Encrypted triangular mesh generation method and device, storage medium and computer program product

Abstract

The invention discloses an encrypted triangular mesh generation method and device, a storage medium and a computer program product, and relates to the technical field of computer-aided engineering.The method comprises the steps that an independent K-dimensional tree in a geometric plane is constructed according to background mesh nodes pre-constructed by a preset geometric model; performing neighborhood query filtering based on topological information of each geometric surface in the geometric model to obtain a to-be-calculated adjacent geometric surface; traversing the background grid nodes, querying a nearest neighbor point according to the K-dimensional tree and the adjacent geometric surface to be calculated, and calculating an adjacent size based on the nearest neighbor point; and establishing an adjacent size field according to the adjacent size, and generating an encrypted triangular mesh based on the adjacent size field. By constructing an in-plane independent K-dimensional tree, a to-be-calculated adjacent geometric surface is accurately identified, a size field is efficiently calculated, and improvement of encryption accuracy, improvement of calculation efficiency and enhancement of simulation precision are comprehensively realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer aided engineering, and particularly relates to a method, device, storage medium and computer program product for encrypted triangular mesh generation. BACKGROUND

[0002] In CAE simulation analysis, for thin-walled structures such as sheet metal parts, composite laminates, electronic component housings and the like, due to the significant stress and strain gradient changes in the thickness direction, in order to balance the efficiency and the accuracy of the solving results, the mesh is only encrypted in the places where encryption is needed. However, when processing complex geometric models such as thin-walled structures using traditional mesh generation methods, it is difficult to dynamically identify the regions in the model where the spatial distance is close but the topology is not adjacent (such as the two sides of a narrow gap with non-shared boundaries), resulting in the inability to perform targeted local mesh encryption, and prone to problems such as mesh penetration, distortion or size field discontinuity, thereby affecting the mesh quality and the accuracy and stability of the subsequent numerical simulation results.

[0003] Therefore, how to dynamically identify the regions in the model where the spatial distance is close but the topology is not adjacent, improve the accuracy and computational efficiency of mesh encryption in CAE simulation analysis, and increase the simulation accuracy, has become a technical problem that needs to be solved by the present application.

[0004] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0005] The main purpose of the present application is to provide a method, device, storage medium and computer program product for encrypted triangular mesh generation, aiming to solve the technical problem of how to dynamically identify the regions in the model where the spatial distance is close but the topology is not adjacent, improve the accuracy and computational efficiency of mesh encryption in CAE simulation analysis, and increase the simulation accuracy.

[0006] To achieve the above purpose, the present application provides a method for generating encrypted triangular mesh, applied to computer aided engineering (CAE) simulation, which comprises: constructing an independent K-dimensional tree in a geometric face according to the background mesh nodes pre-constructed according to a preset geometric model; performing neighborhood query filtering based on the topological information of each geometric face in the geometric model to obtain adjacent geometric faces to be calculated; traversing the background mesh nodes, querying the nearest neighboring points according to the K-dimensional tree and the adjacent geometric faces to be calculated, and calculating the adjacent size based on the nearest neighboring points; establishing an adjacent size field according to the adjacent size, and generating encrypted triangular mesh based on the adjacent size field.

[0007] In an embodiment, the step of constructing the K-dimensional tree independently in the plane according to the background grid nodes pre-constructed according to the preset geometric model further comprises the following steps: obtaining an initial cell size and a minimum limit size input by a user; generating a discrete triangular mesh of each geometric face in the geometric model based on the topological information, and taking the discrete triangular mesh as a background mesh; initializing a size value of the background mesh according to the initial cell size and the minimum limit size, to obtain a background mesh initial size parameter and each background mesh node.

[0008] In an embodiment, the step of traversing the background mesh nodes, querying a nearest neighbor point according to the K-dimensional tree and the adjacent geometric face to be calculated, and calculating an adjacent size based on the nearest neighbor point comprises the following steps: traversing the background mesh nodes, querying a nearest neighbor point according to the K-dimensional tree and the adjacent geometric face to be calculated; obtaining a thickness direction layer number input by a user; calculating an adjacent size according to the thickness direction layer number, a nearest distance of the nearest neighbor point, and the background mesh initial size parameter.

[0009] In an embodiment, the step of establishing an adjacent size field according to the adjacent size comprises the following steps: establishing an initial adjacent size field according to the adjacent size, identifying a size value gradient mutation area of the initial adjacent size field, and performing recursive smoothing processing on the size value gradient mutation area until a preset smoothing condition is reached; obtaining a smoothed adjacent size field under the condition that the size value gradient mutation area reaches the preset smoothing condition; performing linear interpolation calculation on any point in the background mesh pre-constructed for the geometric model, to obtain an arbitrary point size value; performing continuous processing on the smoothed adjacent size field according to the arbitrary point size value, to obtain the adjacent size field.

[0010] In an embodiment, the step of establishing an adjacent size field according to the adjacent size further comprises the following steps: establishing a mapping relationship between the adjacent size field and topological information of each geometric face in the geometric model, and managing a shared boundary according to the mapping relationship.

[0011] In an embodiment, the step of generating an encrypted triangular mesh based on the adjacent size field comprises the following steps: associating a model boundary of the geometric model to the adjacent size field based on a pre-established mapping relationship, and obtaining size field information of the adjacent size field; discretize the model boundary according to the size field information, after the adaptive discretization, each of the model boundaries corresponds to a piecewise linear size field; interpolate according to the size value of the piecewise linear size field and the actual arc length of the model boundary to obtain boundary node coordinates; generate a dense triangular mesh according to the boundary node coordinates.

[0012] In an embodiment, the step of generating a dense triangular mesh according to the boundary node coordinates comprises: According to the boundary node coordinates, a triangular mesh division algorithm is used to generate a mesh for each geometric face of the geometric model to obtain an initial triangular mesh. Perform a mesh optimization operation on the initial triangular mesh to obtain a dense triangular mesh, the mesh optimization operation includes edge splitting, edge collapsing and edge swapping.

[0013] In addition, to achieve the above-mentioned purpose, the present application also provides a dense triangular mesh generation device, the device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program is configured to implement the steps of the dense triangular mesh generation method as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, the storage medium is a computer readable storage medium, the storage medium stores a computer program, the computer program is executed by the processor to implement the steps of the dense triangular mesh generation method as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, the computer program product comprises a computer program, the computer program is executed by the processor to implement the steps of the dense triangular mesh generation method as described above.

[0016] The one or more technical solutions provided by the present application have at least the following technical effects: The K-dimensional tree independent in the geometric face is constructed according to a background grid node constructed in advance according to a preset geometric model; neighborhood query filtering is performed based on topological information of each geometric face in the geometric model, to obtain a to-be-calculated adjacent geometric face; the background grid node is traversed, the nearest neighbor point is queried based on the K-dimensional tree and the to-be-calculated adjacent geometric face, and an adjacent size is calculated based on the nearest neighbor point; an adjacent size field is established according to the adjacent size, and a densified triangular mesh is generated based on the adjacent size field. First, the K-dimensional tree independent in the geometric face is constructed, topological ambiguity caused by cross-face vertex mixed construction is effectively avoided, and a data structure foundation is laid for accurate identification of a spatial adjacent region; further, the to-be-calculated adjacent geometric face is screened based on geometric face topological information, only nodes meeting topological constraints are included in neighborhood query, interference of non-adjacent faces is excluded, and the accuracy of adjacent region identification is ensured; further, when the background grid node is traversed, the nearest neighbor point in the to-be-calculated adjacent geometric face is efficiently queried by using the K-dimensional tree, and the adjacent size is calculated, the region with a close spatial distance but not topologically adjacent in the model is dynamically captured, and then the adjacent size field is established and the densified triangular mesh is generated, local encryption is realized only in the region that needs to be encrypted, the problem of a sharp increase in the number of meshes caused by global encryption in the traditional method is avoided, the calculation efficiency is significantly improved while the accuracy of mesh encryption is improved, the stress gradient change in the thickness direction of the thin-walled structure is effectively captured, and thus the accuracy of CAE simulation analysis is enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, those skilled in the art can obtain other drawings from these drawings without creative effort.

[0019] Figure 1 A flowchart provided by a first embodiment of the triangular mesh densification method of the present application; Figure 2 A flowchart provided by a second embodiment of the triangular mesh densification method of the present application; Figure 3 A K-dimensional tree diagram provided by the present application; Figure 4 A flowchart provided by a fourth embodiment of the triangular mesh densification method of the present application; Figure 5 An electronic component adjacent densification effect diagram provided by the present application; Figure 6A schematic diagram of a rotator housing adjacent encryption effect provided by the present application; Figure 7 A schematic diagram of a rotator adjacent encryption effect provided by the present application; Figure 8 A schematic diagram of a rotator adjacent encryption effect provided by the present application; Figure 9 A schematic diagram of a rotator adjacent encryption effect provided by the present application; Figure 10 A schematic diagram of a rotator adjacent encryption effect provided by the present application; Figure 10 A schematic diagram of a rotator adjacent encryption effect provided by the present application;

[0020] The object, function features and advantages of the present application will be further explained in combination with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and are not intended to limit the present application.

[0022] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the accompanying drawings and specific embodiments.

[0023] The main solution of the embodiments of the present application is: constructing a K-dimensional tree in an independent geometric plane according to the background grid nodes constructed in advance according to a preset geometric model; filtering the neighborhood query based on the topological information of each geometric plane in the geometric model to obtain a to-be-calculated adjacent geometric plane; traversing the background grid nodes, querying the nearest neighbor point according to the K-dimensional tree and the to-be-calculated adjacent geometric plane, and calculating the adjacent size based on the nearest neighbor point; establishing an adjacent size field according to the adjacent size, and generating an encrypted triangular mesh based on the adjacent size field.

[0024] Embodiments of the present application consider that: in CAE simulation analysis, for thin-walled structures such as sheet metal parts, composite laminates, electronic component housings, etc., there are significant stress and strain gradient changes in the thickness direction. In order to balance the efficiency and the accuracy of the solving results, only the grids in the places that need to be encrypted are encrypted. However, when dealing with complex geometric models such as thin-walled structures using traditional grid generation methods, it is difficult to dynamically identify regions in the model that are close in space but not topologically adjacent (such as the two sides of a narrow gap with non-shared boundaries), resulting in the inability to locally encrypt the grid, and prone to problems such as grid penetration, distortion, or discontinuity of the size field, thereby affecting the quality of the grid and the accuracy and stability of the subsequent numerical simulation results. KD-Tree (K-Dimensional Tree) is a binary tree data structure used to organize K-dimensional space data, and is widely used in nearest neighbor search, range query, and space partitioning tasks. Its core idea is to recursively partition the space along different coordinate axes to efficiently organize the data points into a hierarchical structure.

[0025] Therefore, the present application provides a solution, according to the background grid nodes constructed in advance according to the preset geometric model, an independent K-dimensional tree in the geometric face is constructed; based on the topological information of each geometric face in the geometric model, a neighborhood query filter is performed to obtain a to-be-calculated adjacent geometric face; the background grid nodes are traversed, the nearest neighbor point is queried according to the K-dimensional tree and the to-be-calculated adjacent geometric face, and the adjacent size is calculated based on the nearest neighbor point; an adjacent size field is established according to the adjacent size, and an encrypted triangular grid is generated based on the adjacent size field. First, an independent K-dimensional tree in the geometric face is constructed, which effectively avoids the topological ambiguity caused by the mixed construction of cross-face vertices, and lays a data structure foundation for accurately identifying spatial adjacent regions; further, based on the topological information of the geometric face, the to-be-calculated adjacent geometric face is filtered, only the nodes meeting the topological constraints are included in the neighborhood query, the interference of non-adjacent faces is excluded, and the accuracy of the adjacent region identification is ensured; further, when traversing the background grid nodes, the nearest neighbor point in the to-be-calculated adjacent geometric face is efficiently queried using the K-dimensional tree and the adjacent size is calculated, the regions in the model that are close in space but not topologically adjacent are dynamically captured, and then by establishing an adjacent size field and generating an encrypted triangular grid, local encryption is realized only in the regions that need to be encrypted, avoiding the problem of excessive increase in the number of grids caused by global encryption in the traditional method, finally the accuracy of the grid encryption is improved while the computing efficiency is significantly improved, and the stress gradient changes in the thickness direction of the thin-walled structure are effectively captured, thereby enhancing the accuracy of the CAE simulation analysis.

[0026] It should be noted that the execution subject of the embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone or the like, or an electronic device capable of realizing the above functions, an encrypted triangular mesh generation system or the like. The encrypted triangular mesh generation system is taken as an example to describe the embodiment and the following embodiments.

[0027] Based on this, the application provides an encrypted triangular mesh generation method, which is applied to CAE simulation analysis, and the method comprises the steps of Figure 1 , Figure 1 FIG. 1 is a flowchart of a first embodiment of the encrypted triangular mesh generation method of the application.

[0028] In the embodiment, the encrypted triangular mesh generation method comprises steps S10-S40. Step S10, constructing an independent K-dimensional tree in a geometric face according to background mesh nodes of a preset geometric model; The preset geometric model refers to a CAD / CAE model composed of multiple curved surface pieces having common edges or common vertices but not necessarily topologically adjacent in a three-dimensional space, i.e., geometric faces, which are often present in sheet metal parts, composite material plies, aircraft cabin sections and the like thin-walled structures.

[0029] The K-dimensional tree (KD-Tree) is a data structure for recursively dividing a k-dimensional space, which is used for spatial indexing of background mesh nodes to support O(log N) level nearest neighbor search.

[0030] The background mesh node refers to a node of an initial discrete mesh obtained by performing fast triangular partitioning on each geometric face before formally generating a final mesh, and the size of the background mesh unit is equal to the initial unit size input by a user.

[0031] In addition, it should be noted that the application constructs the K-dimensional tree for each sampling vertex of the geometric face, which effectively avoids the topological ambiguity problem caused by mixing all vertices to construct the K-dimensional tree. Since there are multiple vertices shared by multiple faces at the boundary, if all vertices are uniformly constructed into the K-dimensional tree, a complex topological marker must be introduced to distinguish the belonging faces, otherwise the adjacent relationship will be misjudged when the neighborhood is queried.

[0032] Step S20, performing neighborhood query filtering based on topological information of each geometric face in the geometric model to obtain adjacent geometric faces to be calculated; The topological information refers to the adjacent relationship between geometric faces, such as shared edges, shared vertices, face-face angle, edge-face adjacency table and the like, which are explicitly recorded by a boundary representation data structure and are independent of geometric measurement.

[0033] The neighborhood query filtering refers to that the system utilizes the above topological relationship, filters all geometric faces according to preset rules (for example, only retaining the faces with an included angle less than a given threshold value and topologically not adjacent to the target face, or only retaining the faces with a distance less than a set narrow gap threshold value), thereby excluding those faces that are close in space but not needed to participate in the local encryption judgment in the mechanical or geometric sense, and finally outputting a set of adjacent geometric faces to be calculated.

[0034] In addition, it needs to be noted that the neighborhood query filtering is performed according to the topological information of the geometric model, only the nodes meeting the topological adjacency relationship are considered, the interference of non-adjacent topological nodes on the neighborhood calculation is avoided, the accuracy of the size field construction is ensured, and the local encryption only occurs within a reasonable neighborhood range.

[0035] In a possible implementation, the system can read a user-defined adjacency angle threshold value θ, if the dihedral angle of two faces along a common edge is less than θ, the two faces are considered to be continuous transition and are not included in the adjacent encryption consideration; if the dihedral angle is greater than or equal to θ, the two faces are included in the adjacent geometric faces to be calculated.

[0036] For example, in a specific implementation, the automobile front longitudinal beam collision model includes three layers of sheet metal, i.e., an outer plate, an inner plate and a reinforcing plate, the system reads the topological information and finds that the outer plate and the reinforcing plate have a common edge at the turn-up portion and a dihedral angle of 30°, which is less than the set threshold value of 45°, so the outer plate and the reinforcing plate do not mutually serve as the adjacent geometric faces to be calculated; and the outer plate and the inner plate have no common edge but a minimum gap of 1.2 mm in the local energy absorption area, which is less than the set narrow gap threshold value of 2 mm, so the outer plate and the inner plate are mutually listed as the adjacent geometric faces to be calculated and will participate in the subsequent mutual size field compression calculation. The local energy absorption area refers to a specific area of a thin-walled structural member such as an automobile, which is designed to absorb impact energy through plastic deformation in the collision or force field scenario, and the area usually has geometric characteristics of close spatial distance but topologically not adjacent.

[0037] In step S30, the background grid nodes are traversed, the nearest neighbor points are queried according to the K-dimensional tree and the adjacent geometric faces to be calculated, and the adjacent size is calculated based on the nearest neighbor points. The nearest neighbor point refers to the nearest point of each background grid point in the geometric face F in the K-dimensional tree after the K-dimensional tree is constructed independently in the face according to the background grid nodes.

[0038] The adjacent size refers to the grid cell size calculated according to the minimum distance value of the nearest neighbor point and the background grid point, which is used to control the size of the local grid encryption. The value needs to consider the layer number in the thickness direction, the nearest distance and the initial size parameter of the background grid to ensure that the grid size meets the local encryption requirement and avoids excessive small size leading to a large number of grids. The initial size parameter of the background grid includes the initial cell size and the minimum limit size input by the user.

[0039] Specifically, the background grid nodes are traversed, and the nearest neighbor points are queried through the constructed K-dimensional tree. The core purpose of querying the nearest neighbor points through the K-dimensional tree is to obtain the minimum spatial distance between geometric faces, thereby providing basic data for subsequent calculation of the adjacent size. The nearest neighbor query algorithm of the K-dimensional tree can efficiently locate the vertex closest to the current point, thereby greatly improving the calculation efficiency compared with the brute force search. The number of layers in the thickness direction is obtained in order to reasonably distribute the nearest distance to the grid in the thickness direction, thereby ensuring that the size of each layer of the grid meets the accuracy requirement.

[0040] In a possible implementation, the process of querying the nearest neighbor points through the K-dimensional tree can include: taking each background grid point of the current face as a query point, performing a nearest neighbor search in the K-dimensional tree of the adjacent geometric face to be calculated, and recording the coordinates and distance value d of the nearest neighbor point corresponding to each query point.

[0041] In step S40, an adjacent size field is established according to the adjacent size, and a triangular mesh is generated based on the adjacent size field.

[0042] The adjacent size field refers to a size field that is finally used to guide the generation of the mesh after smoothing and continuous processing, and is characterized in that the global size value is continuous, the gradient change is gentle, and the local encryption requirement is met.

[0043] In a possible implementation, the initial adjacent size field established according to the adjacent size can be adjusted recursively, and a plurality of rounds of smoothing-interpolation-re-smoothing operations can be performed to eliminate the size gradient mutation caused by sudden compression. After the size gradient mutation is eliminated, the triangular mesh is generated.

[0044] The embodiment provides a triangular mesh generation method, constructs an independent K-dimensional tree in a geometric face, effectively avoids the topological ambiguity caused by mixed construction of cross-face vertices, and lays a data structure foundation for accurate identification of a spatial adjacent region. Further, based on the topological information of the geometric face, the adjacent geometric face to be calculated is screened, only the nodes meeting the topological constraint are included in the neighborhood query, the interference of non-adjacent faces is excluded, and the accuracy of the adjacent region identification is ensured. Further, when the background grid nodes are traversed, the nearest neighbor points in the adjacent geometric face to be calculated are efficiently queried through the K-dimensional tree, and the adjacent size is calculated, the regions with close spatial distances but non-adjacent topologies in the model are dynamically captured, and then the triangular mesh is generated by establishing the adjacent size field, local encryption is realized only in the region that needs to be encrypted, the problem of explosive increase in the number of meshes caused by global encryption in the traditional method is avoided, the calculation efficiency is significantly improved while the accuracy of the mesh encryption is improved, the stress gradient change in the thickness direction of the thin-walled structure is effectively captured, and thus the accuracy of the CAE simulation analysis is improved.

[0045] Based on the first embodiment of the present application, the second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar contents as the above first embodiment can be referred to the above introduction, and the subsequent will not be described in detail.

[0046] On this basis, please refer to Figure 2 , Figure 2 for the flowchart of the second embodiment of the present application. As shown in Figure 2 , before step S10, the encryption triangular mesh generation method further includes steps S01-S03: Step S01, obtaining the initial cell size and the minimum limit size input by the user; The initial cell size input by the user refers to the "target edge length" scalar value that the CAE simulation personnel explicitly specifies in the software interface or script, which is expected to be used by the mesh in the flat area first, denoted by symbol h u .

[0047] The minimum limit size refers to the lower threshold value that the system allows any mesh edge length or node spacing to be no less than after encryption, denoted by symbol h min , which prevents excessive refinement from causing an explosion in the number of elements and solver overflow.

[0048] In addition, it should be noted that after the system obtains the above two parameters, it will immediately perform a reasonableness check. For example, check whether h min is less than 1 / 5 of the minimum curvature radius of the geometric model, if it is, prompt the user that tooth-like mesh may be generated; through this check, size conflicts can be avoided in advance to ensure the stability of subsequent size field construction and mesh generation.

[0049] Step S02, generating a discrete triangular mesh for each geometric face in the geometric model based on the topological information, taking the discrete triangular mesh as a background mesh; The topological information is used to uniquely determine the shape and mutual connection relationship of each geometric face in the geometric model; each geometric face in the geometric model specifically refers to a multi-face set composed of thin-walled sheet metal, composite material plies, narrow gaps, and reinforcing ribs.

[0050] The discrete triangular mesh refers to sampling and connecting the continuous faces of the above geometric model into a triangular set with fixed or adaptive density, without overlapping, without holes, and meeting the minimum internal angle limit.

[0051] The background mesh refers to the discrete triangular mesh, which is only used as a geometric background for subsequent size field calculation, and the element quality does not need to reach the final simulation level, but must faithfully preserve the boundaries, holes, and characteristic lines of the original face.

[0052] Step S03: Initialize the background mesh size values ​​according to the initial unit size and the minimum limit size to obtain the initial size parameters of the background mesh and each background mesh node.

[0053] The background mesh is initialized with dimensions based on the initial element size and minimum constraint size. All dimensions of the discrete triangular mesh are initialized to the user-inputted initial element size, thus completing the background mesh size initialization and obtaining the initial size parameters of the background mesh, namely the user-inputted initial element size and minimum constraint size. The background mesh after size initialization contains multiple background mesh nodes.

[0054] In this embodiment, by generating a background mesh and initializing the size parameters, the generation of the background mesh ensures the discretization of the geometric model, providing core data support for accurate local encryption.

[0055] Based on the first and / or second embodiments of this application, a third embodiment of this application is proposed. In the third embodiment of this application, the contents that are the same as or similar to the first and / or second embodiments described above can be referred to the above description and will not be repeated hereafter.

[0056] In this embodiment, step S30, which involves traversing the background mesh nodes, querying the nearest neighbor point based on the K-dimensional tree and the neighboring geometric surface to be calculated, and calculating the neighbor size based on the nearest neighbor point, includes steps S31 to S33: Step S31: Traverse the background mesh nodes and query the nearest neighbor points based on the K-dimensional tree and the neighboring geometry to be calculated; refer to Figure 3 , Figure 3 This is a schematic diagram of the K-dimensional tree provided in this application. Traversing the background grid nodes, through methods such as... Figure 3 The K-tree nearest neighbor query shown aims to find the nearest neighbor point for each point on the geometric surface F, providing fundamental data for subsequent calculations of neighboring dimensions. The K-tree nearest neighbor query algorithm efficiently locates the vertex closest to the current point, significantly improving computational efficiency compared to brute-force search. Obtaining the number of layers in the thickness direction is crucial for rationally distributing the nearest distances across the mesh in the thickness direction, ensuring that the size of each mesh layer meets accuracy requirements.

[0057] In one possible implementation, the process of querying the nearest neighbor point in a K-tree may include: taking each background grid point of the current face as the query point, performing a nearest neighbor search in the K-tree of the nearby geometric face to be calculated, and recording the coordinates and distance value d of the nearest neighbor point corresponding to each query point.

[0058] Step S32: Obtain the number of layers in the thickness direction input by the user; The thickness direction layer number refers to a number of layers of grids in a thickness direction of a thin-walled structure that needs to be divided according to a simulation precision requirement of a user, and the parameter directly affects the calculation of the proximity size. It needs to be noted that the thickness direction layer number ≥ 3 is an optimized grid to capture the stress gradient change.

[0059] In addition, it needs to be noted that the thickness direction layer number input by the user can be configured through a man-machine interactive interface, such as a parameter setting panel of CAE software, and the system stores the parameter as a global variable for calling in the proximity size calculation step. In actual application, the number of layers can be dynamically adjusted according to the structure type, for example, for a region with a severe stress gradient, the user can set a higher thickness direction layer number.

[0060] In step S33, the proximity size is calculated according to the thickness direction layer number, the nearest distance of the nearest neighboring point, and the background grid initial size parameter.

[0061] The proximity size refers to a parameter reflecting the size of a grid unit of a region to be encrypted, and the value needs to comprehensively consider the thickness direction layer number, the nearest distance, and the background grid initial size parameter to ensure that the grid size meets the local encryption requirement and avoids an excessive increase in the number of grids due to a too small size. The background grid initial size parameter includes an initial unit size and a minimum limit size input by the user.

[0062] It needs to be noted that the core idea of calculating the proximity size is: first, the theoretical proximity size is calculated according to the nearest distance d and the thickness direction layer number n, and the calculation formula is as follows:

[0063] Wherein, d represents the nearest distance of the nearest neighboring point, and n represents the thickness direction layer number input by the user.

[0064] Further, the proximity size represents a single-layer size when the nearest distance is evenly distributed to n layers of grids; then, the grid unit size of the initial proximity size field is constrained in combination with the background grid initial size parameter, and the specific formula is as follows:

[0065] Wherein, represents the proximity size, represents the minimum limit size, and the right side of the equal sign represents the initial unit size input by the user, that is, the grid unit size of the background grid, and the left side of the equal sign represents the grid unit size of the initial proximity size field obtained after updating.

[0066] After the proximity size of all background grid points is calculated, the proximity size field based on the background grid is established.​

[0067] In the present embodiment, by traversing the background grid nodes, combining the nearest neighbor point query and the thickness direction layer number to calculate the neighborhood size, the neighborhood size field is finally established. The process utilizes the high-efficiency space division capability of the K-dimensional tree to solve the topological ambiguity problem caused by the mixed construction of the tree structure of the multi-surface model vertex in the traditional method, and directly links the geometric distance and the user demand (layer number n) through the neighborhood size formula, ensuring that the encryption region size meets the stress gradient capture demand and avoids the excessive increase of the number of grids caused by the too small size, realizing the preliminary balance of the precision and the efficiency.

[0068] Based on the above-mentioned embodiments of the present application, the fourth embodiment of the present application is proposed. In the fourth embodiment of the present application, the same or similar contents as the above-mentioned embodiments can be referred to the above introduction, and the subsequent will not be described in detail.

[0069] On this basis, please refer to Figure 4 , Figure 4 The flowchart of the fourth embodiment of the present application is shown. As shown in Figure 4 , in the present embodiment, the step S40 of establishing the neighborhood size field according to the neighborhood size includes steps S41-S44: Step S41, establishing an initial neighborhood size field according to the neighborhood size, identifying the size value gradient mutation region of the initial neighborhood size field, and performing recursive smoothing processing on the size value gradient mutation region until the preset smoothing condition is reached. The initial neighborhood size field refers to a continuous distribution field defined on the background grid, which reflects the size demand of each position grid. Its core role is to provide size guidance for subsequent grid generation, ensuring that the grid of the region to be encrypted (such as a narrow gap or a thin wall) is encrypted according to the neighborhood size.

[0070] The size value gradient mutation region refers to a region where the size value change rate of adjacent points in the size field exceeds a preset threshold, for example, a transition zone where the size suddenly increases from 1mm to 5mm. Such mutations may cause cell distortion or size discontinuity during grid generation.

[0071] Recursive smoothing processing refers to the process of adjusting the size value of the mutation region through multiple iterations, gradually reducing the gradient change rate. Each iteration dynamically adjusts the parameters based on the gradient distribution of the current size field. The preset smoothing condition refers to the basis for judging whether the smoothing processing is terminated, which usually includes the gradient change rate threshold, the maximum number of iterations or the size value difference threshold.

[0072] It should be noted that the purpose of identifying the size value gradient mutation region is to locate the abnormal transition zone in the size field that may affect the grid quality. By recursive smoothing processing, these mutations are eliminated, so that the size value transitions smoothly from the encryption region to the non-encryption region, thereby ensuring that the generated grid cells are regular in shape and continuous in size.

[0073] Specifically, the system marks the area where the gradient of each point in the size field and its neighborhood points (such as the difference between the size values of two adjacent points and the spatial distance) exceeds the threshold as a mutation area, and adjusts it using weighted average, Gaussian filtering and other algorithms, and iteratively performs this process until all areas meet the preset smoothing condition.

[0074] Step S42, under the condition that the size value gradient mutation area meets the preset smoothing condition, the smoothed adjacent size field is obtained; The smoothed adjacent size field refers to the size field after recursive smoothing processing, where the size value gradient mutation area has been eliminated, and the size value changes continuously and smoothly in space. This size field retains the original encryption requirements (such as small size in thin-walled areas), while ensuring that the size transition of adjacent areas meets the grid quality requirements of engineering simulation (such as avoiding cell deformity and size jump).

[0075] It should be noted that after the size value gradient mutation area meets the preset smoothing condition, the system automatically terminates the smoothing process and outputs the smoothed adjacent size field. The judgment logic of the preset smoothing condition is the key to ensuring the quality of the size field, for example, when the gradient change rate of all adjacent points is less than 0.3 mm / mm, or the gradient change rate decreases by less than 5% for three consecutive iterations, it is determined that the smoothing condition is met. The smoothed size field provides more reliable size guidance for subsequent boundary discretization and grid generation, reducing the failure of grid generation or loss of simulation accuracy caused by size mutation.

[0076] Step S43, performing linear interpolation calculation on any point in the background grid of the geometric model pre-constructed to obtain the size value of the any point; The background grid refers to the generated discrete triangular mesh, the vertices of which have been assigned values through adjacent size calculation, but the size values of non-vertex positions (such as internal points of triangular faces and midpoints of edges) have not been defined. Any point refers to all spatial position points in the background grid except the vertices, including internal points of triangular faces, midpoints of edges and other non-vertex sampling points. Linear interpolation calculation refers to a method of calculating the size value of a point based on the size values of the three vertices of the triangular face where the point is located through weighted average, and the weight is usually inversely proportional to the distance from the vertex to the any point.

[0077] It should be noted that the purpose of performing linear interpolation calculation on any point in the background grid is to extend the size field from discrete vertices to the entire face domain, achieving global coverage of size values. Since the initial size field only defines the size values of the vertices, the size values of non-vertex positions directly affect the generation accuracy of the grid cells, therefore, through linear interpolation, the continuity of the size field in space can be ensured, providing a data basis for subsequent continuous processing.

[0078] In one possible implementation, the size value of any point in the background mesh is calculated by projecting to the nearest triangular patch in the background mesh and calculating the size value at the point by linear interpolation according to the size values of the three vertices of the triangular patch.

[0079] Exemplarily, taking the chassis beam of an automobile chassis as an example, since the automobile chassis beam has a stress concentration area, it is necessary to smoothly transition from the densification area to the non-densification area of the background mesh. Before the size field gradient optimization and interpolation, the size of the densification area 0.5 mm is directly adjacent to the size of the non-densification area 2 mm, and the gradient changes abruptly. Therefore, the region is smoothed by Gaussian filtering, and the size of the transition area is adjusted to 0.5→1.0→1.5→2.0 mm by Gaussian filtering; for a point P on the surface of the beam, the point P is projected to a triangular patch ABC, and the size of the vertices is h a =0.5 mm, h b =1.0 mm, h c =1.5 mm, and after interpolation, h p =0.5×0.2+1.0×0.5+1.5×0.3=1.05 mm In step S44, the smoothed adjacent size field is continuously processed according to the size value of the arbitrary point, to obtain the adjacent size field.

[0080] The continuous processing refers to integrating the vertex size value and the size value of the arbitrary point into a globally continuous function field, to ensure that the size field is defined at any position of the background mesh and has no abrupt change. The adjacent size field after recursive adjustment refers to the size field after smoothing and continuous processing, which is finally used to guide the generation of the mesh, and is characterized by globally continuous size value, gently changing gradient, and meeting the local densification requirement.

[0081] It should be noted that the continuous processing of the smoothed size field according to the size value of the arbitrary point essentially converts the discrete size data (vertex size value + interpolated size value) into a continuous mathematical model (such as a piecewise linear function or a spline surface), so that the size field is not only defined at the vertices and interpolation points, but also can predict the size value at any spatial position through the model. This step ensures that the mesh generation algorithm (such as boundary discretization and cell division) can obtain accurate size requirements at any position in real time when processing complex geometries, avoiding mesh quality problems caused by data missing.

[0082] In one possible implementation, all vertex size values and interpolation size values of the background mesh are taken as sample points, and a globally continuous function is generated by weighted least squares fitting, wherein the weight of the sample point is inversely proportional to the distance to the predicted point (the closer the distance, the greater the weight). The model can directly calculate the size value according to any spatial coordinates (x, y, z), to realize the global continuous expression of the size field.

[0083] In the embodiment, by identifying the gradient mutation region of the initial size field and recursively smoothing processing, combining linear interpolation and continuous processing, the sharp fluctuation of the size value is eliminated. The process ensures the smooth transition of the size field from the encryption area to the non-encryption area through the preset smoothing condition, avoids the grid distortion caused by the size mutation in the traditional method, and realizes the continuous expression of the size field through the global linear interpolation, thereby providing high-quality size guide data for subsequent boundary discretization and grid generation, and significantly improving the form regularity and size continuity of the grid cells.

[0084] Based on the above-mentioned embodiments of the application, the fifth embodiment of the application is proposed. In the fifth embodiment of the application, the same or similar contents as the above-mentioned embodiments can be referred to the above introduction, and will not be described in detail hereinafter.

[0085] In the embodiment, after the step S40 of establishing the adjacent size field according to the adjacent size, the encryption triangular mesh generation method further includes a step A01. Step A01, a mapping relationship between the adjacent size field and the topological information of each geometric face in the geometric model is established, and the shared boundary is managed according to the mapping relationship.

[0086] The mapping relationship refers to a one-to-one correspondence relationship between the size field data (such as the size value distribution of each face) and the topological information (such as the face ID and the boundary edge identifier) through a data structure (such as an associated array or a hash table). The shared boundary refers to the boundary edge or vertex commonly owned by two or more adjacent geometric faces, for example, the edge line shared by the upper and lower surfaces of a thin-walled structure.

[0087] It should be noted that the core purpose of establishing the mapping relationship is to realize the collaborative management of the size field and the geometric topology, and to ensure that the size information at the shared boundary is consistent between different faces. By associating the size field of each face with the topological identifier (such as the face number and the boundary edge ID) of the face, the system can quickly query the size requirements of any boundary, avoiding the size value conflict caused by the shared boundary belonging to multiple faces (such as one face requiring encryption and another face not requiring encryption), thereby ensuring the seamless connection of the grids between the faces.

[0088] In a possible implementation, the establishment of the mapping relationship can adopt a three-level association structure of “face-boundary-size”: the first level associates the size field data of the face with the face ID as the key; the second level associates the identifier of the shared boundary (such as the face ID sharing the edge) with the boundary edge ID in the face as the key; and the third level associates the specific size value with the boundary vertex coordinates as the key. This structure supports fast retrieval of the size information of the shared boundary in different faces, and ensures synchronous update.

[0089] In the embodiment, by establishing a mapping relationship between the recursively adjusted adjacent size field and the topological information, the unified management of the shared boundary is realized. The mechanism ensures that the size information of the shared boundary node in the multi-face model is consistent in the size field of different faces, avoids the local size field fault, solves the grid insertion or gap problem caused by the discontinuous boundary size in the traditional method, and provides data association guarantee for seamless connection of interfacial grids (such as alignment of upper and lower surface grids on a thin-walled structure).

[0090] Based on the above-mentioned embodiments of the application, the sixth embodiment of the application is proposed. In the sixth embodiment of the application, the same or similar contents as the above-mentioned embodiments can be referred to the above introduction, and will not be described in detail hereinafter.

[0091] In the embodiment, the step S40 of generating the encrypted triangular mesh based on the adjacent size field includes steps A41-A44: In step A41, the model boundary of the geometric model is associated to the adjacent size field based on the pre-established mapping relationship, and the size field information of the adjacent size field is obtained. The size field information refers to specific size parameters associated with the model boundary, such as the grid cell size of each point on the boundary, the size gradient change rate, etc. The size field information can be obtained by querying the mapping relationship, binding the topological identifier (such as edge ID) of each model boundary to the corresponding region in the size field, so as to directly obtain the size value at any position on the boundary, and ensure that the node distribution after the boundary is discretized meets the local encryption requirement.

[0092] In a possible implementation, the association process can be realized by "boundary traversal-identifier matching": the system traverses all the model boundaries of the geometric model, extracts the topological identifier (such as edge ID, face ID to which it belongs) of each boundary, and then finds the size field data corresponding to the identifier through the mapping relationship, binds the size field information (such as the size value array along the boundary) with the boundary and stores it in the memory for subsequent discrete step calling.

[0093] In step A42, the model boundary is adaptively discretized according to the size field information, and after the adaptive discretization, each model boundary corresponds to a piecewise linear size field. The piecewise linear size field refers to a size distribution mode in which the model boundary is divided into several straight line segments according to the size value change law, and the size value in each segment changes linearly with the boundary arc length, for example, a certain boundary is divided into 3 segments, the size increases from 1mm to 2mm in the first segment, remains 2mm in the second segment, and increases from 2mm to 3mm in the third segment.

[0094] It should be noted that the purpose of adaptively discretizing the model boundary is to convert continuous size field information into a discrete node distribution scheme, so that the density of the boundary nodes is accurately matched with the size field requirements. By dividing the boundary into piecewise linear size fields, the system can distribute nodes within each segment according to a linear rule, which meets the local encryption requirements and avoids wasting computing resources due to excessive number of nodes.

[0095] In one possible implementation, the implementation of adaptive discretization can adopt a "size value-arc length segmentation method": first, determine the size mutation points on the boundary according to the size field information (such as the position where the size value changes from 1 mm to 2 mm), and divide the boundary into segments according to the mutation points; then, for each boundary segment, calculate the node spacing according to the starting size value, the ending size value, and the arc length of the segment (such as densely distributing nodes at the starting end according to the small size, and sparsely distributing nodes at the ending end according to the large size), to ensure that the node spacing within each segment changes linearly with the size value.

[0096] Step A43, interpolating according to the size value of the piecewise linear size field and the actual arc length of the model boundary to obtain boundary node coordinates; The size value of the piecewise linear size field refers to the size parameters of the starting point and the ending point on each boundary segment (such as the first segment starting size 1 mm, ending size 2 mm).

[0097] The actual arc length refers to the true curve length of the model boundary in three-dimensional space, rather than the projected length. Interpolation calculation refers to the process of calculating the specific spatial coordinates of each node within the arc length range of each boundary segment according to the size value variation law of the piecewise linear size field, commonly using linear interpolation, spline interpolation, etc.

[0098] The boundary node coordinates refer to the three-dimensional spatial coordinates (x, y, z) of each node on the discretized model boundary, which are the basic data for subsequent mesh generation.

[0099] It should be noted that the purpose of obtaining the boundary node coordinates through interpolation calculation is to convert the "size requirements" of the piecewise linear size field into "geometric positions", to ensure that the distribution of nodes in space strictly meets the density specified by the size field. The system calculates the arc length position of each node according to the actual arc length and the size value variation rate of each boundary segment, and then converts the arc length position into three-dimensional coordinates through geometric curve interpolation, to achieve accurate matching of the size field and the geometric shape.

[0100] In one possible implementation, the interpolation calculation can adopt an arc length parameterized interpolation method: first, express the curve equation of the segmented boundary as a function of arc length; then, according to the linear relationship between the size value and the arc length, calculate the arc length position corresponding to the node, substitute the arc length position into the curve equation, and solve to obtain the three-dimensional coordinates of the node.

[0101] Step A44, generating the encrypted triangular mesh according to the boundary node coordinates.

[0102] Specifically, an initial triangular mesh can be generated according to the boundary node coordinates, and a mesh optimization operation is performed on the initial triangular mesh to obtain the encrypted triangular mesh. The initial triangular mesh is a first triangular mesh generated by using a size field Delaunay front propagation method or a mapping method in a parameter domain with the boundary node coordinates as constraints, and the unit edge length thereof approaches the local size field value as much as possible, but has not undergone topological optimization and geometric quality improvement.

[0103] The mesh optimization operation includes edge splitting (dividing a long edge into two), edge collapse (combining short edges), edge swapping (improving the minimum angle by flipping the diagonal), vertex smoothing (Laplacian or optimized center movement), and normal alignment constraint. The purpose is to improve the minimum angle of the unit, reduce the maximum angle, make the unit area ratio tend to 1, and ensure that the optimized encrypted triangular mesh still strictly adheres to the original CAD surface, and the maximum geometric deviation does not exceed the user-set tolerance.

[0104] In the embodiment, by associating the model boundary to the size field and performing adaptive discretization, the boundary node coordinates are calculated by combining the piecewise linear size field and the arc length interpolation, and the precise control of the boundary mesh is realized. This process dynamically adjusts the node density according to the size field information, and ensures the smooth change of the size value along the boundary through piecewise linear interpolation, avoiding the over-discretization or under-discretization problem caused by traditional uniform discretization. The finally generated boundary node coordinates not only meet the local encryption requirement, but also ensure the geometric accuracy of the boundary line, laying a boundary foundation for the generation of high-quality triangular mesh.

[0105] In a specific embodiment, step A44 can include steps A441-A442: Step A441, generating an initial triangular mesh by using a triangular mesh division algorithm on each geometric face of the geometric model according to the boundary node coordinates; The boundary node coordinates are a set of discrete point coordinates obtained by driving the size field and adaptively discretizing the boundary, and all of these coordinates are located on the boundary of the geometric face. These coordinates have been subjected to piecewise linear interpolation according to the local mesh target size given by the size field, and thus can reflect the geometric feature and mesh density requirement at the same time.

[0106] The triangular mesh division algorithm refers to any mature algorithm that takes the boundary node as input, generates a set of triangular units covering the entire face and meeting the given size field in the parameter domain or physical domain of the geometric face, including but not limited to the Delaunay cavity algorithm, the front propagation method (AFT), the mapping method, the quadtree-triangle cutting hybrid method, etc.

[0107] The initial triangular mesh refers to a set of original triangular elements which have not been optimized in mesh quality, and the element shape may contain obtuse angles, small angles or edges close to degeneration, but has met the statistical requirements of the size field on the edge length.

[0108] In a possible implementation, the system adopts the Delaunay algorithm as the triangular mesh division algorithm: first, a two-dimensional tensor field is constructed according to the size field, so that the element is allowed to be elongated in the long axis direction and the size is limited in the short axis direction, thereby automatically generating elongated but high-quality elements in the area with large curvature. The size information of the inserted point is no longer a weighted average of the lengths of the mesh edges, but directly uses the size value information.

[0109] In another possible implementation, the front propagation method is adopted as the triangular mesh division algorithm: the geometric surface is divided into multiple subdomains according to the K-dimensional tree, and the wave front is synchronously propagated in each subdomain in an independent thread, and the node coincidence is ensured through message passing at the subdomain boundary, and finally the globally consistent initial triangular mesh is obtained. It should be noted that the current propagation size and length are no longer used, but the size value information is directly used.

[0110] Step A442, performing a mesh optimization operation on the initial triangular mesh to obtain an encrypted triangular mesh, the mesh optimization operation including edge splitting, edge collapse and edge swapping.

[0111] Edge splitting refers to inserting a new node at the midpoint of an edge whose length exceeds the given upper limit of the size field, and reconnecting the surrounding elements to match the local mesh density with the size field; edge collapse refers to collapsing a short edge whose length is below the given lower limit of the size field into a node and deleting the redundant elements, thereby eliminating the over-dense mesh and improving the minimum angle; edge swapping refers to, for any pair of co-edge triangles, if the minimum internal angle or the energy function can be improved after swapping the diagonal, performing diagonal flipping to improve the element shape and alleviate the gradient jump.

[0112] Performing the mesh optimization operation including edge splitting, edge collapse and edge swapping on the initial triangular mesh to obtain the encrypted triangular mesh, thereby improving the mesh quality.

[0113] Illustratively, in order to help understand the implementation process of the encrypted triangular mesh generation method obtained after the above-mentioned embodiment, taking an electronic device or a rotator containing adjacent features as an example, the encrypted triangular mesh generation method proposed in the present application is used to perform adjacent triangular mesh encryption on the above-mentioned electronic device or rotator.

[0114] On this basis, please refer to Figures 5 to 8 , Figure 5 is a schematic diagram of the adjacent encryption effect of the electronic device obtained after the electronic device is encrypted by using the encrypted triangular mesh generation method. As shown inFigure 5 As shown, the mesh is automatically refined in the neighborhood while taking into account the quality of the surrounding meshes. Compared to using a larger mesh size for all meshes, the calculation results in the neighborhood will be more accurate. If automatic search and refinement are not considered, global refinement is required to achieve the same calculation accuracy, and the number of meshes will increase dramatically.

[0115] Figure 6 This is a schematic diagram illustrating the proximity encryption effect of the rotator housing provided in this application, as shown below. Figure 6 As shown, Figure 6 This demonstrates the effect of applying a K-tree-based proximity-based triangular mesh generation method to the rotator's outer shell structure. Specifically, through in-plane independent K-tree construction and topological neighborhood filtering, geometric feature regions such as thin walls and narrow slits on the shell surface are automatically identified, and these spatially close regions are locally meshed. As the external structure of the rotator, the shell may have curvature variations or regions adjacent to internal components, such as... Figure 6 As shown, the encrypted mesh is mainly concentrated in areas with complex geometric features of the shell (such as edges, grooves, or junctions with other components), rather than being uniformly encrypted. This achieves the core advantage of encrypting only where necessary, while maintaining a smooth transition and low distortion rate of the overall shell mesh.

[0116] Figure 7 This is a schematic diagram of the proximity encryption effect of the rotator provided in this application, such as... Figure 7 As shown, Figure 7 This demonstration showcases the mesh refinement effect of the overall rotator structure, without specifying any particular location. In stress concentration areas (such as stress connections and thin-walled structures) or areas with close geometric distances (such as narrow slits and slender features), the mesh cell size is significantly reduced, while the density increases. Areas far from critical features maintain a larger mesh size, demonstrating that the proposed mesh refinement algorithm balances efficiency and accuracy, avoiding a surge in mesh count due to global refinement. The mesh transition between refined and unrefined areas is smooth, without obvious size discontinuities, reflecting the technical features of "multi-level recursive adjustment" and "topology constraint filtering" in this application, ensuring the stability of subsequent CAE simulation analysis.

[0117] Figure 8 This is a schematic diagram illustrating the effect of near-field encryption of the rotator provided in this application, such as... Figure 8 As shown, Figure 8 Focusing on the mesh refinement details of the local fine structure of the rotator, the mesh cell size of the fine structure (such as small grooves and gaps) is close to the minimum size threshold hmin set by the user, and there is no mesh overlap or distortion. The refined mesh in the detailed area and the surrounding coarser mesh are smoothly transitioned through the size field, which can demonstrate the high-quality mesh generation capability of the refined triangular mesh generation method of this application under complex geometric boundary conditions, and provide support for the accurate calculation of stress concentration areas in subsequent simulations.

[0118] In summary Figures 5 to 8 The above effect maps jointly verify the applicability of the encryption triangular mesh generation method proposed in the application in complex geometric models such as rotators and electronic components, especially the encryption effect in thin-walled structures, geometric details and stress-sensitive areas, and intuitively show the technical advantages of the local encryption control, mesh quality optimization and efficiency-precision balance of the application.

[0119] It should be noted that the above examples are only used for understanding the application and do not constitute a limitation on the method of the application. More forms of simple changes based on this technical concept are within the protection scope of the application.

[0120] The application also provides an encryption triangular mesh generation device, please refer to Figure 9 The encryption triangular mesh generation device comprises: A construction module 10 is configured to construct an independent K-dimensional tree in a geometric face according to a background mesh node constructed in advance based on a preset geometric model; A query filtering module 20 is configured to perform neighborhood query filtering based on the topological information of each geometric face in the geometric model to obtain a to-be-calculated adjacent geometric face; A calculation module 30 is configured to traverse the background mesh node, query a nearest neighbor point according to the K-dimensional tree and the to-be-calculated adjacent geometric face, and calculate an adjacent size based on the nearest neighbor point; A mesh generation module 40 is configured to establish an adjacent size field according to the adjacent size, and generate an encryption triangular mesh based on the adjacent size field.

[0121] The encryption triangular mesh generation device provided by the application adopts the encryption triangular mesh generation method in the above embodiments and can solve the technical problem of encryption triangular mesh generation. Compared with the prior art, the encryption triangular mesh generation device provided by the application has the same beneficial effects as the encryption triangular mesh generation method provided by the above embodiments, and the other technical features in the encryption triangular mesh generation device are the same as the features disclosed in the above embodiments, which will not be repeated here.

[0122] The application provides an encryption triangular mesh generation device, which comprises at least one processor and a memory connected with the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the encryption triangular mesh generation method in the above embodiment one.

[0123] The following refers to Figure 10The diagram illustrates a structural schematic of an encrypted triangular mesh generation device suitable for implementing embodiments of this application. The encrypted triangular mesh generation device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 10 The encrypted triangular mesh generation device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0124] like Figure 10 As shown, the encrypted triangular mesh generation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the encrypted triangular mesh generation device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the encrypted triangulation generation device to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows encrypted triangulation generation devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0125] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program codes for executing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiments disclosed in the present application are executed.

[0126] The encryption triangular mesh generation device provided by the present application adopts the encryption triangular mesh generation method in the above-mentioned embodiments, and can solve the technical problem of encryption triangular mesh generation. Compared with the prior art, the encryption triangular mesh generation device provided by the present application has the same beneficial effects as the encryption triangular mesh generation method provided by the above-mentioned embodiments, and other technical features in the encryption triangular mesh generation device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0127] It should be understood that various parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above-mentioned embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0128] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0129] The present application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e. computer program) for executing the encryption triangular mesh generation method in the above-mentioned embodiments.

[0130] The computer readable storage medium provided in the application may, for example, be a U disk, but is not limited to an electric, magnetic, optical, electromagnetic, infrared, or semiconductor system or device, or any combination of the above. More specific examples of the computer readable storage medium may include, but are not limited to, an electric connection with one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment, the computer readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer readable storage medium can be transmitted by any suitable medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), and the like, or any suitable combination of the above.

[0131] The computer readable storage medium described above may be contained in the encrypted triangle mesh generation device, or may exist separately without being assembled into the encrypted triangle mesh generation device.

[0132] The computer readable storage medium described above carries one or more programs, which, when executed by the encrypted triangle mesh generation device, cause the encrypted triangle mesh generation device to: construct a K-dimensional tree independent of a geometric face according to a background grid node constructed in advance according to a preset geometric model; perform neighborhood query filtering based on topological information of each geometric face in the geometric model to obtain a to-be-calculated adjacent geometric face; traverse the background grid node, query a nearest neighbor point according to the K-dimensional tree and the to-be-calculated adjacent geometric face, and calculate an adjacent size based on the nearest neighbor point; establish an adjacent size field according to the adjacent size, and generate an encrypted triangle mesh based on the adjacent size field.

[0133] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0134] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0135] The modules involved in the embodiments of the present application can be implemented in software or hardware. In some cases, the name of the module does not constitute a limitation on the module itself.

[0136] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e. computer program) for executing the above-mentioned encrypted triangle mesh generation method, and can solve the technical problem of encrypted triangle mesh generation. Compared with the prior art, the computer readable storage medium provided by the present application has the same beneficial effects as the encrypted triangle mesh generation method provided by the above-mentioned embodiments, which will not be repeated here.

[0137] The application further provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method for generating encrypted triangular meshes as described above.

[0138] The computer program product provided by the application can solve the technical problem of generating encrypted triangular meshes. Compared with the prior art, the computer program product provided by the application has the same beneficial effects as the method for generating encrypted triangular meshes provided by the above-described embodiments, and will not be described here.

[0139] The above only describes some embodiments of the application, and does not limit the patent scope of the application. Any equivalent structural transformation, direct / indirect application in other related technical fields, or the like made by using the content of the application specification and drawings is included in the patent protection scope of the application.

Claims

1. A method for encrypted triangulation mesh generation, characterized in that, The encrypted triangular mesh generation method is applied to computer aided engineering (CAE) simulation analysis, and comprises the following steps: a K-dimensional tree is constructed in a geometric face independently according to background grid nodes constructed in advance according to a preset geometric model; neighborhood query filtering is performed based on topological information of each geometric face in the geometric model, and adjacent geometric faces to be calculated are obtained; the background grid nodes are traversed, the nearest neighbor points are queried according to the K-dimensional tree and the adjacent geometric faces to be calculated, and the adjacent size is calculated based on the nearest neighbor points; an adjacent size field is established according to the adjacent size, and an encrypted triangular mesh is generated based on the adjacent size field.

2. The method of claim 1, wherein, Before the step of constructing the K-dimensional tree in the face independently according to the background grid nodes constructed in advance according to the preset geometric model, the method further comprises the following steps: an initial unit size and a minimum limit size input by a user are acquired; discrete triangular meshes of each geometric face in the geometric model are generated based on the topological information, and the discrete triangular meshes are taken as background meshes; the background meshes are initialized in size value according to the initial unit size and the minimum limit size, and background mesh initial size parameters and each background mesh node are obtained.

3. The method of claim 2, wherein, The step of traversing the background grid nodes, querying the nearest neighbor points according to the K-dimensional tree and the adjacent geometric faces to be calculated, and calculating the adjacent size based on the nearest neighbor points comprises the following steps: the background grid nodes are traversed, and the nearest neighbor points are queried according to the K-dimensional tree and the adjacent geometric faces to be calculated; a thickness direction layer number input by a user is acquired; the adjacent size is calculated according to the thickness direction layer number, the nearest distance of the nearest neighbor points, and the background mesh initial size parameters.

4. The method of claim 1, wherein, The step of establishing the adjacent size field according to the adjacent size comprises the following steps: an initial adjacent size field is established according to the adjacent size, a size value gradient mutation area of the initial adjacent size field is identified, and recursive smoothing processing is performed on the size value gradient mutation area until a preset smoothing condition is reached; the smoothed adjacent size field is obtained under the condition that the size value gradient mutation area reaches the preset smoothing condition; linear interpolation calculation is performed on any point in the background meshes of the geometric model, and an arbitrary point size value is obtained; the smoothed adjacent size field is continuously processed according to the arbitrary point size value, and the adjacent size field is obtained.

5. The method of claim 1, wherein, After the step of establishing the adjacent size field according to the adjacent size, the method further comprises the following steps: a mapping relationship between the adjacent size field and the topological information of each geometric face in the geometric model is established, and the mapping relationship is managed to share a boundary.

6. The method of claim 1, wherein, The step of generating an encrypted triangular mesh based on the adjacent size field comprises the following steps: the model boundary of the geometric model is associated to the adjacent size field based on the mapping relationship established in advance, and size field information of the adjacent size field is acquired; the model boundary is adaptively discretized according to the size field information, and after the adaptive discretization, each model boundary corresponds to a piecewise linear size field; interpolation calculation is performed according to the size value of the piecewise linear size field and the actual arc length of the model boundary, and boundary node coordinates are obtained; an encrypted triangular mesh is generated according to the boundary node coordinates.

7. The method of claim 6, wherein, The step of generating the encrypted triangular mesh according to the boundary node coordinates comprises: According to the boundary node coordinates, a triangular mesh division algorithm is used to generate a mesh for each geometric face of the geometric model to obtain an initial triangular mesh. The initial triangular mesh is subjected to a mesh optimization operation to obtain an encrypted triangular mesh, and the mesh optimization operation comprises edge splitting, edge collapsing and edge swapping.

8. An encrypted triangulated mesh generation device, characterized by, The device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the encrypted triangular mesh generation method according to any one of claims 1 to 7.

9. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the encrypted triangular mesh generation method according to any one of claims 1 to 7.

10. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by a processor to implement the steps of the encrypted triangular mesh generation method according to any one of claims 1 to 7.

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