Three-dimensional point cloud multi-level recursive and weighted smoothing cloud visualization method and device

Abstract

The application relates to a three-dimensional point cloud multi-level recursive and weighted smoothing cloud map visualization method and device, and relates to the technical field of electric power engineering.The method and the device obtain a simulation result of a target structure by performing numerical simulation on the target structure based on finite element software; a three-dimensional model is constructed according to a multi-level recursive algorithm and the simulation result to obtain a target three-dimensional model; the target three-dimensional model is subjected to smoothing processing by using an improved nonlinear regression method to obtain a modified target three-dimensional model; the modified target three-dimensional model is subjected to optimization processing by using a smoothing transition algorithm to obtain a three-dimensional cloud map; and the three-dimensional cloud map is displayed for a user to view. Therefore, the technical scheme of the application jointly constructs a three-dimensional model cloud map based on a multi-level recursive algorithm and a smoothing algorithm and displays the three-dimensional model cloud map, improves display efficiency and accuracy, and realizes better embedding of a finite element software simulation result into a visualization platform, which is more convenient for engineering researchers to make analysis and decision.

Description

Technical Field

[0001] This invention relates to the field of power engineering technology, specifically to a method and apparatus for visualizing three-dimensional point clouds using multi-level recursion and weighted smoothing. Background Technology

[0002] With increasing market competition and shorter product update cycles, enterprises have a more urgent need for new technologies. As an effective means to improve product quality, shorten design cycles, and enhance product competitiveness, the finite element method has received increasing attention and application in engineering design and scientific research.

[0003] In traditional finite element analysis (FEM) procedures, the results are typically displayed as textual data and contour plots using FEM software. However, numerical simulation results cannot be fully visualized in three dimensions, hindering further analysis and decision-making by engineering researchers and directly impacting time and efficiency. In related technologies, when converting the results obtained through finite element numerical simulation into a three-dimensional contour plot model using traditional algorithms, issues arise such as unevenness in the three-dimensional structure due to mesh generation and discontinuities in the contour plot display, failing to accurately convey high-dimensional information and thus affecting subsequent analysis and decision-making by engineering researchers.

[0004] Therefore, existing technologies suffer from low efficiency and low accuracy in cloud map visualization. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and apparatus for visualizing three-dimensional point clouds using multi-level recursion and weighted smoothing, so as to overcome the problems of low efficiency and low accuracy in current cloud image visualization.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] On the one hand, a method for visualizing 3D point clouds using multi-level recursion and weighted smoothing includes:

[0008] Numerical simulation of the target structure was performed using finite element software to obtain simulation results of the target structure; the simulation results include mesh element information, node coordinate information, and simulation result parameter information.

[0009] A three-dimensional model is constructed based on the multi-level recursive algorithm and the simulation results to obtain the target three-dimensional model;

[0010] The target 3D model is smoothed using an improved nonlinear regression method to obtain a corrected target 3D model.

[0011] The modified target 3D model is optimized using a smooth transition algorithm to obtain a 3D cloud map;

[0012] The 3D cloud map is displayed for user viewing.

[0013] Optionally, the mesh cell information includes: mesh cell number; the node coordinate information includes: node number; the step of constructing a 3D model based on a multi-level recursive algorithm and the simulation results to obtain the target 3D model includes:

[0014] Using the grid cells as cells, a vector information C = {E, V, F} is constructed, where E is the grid cell number; each grid cell contains several nodes, and the nodes constitute a node vector V = {V1, V2, V3, ... V}. m}, where m is the node number; F is the simulation result parameter vector value, F={F1,F2,F3,…F m}, where m is the node number;

[0015] A multi-threaded method is used to divide the node vector C into at least two segments, and the resulting sub-vectors C are then... m They are placed in different threads; among them, C m Represents the sub-vectors after partitioning;

[0016] Using a recursive algorithm, C is processed simultaneously in each thread. i A three-dimensional unit mesh model is established in the form of cells, where the node coordinate information corresponds one-to-one with the node number of the node vector V, thus constructing the target three-dimensional model.

[0017] Optionally, the step of using a smooth transition algorithm to optimize the modified target 3D model to obtain a 3D cloud map includes:

[0018] Determine the smoothed coordinate information Z, Z = W1 T X+W2 T Y+W3, where W is the weight parameter, and X and Y are [1, x, x ∈ [1 ... 2 [,…] and [1,y,y] 2 The smoothed coordinate information is used as the coordinate information of the point cloud in the modified target 3D model;

[0019] The fitted surface function is determined based on the smoothed coordinate information, and the Gaussian distance matrix loss function is used as the loss function to determine whether the loss function converges; wherein, the Gaussian distance matrix loss function A=exp(-(ZZ) 原始 ) 2 / Z 2 );

[0020] If the Gaussian distance matrix loss function converges, the corrected target 3D model is obtained.

[0021] Optionally, the node coordinate information includes vertex coordinate information; the step of optimizing the modified target 3D model using a smooth transition algorithm to obtain a 3D cloud map includes:

[0022] Based on the vertex coordinate information and the corrected target 3D model, a 3D cloud map is obtained;

[0023] The optimized 3D cloud map is obtained by performing intra-domain weight difference on the 3D cloud map.

[0024] Optionally, the step of performing neighborhood weight difference on the 3D cloud map to obtain an optimized 3D cloud map includes:

[0025] Determine the interpolation coordinates (x, y, z) from the coordinates of the three-dimensional cloud map;

[0026] Take the 6*6 neighborhood points (x) of the coordinate point to be interpolated i y j , z l ), where i, j, l = 0, 1, 2, 3, 4, 5;

[0027] Interpolation calculations are performed on the coordinate points to be interpolated based on the neighborhood points, wherein the interpolation calculation formula is:

[0028]

[0029] Where W is the weighting coefficient.

[0030] Optionally, the simulation result parameter information corresponds to the node coordinate information.

[0031] Optionally, the simulation result parameter information includes: stress parameters and temperature parameters.

[0032] Optional, also includes:

[0033] The simulation results are stored in text information or matrix form.

[0034] On another front, a cloud visualization device for three-dimensional point clouds using multi-level recursion and weighted smoothing includes:

[0035] The simulation module is used to perform numerical simulation of the target structure based on finite element software, and obtain the simulation results of the target structure; the simulation results include mesh element information, node coordinate information and simulation result parameter information.

[0036] A construction module is used to construct a three-dimensional model based on a multi-level recursive algorithm and the simulation results to obtain the target three-dimensional model.

[0037] The correction module is used to smooth the target 3D model using an improved nonlinear regression method to obtain a corrected target 3D model.

[0038] The optimized display module is used to optimize the modified target 3D model using a smooth transition algorithm to obtain a 3D cloud map; the 3D cloud map is then displayed for user viewing.

[0039] On another front, a cloud visualization device for multi-level recursive and weighted smoothing of three-dimensional point clouds includes a processor and a memory, wherein the processor is connected to the memory.

[0040] The processor is used to call and execute the program stored in the memory;

[0041] The memory is used to store the program, which is at least used to execute the cloud visualization method for multi-level recursion and weighted smoothing of three-dimensional point clouds as described in any of the above-mentioned methods.

[0042] The technical solution provided by this invention has at least the following beneficial effects:

[0043] The present invention provides a method and apparatus for visualizing 3D point clouds using multi-level recursion and weighted smoothing. This method involves numerically simulating a target structure using finite element method (FE) software to obtain simulation results; constructing a 3D model based on the multi-level recursive algorithm and simulation results to obtain a target 3D model; smoothing the target 3D model using an improved nonlinear regression method to obtain a corrected target 3D model; optimizing the modified target 3D model using a smoothing transition algorithm to obtain a 3D point cloud; and displaying the 3D point cloud for user viewing. Therefore, the technical solution of this application, based on a combination of multi-level recursive and smoothing algorithms, constructs and displays a 3D model point cloud, improving display efficiency and accuracy, better embedding finite element method (FE) simulation results into the visualization platform, and facilitating analysis and decision-making by engineering researchers. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0045] Figure 1 A flowchart illustrating a method for visualizing three-dimensional point clouds using multi-level recursion and weighted smoothing, as provided in an embodiment of the present invention.

[0046] Figure 2A schematic diagram of a two-dimensional model constructed using a recursive algorithm, provided in an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of the structure of a cloud image visualization device for multi-level recursion and weighted smoothing of three-dimensional point clouds provided in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the structure of a cloud visualization device for multi-level recursive and weighted smoothing of three-dimensional point clouds, provided in an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0050] In traditional finite element analysis (FEM) procedures, the results are typically displayed as textual data and contour plots using FEM software. However, numerical simulation results cannot be fully visualized in three dimensions, hindering further analysis and decision-making by engineering researchers and directly impacting time and efficiency. In related technologies, when converting the results obtained through finite element numerical simulation into a three-dimensional contour plot model using traditional algorithms, issues arise such as unevenness in the three-dimensional structure due to mesh generation and discontinuities in the contour plot display, failing to accurately convey high-dimensional information and thus affecting subsequent analysis and decision-making by engineering researchers.

[0051] Based on this, embodiments of the present invention provide a method, apparatus, and device for visualizing three-dimensional point clouds using multi-level recursion and weighted smoothing.

[0052] Figure 1 This is a flowchart illustrating a method for visualizing 3D point clouds using multi-level recursion and weighted smoothing, as provided in an embodiment of the present invention. Please refer to [link / reference]. Figure 1 This embodiment may include the following steps:

[0053] Step S1: Perform numerical simulation of the target structure using finite element software to obtain the simulation results of the target structure; the simulation results include mesh element information, node coordinate information and simulation result parameter information.

[0054] In a specific implementation process, any structure can be used as the target structure, and cloud visualization processing can be performed on the target structure. For example, based on the target structure and computational requirements, a numerical simulation model system can be built based on the secondary development of finite element software, and simulation results can be obtained by co-simulating with programming languages ​​and finite element software.

[0055] It is understandable that the simulation results correspond to the node coordinate information.

[0056] In some embodiments, the simulation result parameter information includes: stress parameters and temperature parameters.

[0057] In some embodiments, after obtaining the simulation results, the simulation results can be stored in the form of text information or matrix form.

[0058] In this embodiment of the application, the technical solution of the present invention is illustrated by visually displaying a cloud map of a two-dimensional model. In this application, the node coordinate data and stress value data of the two-dimensional model can be statistically analyzed.

[0059] Table 1 shows the node coordinate information and stress value information, which is a simulation result stored in text information format.

[0060] Table 1. Node coordinate information and stress value information

[0061]

[0062] Step S2: Construct a three-dimensional model based on the multi-level recursive algorithm and simulation results to obtain the target three-dimensional model.

[0063] In some embodiments, the mesh element information includes: mesh element number; the node coordinate information includes: node number; a three-dimensional model is constructed based on a multi-level recursive algorithm and simulation results to obtain the target three-dimensional model, including:

[0064] Using grid cells as cells, construct vector information C = {E, V, F}, where E is the grid cell number; each grid cell contains several nodes, and the nodes form a node vector V = {V1, V2, V3, ... V}. m}, where m is the node number; F is the simulation result parameter vector value, F={F1,F2,F3,…F m}, where m is the node number;

[0065] A multi-threaded method is used to divide the node vector C into at least two segments, and the resulting sub-vectors C are then... m They are placed in different threads; among them, C m Represents the sub-vectors after partitioning;

[0066] Using a recursive algorithm, C is processed simultaneously in each thread. iA three-dimensional unit mesh model is established in the form of cells, where the node coordinate information corresponds one-to-one with the node number of the node vector V, thus constructing the target three-dimensional model.

[0067] It is worth noting that the number of segments into which the node vector C is divided can be determined based on computer performance, and no specific limitation is made in this application.

[0068] In this embodiment of the invention, E = {[0,1,6],[1,6,7],[1,2,7],[2,7,8],[2,3,8],[3,4,8],[4,5,8],[5,6,8],[0,5,6],[6,7,8]}; V = {1,2,3,4,5,6,7,8,9}; F = {15,13,17,18,11,20,19,22,10}; thus, the vector information C = {E,V,F} and the node and result parameter matrix D = {V,F} can be obtained.

[0069] Using a recursive algorithm, C is processed simultaneously in each thread. i A three-dimensional unit mesh model is established in the form of cells, where the node coordinate information is directly corresponding one-to-one with the node number of the vector V, thereby avoiding a lot of search time and finally building the target three-dimensional model. Figure 2 This is a schematic diagram of a two-dimensional model constructed using a recursive algorithm, as provided in an embodiment of the present invention.

[0070] Step S3: Smooth the target 3D model using the improved nonlinear regression method to obtain the corrected target 3D model.

[0071] In some embodiments, a smooth transition algorithm is used to optimize the modified target 3D model to obtain a 3D cloud map, including:

[0072] Determine the smoothed coordinate information Z, Z = W1 T X+W2 T Y+W3, where W is the weight parameter, and X and Y are [1, x, x ∈ [1 ... 2 [,…] and [1,y,y] 2 The smoothed coordinate information is used as the coordinate information of the point cloud in the modified target 3D model;

[0073] The fitted surface function is determined based on the smoothed coordinate information, and the Gaussian distance matrix loss function is used as the loss function to determine whether the loss function converges; where, the Gaussian distance matrix loss function A=exp(-(ZZ) 原始 ) 2 / Z 2 );

[0074] If the Gaussian distance matrix loss function converges, the corrected 3D target model is obtained.

[0075] Specifically, an improved nonlinear regression method is used to smooth the three-dimensional model, i.e., Z = W1. T X+W2 T Y+W3, where W is the weight parameter, and X and Y are [1, x, x ∈ [1 ... 2 [,…] and [1,y,y] 2 [,…], where Z represents the smoothed coordinate information, and the loss function uses the Gaussian distance matrix A = exp(-(ZZ)). 原始 ) 2 / Z 2 The algorithm updates A with each iteration until convergence. Notably, this application uses an improved nonlinear regression algorithm for smoothing in local regions, avoiding errors caused by large-scale data computation.

[0076] Step S4: Optimize the modified target 3D model using a smooth transition algorithm to obtain a 3D cloud map; display the 3D cloud map for user viewing.

[0077] In some embodiments, node coordinate information includes vertex coordinate information; a smooth transition algorithm is used to optimize the modified target 3D model to obtain a 3D cloud map, including:

[0078] Based on the vertex coordinate information and the corrected target 3D model, a 3D cloud map is obtained;

[0079] The optimized 3D cloud map is obtained by performing intra-domain weight difference on the 3D cloud map.

[0080] Specifically, after determining the vertex coordinate information and the corrected target 3D model, a 3D cloud map is generated, and the intra-domain weight difference of the 3D cloud map is calculated to obtain the optimized 3D cloud map.

[0081] In some embodiments, performing neighborhood weight difference on the 3D cloud map to obtain an optimized 3D cloud map includes:

[0082] Determine the coordinate points (x, y, z) to be interpolated in the coordinate points of the 3D cloud map;

[0083] Take the 6*6 neighborhood points (x) of the coordinate point to be interpolated i y j , z l ), where i, j, l = 0, 1, 2, 3, 4, 5;

[0084] Interpolation calculations are performed on the coordinates of the points to be interpolated based on the neighborhood points. The interpolation calculation formula is as follows:

[0085]

[0086] Where W is the weighting coefficient.

[0087] Specifically, this application employs a smooth transition algorithm for displaying the result cloud map, ensuring uniform and continuous color changes. For the coordinate point (x, y, z) to be interpolated (x, y, and z can be floating-point numbers), its nearest 6×6 neighborhood points (xi, yj, zl) are taken, where i, j, l = 0, 1, 2, 3, 4, 5. Interpolation is calculated using the following formula, where W represents the weighting coefficient:

[0088]

[0089] It is understandable that the calculation scheme provided in this application can quickly transform finite element numerical simulation results into three-dimensional structures and display corresponding cloud map information, thereby better embedding finite element software simulation results into the visualization platform, making it easier for engineering researchers to make analyses and decisions.

[0090] The three-dimensional point cloud visualization method based on multi-level recursion and weighted smoothing provided in this invention involves numerically simulating a target structure using finite element method (FE) software to obtain simulation results; constructing a three-dimensional model based on the multi-level recursive algorithm and simulation results to obtain the target three-dimensional model; smoothing the target three-dimensional model using an improved nonlinear regression method to obtain a corrected target three-dimensional model; optimizing the modified target three-dimensional model using a smoothing transition algorithm to obtain a three-dimensional point cloud; and displaying the three-dimensional point cloud for user viewing. Therefore, the technical solution of this application constructs and displays a three-dimensional model point cloud based on a combination of multi-level recursive algorithm and smoothing algorithm, improving display efficiency and accuracy, thereby better embedding finite element method (FE) simulation results into the visualization platform, making it easier for engineering researchers to make analyses and decisions.

[0091] Based on a general inventive concept, embodiments of the present invention also provide a cloud map visualization device for multi-level recursion and weighted smoothing of three-dimensional point clouds.

[0092] Figure 3 This is a schematic diagram of a cloud visualization device for multi-level recursive and weighted smoothing of three-dimensional point clouds provided in an embodiment of the present invention. (See attached diagram.) Figure 3 The apparatus provided in this application may include:

[0093] Simulation module 31 is used to perform numerical simulation of the target structure based on finite element software to obtain the simulation results of the target structure; the simulation results include mesh element information, node coordinate information and simulation result parameter information.

[0094] Module 32 is used to construct a three-dimensional model based on a multi-level recursive algorithm and simulation results to obtain the target three-dimensional model;

[0095] The correction module 33 is used to smooth the target three-dimensional model using an improved nonlinear regression method to obtain the corrected target three-dimensional model.

[0096] The optimized display module 34 is used to optimize the modified target 3D model using a smooth transition algorithm to obtain a 3D cloud map; the 3D cloud map is then displayed for user viewing.

[0097] Optionally, a construction module is used to construct vector information C = {E, V, F} using grid cells as cells, where E is the grid cell number; each grid cell contains several nodes, and the nodes form a node vector V = {V1, V2, V3, ... V}. m}, where m is the node number; F is the simulation result parameter vector value, F={F1,F2,F3,…F m}, where m is the node number;

[0098] A multi-threaded method is used to divide the node vector C into at least two segments, and the resulting sub-vectors C are then... m They are placed in different threads; among them, C m Represents the sub-vectors after partitioning;

[0099] Using a recursive algorithm, C is processed simultaneously in each thread. i A three-dimensional unit mesh model is established in the form of cells, where the node coordinate information corresponds one-to-one with the node number of the node vector V, thus constructing the target three-dimensional model.

[0100] Optional, a correction module, specifically used to determine the smoothed coordinate information Z, Z = W1 T X+W2 T Y+W3, where W is the weight parameter, and X and Y are [1, x, x ∈ [1 ... 2 [,…] and [1,y,y] 2 The smoothed coordinate information is used as the coordinate information of the point cloud in the modified target 3D model;

[0101] The fitted surface function is determined based on the smoothed coordinate information, and the Gaussian distance matrix loss function is used as the loss function to determine whether the loss function converges; where, the Gaussian distance matrix loss function A=exp(-(ZZ) 原始 ) 2 / Z 2 );

[0102] If the Gaussian distance matrix loss function converges, the corrected 3D target model is obtained.

[0103] Optionally, the display module is optimized, specifically used to obtain a 3D cloud map based on vertex coordinate information and the corrected target 3D model;

[0104] The optimized 3D cloud map is obtained by performing intra-domain weight difference on the 3D cloud map.

[0105] Optionally, the display module is optimized, specifically for determining the coordinate points (x, y, z) to be interpolated in the coordinate points of the 3D cloud map;

[0106] Take the 6*6 neighborhood points (x) of the coordinate point to be interpolated i y j , z l ), where i, j, l = 0, 1, 2, 3, 4, 5;

[0107] Interpolation calculations are performed on the coordinates of the points to be interpolated based on the neighborhood points. The interpolation calculation formula is as follows:

[0108]

[0109] Where W is the weighting coefficient.

[0110] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0111] The 3D point cloud visualization device based on multi-level recursion and weighted smoothing provided in this invention performs numerical simulation of the target structure using finite element method software to obtain the simulation results. A 3D model is constructed based on the multi-level recursive algorithm and the simulation results to obtain the target 3D model. An improved nonlinear regression method is used to smooth the target 3D model to obtain a corrected target 3D model. A smoothing transition algorithm is then used to optimize the modified target 3D model to obtain a 3D point cloud map. The 3D point cloud map is then displayed for user viewing. Therefore, the technical solution of this application constructs and displays a 3D model point cloud map based on a combination of multi-level recursive algorithm and smoothing algorithm, improving display efficiency and accuracy. This allows for better embedding of finite element method simulation results into the visualization platform, facilitating analysis and decision-making by engineering researchers.

[0112] Based on a general inventive concept, embodiments of the present invention also provide a cloud visualization device for multi-level recursive and weighted smoothing of three-dimensional point clouds.

[0113] Figure 4 This is a schematic diagram of the structure of a cloud visualization device for multi-level recursive and weighted smoothing of three-dimensional point clouds provided in an embodiment of the present invention. (See attached diagram.) Figure 4 The device provided in this application includes a processor 41 and a memory 42, with the processor 41 connected to the memory 42. The processor 41 is used to call and execute a program stored in the memory 42; the memory 42 is used to store the program, which is at least used to execute the three-dimensional point cloud multi-level recursive and weighted smoothing cloud map visualization method described in the above embodiments.

[0114] The specific implementation scheme of the cloud visualization device for multi-level recursion and weighted smoothing of three-dimensional point clouds provided in this application embodiment can refer to the implementation scheme of the cloud visualization method for multi-level recursion and weighted smoothing of three-dimensional point clouds in any of the above embodiments, and will not be repeated here.

[0115] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0116] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0117] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0118] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0119] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0120] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0121] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0122] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0123] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for visualizing 3D point clouds using multi-level recursion and weighted smoothing, characterized in that, include: Numerical simulation of the target structure was performed using finite element software to obtain the simulation results of the target structure. The simulation results include mesh cell information, node coordinate information, and simulation result parameter information; A three-dimensional model is constructed based on the multi-level recursive algorithm and the simulation results to obtain the target three-dimensional model; The target 3D model is smoothed using an improved nonlinear regression method to obtain a corrected target 3D model. The corrected target 3D model is optimized using a smooth transition algorithm to obtain a 3D cloud map; The 3D cloud map is displayed for user viewing; in, The mesh cell information includes: mesh cell number; the node coordinate information includes: node number; the step of constructing a 3D model based on a multi-level recursive algorithm and the simulation results to obtain the target 3D model includes: Using grid cells as cells, construct vector information C = {E, V, F}, where E is the grid cell number; each grid cell contains several nodes, and the nodes constitute a node vector V = {V1, V2, V3, ... V}. m }, m is the node number; F is the simulation result parameter vector value, F={F1, F2, F3,… F m }, m is the node number; A multi-threaded method is used to divide the vector information C into at least two segments, and the resulting sub-vectors C are then... m They are placed in different threads; among them, C m Represents the sub-vectors after partitioning; Using a recursive algorithm, C is processed simultaneously in each thread. m A three-dimensional unit mesh model is established in the form of cells, where the node coordinate information corresponds one-to-one with the node number of the node vector V to construct the target three-dimensional model. The step of smoothing the target 3D model using an improved nonlinear regression method to obtain a corrected target 3D model includes: Determine the smoothed coordinate information Z, Z=W1 T X+ W2 T Y+W3, where W is the weight parameter, and X and Y are [1, x, x]. 2 [,…] and [1,y, y] 2 The smoothed coordinate information is used as the coordinate information of the point cloud in the corrected target 3D model; The fitted surface function is determined based on the smoothed coordinate information, and the Gaussian distance matrix loss function is used as the loss function to determine whether the loss function converges; wherein, the Gaussian distance matrix loss function A = exp(-(ZZ) 原始 ) 2 / Z 2 Z 原始 To smooth out the coordinate information beforehand; If the Gaussian distance matrix loss function converges, the corrected target 3D model is obtained.

2. The method according to claim 1, characterized in that, The node coordinate information includes vertex coordinate information; the optimization processing of the corrected target 3D model using a smooth transition algorithm to obtain a 3D cloud map includes: Based on the vertex coordinate information and the corrected target 3D model, a 3D cloud map is obtained; The optimized 3D cloud map is obtained by performing intra-domain weight difference on the 3D cloud map.

3. The method according to claim 2, characterized in that, The step of performing neighborhood weight difference on the 3D cloud map to obtain an optimized 3D cloud map includes: Determine the interpolation coordinates (x, y, z) from the coordinates of the three-dimensional cloud map; Take the 6*6 neighborhood points (x) of the coordinate point to be interpolated i y j , z l ),in, i , j , l = 0, 1, 2, 3, 4, 5; Interpolation calculations are performed on the coordinate points to be interpolated based on the neighborhood points, wherein the interpolation calculation formula is: ； Where W is the weighting coefficient.

4. The method according to claim 1, characterized in that, The simulation result parameter information corresponds to the node coordinate information.

5. The method according to claim 1, characterized in that, The simulation results parameters include: stress parameters and temperature parameters.

6. The method according to claim 1, characterized in that, Also includes: The simulation results are stored in text information or matrix form.

7. A cloud image visualization device for three-dimensional point clouds using multi-level recursion and weighted smoothing, characterized in that, include: The simulation module is used to perform numerical simulation of the target structure based on finite element software, and obtain the simulation results of the target structure. The simulation results include mesh cell information, node coordinate information, and simulation result parameter information; A construction module is used to construct a three-dimensional model based on a multi-level recursive algorithm and the simulation results to obtain the target three-dimensional model. The correction module is used to smooth the target 3D model using an improved nonlinear regression method to obtain a corrected target 3D model. The optimized display module is used to optimize the corrected target 3D model using a smooth transition algorithm to obtain a 3D cloud map; The 3D cloud map is displayed for user viewing; in, The mesh cell information includes: mesh cell number; the node coordinate information includes: node number; the step of constructing a 3D model based on a multi-level recursive algorithm and the simulation results to obtain the target 3D model includes: Using grid cells as cells, construct vector information C = {E, V, F}, where E is the grid cell number; each grid cell contains several nodes, and the nodes constitute a node vector V = {V1, V2, V3, ... V}. m }, m is the node number; F is the simulation result parameter vector value, F={F1, F2, F3,… F m }, m is the node number; A multi-threaded method is used to divide the vector information C into at least two segments, and the resulting sub-vectors C are then... m They are placed in different threads; among them, C m Represents the sub-vectors after partitioning; Using a recursive algorithm, C is processed simultaneously in each thread. m A three-dimensional unit mesh model is established in the form of cells, where the node coordinate information corresponds one-to-one with the node number of the node vector V to construct the target three-dimensional model. The step of smoothing the target 3D model using an improved nonlinear regression method to obtain a corrected target 3D model includes: Determine the smoothed coordinate information Z, Z=W1 T X+ W2 T Y+W3, where W is the weight parameter, and X and Y are [1, x, x]. 2 [,…] and [1,y, y] 2 The smoothed coordinate information is used as the coordinate information of the point cloud in the corrected target 3D model; The fitted surface function is determined based on the smoothed coordinate information, and the Gaussian distance matrix loss function is used as the loss function to determine whether the loss function converges; wherein, the Gaussian distance matrix loss function A = exp(-(ZZ) 原始 ) 2 / Z 2 Z 原始 To smooth out the coordinate information beforehand; If the Gaussian distance matrix loss function converges, the corrected target 3D model is obtained.

8. A cloud image visualization device for three-dimensional point clouds using multi-level recursion and weighted smoothing, characterized in that, It includes a processor and a memory, wherein the processor is connected to the memory: The processor is used to call and execute the program stored in the memory; The memory is used to store the program, which is at least used to execute the cloud visualization method for multi-level recursion and weighted smoothing of three-dimensional point clouds as described in any one of claims 1-6.

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