Method and device for structural optimization in three-dimensional digital model design

Abstract

This invention discloses a method and apparatus for structural optimization in three-dimensional digital model design. The method includes: identifying the type of the three-dimensional digital model to be analyzed, and classifying the structures in the three-dimensional digital model based on the type identification results to obtain different structural types; partitioning the three-dimensional digital model into multiple detection intervals based on the different structural types, and performing interference detection on the multiple detection intervals to obtain interference position data, wherein each detection interval includes a structure as the interference detection object; performing interference analysis on the multiple interference detection objects corresponding to the interference position data, and automatically adjusting the corresponding structures in the three-dimensional digital model based on the interference analysis results. This invention solves the technical problems of low efficiency and poor accuracy in existing fixture design due to the reliance on manual experience for interference detection.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle design, and more specifically, to a method and apparatus for structural optimization in three-dimensional digital model design. Background Technology

[0002] In fixture design, issues often arise regarding the relative positions of welding torches, tooling, and parts, such as interference or insufficient clearance. Failure to identify and address these problems promptly can lead to a series of serious consequences, including parts failing to fit correctly into the fixture (e.g., misalignment of locating pins and holes), deformation of parts under stress during positioning, interference blocking the movement path during assembly, surface damage, or dimensional deviations, ultimately resulting in rework, part scrap, and even safety risks. Therefore, accurate and timely interference detection and structural optimization are crucial for ensuring assembly quality and efficiency during fixture design.

[0003] In existing technologies, the interference relationship between fixtures, welding torches, and parts is mainly determined manually or through static interference analysis using 3D design software such as CATIA and UG. Designers then make structural adjustments and optimizations based on their experience. This method is highly dependent on the professional level and experience of designers, resulting in low design efficiency, long cycles, and difficulty in achieving rapid and accurate judgment and optimization when facing complex structures or multi-station assembly scenarios.

[0004] With the advent of Industry 4.0, the manufacturing industry is accelerating its transformation towards intelligence and data, driving the evolution of traditional design processes towards automation and intelligent decision-making. Existing fixture design methods have not yet achieved intelligent identification and optimization of interference relationships in complex structures, and lack the ability to analyze, learn, and optimize typical structures and assembly scenarios through algorithmic models.

[0005] Therefore, there is an urgent need for a fixture design method that combines static interference analysis and intelligent optimization algorithms to improve structural adaptability and design efficiency, and meet the urgent needs of new industrial systems for high-precision and high-efficiency fixture design. Summary of the Invention

[0006] This invention provides a method and apparatus for structural optimization in three-dimensional digital model design, which at least solves the technical problems of low efficiency and poor accuracy in existing fixture design due to the reliance on manual experience for interference detection.

[0007] According to one aspect of the present invention, a method for structural optimization in three-dimensional digital model design is provided, comprising: identifying the type of the three-dimensional digital model to be analyzed, and classifying the structures in the three-dimensional digital model based on the result of the type identification to obtain different structural types; partitioning the three-dimensional digital model based on the different structural types to divide the three-dimensional digital model into multiple detection intervals, and performing interference detection on the multiple detection intervals to obtain interference position data, wherein each detection interval includes a structure as an interference detection object; performing interference analysis on the multiple interference detection objects corresponding to the interference position data respectively, and automatically adjusting the corresponding structures in the three-dimensional digital model based on the result of the interference analysis.

[0008] According to another aspect of the present invention, an apparatus for structural optimization in three-dimensional digital model design is also provided, comprising: a classification and identification module configured to perform type identification on the three-dimensional digital model to be analyzed, and classify the structures in the three-dimensional digital model based on the type identification result to obtain different structure types; an interference detection module configured to perform partitioning processing on the three-dimensional digital model based on the different structure types to divide the three-dimensional digital model into multiple detection intervals, and perform interference detection on the multiple detection intervals to obtain interference position data, wherein each detection interval includes a structure as an interference detection object; an interference analysis module configured to perform interference analysis on the multiple interference detection objects corresponding to the interference position data respectively; and a structure optimization module configured to automatically adjust the corresponding structures in the three-dimensional digital model based on the interference analysis result.

[0009] In this embodiment of the invention, the three-dimensional digital model to be analyzed is type-identified, and the structures in the three-dimensional digital model are classified based on the type identification results to obtain different structure types. Based on the different structure types, the three-dimensional digital model is partitioned to divide it into multiple detection intervals, and interference detection is performed on the multiple detection intervals to obtain interference position data. Each detection interval includes a structure as the interference detection object. Interference analysis is performed on the multiple interference detection objects corresponding to the interference position data, and based on the results of the interference analysis, the corresponding structures in the three-dimensional digital model are automatically adjusted. This method solves the technical problems of low efficiency and poor accuracy in existing fixture designs due to the reliance on manual experience for interference detection. Attached Figure Description

[0010] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0011] Figure 1 This is a flowchart of a method for structural optimization in three-dimensional digital model design according to an embodiment of the present invention;

[0012] Figure 2 This is a flowchart of another method for structural optimization in three-dimensional digital model design according to an embodiment of the present invention;

[0013] Figure 3 This is a schematic diagram illustrating different structural types identified according to an embodiment of the present invention;

[0014] Figure 4 (a) and (b) in the figure are two different forms of interference diagrams according to embodiments of the present invention;

[0015] Figure 5 This is a schematic diagram of an optimized opponent structure according to an embodiment of the present invention;

[0016] Figure 6 This is a schematic diagram of an optimized overall structure according to an embodiment of the present invention;

[0017] Figure 7 This is an output diagram showing the result of structural optimization according to an embodiment of the present invention;

[0018] Figure 8 This is a schematic diagram of a structural optimization device in three-dimensional digital model design according to an embodiment of the present invention;

[0019] Figure 9 A schematic diagram of the structure of a computer device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] According to an embodiment of the present invention, a method embodiment of a structural optimization method in three-dimensional digital model design is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0023] Figure 1 This is a method for structural optimization in three-dimensional digital model design according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0024] Step S102: Perform type identification on the three-dimensional digital model to be analyzed, and classify the structures in the three-dimensional digital model based on the results of the type identification to obtain different structure types.

[0025] For example, the structure tree information in the three-dimensional digital model is parsed, and the identification information of each node in the structure tree information is extracted; based on the preset identifier keywords and the extracted identification information, the nodes in the structure tree information are identified by type; based on the result of the type identification, the structure in the three-dimensional digital model is classified into the different structure types, wherein the different structure types include at least one of the following: part digital model, fixture digital model and connecting device digital model.

[0026] Step S104: Based on the different structure types, the three-dimensional digital model is partitioned to divide the three-dimensional digital model into multiple detection intervals, and interference detection is performed on the multiple detection intervals to obtain interference position data, wherein each detection interval includes a structure as the object of interference detection.

[0027] First, partitioning is performed. Based on the different structure types, the 3D digital model is partitioned into multiple detection intervals. Next, interference position data is acquired. For example, the multiple detection intervals are each meshed to construct corresponding distance fields; based on the distance fields, the mesh boundary points of each detection interval are extracted, and the boundary point distance between every two detection intervals is calculated; based on the boundary point distance, an activation function is used to calculate the risk entropy value between every two detection intervals; based on the risk entropy value, the interference position data between every two detection intervals is determined.

[0028] This embodiment efficiently identifies potential interference regions between multiple structures by partitioning the 3D digital model and combining distance field and risk entropy value calculations. This method avoids the high computational cost of global detection of the entire digital model, improves detection accuracy and efficiency, and facilitates timely detection of interference risks.

[0029] Step S106: Perform interference analysis on multiple interference detection objects corresponding to the interference position data, and automatically adjust the corresponding structure in the three-dimensional digital model based on the results of the interference analysis.

[0030] For example, for each interference detection object, the number of structures that have interference relationships with it is determined and used as the interference quantity; the multiple interference detection objects are sorted based on the interference quantity, and the interference detection object with the most interference quantities is selected as the optimization object; for the optimization object, an adjustment strategy is constructed using the principle of minimum change; based on the adjustment strategy, the optimization object is updated in real time using a dynamic update engine, and the opposing structures that have assembly relationships with the optimization object are simultaneously optimized, wherein the opposing structures are other interference detection objects that have assembly relationships with the optimization object in the three-dimensional digital model in terms of spatial location.

[0031] This embodiment selects optimization targets based on the number of interferences and constructs an adjustment strategy using the principle of minimizing changes. This enables priority optimization of key interference structures, improving optimization efficiency. Furthermore, by combining a dynamic update engine to perform real-time adjustments to the optimization targets and their counterparts, the uncertainty caused by repeated modifications and local corrections can be effectively reduced, ensuring overall structural consistency and the accuracy of interference elimination, thus optimizing the design process.

[0032] In some embodiments, synchronous optimization of a counterpart structure that has an assembly relationship with the optimization object may include: obtaining three-dimensional position data of the counterpart structure that has an assembly relationship with the optimization object based on the interference position data; constructing a multi-objective topology optimization function based on the three-dimensional position data, and performing dimensional filtering based on the gradient changes of multiple interference variables of the counterpart structure according to the multi-objective topology optimization function to determine the key interference variables that need to be adjusted; and performing synchronous optimization of the counterpart structure based on the key interference variables.

[0033] By constructing a multi-objective topology optimization function and combining it with dimensionality filtering based on the gradient changes of interference variables, this method can accurately identify key variables in the opponent structure that have a significant impact on interference, thus enabling targeted adjustments. This approach improves the targeting and efficiency of optimization, avoids ineffective modifications to non-critical variables, achieves refined and synchronous optimization of the opponent structure, and further enhances the overall interference elimination effect and design rationality of the structure.

[0034] Figure 2 This is another structural optimization method in three-dimensional digital model design according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0035] Step S202, node selection.

[0036] Users click the "Select Product Model" button through the human-computer interaction interface to select the product model node to be inspected. The system automatically obtains the number information of the selected node, retrieves the corresponding 3D digital model based on the number information, and then imports the 3D digital model into the system for subsequent analysis and processing.

[0037] Step S204, type identification.

[0038] The system performs type identification on imported 3D digital models, automatically parses the structure tree information of the 3D digital models, and classifies and marks the structures according to the identification information of each node in the structure tree. At the same time, it records the corresponding structure category, name, hierarchical path and other information. In order to adapt to input scenarios with different digital model formats, the system has the ability to automatically identify node data, including format differences, hidden state or active state of nodes, all of which can be automatically identified and processed by algorithms.

[0039] Specifically, the system performs structural judgments based on preset identifier keywords. For example... Figure 3As shown, if the node name contains keywords such as "SM", "digital model", or "date", then the part or assembly to which the node is located is identified as a part digital model; if the node name contains keywords such as "positioning unit", "JIG", or "unit", then it is identified as a fixture digital model; if the node name contains keywords such as "gun", "welding gun digital model", or "welding gun", then it is identified as a connecting device digital model.

[0040] During the recognition process, if some input models cannot be automatically recognized using the aforementioned keywords due to a lack of standard naming rules, the system also provides a manual assistance mechanism. Users can manually select model nodes and their types through the "Manual Node Selection" interface on the CATIA platform. The system will read the node information and complete the type recognition and classification, ensuring the integrity and flexibility of the overall type recognition process.

[0041] Step S206, interference detection.

[0042] Based on the aforementioned type identification results, the system first partitions the structures according to different types, dividing the 3D digital model into several regions to be detected. Subsequently, the system can employ two optional schemes to detect the interference positions between the regions:

[0043] Option 1: Static Interference Detection Based on CATIA Built-in Tools. The system calls CATIA's CAACheckClash (interference detection module) and Sectioning Definition (section definition tool) to analyze the spatial relationships between the sections to be detected, in order to identify whether there is structural overlap or interference. After the detection is completed, the system will automatically output the interference detection results for each section and record the corresponding interference position data, providing a basis for subsequent structural optimization.

[0044] Option 2: Interference detection based on dynamic-static fusion analysis. This option achieves a more refined interference detection process through dynamic-static fusion analysis, specifically including the following steps:

[0045] 1) Construct a distance field.

[0046] The system performs meshing on each detection interval obtained after partitioning to generate a corresponding mesh model. Each detection interval contains a structure that serves as the object of interference detection, such as a part model, fixture model, and welding torch model. The system will perform meshing modeling on these structures respectively to provide basic data for subsequent distance calculation and interference analysis.

[0047] Specifically, for structures such as part models, fixture models, and welding gun models, the system performs triangular facet reconstruction operations and adjusts the mesh density through an adaptive meshing strategy to adapt to different structural complexities. For example, in corners, high curvature, or edge-sensitive areas, the system automatically densifies the mesh nodes to improve geometric accuracy; while in flat or structurally insignificant areas, a sparse mesh is used to save computational resources. To ensure the robustness of subsequent distance field calculations, the system uses an octree spatial partitioning algorithm to divide the entire 3D model into voxels and establishes node index relationships to form the basic structure of the sparse distance matrix. Each structural unit ultimately corresponds to an independent sparse mesh block, which facilitates local calls and parallel computation in subsequent analysis.

[0048] 2) Calculate the boundary distance.

[0049] Boundary points (contour points) of each mesh model are extracted, and the distances between corresponding boundary points between different structures are calculated to form complete boundary distance data. Specifically, boundary points (i.e., contour points or shell points) of each mesh model are extracted, and an index structure is built for all boundary points using the KD-tree (k-dimensional tree) algorithm to accelerate the process. Then, the nearest distance between boundary points is calculated between each region to be detected, forming a symmetric distance matrix and recording the minimum Euclidean distance for each pair of boundary points. In some embodiments, only boundary point pairs with opposite normal vector directions can be retained as valid distance pairs, filtering out point pairs with no actual possibility of interference.

[0050] 3) Risk entropy assessment.

[0051] In some embodiments, an interference risk entropy value can be calculated based on boundary distance data using a Sigmoid activation function to measure the severity of potential interference between structures.

[0052] In other embodiments, an improved multi-scale entropy weight fusion model can be used to replace the single Sigmoid function evaluation method. Specifically, the interference risk entropy value can be calculated based on the local proximity index, the boundary normal angle weight, and the structure type weight factor. The local proximity index is determined by the reciprocal of the distance between boundary points, reflecting the degree of geometric proximity. The boundary normal angle weight represents the angle between the normal vectors of two boundary points; the smaller the angle, the more direct the potential contact. The structure type weight factor is a different weight bias introduced according to the differences in structure type (such as fixture to welding gun, part to part).

[0053] The final risk entropy scoring function takes the following form:

[0054]

[0055] where R ijTo score risk entropy, d ij θ is the distance between boundary points. ij Let σ be the angle between the boundary normals and T. ij The structural type weighting factor is set (e.g., the value for fixtures and welding guns is set to 1.2, and the value for parts is set to 1.0), and w1, w2, and w3 are adjustable weighting coefficients.

[0056] This embodiment introduces a risk assessment model that integrates structure type perception and multi-scale geometric feature fusion, which not only improves the accuracy of interference recognition but also has stronger versatility and adaptability to engineering scenarios.

[0057] 4) Interference position extraction.

[0058] Based on the result that the risk entropy value exceeds the preset threshold, the system outputs the high-risk interference position as input for subsequent interference analysis and structural optimization.

[0059] Through the above interference detection process, the system can not only achieve traditional static interference identification, but also obtain more accurate and sensitive interference risk areas through dynamic fusion analysis, significantly improving detection accuracy and engineering applicability.

[0060] Two different forms of interference diagrams are shown in the example. Figure 4 As shown.

[0061] Step S208: Interference analysis and structural optimization.

[0062] First, the results of the aforementioned interference detection stage are analyzed one by one according to the interference location, such as the part number. Specifically, the number of interferences between each interference detection object and other structures (other interference detection objects) is counted and sorted from high to low. The structure with the highest number of interferences is then prioritized as the current optimization target, improving the targeting and efficiency of the optimization.

[0063] For the selected optimization target, the interference situation is further analyzed, and an optimization strategy is formulated following the principle of "minimum modification". During the optimization process, a dynamic update engine is used to perform real-time adjustment calculations on the current structure to ensure the continuity and spatial adaptability of the adjustment process.

[0064] Meanwhile, to ensure coordinated optimization of the overall structure, the system employs a multi-objective topology optimization function to achieve coordinated adjustment between the optimization object and its interference-related structures. Specifically, this includes the following steps:

[0065] 1) Import the interference position data.

[0066] Based on the interference detection results, load the interference position data of the current optimization object and its interference opponent structure.

[0067] 2) Construct a multi-objective optimization function.

[0068] The multi-objective topology optimization function is constructed using local constraints, dual constraints, and system constraints.

[0069] 3) Gradient-guided variable selection.

[0070] In some embodiments, multiple interference variables can be dimensionally filtered based on gradient change guidance to determine key variables. In other embodiments, key variables can be filtered based on the rate of change of the interference area, the degree of influence on the distribution of the contact normal vector, and the functional sensitivity of the structural region in which they are located, thereby quantifying the actual influence of each key variable on the interference relationship. Finally, the system selects only variables with IIF scores higher than a preset threshold as key interference variables. This significantly improves the targeting and optimization effect of variable selection.

[0071] 4) Variable conflict resolution.

[0072] The system analyzes and optimizes the self-conflicting relationships among variables and executes conflict resolution logic. Specifically, when multiple variables are coupled or mutually influential, the system constructs a variable dependency graph and identifies potential conflicting paths using a minimum weight path search algorithm, prioritizing the retention of variables with higher interference factors and smaller structural influence ranges. For variables that still have conflicts, the system performs vector decomposition, retaining only the principal component directions for adjustment. If the conflict cannot be effectively resolved, the system reverts to the previous stage to re-select variables, ensuring the stability and controllability of the optimization process.

[0073] 5) Optimize the dissemination of results.

[0074] The structural adjustment results are propagated forward or backward according to logical relationships to ensure that all interference-related structures are adjusted synchronously. Specifically, when there is a parent-child hierarchical relationship between structures, forward propagation is performed to maintain the continuity of the structural chain; when the interference involves force feedback paths or the reverse influence of constraints, a backward propagation mechanism is used. In addition, for scenarios with bidirectional dependencies such as welding torch and fixture, fixture and parts, the system adopts a bidirectional linkage propagation mechanism to ensure that all interference-related structures remain coordinated and consistent after adjustment.

[0075] For example, when the optimization object changes, the amount of change in the opponent's structure is:

[0076]

[0077] Where ΔP represents a small change in the optimized object, such as displacement or angle adjustment. ΔQ represents a small change in the opponent's structure. The Jacobian matrix J... PQ Let H be the first-order partial derivative matrix, representing the linear effect of ΔP on ΔQ. The Hessian matrix H... PQis a second-order partial derivative matrix, representing the nonlinear (curvature) effect of ΔP on ΔQ.

[0078] Each opponent's structure is optimized as follows:

[0079]

[0080] in,

[0081]

[0082] in, Let be the weighted norm, represent the weighted sum of squared errors, W be the weight matrix, and b be the weighted norm. k Let g be the threshold vector of the system constraints. i h represents a local constraint. i Denotes duality constraint, A k Represents system constraints.

[0083] like Figure 5 As shown, when the optimized object is adjusted, other interference detection objects (such as structure 2, structure 3, structure 4 and structure 5) will automatically make corresponding matching adjustments to maintain the overall consistency of the assembly structure.

[0084] During the optimization process, if interference is detected between the welding torch and the fixture or part, the system will prioritize optimizing the welding torch's posture or position. If welding torch optimization fails to effectively resolve the interference, then the fixture structure will be adjusted. After optimizing each component, the system immediately performs a secondary interference check on that component to verify the effectiveness of the adjustment. It then proceeds to the next component to be optimized, re-performing a static interference check before starting to ensure that all interference data used is up-to-date.

[0085] like Figure 6 As shown, after all interference relationships between structures have been eliminated, the system outputs the final adjustment scheme according to the optimization order. The output includes key parameters such as the adjustment angle, movement vector, and changed position for each structure, facilitating recording, verification, and application by engineers.

[0086] Step S210, output the result.

[0087] After structural optimization is complete, the system outputs all adjustment schemes sequentially according to the actual adjustment order of each structure, and records each adjustment operation in detail. The output content is as follows: Figure 7The data includes, but is not limited to: the adjustment angles of each structure, used to describe changes in attitude or direction; the adjustment amounts of each structure, i.e., displacement vectors in three-dimensional space; and the structural change locations of each part, including information such as shape adjustments, connection point modifications, or topological changes. Systematic recording of the above data facilitates subsequent verification, simulation, assembly guidance, and design iteration, ensuring the traceability and engineering usability of the structural optimization results.

[0088] This invention utilizes a fusion of software recognition and algorithms to mesh the 3D digital models of fixtures, connecting devices, and parts. Based on this meshing, structural analysis and optimization are performed, and combined with the coordinated optimization of related structures, achieving full automation of interference detection and structural adjustment. This method significantly improves the standardization of the design process and the accuracy and efficiency of the output results, reduces reliance on the professional experience of designers, and effectively saves design time and labor costs.

[0089] This application also provides a device for structural optimization in three-dimensional digital model design, such as... Figure 8 As shown, it includes: a classification and identification module 82, configured to identify the type of the 3D digital model to be analyzed, and classify the structures in the 3D digital model based on the result of the type identification to obtain different structure types; an interference detection module 84, configured to partition the 3D digital model based on the different structure types to divide the 3D digital model into multiple detection intervals, and perform interference detection on the multiple detection intervals to obtain interference position data, wherein each detection interval includes a structure as the interference detection object; an interference analysis module 86, configured to perform interference analysis on the multiple interference detection objects corresponding to the interference position data respectively; and a structure optimization module 88, configured to automatically adjust the corresponding structures in the 3D digital model based on the result of the interference analysis.

[0090] It should be noted that the structural optimization device for 3D digital model design provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the structural optimization device for 3D digital model design and the method embodiment for structural optimization in 3D digital model design provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0091] Figure 9 A schematic diagram of a computer device suitable for implementing embodiments of the present disclosure is shown. It should be noted that... Figure 9 The computer device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0092] like Figure 9 As shown, the computer device includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0093] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 1010 as needed so that computer programs read from it can be installed into storage section 1008 as needed.

[0094] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for structural optimization in three-dimensional digital model design, characterized in that, include: Type identification is performed on the three-dimensional digital model to be analyzed, and the structures in the three-dimensional digital model are classified based on the results of the type identification to obtain different structure types; Based on the different structural types, the three-dimensional digital model is partitioned to divide it into multiple detection intervals, and interference detection is performed on the multiple detection intervals to obtain interference position data. Each detection interval includes a structure that is the object of interference detection. Interference analysis is performed on multiple interference detection objects corresponding to the interference position data, and the corresponding structure in the three-dimensional digital model is automatically adjusted based on the results of the interference analysis. The process involves performing interferometric analysis on multiple interferometric detection objects corresponding to the interferometric position data, and automatically adjusting the corresponding structures in the 3D digital model based on the results of the interferometric analysis. This includes: determining the number of structures with which each interferometric detection object has an interferometric relationship, and using this number as the number of interferometrics; sorting the multiple interferometric detection objects based on the number of interferometrics, and selecting the interferometric detection object with the most interferometrics as the optimization object; constructing an adjustment strategy for the optimization object using the principle of minimum change; and updating the structure of the optimization object in real time based on the adjustment strategy, while simultaneously optimizing the opposing structures that have an assembly relationship with the optimization object. The opposing structures are other interferometric detection objects in the 3D digital model that have an assembly relationship with the optimization object in spatial position. The synchronous optimization process for the opponent structure that has an assembly relationship with the optimization object includes: acquiring three-dimensional position data of the opponent structure that has an assembly relationship with the optimization object based on the interference position data; constructing a multi-objective topology optimization function based on the three-dimensional position data, and performing dimensional filtering based on the gradient changes of multiple interference variables of the opponent structure according to the multi-objective topology optimization function to determine the key interference variables that need to be adjusted; and performing synchronous optimization process on the opponent structure based on the key interference variables.

2. The method according to claim 1, characterized in that, The three-dimensional digital model to be analyzed is type-identified, and the structures in the three-dimensional digital model are classified based on the type-identification results to obtain different structure types, including: The structure tree information in the three-dimensional digital model is parsed, and the identification information of each node in the structure tree information is extracted; Based on preset identifier keywords and extracted identifier information, type identification is performed on each node in the structure tree information; Based on the results of type recognition, the structures in the three-dimensional digital model are classified into different structural types, wherein the different structural types include at least one of the following: part digital model, fixture digital model, and connecting device digital model.

3. The method according to claim 1, characterized in that, Interference detection is performed on the multiple regions to be detected to obtain interference position data, including: The multiple regions to be detected are respectively processed into grids to construct corresponding distance fields; Based on the distance field, the grid boundary points of each interval to be detected are extracted, and the distance between the boundary points of each two intervals to be detected is calculated based on the grid boundary points. Based on the boundary point distance, the risk entropy value between every two intervals to be detected is calculated using an activation function; Based on the risk entropy value, the interference position data of every two intervals to be detected is determined.

4. The method according to claim 1, characterized in that, Based on the three-dimensional location data, a multi-objective topology optimization function is constructed, including: based on the three-dimensional location data, using local constraints, dual constraints, and system constraints to construct the multi-objective topology optimization function.

5. A device for structural optimization in three-dimensional digital model design, characterized in that, include: The classification and recognition module is configured to perform type recognition on the three-dimensional digital model to be analyzed, and classify the structures in the three-dimensional digital model based on the type recognition results to obtain different structure types; The interference detection module is configured to partition the three-dimensional digital model based on the different structure types, so as to divide the three-dimensional digital model into multiple detection intervals, and perform interference detection on the multiple detection intervals to obtain interference position data, wherein each detection interval includes a structure as the interference detection object; The interference analysis module is configured to perform interference analysis on multiple interference detection objects corresponding to the interference position data respectively; The structure optimization module is configured to automatically adjust the corresponding structure in the three-dimensional digital model based on the results of interferometry analysis; The device is further configured to: for each interference detection object, determine the number of structures that have an interference relationship with it, and use this number as the interference quantity; sort the multiple interference detection objects based on the interference quantity, and select the interference detection object with the most interference quantities as the optimization object; for the optimization object, construct an adjustment strategy using the principle of minimum change; based on the adjustment strategy, perform real-time structural updates on the optimization object, and simultaneously perform synchronous optimization processing on the opposing structures that have an assembly relationship with the optimization object, wherein the opposing structures are other interference detection objects that have an assembly relationship with the optimization object in the three-dimensional digital model in terms of spatial position; The device is further configured to: acquire three-dimensional position data of the opponent structure that has an assembly relationship with the optimization object based on the interference position data; construct a multi-objective topology optimization function based on the three-dimensional position data; and perform dimensional filtering based on the gradient changes of multiple interference variables of the opponent structure according to the multi-objective topology optimization function to determine the key interference variables that need to be adjusted; and perform synchronous optimization processing on the opponent structure based on the key interference variables.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 4.

7. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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