Collaborative Design Method and System Based on CAD Platform

Abstract

The present invention discloses a collaborative design method and system based on a CAD platform, specifically relating to the technical field of data processing; collecting and format-parsing data of multiple heterogeneous CAD software, evaluating geometric accuracy loss through surface deviation analysis, monitoring parameter changes during the collaborative design process by using an event listening mechanism and differential update technology, calculating the parametric constraint retention rate between different CAD platforms, evaluating the integrity of parameter information conversion, and further constructing a data conversion quality evaluation model, comprehensively analyzing the geometric accuracy and parameter retention situation, and for low-quality conversion data, adopting an adaptive optimization algorithm to dynamically adjust the CAD data conversion strategy to improve cross-platform data integrity; the present invention effectively improves the data interoperability, design consistency and conversion quality of CAD collaborative design, and provides an efficient and intelligent solution for multi-software collaborative design and manufacturing.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a collaborative design method and system based on a CAD platform. Background Art

[0002] Computer-Aided Design (CAD) technology is widely used in multiple fields such as industrial manufacturing, architectural design, and electronic circuit design. The emergence of the CAD platform has greatly improved the design efficiency, enabling engineers to perform modeling, simulation, and optimization in a virtual environment. However, with the increasing complexity of products and the growing demand for global collaborative design, the traditional CAD design mode has gradually revealed some limitations. Currently, collaborative design mainly relies on a CAD platform shared by a local network or the cloud, allowing multiple designers to access and edit the same design file simultaneously. Typical collaborative design methods include: the file sharing mode, which manages the files submitted by different designers through a version control system (such as PDM / PLM) to ensure data consistency. The real-time collaboration mode, based on the real-time editing function of cloud computing or a local area network, allows multiple users to modify the model simultaneously and automatically synchronize the updates. Although these modes have improved the collaborative efficiency to a certain extent, there are still problems in aspects such as data synchronization, version conflicts, permission management, and remote collaboration efficiency. Especially when facing large-scale complex projects across platforms and teams, traditional methods are difficult to meet the efficient and accurate collaborative requirements.

[0003] The existing technologies have the following deficiencies:

[0004] In a CAD collaborative design environment, there are data compatibility problems between cross-software and heterogeneous CAD systems. Different CAD software uses different data formats, modeling kernels, and parameter expression methods, making it extremely difficult to perform lossless conversion of data when multiple software are involved in the collaborative design process. For example, when a complex 3D model is transferred between different CAD platforms, the model topology structure may change, parameters may be lost, or feature parsing may fail due to incompatible geometric kernels, thus affecting the consistency and integrity of the design. Although existing data exchange standards (such as STEP and IGES) can partially alleviate this problem, they still cannot completely solve the full-parameter interoperability and semantic preservation in cross-platform design. Summary of the Invention

[0005] The purpose of the present invention is to provide a collaborative design method and system based on a CAD platform to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A collaborative design method based on a CAD platform, including:

[0007] Collect the design data of multiple heterogeneous CAD software, parse their formats, and convert them based on a unified data standard. By analyzing the surface deviation degree of the CAD model after conversion on different platforms, evaluate the impact of data conversion on design accuracy;

[0008] Adopt cloud computing architecture technology to build a collaborative design platform that supports multi-user real-time access. During the collaborative design process, use the event listening mechanism and differential update technology to detect the modification of design parameters by different users, judge the retention rate of parametric constraint relationships between different CAD platforms, and evaluate the conversion integrity of parametric information;

[0009] Combine the impact of data conversion on design accuracy and the conversion integrity of parametric information to build a data conversion quality evaluation model to evaluate the data conversion quality between different CAD software;

[0010] For low-quality conversion data, use an optimization algorithm to perform adaptive optimization according to the conversion characteristics of different CAD software to improve the data integrity of cross-platform collaborative design;

[0011] After the collaborative design is completed, export the final design results into multiple common formats and can be docked with the PDM / PLM system to achieve product full life cycle management.

[0012] Preferably, the multiple heterogeneous CAD software includes SolidWorks, CATIA, Siemens NX, PTC Creo, Autodesk Inventor, and FreeCAD, the unified data standard includes STEP, IGES, Parasolid, JT, STL, DXF, and 3D PDF, and the format parsing includes directly parsing the CAD native format, bridging through a neutral format, and using API for conversion.

[0013] Preferably, after analyzing the surface deviation degree of the CAD model after conversion on different platforms, generate a surface deviation anomaly index. The method for obtaining the surface deviation anomaly index is as follows:

[0014] Set the original CAD surface as and the converted CAD surface as , from sample , from sample ; Calculate the bidirectional Hausdorff distance, marked as , and the calculation formula is: ;

[0015] where: sup represents the maximum deviation, inf represents the nearest point, and for each point in In, find its The closest point in , calculate the Euclidean distance; for each point exist In, find its The closest point in , calculate the distance, take the maximum value as the Hausdorff distance, calculate the minimum deviation root mean square error RMS of all points, and calculate the weighted average sum of the Hausdorff distance and RMS error to get the surface deviation anomaly index.

[0016] Preferably, after analyzing the retention rates of parameterized constraint relationships between different CAD platforms, a constraint relationship retention rate drift index is generated, and the constraint relationship retention rate drift index is obtained by:

[0017] Construct a CAD constraint relationship vector, where each element corresponds to the weight of a CAD constraint, and define the original CAD constraint vector and the converted CAD constraint vector : ;in: Indicates the number of certain constraints in the original CAD model. Represents the number of corresponding constraints in the converted CAD model, and calculates its cosine similarity S, which is expressed as: ; Where: the numerator is the dot product of the vectors, indicating the similarity of the two CAD constraint vectors; the constraint relationship retention rate drift index CRDI is calculated, and the expression is: .

[0018] Preferably, a data conversion quality evaluation model is constructed based on the impact of data conversion on design accuracy and the conversion integrity of parametric information to evaluate the data conversion quality between different CAD software, including:

[0019] The surface deviation anomaly index and the constraint relationship retention rate drift index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model predicts the data conversion quality value labels between different CAD software with each group of comprehensive feature vectors as the prediction target, and minimizes the sum of prediction errors of data conversion quality value labels between all different CAD software as the training target. The machine learning model is trained until the sum of prediction errors converges, and the model training is stopped. The data conversion quality values ​​between different CAD software are determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0020] Preferably, compare the obtained data conversion quality value between different CAD software with a preset threshold. If the data conversion quality value between different CAD software is greater than or equal to the preset threshold, it indicates that the data conversion quality between different CAD software is high, and mark it as high-quality conversion data; if the data conversion quality value between different CAD software is less than the preset threshold, it indicates that the data conversion quality between different CAD software is low, and mark it as low-quality conversion data.

[0021] Preferably, for low-quality conversion data, adopt an adaptive optimization algorithm to automatically adjust parameters according to the conversion characteristics of different CAD software, including: in the initial population of the genetic algorithm, each individual represents a combination of CAD conversion parameters, and define the individual gene encoding: ; where: is the geometric tolerance setting, is the feature mapping method, is the parameter conversion weight, is the surface reconstruction method;

[0022] Randomly generate N individuals to form a population: ; where, is a combination of CAD conversion parameters, define the fitness function to evaluate the conversion quality of each individual: ; where: is the weight coefficient, calculate the fitness values of all individuals, and sort the individuals. HSK is the surface deviation abnormal index, and the value range of HSK is [0,1];

[0023] Reduce the mutation rate for individuals with large gradients and increase the mutation rate for individuals with small gradients. Use roulette wheel selection to select individuals with high fitness to enter the next generation. Use single-point crossover to generate new individuals, and use adaptive gradient adjustment to mutate the rate, and randomly perturb the individual genes.

[0024] Preferably, repeat the calculation until the conversion quality converges: ; where: is the convergence threshold, set to 0.001 or dynamically adjusted according to the error change rate, is the conversion quality of the i-th individual in the current iteration, is the conversion quality of the i-th individual in the previous iteration. When the fitness change tends to be stable, the algorithm stops optimizing.

[0025] The present invention also provides a collaborative design system based on a CAD platform, including a format parsing module, a parameter conversion analysis module, a data conversion quality evaluation module, a low-quality conversion data optimization module, and a design result export module;

[0026] Format parsing module: Collect the design data of multiple heterogeneous CAD software, parse their formats, and perform conversions based on a unified data standard. By analyzing the surface deviation degree of the CAD model after conversion on different platforms, evaluate the impact of data conversion on design accuracy;

[0027] Parameter conversion analysis module: Adopt cloud computing architecture technology to build a collaborative design platform that supports multi-user real-time access. During the collaborative design process, use the event listening mechanism and differential update technology to detect the modification of design parameters by different users, judge the retention rate of parametric constraint relationships between different CAD platforms, and evaluate the conversion integrity of parametric information;

[0028] Data conversion quality evaluation module: Combine the impact of data conversion on design accuracy and the conversion integrity of parametric information to build a data conversion quality evaluation model to evaluate the data conversion quality between different CAD software;

[0029] Low-quality conversion data optimization module: For low-quality conversion data, adopt an optimization algorithm to perform adaptive optimization according to the conversion characteristics of different CAD software to improve the data integrity of cross-platform collaborative design;

[0030] Design result export module: After the collaborative design is completed, export the final design result into multiple common formats and can be docked with the PDM / PLM system to achieve product full life cycle management.

[0031] In the above technical solutions, the technical effects and advantages provided by the present invention are:

[0032] 1. Through format parsing and standardization conversion, the method of the present invention can be compatible with multiple CAD platforms such as SolidWorks, CATIA, NX, Creo, Inventor, FreeCAD, etc., and perform data conversion based on standard formats such as STEP, IGES, Parasolid, JT, etc. to improve cross-platform design consistency. By calculating the surface deviation abnormal index (HSK) and the constraint relationship retention rate drift index (CRDI), quantify the impact of CAD data conversion on geometric accuracy and parameter integrity, and use the polynomial regression model to predict the data conversion quality between different CAD software, providing optimization suggestions for enterprise collaborative design in a multi-CAD environment. In addition, the system is based on a cloud computing architecture, supports multi-user real-time access, and uses the event listening mechanism and differential update technology to ensure that design data remains synchronized during the collaboration process of multiple people, improving the efficiency and reliability of collaborative design.

[0033] 2. By adopting the Adaptive Gradient Genetic Algorithm (AGGA), the present invention adaptively adjusts parameters according to the conversion characteristics of different CAD software, optimizes key factors such as geometric tolerances, feature mapping, and parameter weights, thereby improving the integrity and accuracy of CAD data conversion. The optimized design data can support export in multiple formats and be seamlessly integrated into PDM / PLM to achieve data management throughout the product life cycle. The present invention not only improves data interoperability across CAD platforms, but also enhances the quality of collaborative design, reduces design errors, and provides an efficient and accurate CAD data management solution for fields such as intelligent manufacturing, engineering design, and product R & D. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0035] Figure 1 It is a flowchart of the method of the present invention.

[0036] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1. Please refer to Figure 1 As shown, the collaborative design method based on the CAD platform in this embodiment includes:

[0039] Collect the design data of multiple heterogeneous CAD software, perform format parsing on it, and convert it based on a unified data standard. By analyzing the surface deviation degree of the CAD model after conversion on different platforms, evaluate the impact of data conversion on design accuracy;

[0040] Adopt cloud computing architecture technology to build a collaborative design platform that supports multi-user real-time access. During the collaborative design process, use the event listening mechanism and differential update technology to detect the modification of design parameters by different users, judge the retention rate of parametric constraint relationships between different CAD platforms, and evaluate the conversion integrity of parametric information;

[0041] Construct a data conversion quality evaluation model by combining the impact of data conversion on design accuracy and the conversion integrity of parametric information to evaluate the data conversion quality between different CAD software;

[0042] For low-quality converted data, adopt an optimization algorithm to perform adaptive optimization according to the conversion characteristics of different CAD software to improve the data integrity of cross-platform collaborative design;

[0043] After the collaborative design is completed, export the final design results into multiple common formats and interface with the PDM / PLM system to achieve product full life cycle management.

[0044] Collect the design data of heterogeneous CAD software: Different CAD software use their own proprietary formats, such as: SolidWorks (SLDPRT, SLDASM), CATIA (CATPart, CATProduct), Siemens NX (PRT), PTCCreo (PRT, ASM), Autodesk Inventor (IPT, IAM), open-source CAD (such as FreeCAD, using STEP, IGES).

[0045] In addition, there are some neutral formats for cross-platform data exchange: STEP (ISO 10303): Widely supported and suitable for precise geometry conversion, IGES: An early standard, and topological information may be lost in some cases, Parasolid (X_T, X_B): Developed by Siemens and supports multiple CAD platforms, JT (Jupiter Tessellation): Used for lightweight visualization.

[0046] To convert between different CADs, it is necessary to parse their respective file formats and unify them into a common data structure, such as: geometric data: B-Rep (Boundary Representation), NURBS (Non-Uniform Rational B-Spline), topological structure: relationships of points, edges, faces, solids, etc.; parametric information: constraints, feature trees, dimension parameters; materials and properties: colors, densities, material properties, etc.;

[0047] Conversion methods: Directly parse the CAD native format (requiring official SDK or reverse engineering), bridge through neutral formats (such as STEP, Parasolid), and adopt APIs or plugins (such as Open Cascade, CGM Core).

[0048] Evaluate the impact of data conversion on design accuracy, mainly evaluate the geometric errors after CAD data conversion, including: surface deviation: calculate the Hausdorff distance of NURBS surfaces and analyze the curvature change; boundary deviation: check whether the topology after conversion is broken or self-intersecting; volume / mass change: evaluate whether the mass properties are shifted; feature loss: for example, whether the information of holes, chamfers, and draft angles is completely retained. Directly compare the geometric data point clouds before and after conversion (for example, use the ICP algorithm for registration), through the geometric analysis tools of CAD software (such as CATIA Compare, NX Validation), and use mathematical analysis methods to calculate the error range.

[0049] After analyzing the degree of surface deviation of the CAD model after conversion on different platforms, generate a surface deviation abnormality index. The method for obtaining the surface deviation abnormality index is as follows:

[0050] Set the original CAD surface as and the converted CAD surface as . Since CAD surfaces are usually in NURBS or B-Rep form, it is first necessary to discretize (sample) the surface and convert it into a point cloud:

[0051] Sample from with samples, and sample from with samples; The sampling methods include: uniform sampling: uniformly take points in the parameter space (u, v), adaptive sampling: increase the sampling density in areas with larger curvature, and grid sampling: take points after converting the surface into an STL grid. Calculate the bidirectional Hausdorff distance, marked as , and the calculation formula is: ;

[0052] where: sup represents the maximum deviation, inf represents the closest point, for each point in , find its closest point in , and calculate the Euclidean distance; for each point in , find its closest point in , calculate the distance, and take the maximum value as the Hausdorff distance.

[0053] Calculate the root mean square error RMS of the minimum deviation of all points, and perform a weighted average summation calculation on the Hausdorff distance and RMS error to obtain the surface deviation abnormality index.

[0054] If the surface deviation abnormal index is large, it indicates that there are significant geometric errors in the CAD model after conversion, which may affect manufacturing or simulation accuracy.

[0055] If the surface deviation abnormal index is moderate, the converted model can be used in most application scenarios.

[0056] If the surface deviation abnormal index is small, it indicates that the error in the conversion process is negligible and the model has high accuracy.

[0057] To support multi-user real-time collaborative design, a cloud computing architecture needs to be adopted, including the front end (Web CAD side), the back end (CAD calculation and data storage), real-time communication (event listening and synchronization), and the storage layer (CAD model database).

[0058] Common architecture solutions: SaaS (Software as a Service): Users access cloud-based CAD applications (such as Onshape, Autodesk Fusion 360) through a browser. PaaS (Platform as a Service): Provides CAD calculation and storage capabilities, allowing enterprises to integrate CAD components (such as Siemens NX Open API, PTC Onshape API). IaaS (Infrastructure as a Service): Provides underlying computing resources to host CAD applications and databases.

[0059] To detect modifications to design parameters by different users, an event listening mechanism needs to be established to capture changes in CAD design parameters.

[0060] Based on WebSocket / Socket.IO: Suitable for Web-based CAD collaborative design, real-time pushing of change information of design parameters. For example: User A modifies the dimension D1 = 10mm → 12mm on the Web side, and this change will be broadcast to other online users through WebSocket.

[0061] Based on database triggers: Suitable for back-end databases (such as PostgreSQL, MongoDB), monitors changes in CAD parameters, and triggers corresponding synchronization operations.

[0062] Based on CAD API Hook (plugin-level listening): Listens for geometric constraints and parameters modified by users in CAD software and records the change history. For example, uses Onshape API, Siemens NX Open API to listen for users to adjust feature dimensions, sketch constraints, etc.

[0063] Example event listening process: User A modifies parameters to listen for changes in dimensional parameters (D1, D2...) or geometric constraints (parallel, perpendicular...). Record the change log, including the user ID, time, and values before and after the modification, and store it in the database. Synchronize the changes. Through WebSocket or CAD API, synchronize the changes to other users. During collaborative design, instead of transmitting the entire CAD model, lightweight data synchronization is performed through delta update to improve efficiency.

[0064] Delta update methods include: Based on JSON Patch, only transmit the modified part instead of the entire parameter list: suitable for parametric model updates. Based on Git-like version management, adopt version control (such as Git), and only store the "increments" of parameter changes. For example: Onshape uses FeatureScript to record parameter changes and can trace the design history. Update based on CAD constraint relationships: Only update the affected geometric features to avoid global updates and improve computational efficiency.

[0065] Between different CAD platforms (such as SolidWorks CATIA Siemens NX), parametric information may be lost or distorted, so it is necessary to evaluate the conversion integrity.

[0066] The following problems may occur in the conversion of parametric constraints between different CADs:

[0067] Constraint loss: Such as dimensional constraints, symmetry, concentricity, perpendicularity, etc. disappear.

[0068] Constraint substitution: Some CAD software may automatically substitute constraints (for example, CATIA → NX may substitute the symmetry constraint with equal length). Parameter drift: There are slight deviations in the dimension values after conversion (such as 10.000mm → 9.999mm).

[0069] After analyzing the retention rate of parametric constraint relationships between different CAD platforms, a constraint relationship retention rate drift index is generated. The method for obtaining the constraint relationship retention rate drift index is:

[0070] Construct a CAD constraint relationship vector. Different CAD constraints can be represented as a vector, where each element corresponds to the weight of a CAD constraint. Set the constraint types as: Parallel (parallel), Coincident (coincident), Concentric (concentric), EqualLength (equal length), Symmetric (symmetric), and Tangent (tangent).

[0071] Define the original CAD constraint vector and the transformed CAD constraint vector : ; where: represents the quantity of a certain constraint in the original CAD model, represents the quantity of the corresponding constraint in the transformed CAD model. Calculate the cosine similarity S, and the expression is: ; where: The numerator is the dot product of the vectors, representing the similarity degree of the two CAD constraint vectors. The denominator is the product of the vector norms, used to normalize the result so that its value is between 0 and 1: S = 1 indicates that the two CAD constraints are exactly the same, and S = 0 indicates that the two CAD constraints are completely different.

[0072] Calculate the constraint relationship retention rate drift index CRDI, and the expression is: ; where: When CRDI = 0, it indicates that there is no drift in the CAD constraint transformation and the parametric integrity is extremely high; when CRDI = 1, it indicates that the CAD constraints are completely lost or changed, and the transformation quality is extremely poor.

[0073] Combining the influence of data transformation on design accuracy and the transformation integrity of parametric information, construct a data transformation quality evaluation model to evaluate the data transformation quality between different CAD software, including:

[0074] Convert the surface deviation anomaly index and the constraint relationship retention rate drift index into a comprehensive feature vector, use the comprehensive feature vector as the input of the machine learning model, take predicting the data transformation quality value label between different CAD software for each group of comprehensive feature vectors as the prediction target, and take minimizing the sum of the prediction errors for the data transformation quality value labels between all different CAD software as the training target, train the machine learning model until the sum of the prediction errors reaches convergence and then stop the model training, and determine the data transformation quality value between different CAD software according to the model output result, where the machine learning model is a polynomial regression model.

[0075] The method for obtaining the data transformation quality value between different CAD software is: Obtain the corresponding function expression from the comprehensive feature vector training data of the trained machine learning model: ; In the formula, F is the output function of the model, HSK is the surface deviation anomaly index, CRDI is the constraint relationship retention rate drift index, and LRR is the data transformation quality value between different CAD software.

[0076] Compare the obtained data conversion quality values between different CAD software with a pre - set threshold. If the data conversion quality value between different CAD software is greater than or equal to the pre - set threshold, it indicates that the data conversion quality between different CAD software is high, and mark it as high - quality conversion data; if the data conversion quality value between different CAD software is less than the pre - set threshold, it indicates that the data conversion quality between different CAD software is low, and mark it as low - quality conversion data.

[0077] For low - quality conversion data, an adaptive optimization algorithm can be used to automatically adjust parameters according to the conversion characteristics of different CAD software, improving the data integrity of cross - platform collaborative design.

[0078] In the initial population of the genetic algorithm, each individual represents a combination of CAD conversion parameters. For example: geometric tolerance, feature mapping strategy, parameter conversion weights, surface reconstruction method.

[0079] Define the individual gene encoding (such as binary encoding): , where: is the geometric tolerance setting (0.001mm - 0.1mm). is the feature mapping method (point cloud matching, B - Rep approximation, etc.). is the parameter conversion weight (dimensions, constraints, materials, etc.). is the surface reconstruction method (NURBS approximation, STL repair, etc.).

[0080] Randomly generate N individuals to form a population: ; where, is a combination of CAD conversion parameters. Define the fitness function to evaluate the conversion quality of each individual: ; where: is the weight coefficient (which can be optimized through experiments). Minimize HSK and maximize CRDI to ensure that more information is retained in the converted model. Calculate the fitness values of all individuals and sort the individuals. HSK is the surface deviation anomaly index, and the value range of HSK is [0,1].

[0081] Reduce the mutation rate for individuals with large gradients to avoid drastic fluctuations; increase the mutation rate for individuals with small gradients to enhance exploration; use roulette wheel selection to select individuals with high fitness to enter the next generation, use single-point crossover to generate new individuals, and use adaptive gradient to adjust the mutation rate to randomly perturb individual genes to ensure search diversity.

[0082] Repeat the process of calculating fitness → selection → crossover → mutation until the conversion quality converges: ; where: is the convergence threshold, set to 0.001 or dynamically adjusted according to the error change rate. When the change in conversion quality for 10 consecutive generations is less than , the algorithm stops iterating to ensure computational efficiency and optimization effect. is the conversion quality of the i-th individual in the current iteration, is the conversion quality of the i-th individual in the previous iteration. When the change in fitness tends to be stable, the algorithm stops optimizing.

[0083] After the collaborative design is completed, export the final design results into multiple common formats and interface with the PDM / PLM system to achieve product full life cycle management.

[0084] After the collaborative design is completed, the system supports exporting the final design results into multiple common formats, such as STEP (ISO10303), IGES, Parasolid, JT, STL, 3MF, DXF, PDF 3D, etc., to ensure data compatibility across CAD platforms. For parametric models, the native CAD format (such as SolidWorks SLDPRT, CATIA CATPart, NXPRT) can also be exported to maintain complete feature information. At the same time, the system provides batch export and automatic conversion functions to support the specific data requirements of different downstream applications (such as CAM, CAE, 3D printing), thereby improving the reusability and circulation of design data.

[0085] In addition, the system can seamlessly interface with the PDM (Product Data Management) / PLM (Product Life Cycle Management) system to implement functions such as design data version control, permission management, change tracking, approval process management, etc., to ensure the traceability and collaborative efficiency of the product development process. Through APIs or standard data interfaces (such as RESTful API, ODBC, XML, JSON), design data can be synchronized to the enterprise PLM system (such as Siemens Teamcenter, PTC Windchill, Dassault ENOVIA, Autodesk Vault) to achieve full life cycle data management from conceptual design to production manufacturing, improving product R & D efficiency and data security.

[0086] Example 2, please refer to Figure 2 As shown in the figure, the collaborative design system based on the CAD platform in this embodiment includes a format parsing module, a parameter conversion analysis module, a data conversion quality evaluation module, a low-quality conversion data optimization module, and a design result export module;

[0087] Format parsing module: Collect the design data of multiple heterogeneous CAD software, parse its format, and perform conversion based on a unified data standard. By analyzing the surface deviation degree of the CAD model after conversion on different platforms, evaluate the impact of data conversion on design accuracy;

[0088] Parameter conversion analysis module: Adopt cloud computing architecture technology to build a collaborative design platform that supports multi-user real-time access. During the collaborative design process, use the event listening mechanism and differential update technology to detect the modification of design parameters by different users, judge the retention rate of parametric constraint relationships between different CAD platforms, and evaluate the conversion integrity of parametric information;

[0089] Data conversion quality evaluation module: Combine the impact of data conversion on design accuracy and the conversion integrity of parametric information to build a data conversion quality evaluation model to evaluate the data conversion quality between different CAD software;

[0090] Low-quality conversion data optimization module: For low-quality conversion data, adopt an optimization algorithm to perform adaptive optimization according to the conversion characteristics of different CAD software to improve the data integrity of cross-platform collaborative design;

[0091] Design result export module: After the collaborative design is completed, export the final design result into multiple general formats and can be docked with the PDM / PLM system to realize the full life cycle management of products.

[0092] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0093] It should be understood that the term "and / or" in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. These three situations, where A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. Specifically, it can be understood by referring to the context before and after.

[0094] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0095] As described above, the above is only a specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application.

Claims

1. A collaborative design method based on a CAD platform, characterized by: include: Collect design data from multiple heterogeneous CAD software, parse its format, and convert it based on unified data standards. By analyzing the degree of surface deviation after CAD models are converted on different platforms, the impact of data conversion on design accuracy can be evaluated; Adopt cloud computing architecture technology to build a collaborative design platform that supports real-time access by multiple users. In the collaborative design process, use event monitoring mechanism and differential update technology to detect the modification of design parameters by different users, determine the retention rate of parametric constraint relationships between different CAD platforms, and evaluate the conversion integrity of parametric information. Combining the impact of data conversion on design accuracy and the conversion integrity of parametric information, a data conversion quality evaluation model is constructed to evaluate the data conversion quality between different CAD software; For low-quality conversion data, an optimization algorithm is used to adaptively optimize the conversion characteristics of different CAD software to improve the data integrity of cross-platform collaborative design; After the collaborative design is completed, the final design results can be exported into a variety of common formats and can be connected to the PDM / PLM system to achieve full product life cycle management.

2. The collaborative design method based on the CAD platform according to claim 1, characterized in that: The multiple heterogeneous CAD software include SolidWorks, CATIA, Siemens NX, PTC Creo, Autodesk Inventor and FreeCAD; the unified data standards include STEP, IGES, Parasolid, JT, STL, DXF and 3D PDF; the format parsing includes directly parsing the CAD native format, bridging through a neutral format and converting using an API.

3. The collaborative design method based on the CAD platform according to claim 1, characterized in that: The surface deviation anomaly index is generated by analyzing the surface deviation degree of the CAD model after conversion on different platforms. The method for obtaining the surface deviation anomaly index is as follows: Set the original CAD surface to And the converted CAD surface is ,from sampling ,from sampling ; Compute the bidirectional Hausdorff distance, denoted as , the calculation formula is: ; Among them: sup represents the maximum deviation, inf represents the nearest point, for each point exist In, find its The closest point in , calculate the Euclidean distance; for each point exist In, find its The closest point in , calculate the distance, take the maximum value as the Hausdorff distance, calculate the minimum deviation root mean square error RMS of all points, and calculate the weighted average sum of the Hausdorff distance and RMS error to get the surface deviation anomaly index.

4. The collaborative design method based on the CAD platform according to claim 3, characterized in that: After analyzing the retention rates of parametric constraint relationships between different CAD platforms, a constraint relationship retention rate drift index is generated. The method for obtaining the constraint relationship retention rate drift index is as follows: Construct a CAD constraint relationship vector, where each element corresponds to the weight of a CAD constraint, and define the original CAD constraint vector and the converted CAD constraint vector : ;in: Indicates the number of certain constraints in the original CAD model. Represents the number of corresponding constraints in the converted CAD model, and calculates its cosine similarity S, which is expressed as: ; Where: the numerator is the dot product of the vectors, indicating the similarity of the two CAD constraint vectors; the constraint relationship retention rate drift index CRDI is calculated, and the expression is: .

5. The collaborative design method based on the CAD platform according to claim 4, characterized in that: Combining the impact of data conversion on design accuracy and the conversion integrity of parametric information, a data conversion quality evaluation model is constructed to evaluate the data conversion quality between different CAD software, including: The surface deviation anomaly index and the constraint relationship retention rate drift index are converted into comprehensive feature vectors, and the comprehensive feature vectors are used as the input of the machine learning model. The machine learning model predicts the data conversion quality value labels between different CAD software with each group of comprehensive feature vectors as the prediction target, and minimizes the sum of prediction errors of data conversion quality value labels between all different CAD software as the training target. The machine learning model is trained until the sum of prediction errors converges, and the model training is stopped. The data conversion quality values ​​between different CAD software are determined according to the model output results, wherein the machine learning model is a polynomial regression model.

6. The collaborative design method based on the CAD platform according to claim 5, characterized in that: The obtained data conversion quality values ​​between different CAD software are compared with the preset threshold. If the data conversion quality values ​​between different CAD software are greater than or equal to the preset threshold, it means that the data conversion quality between different CAD software is high and it is marked as high-quality conversion data; if the data conversion quality values ​​between different CAD software are less than the preset threshold, it means that the data conversion quality between different CAD software is low and it is marked as low-quality conversion data.

7. The collaborative design method based on the CAD platform according to claim 6, characterized in that: For low-quality conversion data, an adaptive optimization algorithm is used to automatically adjust parameters according to the conversion characteristics of different CAD software, including: In the initial population of the genetic algorithm, each individual represents a CAD conversion parameter combination, and the individual gene code is defined: ;in: For geometric tolerance settings, is the feature mapping method, is the parameter conversion weight, It is the surface reconstruction method; Randomly generate N individuals to form a population: ;in, is a combination of CAD transformation parameters that defines the fitness function Evaluate the quality of each individual transformation: ;in: is the weight coefficient, calculates the fitness values ​​of all individuals, and sorts the individuals. HSK is the surface deviation abnormality index, and the value range of HSK is [0,1]; The mutation rate is reduced for individuals with large gradients, and increased for individuals with small gradients. Roulette wheel selection is used to select individuals with high fitness to enter the next generation. Single-point crossover is used to generate new individuals. Adaptive gradient is used to adjust the mutation rate, and individual genes are randomly perturbed.

8. The collaborative design method based on the CAD platform according to claim 7, characterized in that: Repeat the calculation until the transformation quality converges: ;in: is the convergence threshold, set to 0.001 or dynamically adjusted according to the error change rate. is the conversion quality of the i-th individual in the current iteration, is the conversion quality of the i-th individual in the previous iteration. When the fitness change tends to be stable, the algorithm stops optimizing.

9. A collaborative design system based on a CAD platform, used to implement the collaborative design method based on a CAD platform according to any one of claims 1 to 8, characterized in that: It includes format parsing module, parameter conversion analysis module, data conversion quality assessment module, low-quality conversion data optimization module and design results export module; Format parsing module: collects design data from multiple heterogeneous CAD software, performs format parsing on it, and converts it based on a unified data standard. By analyzing the degree of surface deviation after CAD models are converted on different platforms, the impact of data conversion on design accuracy is evaluated; Parameter conversion analysis module: Using cloud computing architecture technology, a collaborative design platform that supports real-time access by multiple users is constructed. During the collaborative design process, event monitoring mechanism and differential update technology are used to detect the modification of design parameters by different users, determine the retention rate of parametric constraint relationships between different CAD platforms, and evaluate the conversion integrity of parametric information; Data conversion quality assessment module: Combined with the impact of data conversion on design accuracy and the conversion integrity of parametric information, a data conversion quality assessment model is constructed to evaluate the data conversion quality between different CAD software; Low-quality conversion data optimization module: For low-quality conversion data, an optimization algorithm is used to adaptively optimize the conversion characteristics of different CAD software to improve the data integrity of cross-platform collaborative design; Design results export module: After the collaborative design is completed, the final design results will be exported into a variety of common formats and can be connected to the PDM / PLM system to achieve full product life cycle management.

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