Method and device for establishing full-parameterized model of three-dimensional special-shaped spherical object

Abstract

The invention discloses a method and a device for establishing a full-parameterized model of a three-dimensional special-shaped spherical object. The method comprises the following steps: generating a spherical grid according to an input bubble foreign matter parameter; analyzing the deformation parameters of the spherical network to perform parameter format and content verification to obtain analysis parameters; whether random parameters are applied or not is determined according to the analysis parameters, if not, surface deformation operation is executed according to the analysis parameters, if yes, the random parameters are generated based on the analysis parameters, and the surface deformation operation is executed according to the analysis parameters and the generated random parameters; after the spherical grid is subjected to surface deformation operation, distribution characteristic parameters of final space grid nodes are generated and obtained; and according to the distribution characteristic parameters of the space grid nodes, generating a Delou inner triangle patch.

Description

Technical Field

[0001] This invention relates to the field of geometric modeling technology for large oil-filled equipment, and more specifically, to a method and apparatus for establishing a fully parametric model of a three-dimensional irregular spherical object. Background Technology

[0002] Large oil-filled equipment is a core main equipment of the power grid, and its insulation performance is highly dependent on the quality of the insulating oil. Air bubbles and other foreign matter generated during operation are a key factor leading to a decline in insulation performance. However, traditional disassembly inspections and online monitoring cannot recreate the process by which air bubble migration affects insulation performance. Current equipment operation and maintenance and fault analysis require CAE simulation technology to accurately assess the impact of air bubbles and other foreign matter on the equipment's insulation performance. However, modeling the morphology of these non-basic geometric foreign matter is complex, facing challenges such as long modeling times and shape simplification distortion. Therefore, it is necessary to combine CAD technology to solve the above-mentioned air bubble and foreign matter modeling problems.

[0003] In the field of industrial design, the collaboration between CAD geometric modeling and CAE physics simulation forms the technological foundation. Traditional processes rely on CAD software (such as SolidWorks) to construct geometric models, which are then imported into CAE platforms (such as ANSYS) for mesh generation and physics calculations. There are five main types of geometric modeling techniques: 1) Solid modeling, combining basic voxels through Boolean operations; 2) Surface modeling, constructing closed shapes using parametric surfaces; 3) Polygonal mesh modeling, creating discretized surfaces from vertices, edges, and faces; 4) Implicit modeling, defining surfaces through functions; and 5) Point cloud modeling, constructing models using 3D scan points. Most mainstream CAD software supports these five modeling methods, but none specifically addresses modeling of air bubbles or other foreign objects. Mainstream CAE software only supports partial solid modeling capabilities. Therefore, a modeling method for large-scale oil-filled equipment is urgently needed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and apparatus for establishing a fully parametric model of a three-dimensional irregular spherical object.

[0005] According to one aspect of the present invention, a method for establishing a fully parametric model of a three-dimensional irregular spherical object is provided, comprising:

[0006] Generate a spherical mesh based on the input bubble foreign object parameters;

[0007] The deformation parameters of the spherical network are analyzed, and the parameter format and content are validated to obtain the analytical parameters.

[0008] Whether to apply random parameters is determined based on the analytical parameters. If no random parameters are applied, the surface deformation operation is performed based on the analytical parameters. If random parameters are applied, random parameters are generated based on the analytical parameters, and the surface deformation operation is performed based on the analytical parameters and the generated random parameters.

[0009] After performing surface deformation operations on the spherical mesh, the distribution characteristic parameters of the final spatial mesh nodes are generated;

[0010] Based on the distribution characteristics of the spatial grid nodes, Delaunay triangle patches are generated.

[0011] Optionally, a spherical mesh is generated based on the input bubble foreign object parameters, including:

[0012] In spherical coordinates, a uniformly distributed sequence from 0 to 2π is generated from the azimuth angle Φ, where the number of segments is determined by the discretization point resolution parameter.

[0013] A uniformly distributed sequence from 0 to π is generated in the polar angle θ direction, and the segmentation density is also controlled by the discretization point resolution parameter.

[0014] Next, a two-dimensional parametric mesh (u,v) is generated through a meshing operation, where u corresponds to the azimuth angle and v corresponds to the polar angle;

[0015] A spherical mesh is generated based on a two-dimensional parametric mesh and the radius of the bubble.

[0016] Optionally, the analyzed deformation parameters include at least one of the following: shape function, number of deformations, deformation direction, deformation amplitude, deformation range, overall deformation amount, and compensation type.

[0017] Optionally, random parameters are generated based on the analytical parameters, and surface deformation operations are performed based on the analytical parameters and the generated random parameters, including:

[0018] Random parameters are obtained by randomly controlling the geometric, morphological, and distribution parameters of the surface deformation of the spherical mesh based on analytical parameters.

[0019] Surface deformation operation on spherical mesh based on random parameters;

[0020] After the surface deformation operation is completed, spatial conflict detection is performed on the newly generated spherical mesh;

[0021] If no spatial conflict exists, output the spherical mesh after the surface deformation operation; otherwise, regenerate random parameters and perform the surface deformation operation until no spatial conflict exists.

[0022] Optionally, the randomization mechanism for the geometric parameters is as follows: the deformation axis parameters are randomly distributed uniformly on a sphere, the azimuth angle axis_phi in the spherical coordinate system follows a random distribution in the range [0, 2π], and the polar angle axis_theta in the spherical coordinate system follows a random distribution in the range [0, π], ensuring a uniform random distribution of the deformation axis on the sphere.

[0023] The basic value of the deformation amplitude parameter A ampThe algorithm applies a uniformly distributed random perturbation within the range of % to 100%, and the calculation formula is as follows:

[0024] amp random =amp deform ×(A amp +B amp ×rand(0,1))

[0025] Where, rand(0, 1) is a uniformly random number in the range [0, 1]; A amp This is the random disturbance amplitude coefficient, with a value in the range (0, 1), which controls the disturbance amplitude B. amp =1-A amp ;

[0026] The deformation range parameter is within the base value A. range The random perturbation algorithm is applied within the range of % to 100%, and the calculation formula is as follows:

[0027] range random =range deform ×(A range +B range ×rand(0,1))

[0028] Among them, A range B is the random disturbance range coefficient, taking values ​​within the range (0, 1), which controls the disturbance range. range =1-A range .

[0029] Optionally, the randomization mechanism for the morphological parameters is as follows: the mapping relationship between the deformation direction and the probability of the concave-convex deformation direction, the model, and the parameters are randomly selected using a Bernoulli distribution. When the deformation_direct parameter is set to (-η, η), the probability of randomly selecting the convex shape is controlled by the following formula:

[0030] (direct deform +η / 2) / η×100%

[0031] Where, direct deform The deformation direction is the input parameter. When the deform_direct parameter is set to 0, the system randomly selects a concave or convex shape with a 50% probability. When the deform_direct parameter is set to -0.5, the system randomly selects a concave shape with a 75% probability or a convex shape with a 25% probability, respectively.

[0032] Optionally, spatial conflict detection is performed on the newly generated spherical mesh, including:

[0033] Based on the influence range of existing deformations, the spatial threshold angle of the candidate deformation parameters of the newly generated spherical mesh is calculated, where the spatial threshold angle θ threshold The calculation expression is: θ threshold = 2 × arcsin(range / 2), where range is the range of the input parameter;

[0034] If the angle between any two points on the newly generated spherical mesh is less than the corresponding spatial threshold angle, it is determined to be a spatial conflict; otherwise, there is no spatial conflict.

[0035] Optionally, the processing steps for surface deformation operations include:

[0036] For a discrete point on a spherical discretized mesh, calculate the angle between that point and the spherical surface with respect to the current deformation axis;

[0037] Calculate the angle threshold of deformation influence based on the deformation range parameter;

[0038] If the included angle of the sphere is less than the angle threshold, the discrete point is determined to be located within the deformation influence area, and the included angle is normalized to the σ% confidence interval of the Gaussian distribution, and the normalized coordinates are calculated.

[0039] The deformation amplitude at the point is calculated using a Gaussian function based on the normalized coordinates.

[0040] Based on the deformation amplitude, the sign of the deformation is determined according to the deformation direction parameter, and the radius value of that point is updated.

[0041] Optionally, the shaping operation is based on a gradient function, using the form of a cosine function: gradient = 1 - cosθ. This function takes a value of 0 at θ = 0 and a value of 2 at θ = π. The overall deformation is achieved by modifying the z-coordinate, and the specific calculation formula is as follows:

[0042] z new =z0+deform amp ×gradient

[0043] In the formula, deform amp The overall deformation amplitude parameter; z0 is the z-coordinate of each deformed node after local deformation, and gradient is the deformation gradient along the z-axis; z new This is the modified z-coordinate.

[0044] Optionally, after performing surface deformation operations on the spherical mesh, the distribution characteristic parameters of the final spatial mesh nodes are generated, including:

[0045] After performing surface deformation operations on the spherical mesh, determine whether overall deformation compensation is required;

[0046] If overall deformation is required, the overall deformation parameters are generated based on the overall deformation amount and compensation type, and the shaping deformation is performed to obtain the final spatial grid node distribution characteristic parameters; otherwise, the current parameters are used as the final spatial grid node distribution characteristic parameters.

[0047] Optionally, based on the distribution characteristic parameters of the spatial grid nodes, a Delaunay triangle patch is generated, including:

[0048] For each internal node (i,j) in the grid, it forms a quadrilateral cell with its four adjacent nodes (i+1,j), (i,j+1), and (i+1,j+1).

[0049] Each quadrilateral unit is decomposed into two triangles:

[0050] The vertex indices of the first triangle are [v0, v1, v2], the vertex indices of the second triangle are [v0, v2, v3], and so on, generating all the triangular faces.

[0051] For a spherical mesh with a resolution of resolution×resolution, the number of Delaunay triangles generated is: (resolution-1)×(resolution-1)×2.

[0052] According to another aspect of the present invention, a device for establishing a fully parametric model of a three-dimensional irregular spherical object is provided, comprising:

[0053] The first generation module is used to generate a spherical mesh based on the input bubble foreign object parameters;

[0054] The parsing module is used to parse the deformation parameters of the spherical network, perform parameter format and content validation, and obtain the parsed parameters.

[0055] The deformation module is used to determine whether to apply random parameters based on the analytical parameters. If no random parameters are applied, the surface deformation operation is performed based on the analytical parameters. If random parameters are applied, random parameters are generated based on the analytical parameters, and the surface deformation operation is performed based on the analytical parameters and the generated random parameters.

[0056] The second generation module is used to generate the distribution characteristic parameters of the final spatial mesh nodes after performing surface deformation operations on the spherical mesh.

[0057] The third generation module is used to generate Delaunay triangle patches based on the distribution characteristic parameters of the spatial grid nodes.

[0058] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the methods described in any of the above aspects of the present invention.

[0059] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method described in any of the preceding aspects of the present invention.

[0060] Therefore, this invention adopts a fusion technology of surface modeling and implicit modeling. It achieves three-dimensional modeling through discretized points and surfaces, but the point and surface features are described in the form of parametric surfaces. Finally, they are combined to form a parametric discrete surface model, which realizes the simulation of the concave and convex deformation of the surface of the bubble foreign object or the simulation of the overall extrusion deformation. Attached Figure Description

[0061] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0062] Figure 1 This is a flowchart illustrating a method for establishing a fully parameterized model of a three-dimensional irregular spherical object according to an exemplary embodiment of the present invention.

[0063] Figure 2 This is another flowchart illustrating the method for establishing a fully parameterized model of a three-dimensional irregular spherical object provided in an exemplary embodiment of the present invention;

[0064] Figure 3a This is a schematic diagram illustrating the control parameter requirements provided in an exemplary embodiment of the present invention;

[0065] Figure 3b This is a modeling effect of a parametrically-based random spherical foreign object deformation provided by an exemplary embodiment of the present invention;

[0066] Figure 4 This is a schematic diagram of the structure of a three-dimensional irregular spherical object fully parameterized model building device provided in an exemplary embodiment of the present invention;

[0067] Figure 5 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. Detailed Implementation

[0068] Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0069] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0070] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of the present invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0071] It should also be understood that in the embodiments of the present invention, "multiple" can refer to two or more, and "at least one" can refer to one, two or more.

[0072] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more unless explicitly defined or given contrary instructions in the context.

[0073] Furthermore, the term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this invention generally indicates that the preceding and following related objects have an "or" relationship.

[0074] It should also be understood that the description of the various embodiments in this invention emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0075] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0076] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0077] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0078] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0079] The embodiments of this invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Well-known examples of terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0080] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0081] Exemplary methods

[0082] Figure 1 This is a flowchart illustrating a method for establishing a fully parametric model of a three-dimensional irregular spherical object according to an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as... Figure 1 As shown, the method 100 for establishing a fully parameterized model of a three-dimensional irregular spherical object includes the following steps:

[0083] Step 101: Generate a spherical mesh based on the input bubble foreign object parameters;

[0084] Step 102: Analyze the deformation parameters of the spherical network, verify the parameter format and content, and obtain the analyzed parameters;

[0085] Step 103: Determine whether to apply random parameters based on the analytical parameters. If no random parameters are applied, perform surface deformation operation based on the analytical parameters. If random parameters are applied, generate random parameters based on the analytical parameters and perform surface deformation operation based on the analytical parameters and the generated random parameters.

[0086] Step 104: After performing surface deformation operation on the spherical mesh, the distribution characteristic parameters of the final spatial mesh nodes are generated;

[0087] Step 105: Generate Delaunay triangle patches based on the distribution characteristic parameters of the spatial grid nodes.

[0088] Specifically, this invention employs a fusion technique of surface modeling and implicit modeling. Three-dimensional modeling is achieved through discretized points and surfaces, but the point and surface features are described using parametric surfaces. This combination ultimately forms a parametric discrete surface model, enabling the simulation of the uneven deformation of the surface of a bubble or foreign object, or the simulation of overall extrusion deformation. Its purpose is:

[0089] (1) Solve the technical problems of morphological uniformity and distortion in existing spherical foreign body modeling.

[0090] Existing technologies for modeling spherical foreign objects generally employ regular, smooth spherical models, which cannot accurately reflect the complex morphological changes of foreign objects such as bubbles in real-world multiphysics environments. This invention, through a parametric spherical deformation algorithm, achieves precise control over the local concave-convex deformation and overall extrusion deformation of the foreign object's surface, solving the problem of simulation distortion caused by overly simplified morphology in traditional modeling methods.

[0091] (2) Solving the problem of insufficient precision in the parameterized control of spherical foreign matter morphology

[0092] Traditional modeling methods struggle to achieve precise parameter control over bubble morphology, failing to meet the needs of various simulation scenarios. This invention, through multi-level parametric design, including adjustable parameters such as deformation amplitude, deformation range, and deformation direction, achieves accurate and controllable modeling of bubble morphology.

[0093] (3) Solving the problem of non-physical morphology generation caused by spatial conflicts in multi-deformable regions

[0094] Traditional spherical object modeling techniques lack effective spatial constraint mechanisms. When multiple deformation regions exist, they are prone to overlap or conflict, resulting in spherical object shapes that do not match physical constraints. This invention ensures the reasonable spatial distribution of multiple deformation regions through spherical geometric angle calculation and conflict detection algorithms, thus avoiding deformation conflict problems.

[0095] (4) Solving the technical problem of the disconnect between physical constraints and geometric modeling

[0096] Existing techniques often neglect the constraints of physical laws during the geometric modeling of spherical foreign objects, resulting in a lack of physical realism in the modeling results. This invention achieves effective coupling between geometric deformation and physical constraints through a volume conservation compensation mechanism and axial deformation gradient control, ensuring that the modeling results conform to physical laws.

[0097] (5) Solving the problem of batch random generation of spherical foreign object morphology

[0098] Existing technologies lack effective methods for generating random spherical foreign object shapes, making it difficult to simulate the diversity and uncertainty of bubble shapes in actual engineering. This invention, through a parameter randomization generation mechanism combined with physical constraint verification, achieves batch generation of bubble shapes that are both random and conform to physical laws, thus improving the practicality for engineering applications.

[0099] Furthermore, this invention proposes a method and system for establishing a fully parametric model of a three-dimensional irregular spherical object, the system flowchart of which is attached. Figure 2 As shown.

[0100] 1. Input bubble / foreign object parameters:

[0101] The bubble radius should be entered as a float16 data type, with the unit being mm.

[0102] The input data type for the discretization point resolution is int8;

[0103] The input data type for the shape function is string, such as "Gaussian";

[0104] The number of transformations is input as an int8 data type and is only valid after the random parameter setting is enabled.

[0105] The data type for inputting the deformation direction is float16. If the random parameter is not enabled, it can only be set to -1 or 1, which represent concave deformation and convex deformation, respectively. If the random parameter setting is enabled, the range can be set to [-1, 1].

[0106] The deformation range input data type is float16, and the range is (0,1). It represents the proportional relationship between the deformation range and the radius of the bubble.

[0107] The deformation range input data type is float16, the range is (0,1), and it is the proportional relationship between the deformation range and the area of ​​the large circle of the bubble;

[0108] The overall deformation input data type is float16, with a range of (0,1), which represents the ratio between the overall axial deformation and the diameter of the bubble.

[0109] The input data type for compensation type is string, such as "Volume";

[0110] 2. Spherical mesh generation: A spherical mesh is generated based on the bubble sphere radius and the discretization point resolution. The bubble sphere radius is used to control the radius of each discrete node from the center of the sphere, and the discretization point resolution is used to uniformly divide the azimuth and polar angle distribution in the u and v directions.

[0111] 3. Analyze the surface deformation parameter settings: Verify that parameters such as shape function, deformation number, deformation direction, deformation amplitude, deformation range, overall deformation amount, and compensation type are filled in as required. Only after this analysis is complete can you proceed to the next step.

[0112] The parsing method verifies the data format and content of the output parameters. Once parsed, the data will be correctly associated with the corresponding program variables.

[0113] 4. Apply random parameters: The program will prompt the user to enter whether to apply random parameters. Enter Y if yes, otherwise enter N.

[0114] If the input is Y, the following deformation parameters are first calculated based on parameters such as shape function, number of deformations, deformation direction, deformation amplitude, and deformation range: the shape function determines whether the deformation simulates a Gaussian or conical shape; the number of deformations determines the number of local deformations on the sphere's surface; the deformation direction determines the probability that a given deformation is concave or convex; the deformation amplitude determines the ratio of the maximum amplitude of a given deformation to the radius; and the deformation range determines the ratio of the range of a given deformation to the area of ​​the great circle of the base sphere. The deformation direction, deformation amplitude, and deformation range are applied with random parameters according to the method described in the invention. During the process of applying random deformations one by one, each deformation parameter needs to be checked for spatial conflicts with previously caused deformations using the model conflict detection module. If a conflict exists, the deformation parameters are randomly generated again, and the check is repeated.

[0115] If N is input, the influence of random parameters is not considered. The deformation parameters are directly generated based on the shape function, number of deformations, deformation direction, deformation amplitude, and deformation range, and then the deformation is performed.

[0116] 5. After local deformation is completed, the user inputs whether to consider the overall deformation effect:

[0117] If Y is input, the overall deformation parameters are generated based on the overall deformation amount and compensation type, and then the overall deformation is performed.

[0118] If N is entered, the overall deformation is skipped and the process proceeds directly to the mesh generation step.

[0119] 6. Generate a high-quality mesh. Based on the distribution characteristics of the final spatial mesh nodes (after deformation), generate Delaunay triangle patches.

[0120] 7. Record the generated Delaunay triangle facets as an STL file.

[0121] In a specific embodiment of the present invention:

[0122] (1) Bubble foreign matter parameters

[0123] Input the parameters required for modeling the bubble foreign object, including bubble radius, discretization point resolution, shape function, deformation count, deformation direction, deformation amplitude, deformation range, overall deformation amount, and compensation type, totaling 9 parameters. Parameter requirements are attached. Figure 2 As shown.

[0124] The bubble radius should be entered as a float16 data type, with the unit being mm.

[0125] The input data type for the discretization point resolution is int8;

[0126] The input data type for the shape function is string, such as "Gaussian";

[0127] The number of transformations is input as an int8 data type and is only valid after the random parameter setting is enabled.

[0128] The data type for inputting the deformation direction is float16. If the random parameter is not enabled, it can only be set to -1 or 1, which represent concave deformation and convex deformation, respectively. If the random parameter setting is enabled, the range can be set to [-1, 1].

[0129] The deformation range input data type is float16, and the range is (0,1). It represents the proportional relationship between the deformation range and the radius of the bubble.

[0130] The deformation range input data type is float16, the range is (0,1), and it is the proportional relationship between the deformation range and the area of ​​the large circle of the bubble;

[0131] The overall deformation input data type is float16, with a range of (0,1), which represents the ratio between the overall axial deformation and the diameter of the bubble.

[0132] The input data type for compensation type is string, such as "Volume".

[0133] (2) Spherical Mesh Generation Module

[0134] The spherical mesh generation module is the fundamental technology for solving the problem of uniform morphology of spherical foreign objects in this invention. This module constructs the basic mesh using a spherical coordinate system. The specific implementation process is as follows: First, in the spherical coordinate system, a uniformly distributed sequence from 0 to 2π is generated along the azimuth angle Φ, with the number of segments determined by the discretization point resolution parameter. Then, a uniformly distributed sequence from 0 to π is generated along the polar angle θ, with the segmentation density also controlled by the discretization point resolution parameter. Next, a two-dimensional parametric mesh (u, v) is generated through a meshing operation, where u corresponds to the azimuth angle and v corresponds to the polar angle. Finally, based on the bubble sphere radius r, the initial radius of all mesh points is set to rr. The spherical mesh generated by this module provides a high-precision discretization foundation for subsequent deformation processing, and the mesh density directly affects the accuracy and computational complexity of the final modeling.

[0135] (3) Deformation parameter generation module

[0136] The deformation parameter generation module is the core technology module of this invention for achieving accurate modeling and batch generation of spherical foreign objects. This module supports two working modes: deterministic parameter modeling and randomized batch generation.

[0137] 1) Parametric modeling process for spherical foreign objects

[0138] This invention can generate spherical foreign objects of specific shapes according to the provided precise parameters and a standardized parameter mapping process, achieving complete model reproducibility. The parameter system for deterministic modeling includes:

[0139] Geometric control parameter set: This invention proposes deformation axial parameters (axis_phi, axis_theta) to accurately define the spatial orientation of the deformation, where axis_phi is the azimuth angle in the corresponding spherical coordinate system (range 0 to 2π), and axis_theta is the polar angle in the corresponding spherical coordinate system (range 0 to π).

[0140] The set of shape control parameters: This invention proposes that the deformation direction parameter deform_direct adopts floating-point encoding (-1 indicates inward concave deformation, 1 indicates outward convex deformation); the Gaussian standard deviation parameter gaussian_sigma controls the smoothness of the deformation boundary (the value ranges from 0.1 to 3.0, the smaller the value, the sharper the boundary); the deformation quantity parameter deform_counts supports the superposition of multiple deformations.

[0141] The deterministic parameter modeling process is executed according to the following standardized procedure: A complete parameter configuration file is input, and the system verifies the parameters according to their priority; the geometric rationality of each deformation parameter set is checked, including angle range verification, amplitude ratio check, and direction consistency confirmation; the number of iterations for deformation processing is determined based on the deformation quantity parameter, and the deformation sequence is arranged according to the spatial distribution optimization principle; spatial conflict detection, local deformation processing, and physical constraint verification are performed sequentially for each deformation; the generated spherical foreign object model is correlated with the input parameters to ensure that the same spherical foreign object morphology can be accurately reproduced under the same parameter input.

[0142] 2) Randomized batch generation process

[0143] Based on deterministic modeling, this invention can apply controlled stochastic processes to the geometric parameters, morphological parameters, and distribution parameters to achieve batch random generation of spherical foreign objects within a reasonable engineering range.

[0144] Geometric parameter randomization mechanism: The deformation axial parameters are randomized using a uniform spherical distribution. The azimuth angle axis_phi in the spherical coordinate system follows a random distribution within the range [0, 2π], and the polar angle axis_theta in the spherical coordinate system follows a random distribution within the range [0, π], ensuring a uniform random distribution of the deformation axis on the sphere. This invention proposes that the deformation amplitude parameter A is based on the fundamental value. amp The algorithm applies a uniformly distributed random perturbation within the range of %-100%, and the calculation formula is as follows:

[0145] amp random =amp deform ×(A amp +B amp ×rand(0,1))

[0146] Where rand(0,1) is a uniformly distributed random number in the range [0,1]. A amp B is the random disturbance amplitude coefficient, with a value in the range (0,1), which can control the disturbance amplitude. amp =1-A amp .

[0147] This invention proposes a deformation range parameter A within the basic value. range The random perturbation algorithm is applied within the range of % to 100%, and the calculation formula is as follows:

[0148] range random =range deform ×(A range +B range ×rand(0,1))

[0149] Among them, A rangeThis is the random disturbance range coefficient, with a value in the range (0,1), which controls the disturbance range. B range =1-A range .

[0150] Morphological parameter randomization mechanism: This invention proposes a mapping relationship between the deformation direction and the probability of concave or convex deformation direction. The model and parameters are randomly selected using a Bernoulli distribution. When the deform_direct parameter is set to (-η, η), the system controls the probability of randomly selecting concave or convex shapes using the following formula:

[0151] (direct deform +η / 2) / η×100%

[0152] For example, when the deform_direct parameter is set to 0, the system randomly selects a concave or convex shape with a 50% probability; when the deform_direct parameter is set to -0.5, the system randomly selects a concave shape with a 75% probability or a convex shape with a 25% probability, respectively.

[0153] Batch generation control strategy: The system supports random generation of batches with a specified batch size, typically consisting of any number of spherical foreign object models; the random sequence is controlled by seed parameters to ensure the reproducibility of batch generation results; a quality screening mechanism is established to automatically remove abnormal shapes that do not meet engineering requirements, including excessive deformation, spatial conflicts, and volume anomalies; and parameter distribution statistics are provided to record the parameter distribution characteristics of the generated spherical foreign object group, providing statistical data for engineering analysis.

[0154] Through the dual mechanisms of deterministic modeling and randomized generation, the deformation parameter generation module of this invention can not only meet the requirements of model accuracy and reproducibility in scientific research, but also adapt to the needs of diverse morphologies and batch processing of spherical foreign objects in engineering applications, providing a complete technical solution for modeling spherical foreign objects.

[0155] (4) Model conflict detection module

[0156] The model conflict detection module is the core technology of this invention for solving spatial conflict problems in multi-deformable regions. This module achieves accurate conflict detection based on the principles of spherical geometry. The mathematical basis of conflict detection is the calculation of the geodesic distance between two points on a sphere. and The formula for calculating the included angle θ is:

[0157]

[0158] The detection process of this module is as follows: For newly generated candidate deformation parameters, this invention proposes to calculate the angle between them and the axial direction of all existing deformations; based on the influence range of existing deformations, the formula for calculating their spatial threshold angle is:

[0159] θ nreshold = 2 × arcsin(range / 2)

[0160] If any included angle is less than the corresponding threshold angle, it is determined to be a spatial conflict; if a conflict exists, candidate parameters are regenerated; otherwise, the parameter is added to the valid deformation set. This module ensures the reasonable spatial distribution of multiple deformation regions and avoids non-physical shapes caused by deformation overlap.

[0161] (5) Surface deformation processing module

[0162] The local deformation processing module enables precise deformation control based on a Gaussian distribution, and this module is a key technology for achieving parameterized control accuracy in this invention. The mathematical model for deformation processing is based on a Gaussian probability density function:

[0163]

[0164] Where σ is the Gaussian standard deviation parameter. This invention proposes a deformation processing flow: for each discrete point (u) of the spherical mesh... i，j v i，j First, the spherical angle between the point and the current deformation axis (grid_axis_theta) is calculated. Then, the angle threshold (theta_range) of the deformation influence is calculated based on the deformation range parameter. If the angle is less than the threshold, the point is located within the deformation influence region. Next, the angle is normalized to the σ% confidence interval of a Gaussian distribution, and the normalized coordinate (normalized_theta) is calculated. The deformation amplitude (deform_amp) of the point is calculated using a Gaussian function. Finally, the sign of the deformation is determined based on the deformation direction parameter, and the radius value of the point is updated. This module ensures a smooth transition of the deformation boundary through a Gaussian distribution, avoiding mesh quality problems caused by abrupt changes.

[0165] (6) Overall Deformation Processing Module

[0166] The overall deformation processing module is responsible for adjusting the macroscopic shape of the bubble. This module controls the overall shape of the bubble through axial compression and gradient deformation. The overall deformation employs a gradient function based on the polar angle. The deformation amplitude continuously changes with the polar angle position, with the largest deformation at the top of the bubble (θ = 0), moderate deformation in the middle (θ = π / 2), and the smallest deformation at the bottom (θ = π). This invention proposes a gradient function in the form of a cosine function: gradient = 1 - cosθ. This function takes a value of 0 at θ = 0 and a value of 2 at θ = π, ensuring the continuity and physical rationality of the deformation. The overall deformation is achieved by modifying the z-coordinate.

[0167] z new =z0+deform amp ×gradient

[0168] Where deformation_amp is the overall deformation amplitude parameter. This module implements physical reality constraints based on the principle of volume conservation. The theoretical basis of volume compensation is the formula for the volume of a sphere: V = (4 / 3)πr. 3 When the bubble undergoes axial deformation, radial compensation must be used to maintain the total volume, thus ensuring volume conservation while controlling the overall shape.

[0169] (7) Mesh construction and output module

[0170] The mesh generation output module is responsible for organizing discrete 3D coordinate points into a standard triangular mesh and outputting an STL format file usable in engineering. The triangular mesh is built based on the topological relationships of the structured mesh. For a mesh with a resolution of resolution×resolution, the number of triangular faces generated is:

[0171] (resolution-1)×(resolution-1)×2

[0172] The construction process involves the following steps: For each internal node (i, j) in the mesh, it forms a quadrilateral element with its four adjacent nodes (i+1, j), (i, j+1), and (i+1, j+1). Each quadrilateral element is decomposed into two triangles: the vertex indices of the first triangle are [v0, v1, v2], and the vertex indices of the second triangle are [v0, v2, v3]. This process is repeated to generate all the triangular facets, which are then converted into a geometric model file that meets industry standards and satisfies the requirements of an STL file. This model can be directly used for CAE simulation analysis.

[0173] In another exemplary embodiment of the present invention, for example, if it is necessary to generate random-shaped bubble foreign objects in batches, the following steps need to be performed:

[0174] (1) Parameter input

[0175] According to the appendix Figure 3a The requirements are as follows: input bubble radius (1.0), discretization point resolution (100), shape function ("Gaussian"), deformation number (13), deformation direction (0.5), deformation amplitude (0.85), deformation range (0.2), overall deformation amount (0.85), and compensation type parameter ("Volume").

[0176] (2) Modeling process

[0177] During program execution, the user will be prompted to confirm whether to apply random parameters. For example, entering "Y" will apply random perturbations to the deformation process, as explained in the main text. Then, spatial conflict detection will be automatically completed, and finally, surface deformation will be automatically finished. In this example, the overall deformation parameter was entered, so the deformation will be automatically completed according to the parameter design.

[0178] (3) Model export

[0179] The model after modeling is completed (e.g.) Figure 3b As shown, it still only stores an array of all discrete point coordinates. It generates a high-quality mesh for the model using the Delaunay triangle principle and finally exports it as an STL structure model file.

[0180] Therefore, the present invention has the following beneficial technical effects:

[0181] (1) In terms of modeling accuracy, compared with the traditional simplified spherical modeling method, this invention provides parameterized adjustable arbitrary resolution spherical mesh and sphere radius, local functions and corresponding parameters, and overall deformation functions and corresponding parameter control. The parameterized control mechanism fundamentally improves the adjustability and reproducibility of the spherical foreign object morphology, and can accurately reflect the real deformation characteristics of the spherical foreign object in a multi-physics environment, thus solving the limitation of the single morphology of the traditional method.

[0182] (2) In terms of modeling efficiency, through procedural and parametric modeling methods, optimized spherical geometry algorithms, and conflict detection mechanisms, one-click construction of spherical foreign object models is achieved. Compared with the manual modeling method of traditional CAD software, this improves modeling efficiency and reduces the time cost of engineering design.

[0183] (3) The random generation mechanism of the present invention makes it possible to model spherical foreign objects in batches. A variety of spherical foreign object models that meet engineering requirements can be generated in a single generation, providing basic technical support for electric field simulation analysis of large-scale spherical foreign object groups.

[0184] Exemplary device

[0185] Figure 4This is a schematic diagram of the structure of a three-dimensional irregular spherical object fully parameterized model building device provided in an exemplary embodiment of the present invention. Figure 4 As shown, the device 400 includes:

[0186] The first generation module 410 is used to generate a spherical mesh based on the input bubble foreign object parameters;

[0187] The parsing module 420 is used to parse the deformation parameters of the spherical network, perform parameter format and content verification, and obtain the parsed parameters.

[0188] The deformation module 430 is used to determine whether to apply random parameters based on the analytical parameters. If no random parameters are applied, the surface deformation operation is performed based on the analytical parameters. If random parameters are applied, random parameters are generated based on the analytical parameters, and the surface deformation operation is performed based on the analytical parameters and the generated random parameters.

[0189] The second generation module 440 is used to generate the distribution characteristic parameters of the final spatial mesh nodes after performing surface deformation operations on the spherical mesh.

[0190] The third generation module 450 is used to generate Delaunay triangle patches based on the distribution characteristic parameters of the spatial grid nodes.

[0191] Optionally, the first generation module 410 includes:

[0192] In spherical coordinates, a uniformly distributed sequence from 0 to 2π is generated from the azimuth angle Φ, where the number of segments is determined by the discretization point resolution parameter.

[0193] A uniformly distributed sequence from 0 to π is generated in the polar angle θ direction, and the segmentation density is also controlled by the discretization point resolution parameter.

[0194] Next, a two-dimensional parametric mesh (u, v) is generated through a meshing operation, where u corresponds to the azimuth angle and v corresponds to the polar angle;

[0195] A spherical mesh is generated based on a two-dimensional parametric mesh and the radius of the bubble.

[0196] Optionally, the analyzed deformation parameters include at least one of the following: shape function, number of deformations, deformation direction, deformation amplitude, deformation range, overall deformation amount, and compensation type.

[0197] Optionally, the deformation module 430 generates random parameters based on the analytical parameters, and performs surface deformation operations according to the analytical parameters and the generated random parameters, including:

[0198] Random parameters are obtained by randomly controlling the geometric, morphological, and distribution parameters of the surface deformation of the spherical mesh based on analytical parameters.

[0199] Surface deformation operation on spherical mesh based on random parameters;

[0200] After the surface deformation operation is completed, spatial conflict detection is performed on the newly generated spherical mesh;

[0201] If no spatial conflict exists, output the spherical mesh after the surface deformation operation; otherwise, regenerate random parameters and perform the surface deformation operation until no spatial conflict exists.

[0202] Optionally, the randomization mechanism for the geometric parameters is as follows: the deformation axis parameters are randomly distributed uniformly on a sphere, the azimuth angle axis_phi in the spherical coordinate system follows a random distribution in the range [0, 2π], and the polar angle axis_theta in the spherical coordinate system follows a random distribution in the range [0, π], ensuring a uniform random distribution of the deformation axis on the sphere.

[0203] The basic value of the deformation amplitude parameter A amp The algorithm applies a uniformly distributed random perturbation within the range of % to 100%, and the calculation formula is as follows:

[0204] amp random =amp deform ×(A amp +B amp ×rand(0,1))

[0205] Where rnad(0,1) is a uniformly random number in the range [0,1]; A amp This is the random disturbance amplitude coefficient, with a value in the range (0, 1), which controls the disturbance amplitude B. amp =1-A amp ;

[0206] The deformation range parameter is within the base value A. range The random perturbation algorithm is applied within the range of % to 100%, and the calculation formula is as follows:

[0207] range random =range deform ×(A range +B range ×rand(0,1))

[0208] Among them, A range B is the random disturbance range coefficient, taking values ​​within the range (0, 1), which controls the disturbance range. range =1-A range .

[0209] Optionally, the randomization mechanism for the morphological parameters is as follows: the mapping relationship between the deformation direction and the probability of the concave-convex deformation direction, the model, and the parameters are randomly selected using a Bernoulli distribution. When the deformation_direct parameter is set to (-η, η), the probability of randomly selecting the convex shape is controlled by the following formula:

[0210] (direct deform +η / 2) / η×100%

[0211] Where, direct deform The deformation direction is the input parameter. When the deform_direct parameter is set to 0, the system randomly selects a concave or convex shape with a 50% probability. When the deform_direct parameter is set to -0.5, the system randomly selects a concave shape with a 75% probability or a convex shape with a 25% probability, respectively.

[0212] Optionally, spatial conflict detection is performed on the newly generated spherical mesh, including:

[0213] Based on the influence range of existing deformations, the spatial threshold angle of the candidate deformation parameters of the newly generated spherical mesh is calculated, where the spatial threshold angle θ threshold The calculation expression is: θ threshold = 2 × arcsin(range / 2), where range is the range of the input parameter;

[0214] If the angle between any two points on the newly generated spherical mesh is less than the corresponding spatial threshold angle, it is determined to be a spatial conflict; otherwise, there is no spatial conflict.

[0215] Optionally, the processing steps for surface deformation operations include:

[0216] For a discrete point on a spherical discretized mesh, calculate the angle between that point and the spherical surface with respect to the current deformation axis;

[0217] Calculate the angle threshold of deformation influence based on the deformation range parameter;

[0218] If the included angle of the sphere is less than the angle threshold, the discrete point is determined to be located within the deformation influence area, and the included angle is normalized to the σ% confidence interval of the Gaussian distribution, and the normalized coordinates are calculated.

[0219] The deformation amplitude at the point is calculated using a Gaussian function based on the normalized coordinates.

[0220] Based on the deformation amplitude, the sign of the deformation is determined according to the deformation direction parameter, and the radius value of that point is updated.

[0221] Optionally, the shaping operation is based on a gradient function, using the form of a cosine function: gradient = 1 - cosθ. This function takes a value of 0 at θ = 0 and a value of 2 at θ = π. The overall deformation is achieved by modifying the z-coordinate, and the specific calculation formula is as follows:

[0222] z new =z0+deform amp ×gradient

[0223] In the formula, deform amp The overall deformation amplitude parameter; z0 is the z-coordinate of each deformed node after local deformation, and gradient is the deformation gradient along the z-axis; z new This is the modified z-coordinate.

[0224] Optionally, the second generation module 440 includes:

[0225] After performing surface deformation operations on the spherical mesh, determine whether overall deformation compensation is required;

[0226] If overall deformation is required, the overall deformation parameters are generated based on the overall deformation amount and compensation type, and the shaping deformation is performed to obtain the final spatial grid node distribution characteristic parameters; otherwise, the current parameters are used as the final spatial grid node distribution characteristic parameters.

[0227] Optionally, the third generation module 450 includes:

[0228] For each internal node (i,j) in the grid, it forms a quadrilateral cell with its four adjacent nodes (i+1,j), (i,j+1), and (i+1,j+1).

[0229] Each quadrilateral unit is decomposed into two triangles:

[0230] The vertex indices of the first triangle are [v0, v1, v2], the vertex indices of the second triangle are [v0, v2, v3], and so on, generating all the triangular faces.

[0231] For a spherical mesh with a resolution of resolution×resolution, the number of Delaunay triangles generated is: (resolution-1)×(resolution-1)×2.

[0232] Exemplary electronic devices

[0233] Figure 5 This is the structure of an electronic device provided in an exemplary embodiment of the present invention. For example... Figure 5 As shown, the electronic device 50 includes one or more processors 51 and memory 52.

[0234] The processor 51 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0235] The memory 52 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 51 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above, and / or other desired functions. In one example, the electronic device may also include an input device 53 and an output device 54, these components being interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0236] In addition, the input device 53 may also include, for example, a keyboard, a mouse, etc.

[0237] The output device 54 can output various information to the outside. The output device 54 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0238] Of course, for the sake of simplicity, Figure 5 Only some of the components of this electronic device relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0239] Exemplary computer program products and computer-readable storage media

[0240] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0241] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0242] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0243] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0244] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0245] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0246] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0247] The methods and systems of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0248] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0249] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for establishing a fully parameterized model of a three-dimensional irregular spherical object, characterized in that, include: Generate a spherical mesh based on the input bubble foreign object parameters; The deformation parameters of the spherical network are analyzed, and the parameter format and content are validated to obtain the analyzed parameters; Whether to apply random parameters is determined based on the analytical parameters. If no random parameters are applied, a surface deformation operation is performed based on the analytical parameters. If random parameters are applied, random parameters are generated based on the analytical parameters, and a surface deformation operation is performed based on the analytical parameters and the generated random parameters. After performing surface deformation operations on the spherical mesh, the distribution characteristic parameters of the final spatial mesh nodes are generated; Based on the distribution characteristic parameters of the spatial grid nodes, Delaunay triangle patches are generated.

2. The method according to claim 1, characterized in that, Based on the input bubble foreign object parameters, a spherical mesh is generated, including: In spherical coordinates, a uniformly distributed sequence from 0 to 2π is generated from the azimuth angle Φ, where the number of segments is determined by the discretization point resolution parameter. A uniformly distributed sequence from 0 to π is generated in the polar angle θ direction, and the segmentation density is also controlled by the discretization point resolution parameter. Next, a two-dimensional parametric mesh (u,v) is generated through a meshing operation, where u corresponds to the azimuth angle and v corresponds to the polar angle; The spherical mesh is generated based on the two-dimensional parameter mesh and the bubble radius.

3. The method according to claim 1, characterized in that, The deformation parameters analyzed include at least one of the following: shape function, number of deformations, deformation direction, deformation amplitude, deformation range, overall deformation amount, and compensation type.

4. The method according to claim 1, characterized in that, Generating random parameters based on the analytical parameters, and performing surface deformation operations according to the analytical parameters and the generated random parameters, including: Based on the analytical parameters, the geometric parameters, morphological parameters, and distribution parameters of the surface deformation of the spherical mesh are randomly controlled to obtain random parameters; Perform surface deformation operation on the spherical mesh based on the random parameters; After the surface deformation operation is completed, spatial conflict detection is performed on the newly generated spherical mesh; If no spatial conflict exists, output the spherical mesh after the surface deformation operation; otherwise, regenerate random parameters and perform the surface deformation operation until no spatial conflict exists.

5. The method according to claim 4, characterized in that, The randomization mechanism for the geometric parameters is as follows: the deformation axis parameters are randomly distributed uniformly on a sphere; the azimuth angle axis_phi in the spherical coordinate system follows a random distribution in the range [0, 2π]; and the polar angle axis_theta in the spherical coordinate system follows a random distribution in the range [0, π], ensuring a uniform random distribution of the deformation axis on the sphere. The basic value of the deformation amplitude parameter A amp The algorithm applies a uniformly distributed random perturbation within the range of % to 100%, and the calculation formula is as follows: amp random =amp deform ×(A amp +B amp ×rand(0,1)) Where, ranf(0,1) is a uniformly random number in the range [0,1]; A amp This is the random disturbance amplitude coefficient, with a value in the range (0,1), which controls the disturbance amplitude B. amp =1-A amp ; The deformation range parameter is within the base value A. range The random perturbation algorithm is applied within the range of % to 100%, and the calculation formula is as follows: range random =range deform ×(A range +B range ×rand(0,1)) Among them, A range This is the random disturbance range coefficient, with a value in the range (0,1), which controls the disturbance range. B range =1-A range .

6. The method according to claim 4, characterized in that, The randomization mechanism of the morphological parameters is as follows: the mapping relationship between the deformation direction and the probability of concave-convex deformation direction, the model, and the parameters are randomly selected using a Bernoulli distribution. When the deformation_direct parameter is set to (-η, η), the probability of randomly selecting the convex shape is controlled by the following formula: (directly deform +n / 2) / n×100% Where, direct deform The deformation direction is the input parameter. When the deform_direct parameter is set to 0, the system randomly selects a concave or convex shape with a 50% probability. When the deform_direct parameter is set to -0.5, the system randomly selects a concave shape with a 75% probability or a convex shape with a 25% probability, respectively.

7. The method according to claim 4, characterized in that, Spatial conflict detection is performed on the newly generated spherical mesh, including: Based on the influence range of existing deformations, the spatial threshold angle of the candidate deformation parameters of the newly generated spherical mesh is calculated, where the spatial threshold angle θ threshold The calculation expression is: θ threshold = 2 × arcsin(range / 2), where range is the range of the input parameter; If the angle between any two points on the newly generated spherical mesh is less than the corresponding spatial threshold angle, it is determined to be a spatial conflict; otherwise, there is no spatial conflict.

8. The method according to claim 1, characterized in that, The surface deformation operation includes the following steps: For a discrete point on the spherical discretized mesh, calculate the spherical angle between that point and the current deformation axis; Calculate the angle threshold of deformation influence based on the deformation range parameter; If the included angle of the sphere is less than the angle threshold, the discrete point is determined to be located within the deformation influence area, and the included angle is normalized to the σ% confidence interval of the Gaussian distribution, and the normalized coordinates are calculated. The deformation amplitude at the point is calculated using a Gaussian function based on the normalized coordinates. Based on the deformation amplitude, the sign of the deformation is determined according to the deformation direction parameter, and the radius value of that point is updated.

9. The method according to claim 1, characterized in that, The shaping and deformation operation is based on a gradient function, specifically a cosine function: gradient = 1 - cosθ. This function takes a value of 0 at θ = 0 and a value of 2 at θ = π. The overall deformation is achieved by modifying the z-coordinate, and the specific calculation formula is as follows: from new =z0+deform amp ×gradient In the formula, deform amp The overall deformation amplitude parameter; z0 is the z-coordinate of each deformed node after local deformation, and gradient is the deformation gradient along the z-axis; z new This is the modified z-coordinate.

10. The method according to claim 1, characterized in that, After performing surface deformation on the spherical mesh, the distribution characteristic parameters of the final spatial mesh nodes are generated, including: After performing surface deformation on the spherical mesh, determine whether overall deformation compensation is required. If overall deformation is required, the overall deformation parameters are generated based on the overall deformation amount and compensation type, and the shaping deformation is performed to obtain the final spatial grid node distribution characteristic parameters; otherwise, the current parameters are used as the final spatial grid node distribution characteristic parameters.

11. The method according to claim 1, characterized in that, Based on the distribution characteristic parameters of the spatial grid nodes, a Delaunay triangle patch is generated, including: For each internal node (i, j) in the grid, it forms a quadrilateral cell with four adjacent nodes (i+1, j), (i, j+1), and (i+1, j+1). Each quadrilateral unit is decomposed into two triangles: The vertex indices of the first triangle are [v0, v1, v2], the vertex indices of the second triangle are [v0, v2, v3], and so on, generating all the triangular faces. For a spherical mesh with a resolution of resolution×resolution, the number of Delaunay triangles generated is: (resolution-1)×(resolution-1)×2.

12. A device for establishing a fully parametric model of a three-dimensional irregular spherical object, characterized in that, include: The first generation module is used to generate a spherical mesh based on the input bubble foreign object parameters; The parsing module is used to parse the deformation parameters of the spherical network, perform parameter format and content verification, and obtain parsed parameters; The deformation module is used to determine whether to apply random parameters based on the analytical parameters. If no random parameters are applied, the surface deformation operation is performed based on the analytical parameters. If random parameters are applied, random parameters are generated based on the analytical parameters, and the surface deformation operation is performed based on the analytical parameters and the generated random parameters. The second generation module is used to generate the distribution characteristic parameters of the final spatial grid nodes after the surface deformation operation is performed on the spherical grid; The third generation module is used to generate Delaunay triangle patches based on the distribution characteristic parameters of the spatial grid nodes.

13. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-11.

14. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method described in any one of claims 1-11.

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