3D modeling intelligent point cloud generation method and system based on topological optimization

Abstract

The invention discloses a 3D modeling intelligent point cloud generation method and system based on topological optimization, and relates to the technical field of 3D point cloud generation, and the method comprises the steps: collecting data, and defining initial 3D model data; setting an optimization target and variable definition to generate an optimization model, and performing iteration to determine a 3D model of optimal material distribution; and extracting the point cloud data, and performing data quality optimization processing on the point cloud data. According to the topological optimization-based 3D modeling intelligent point cloud generation method provided by the invention, the density distribution is set as a variable in an optimization process, so that reasonable distribution of materials in a design space is realized, and the materials can be distributed in an area with relatively large bearing force in a concentrated manner, thereby improving the strength and rigidity of the structure. Through principal component analysis, principal components which can best represent data distribution characteristics can be extracted from complex point cloud data, data dimensions are compressed, important information in the data is reserved, and subsequent 3D model reconstruction and analysis are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D point cloud generation, and in particular to a 3D modeling intelligent point cloud generation method and system based on topology optimization. Background Art

[0002] With the rapid development of computer-aided design (CAD) technology and three-dimensional printing technology, 3D modeling methods based on topology optimization have been widely used in industrial design, construction engineering, biomedical engineering and other fields. Traditional 3D modeling methods mainly rely on the experience of designers to construct shapes, which is not only time-consuming but also prone to the use of redundant materials. In this context, topology optimization, as a mathematical optimization method, can automatically generate the optimal shape of the structure in a given design space according to the predetermined physical performance requirements, thereby significantly reducing the use of materials and improving structural performance. This method uses finite element analysis (FEA) and optimization algorithms (such as SIMP method and BESO method) to achieve a reasonable distribution of materials in the design space through repeated iterative calculations, so that the final 3D model can meet the requirements of strength, stiffness, etc. while maximizing material savings. However, although topology optimization has shown great potential in 3D modeling, existing technologies still face some challenges in practical applications. For example, when converting the optimized model into a manufacturable 3D model, it is often necessary to rely on high-precision point cloud data, and how to effectively extract and process these point cloud data to ensure their accuracy and availability is still a problem to be solved.

[0003] The existing topology optimization 3D modeling technology still has some shortcomings in model data processing and point cloud generation. First, the current point cloud data extraction method is usually based on uniform sampling, which is difficult to adapt to the local details of complex surfaces, resulting in insufficient accuracy of the generated point cloud data in high curvature areas, thus affecting the fineness of the final model. Secondly, the existing point cloud processing methods lack intelligent processing means in terms of noise reduction and filtering. Traditional linear filtering methods often lose important geometric information while removing noise, resulting in blurring of model surface details. Furthermore, in the feature extraction process of point cloud data, although the principal component analysis (PCA) method can effectively identify the main direction and distribution characteristics, it is difficult to process the nonlinear characteristics of complex surfaces due to its linear nature. Therefore, there is an urgent need for an intelligent point cloud generation method for 3D modeling based on topology optimization, which can not only reasonably distribute materials during the optimization process, but also perform high-precision sampling and intelligent processing for complex geometric structures in the point cloud extraction and processing stage, so as to ensure that the generated 3D model has high accuracy and detail fidelity while meeting manufacturing requirements. This improvement can not only improve the efficiency and quality of 3D modeling, but also be promoted in a wider range of industrial and engineering applications. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing 3D modeling and structural optimization methods have the problems of low material utilization, insufficient structural performance optimization, insufficient point cloud data accuracy, and how to efficiently and accurately generate the optimal 3D model and achieve high-quality point cloud data extraction and processing.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: A 3D modeling intelligent point cloud generation method based on topology optimization, including collecting data, defining initial 3D model data. Setting optimization goals and variable definitions to generate an optimization model, iteratively determining a 3D model with optimal material distribution. Extracting point cloud data, and performing data quality optimization processing on the point cloud data.

[0007] As a preferred solution of the 3D modeling intelligent point cloud generation method based on topology optimization described in the present invention, wherein: the collected data and the definition of the initial 3D model data include defining the initial model, and before starting the topology optimization, the initial 3D model data are defined, including the geometric shape, material properties, boundary conditions and load conditions.

[0008] Input the initial 3D model data provided by the user, including geometry, material properties, boundary conditions, and loading conditions.

[0009] Geometry defines the external and internal structure of a model, including size and shape.

[0010] Material properties are the physical properties of the model material, including elastic modulus E, Poisson's ratio ν, and density ρ.

[0011] Boundary conditions are the fixed points, support conditions, and constraints that define the model.

[0012] Load conditions are specified external forces, pressures, and temperatures acting on the model.

[0013] As a preferred solution of the 3D modeling intelligent point cloud generation method based on topology optimization described in the present invention, wherein: the setting of optimization objectives and variable definitions to generate an optimization model includes constructing a topology optimization algorithm based on initial model data, and achieving the optimization objectives of minimizing material usage and maximizing structural strength by optimizing the density distribution of design variables. The optimization objectives are expressed as:

[0014]

[0015] Where ρ(x) represents density distribution, E(x) represents elastic modulus, ε(x) represents strain, λ and μ are Lagrange multipliers, p is penalty factor, V0 represents volume constraint, represents the stiffness matrix, u represents the displacement vector, and f represents the external force.

[0016] As a preferred solution of the 3D modeling intelligent point cloud generation method based on topology optimization described in the present invention, wherein: the setting of optimization objectives and variable definitions to generate an optimization model also includes setting density distribution as a variable in the optimization process, indicating the existence probability of materials at different positions in the design space, adjusting the density value of each unit, and considering that it is close to 0 as no material and close to 1 as having material. After constructing a topology optimization algorithm, the optimization model is generated by iteratively solving the optimization objective function. In each iteration, the density distribution is updated until it converges to the optimal solution, which is expressed as:

[0017]

[0018] Among them, η k is the adaptive learning rate, β is the inertia term weight, and k is the number of iterations.

[0019] As a preferred solution of the 3D modeling intelligent point cloud generation method based on topology optimization described in the present invention, the extraction of point cloud data includes converting the model into a point cloud data format after generating and verifying the optimized 3D model, and storing the optimized 3D model data in STL, OBJ, and STEP formats.

[0020] Sampling of geometric information is performed, and the surface of the optimized 3D model is segmented. The model surface is divided into several uniform triangular grids using a gridding algorithm. The vertices of each grid are used as initial sampling points. At the determined sampling points, the three-dimensional coordinate data on the model surface is calculated and recorded. On curved surfaces or boundaries, an adaptive sampling strategy is used to automatically adjust the spacing of the sampling points.

[0021] The point cloud density is set by preset requirements.

[0022] The sampled 3D coordinate point set is stored as a point cloud format file.

[0023] As a preferred solution of the 3D modeling intelligent point cloud generation method based on topology optimization described in the present invention, the data quality optimization processing of the point cloud data includes denoising and filtering the point cloud data, and denoising is achieved by removing noise points through nonlinear filtering, which is expressed as:

[0024]

[0025] Among them, p i represents a point in the point cloud, is the neighborhood of point i, and σ is the filter scale parameter.

[0026] The point cloud data is filtered using a multi-scale Gaussian filter to eliminate discrete jumps, which can be expressed as:

[0027]

[0028] Among them, G1 and G2 are Gaussian kernel functions of different scales, and α1 and α2 are the corresponding weight coefficients.

[0029] As a preferred solution of the 3D modeling intelligent point cloud generation method based on topology optimization described in the present invention, the data quality optimization processing of the point cloud data also includes performing principal component analysis on the processed point cloud data to analyze the main direction and distribution characteristics of the point cloud data, which is expressed as:

[0030]

[0031] Among them, φ is a nonlinear mapping function, is the mean of the point cloud data, C is the covariance matrix, and n is the number of points in the point cloud data.

[0032] Another object of the present invention is to provide a 3D modeling intelligent point cloud generation system based on topology optimization, which can achieve reasonable distribution of materials in the design space by setting density distribution as a variable in the optimization process, so that materials can be concentrated in areas with greater bearing capacity, thereby improving the strength and stiffness of the structure. This solves the problem that the current 3D modeling and structural optimization technology contains insufficient optimization of structural performance.

[0033] As a preferred solution of the 3D modeling intelligent point cloud generation system based on topology optimization described in the present invention, it includes: a data acquisition module, a topology optimization module, and a point cloud processing module. The data acquisition module is used to collect data and define initial 3D model data. The topology optimization module is used to set optimization goals and variable definitions to generate an optimization model, and iteratively determine the 3D model with the optimal material distribution. The point cloud processing module is used to extract point cloud data and perform data quality optimization processing on the point cloud data.

[0034] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a 3D modeling intelligent point cloud generation method based on topology optimization.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a 3D modeling intelligent point cloud generation method based on topology optimization.

[0036] Beneficial effects of the present invention: The 3D modeling intelligent point cloud generation method based on topology optimization provided by the present invention realizes the reasonable distribution of materials in the design space by setting the density distribution as a variable in the optimization process, so that the materials can be concentrated in the area with greater bearing capacity, thereby improving the strength and stiffness of the structure. The introduction of adaptive learning rate and inertia term ensures the efficiency and stability of the optimization process. Through principal component analysis, the principal component that best represents the data distribution characteristics can be extracted from complex point cloud data, compressing the data dimension while retaining important information in the data, which is convenient for subsequent 3D model reconstruction and analysis. The present invention achieves better results in terms of cost reduction and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0038] Figure 1 An overall flow chart of a 3D modeling intelligent point cloud generation method based on topology optimization provided in the first embodiment of the present invention.

[0039] Figure 2 A schematic diagram of a module of a 3D modeling intelligent point cloud generation system based on topology optimization provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a 3D modeling intelligent point cloud generation method based on topology optimization, comprising:

[0042] S1: Collect data and define initial 3D model data.

[0043] Furthermore, collecting data and defining initial 3D model data includes defining an initial model, and before starting topology optimization, defining initial 3D model data including geometry, material properties, boundary conditions, and load conditions.

[0044] Input the initial 3D model data provided by the user, including geometry, material properties, boundary conditions, and loading conditions.

[0045] Geometry defines the external and internal structure of a model, including size and shape.

[0046] Material properties are the physical properties of the model material, including elastic modulus E, Poisson's ratio ν, and density ρ.

[0047] Boundary conditions are the fixed points, support conditions, and constraints that define the model.

[0048] Load conditions are specified external forces, pressures, and temperatures acting on the model.

[0049] S2: Set the optimization goal and variable definition to generate the optimization model, and iterate to determine the 3D model of the optimal material distribution.

[0050] Furthermore, setting the optimization goal and variable definition to generate the optimization model includes building a topology optimization algorithm based on the initial model data, and achieving the optimization goal of minimizing material usage and maximizing structural strength by optimizing the density distribution of design variables. The optimization goal is expressed as:

[0051]

[0052] Where ρ(x) represents density distribution, E(x) represents elastic modulus, ε(x) represents strain, λ and μ are Lagrange multipliers, p is penalty factor, V0 represents volume constraint, represents the stiffness matrix, u represents the displacement vector, and f represents the external force.

[0053] Optimization problems are usually expressed as minimization or maximization problems of an objective function, which reflects the structural performance (such as stiffness, strength) or material usage. The problem also includes a series of constraints, such as volume constraints, stress constraints, and displacement constraints. By introducing the Lagrange multiplier method, these constraints can be integrated into the optimization objective for solution.

[0054] It should be noted that setting the optimization goal and variable definition to generate the optimization model also includes setting the density distribution as a variable in the optimization process, indicating the probability of existence of the material at different positions in the design space, adjusting the density value of each unit, and considering it as no material when it is close to 0 and as having material when it is close to 1. After constructing the topology optimization algorithm, the optimization model is generated by iteratively solving the optimization objective function. In each iteration, the density distribution is updated, and the optimized model is verified using finite element analysis (FEA) software (such as ANSYS, ABAQUS) to ensure that it meets the design requirements until it converges to the optimal solution, which is expressed as:

[0055]

[0056] Among them, η k is the adaptive learning rate, β is the inertia term weight, and k is the number of iterations.

[0057] S3: Extract point cloud data and optimize the data quality of the point cloud data.

[0058] Furthermore, extracting the point cloud data includes converting the model into a point cloud data format after generating and verifying the optimized 3D model, and storing the optimized 3D model data in STL, OBJ, and STEP formats.

[0059] Sampling of geometric information is performed, and the surface of the optimized 3D model is segmented. The model surface is divided into several uniform triangular grids using a gridding algorithm. The vertices of each grid are used as initial sampling points. At the determined sampling points, the three-dimensional coordinate data on the model surface is calculated and recorded. On curved surfaces or boundaries, an adaptive sampling strategy is used to automatically adjust the spacing of the sampling points.

[0060] The point cloud density is set by preset requirements.

[0061] The sampled 3D coordinate point set is stored as a point cloud format file.

[0062] It should be noted that the data quality optimization processing of point cloud data includes denoising and filtering. Point cloud data may be affected by measurement errors and environmental noise during the extraction process, resulting in some invalid or erroneous data points. Through intelligent denoising algorithms, such as nonlinear filtering, these noise points can be effectively removed to retain the true geometry of the model. Noise reduction is achieved by removing noise points through nonlinear filtering, which is expressed as:

[0063]

[0064] Among them, p i represents a point in the point cloud, is the neighborhood of point i, and σ is the filter scale parameter.

[0065] The point cloud data is filtered using a multi-scale Gaussian filter to eliminate discrete jumps, which can be expressed as:

[0066]

[0067] Among them, G1 and G2 are Gaussian kernel functions of different scales, and α1 and α2 are the corresponding weight coefficients.

[0068] It should also be noted that the data quality optimization processing of point cloud data also includes principal component analysis of the processed point cloud data to analyze the main direction and distribution characteristics of the point cloud data, which can be expressed as:

[0069]

[0070] Among them, φ is a nonlinear mapping function, is the mean of the point cloud data, C is the covariance matrix, and n is the number of points in the point cloud data.

[0071] Example 2, an embodiment of the present invention, provides a 3D modeling intelligent point cloud generation method based on topology optimization. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0072] First, in order to verify the effectiveness of a 3D modeling intelligent point cloud generation method based on topology optimization, the experiment prepared 3D model data with different geometric shapes and material properties, and performed topology optimization and point cloud data generation and processing on these models. The experiment selected three geometric shape models of different complexity as research objects, namely column model, L-shaped structure model and honeycomb structure model. These models represent several common structural forms in actual engineering applications. Each model is set with corresponding material properties, including elastic modulus E, Poisson's ratio ν, density ρ, as well as boundary conditions and load conditions. The external force conditions of the model are designed to be uniformly distributed loads, and constraints are imposed at fixed points to simulate actual working conditions.

[0073] The experiment collected the initial 3D model data, including geometry, material properties, boundary conditions, and load conditions. Then, the optimization goals and variables were set, and the density distribution of each model was optimized by constructing a topology optimization algorithm. The optimization goal was to minimize the amount of material used while ensuring structural strength. After multiple iterative solutions, the optimal material distribution model was generated. Subsequently, the experiment converted the optimized model into a point cloud data format, segmented the model surface using a meshing algorithm, and sampled geometric information within these segmented areas. For curved surfaces and boundary areas, an adaptive sampling strategy was used to ensure that the sampling density matched the geometric complexity. The sampled point cloud data was stored in PLY format.

[0074] In the point cloud data processing stage, the point cloud data was first subjected to nonlinear filtering to remove noise points, and then the data was smoothed using a multi-scale Gaussian filter to eliminate possible discrete jumps. Finally, the principal component analysis (PCA) method was used to extract features from the processed point cloud data, analyze the main direction and distribution characteristics of the point cloud data, and provide an optimized data basis for subsequent 3D model reconstruction and simulation.

[0075] Table 1 Experimental data comparison table

[0076]

[0077] Embodiment It can be seen from the test data that under the same initial volume conditions, the method of the present invention shows obvious advantages in the final volume after optimization, material utilization rate and stress distribution. First, in the column model, the final volume of the method of the present invention is reduced by 20%, the material utilization rate is reduced from 75% to 60%, and the maximum stress is reduced to 32MPa, which is slightly lower than the 35MPa of the prior art, indicating that the structural strength is maintained or even slightly enhanced. This effect is also reflected in the L-shaped structure and honeycomb structure models. The material utilization rate is reduced from 72% to 56% and from 73% to 60%, respectively, and the maximum stress is also reduced accordingly, proving that the present invention can reduce the amount of material used while also ensuring or improving the mechanical properties of the structure.

[0078] Through these data comparisons, the advantages of the present invention in achieving optimal material distribution and efficient material utilization are clarified. In the optimization process, the existing technology usually cannot take into account both material conservation and structural performance, resulting in material waste or insufficient structural strength. The present invention generates a more reasonable material distribution scheme by setting optimization goals and iterative solutions, so that the final model reaches a new balance in material usage and structural strength. In addition, in terms of point cloud data processing, the present invention further optimizes the data quality by introducing nonlinear filtering and multi-scale Gaussian filters, ensuring that the details of the model surface are retained during the data processing process. This not only improves the accuracy of point cloud data, but also provides a more reliable data foundation for subsequent 3D modeling and simulation.

[0079] In summary, the method of the present invention has significant advantages in terms of material utilization efficiency, structural performance optimization and data processing accuracy, and has obvious innovation and practical value compared with the prior art. These advantages make the present invention have a broad promotion prospect in practical engineering applications, and can significantly improve the overall efficiency and effect of 3D modeling and structural optimization.

[0080] Reference Figure 2 , as an embodiment of the present invention, provides a 3D modeling intelligent point cloud generation system based on topology optimization, including a data acquisition module, a topology optimization module, and a point cloud processing module.

[0081] The data acquisition module is used to collect data and define the initial 3D model data. The topology optimization module is used to set the optimization target and variable definition to generate the optimization model, and iterate to determine the 3D model with the optimal material distribution. The point cloud processing module is used to extract point cloud data and optimize the data quality of the point cloud data.

[0082] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0083] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0084] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0085] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A 3D modeling intelligent point cloud generation method based on topology optimization, characterized in that: include: Collect data and define initial 3D model data; Set optimization goals and variable definitions to generate an optimization model, and iterate to determine the 3D model of the optimal material distribution; Extract point cloud data and optimize the data quality of the point cloud data.

2. The 3D modeling intelligent point cloud generation method based on topology optimization according to claim 1, characterized in that: The collecting data and defining the initial 3D model data include defining an initial model, and before starting the topology optimization, defining the initial 3D model data, including geometric shape, material properties, boundary conditions and load conditions; Input the initial 3D model data provided by the user, including geometry, material properties, boundary conditions, and load conditions; Geometry is the external and internal structure that defines the model, including size and shape; Material properties are the physical properties of the model material, including elastic modulus E, Poisson's ratio ν, and density ρ; Boundary conditions are the fixed points, support conditions, and constraints that define the model; Load conditions are specified external forces, pressures, and temperatures acting on the model.

3. The 3D modeling intelligent point cloud generation method based on topology optimization according to claim 2, characterized in that: The setting of optimization objectives and variable definition to generate an optimization model includes constructing a topology optimization algorithm based on initial model data, and achieving the optimization objectives of minimizing material usage and maximizing structural strength by optimizing the density distribution of design variables. The optimization objectives are expressed as: in, represents density distribution, E(x) represents elastic modulus, ε(x) represents strain, λ and μ are Lagrange multipliers, p is penalty factor, V0 represents volume constraint, represents the stiffness matrix, u represents the displacement vector, and f represents the external force.

4. The 3D modeling intelligent point cloud generation method based on topology optimization according to claim 3, characterized in that: The setting of optimization objectives and variable definitions to generate an optimization model also includes setting density distribution as a variable in the optimization process, indicating the existence probability of materials at different positions in the design space, adjusting the density value of each unit, and treating it as no material when it is close to 0 and treating it as having material when it is close to 1. After constructing a topology optimization algorithm, the optimization model is generated by iteratively solving the optimization objective function. In each iteration, the density distribution is updated until it converges to the optimal solution, which is expressed as: Among them, η k is the adaptive learning rate, β is the inertia term weight, and k is the number of iterations.

5. The 3D modeling intelligent point cloud generation method based on topology optimization according to claim 4, characterized in that: The extracting of point cloud data includes converting the model into a point cloud data format after generating and verifying the optimized 3D model, and storing the optimized 3D model data in STL, OBJ, or STEP formats; Sampling geometric information, segmenting the surface of the optimized 3D model, using a meshing algorithm to divide the model surface into several uniform triangular meshes, using the vertex of each mesh as the initial sampling point, calculating and recording the three-dimensional coordinate data on the model surface at the determined sampling point, and using an adaptive sampling strategy to automatically adjust the spacing of the sampling points on the curved surface or boundary; Point cloud density is set by preset requirements; The sampled 3D coordinate point set is stored as a point cloud format file.

6. The 3D modeling intelligent point cloud generation method based on topology optimization according to claim 5, characterized in that: The data quality optimization processing of the point cloud data includes denoising and filtering the point cloud data, and denoising is achieved by removing noise points through nonlinear filtering, which is expressed as: Among them, p i represents a point in the point cloud, is the neighborhood of point i, σ is the filter scale parameter; The point cloud data is filtered using a multi-scale Gaussian filter to eliminate discrete jumps, which can be expressed as: Among them, G1 and G2 are Gaussian kernel functions of different scales, and α1 and α2 are the corresponding weight coefficients.

7. The 3D modeling intelligent point cloud generation method based on topology optimization according to claim 6, characterized in that: The data quality optimization processing of the point cloud data further includes performing principal component analysis on the processed point cloud data to analyze the main direction and distribution characteristics of the point cloud data, which is expressed as: Among them, φ is a nonlinear mapping function, is the mean of the point cloud data, C is the covariance matrix, and n is the number of points in the point cloud data.

8. A system using the 3D modeling intelligent point cloud generation method based on topology optimization as claimed in any one of claims 1 to 7, characterized in that: Including data acquisition module, topology optimization module, point cloud processing module; The data acquisition module is used to collect data and define initial 3D model data; The topology optimization module is used to set the optimization target and variable definition to generate the optimization model, and iterate to determine the 3D model of the optimal material distribution; The point cloud processing module is used to extract point cloud data and perform data quality optimization processing on the point cloud data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the 3D modeling intelligent point cloud generation method based on topology optimization described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the 3D modeling intelligent point cloud generation method based on topology optimization described in any one of claims 1 to 7 are implemented.

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