Collaborative design method and system based on CAD platform

Abstract

The invention discloses a collaborative design method and system based on a CAD platform, and particularly relates to the technical field of data processing. Data of a plurality of heterogeneous CAD software are collected and subjected to format analysis, geometric accuracy loss is analyzed and evaluated through curved surface deviation, parameter changes in the collaborative design process are monitored through an event monitoring mechanism and a difference updating technology, parameterized constraint retention rates among different CAD platforms are calculated, and parameter information conversion integrity is evaluated; constructing a data conversion quality evaluation model, comprehensively analyzing geometric accuracy and parameter retention conditions, and dynamically adjusting a CAD data conversion strategy by adopting an adaptive optimization algorithm for low-quality conversion data to improve cross-platform data integrity; according to the method, the data intercommunity, the design consistency and the conversion quality of CAD collaborative design are effectively improved, and an efficient and intelligent solution is provided for multi-software collaborative design and manufacturing.

Description

Technical Field

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

[0002] Computer-Aided Design (CAD) technology is widely used in many fields such as industrial manufacturing, architectural design, and electronic circuit design. The emergence of CAD platforms has greatly improved design efficiency, enabling engineers to model, simulate, and optimize in a virtual environment. However, with the increase in product complexity and the growing demand for global collaborative design, traditional CAD design models have gradually exposed some limitations. At present, collaborative design mainly relies on CAD platforms shared on local networks or in the cloud, allowing multiple designers to access and edit the same design file at the same time. Typical collaborative design methods include: file sharing mode, which manages files submitted by different designers through version control systems (such as PDM / PLM) to ensure data consistency. Real-time collaborative mode, based on the real-time editing function of cloud computing or local area networks, allows multiple users to modify the model at the same time and automatically update it synchronously. Although these modes have improved collaborative efficiency to a certain extent, there are still problems in data synchronization, version conflicts, permission management, and remote collaboration efficiency. Especially in the face of large and complex projects across platforms and teams, traditional methods are difficult to meet the needs of efficient and accurate collaboration.

[0003] The prior art has the following deficiencies: In the CAD collaborative design environment, there are data compatibility issues between cross-software and heterogeneous CAD systems. Different CAD software uses different data formats, modeling kernels, and parameter expressions, which makes it extremely difficult to convert data losslessly when multiple software are involved in the collaborative design process. For example, when a complex 3D model is transmitted between different CAD platforms, the model topology 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

[0004] 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 technology.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a collaborative design method based on a CAD platform, comprising: 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.

[0006] Preferably, 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, and the format parsing includes directly parsing the CAD native format, bridging through a neutral format and converting using an API.

[0007] Preferably, 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: Set the original CAD surface to And the converted CAD surface is ,from sampling ,from sampling ; Calculate the bidirectional Hausdorff distance, marked 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.

[0008] 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: 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: .

[0009] 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: 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.

[0010] Preferably, the acquired data conversion quality values ​​between different CAD software are compared with a preset threshold value. If the data conversion quality values ​​between different CAD software are greater than or equal to the preset threshold value, 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 value, it means that the data conversion quality between different CAD software is low, and it is marked as low-quality conversion data.

[0011] Preferably, 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 a 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.

[0012] Preferably, the calculation is repeated 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.

[0013] 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 assessment module, a low-quality conversion data optimization module, and a design result 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.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. Through format parsing and standardized conversion, the present invention is compatible with multiple CAD platforms such as SolidWorks, CATIA, NX, Creo, Inventor, FreeCAD, etc., and performs data conversion based on standard formats such as STEP, IGES, Parasolid, JT, etc., to improve cross-platform design consistency. By calculating the surface deviation anomaly index (HSK) and the constraint relationship retention rate drift index (CRDI), the impact of CAD data conversion on geometric accuracy and parameter integrity is quantified, and the data conversion quality between different CAD software is predicted using a polynomial regression model, providing optimization suggestions for collaborative design of enterprises in a multi-CAD environment. In addition, the system is based on a cloud computing architecture, supports real-time access by multiple users, and uses event monitoring mechanisms and differential update technology to ensure that design data remains synchronized during multi-person collaboration, thereby improving the efficiency and reliability of collaborative design.

[0015] 2. The present invention uses an adaptive gradient genetic algorithm (AGGA) to perform parameter adaptive adjustment according to the conversion characteristics of different CAD software, optimize key factors such as geometric tolerance, feature mapping, parameter weight, etc., 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 improves the quality of collaborative design and reduces design errors, providing efficient and accurate CAD data management solutions for intelligent manufacturing, engineering design, product development and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 The figure is a flow chart of the method of the present invention.

[0018] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1, please refer to Figure 1 As shown, the collaborative design method based on the CAD platform described in this embodiment includes: 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.

[0021] Collect design data from heterogeneous CAD software: Different CAD software uses their own proprietary formats, such as: SolidWorks (SLDPRT, SLDASM), CATIA (CATPart, CATProduct), Siemens NX (PRT), PTC Creo (PRT, ASM), Autodesk Inventor (IPT, IAM), open source CAD (such as FreeCAD, using STEP, IGES).

[0022] In addition, there are some neutral formats for cross-platform data exchange: STEP (ISO 10303): widely supported, suitable for accurate geometry conversion, IGES: an early standard, topology information is lost in some cases, Parasolid (X_T, X_B): developed by Siemens, supports multiple CAD platforms, JT (Jupiter Tessellation): used for lightweight visualization.

[0023] In order 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 between points, edges, faces, and bodies; parametric information: constraints, feature trees, size parameters; materials and attributes: color, density, material attributes, etc.; Conversion methods: Directly parse CAD native formats (requires official SDK or reverse engineering), bridge through neutral formats (such as STEP, Parasolid), use API or plug-ins (such as Open Cascade, CGM Core).

[0024] Evaluate the impact of data conversion on design accuracy, mainly evaluating the geometric errors after CAD data conversion, including: surface deviation: calculate the Hausdorff distance of NURBS surface, analyze curvature changes, boundary deviation: check whether the topological structure is broken or self-intersecting after conversion, volume / mass change: evaluate whether the mass attribute is offset, feature loss: for example, whether the hole, chamfer, and draft angle information is completely preserved. Directly compare the geometric data point cloud before and after conversion (for example, use the ICP algorithm for registration), and use mathematical analysis methods to calculate the error range through the geometric analysis tools of CAD software (such as CATIA Compare, NX Validation).

[0025] 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 , since CAD surfaces are usually in NURBS or B-Rep form, the surface needs to be discretized (sampled) and converted into a point cloud: from sampling ,from sampling ; Sampling methods include: uniform sampling: uniformly taking points in the parameter space (u, v), adaptive sampling: increasing the sampling density in areas with large curvature, grid sampling: taking points after converting the surface into an STL grid. Calculate the bidirectional Hausdorff distance, marked 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 and take the maximum value as the Hausdorff distance.

[0026] The minimum deviation root mean square error (RMS) of all points is calculated, and the surface deviation anomaly index is obtained by weighted average summation of the Hausdorff distance and the RMS error.

[0027] If the surface deviation anomaly index is large, it means that the CAD model has a large geometric error after conversion, which may affect the manufacturing or simulation accuracy.

[0028] If the surface deviation anomaly index is moderate, the converted model can be used in most application scenarios.

[0029] If the surface deviation anomaly index is small, it means that the error in the conversion process is negligible and the model accuracy is high.

[0030] In order to support multi-user real-time collaborative design, a cloud computing architecture is required, including the front end (Web CAD end), the back end (CAD calculation and data storage), real-time communication (event monitoring and synchronization) and the storage layer (CAD model database).

[0031] Common architecture solutions: SaaS (Software as a Service): Users access cloud CAD applications (such as Onshape, Autodesk Fusion 360) through a browser. PaaS (Platform as a Service): Provides CAD computing 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 and hosts CAD applications and databases.

[0032] In order to detect the modifications of design parameters by different users, it is necessary to establish an event monitoring mechanism to capture the changes of CAD design parameters.

[0033] Based on WebSocket / Socket.IO: Suitable for Web-based CAD collaborative design, real-time push of design parameter change information. For example: User A modifies the dimension D1=10mm → 12mm on the Web side, and the change will be broadcast to other online users via WebSocket.

[0034] Based on Database Triggers: Applicable to back-end databases (such as PostgreSQL, MongoDB), monitor changes in CAD parameters, and trigger corresponding synchronization operations.

[0035] Based on CAD API Hook (plug-in level monitoring): monitor the geometric constraints and parameters modified by users in CAD software and record the change history. For example, use Onshape API and Siemens NX Open API to monitor users adjusting feature dimensions, sketch constraints, etc.

[0036] Example event monitoring process: User A modifies parameters to monitor changes in dimensional parameters (D1, D2...) or geometric constraints (parallel, vertical...). Record change logs to record user ID, time, and values ​​before and after modification, and store them in the database. Synchronize changes to other users through WebSocket or CAD API. When designing collaboratively, there is no need to transmit the entire CAD model, but to perform lightweight data synchronization through differential updates (Delta Update) to improve efficiency.

[0037] Differential update methods include: Based on JSON Patch, only the modified part is transmitted, not the entire parameter list: suitable for parametric model updates. Based on Git-like version management, version control (such as Git) is used to store only the "increments" of parameter changes. For example: Onshape uses FeatureScript to record parameter changes and can trace back the design history. Updates based on CAD constraint relationships: only the affected geometric features are updated to avoid global updates and improve calculation efficiency.

[0038] Between different CAD platforms (such as SolidWorks CATIA Siemens NX), parametric information may be lost or distorted, so the conversion integrity needs to be evaluated.

[0039] The following problems may occur when converting parametric constraints between different CAD programs: Constraints are lost: size constraints, symmetry, concentricity, verticality, etc. disappear.

[0040] Constraint substitution: Some CAD software may automatically replace constraints (e.g. CATIA → NX may replace symmetry constraints with equal lengths). Parameter drift: After conversion, the dimension value may deviate slightly (e.g. 10.000mm → 9.999mm).

[0041] 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. Different CAD constraints can be expressed as a vector, in which each element corresponds to the weight of a CAD constraint. The constraint types are set as: Parallel, Coincident, Concentric, EqualLength, Symmetric, and Tangent.

[0042] 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. Calculate its cosine similarity S, the expression is: ; Where: The numerator is the dot product of the vectors, which indicates the similarity of the two CAD constraint vectors. The denominator is the vector modulus product, which is used to normalize the result so that its value is between 0 and 1: S=1 means that the two CAD constraints are exactly the same, and S=0 means that the two CAD constraints are completely different.

[0043] Calculate the constraint relationship retention rate drift index CRDI, the expression is: ; Among them: when CRDI=0, it means that the CAD constraint conversion is completely drift-free and the parametric integrity is extremely high; when CRDI=1, it means that the CAD constraints are completely lost or changed and the conversion quality is extremely poor.

[0044] 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.

[0045] The method for obtaining the data conversion quality value between different CAD software is as follows: from the comprehensive feature vector training data of the trained machine learning model, the corresponding function expression is obtained: ; Where 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 conversion quality value between different CAD software.

[0046] 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.

[0047] 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 to improve the data integrity of cross-platform collaborative design.

[0048] In the initial population of the genetic algorithm, each individual represents a combination of CAD conversion parameters, such as geometric tolerance, feature mapping strategy, parameter retention weights, and surface reconstruction method. Define individual gene encodings (e.g. binary encoding): ,in: Set for geometric tolerance (0.001mm - 0.1mm). It is the feature mapping method (point cloud matching, B-Rep approximation, etc.). Convert weights to parameters (size, constraints, materials, etc.). It is the surface reconstruction method (NURBS approximation, STL repair, etc.).

[0049] Randomly generate N individuals to form a population: ;in, is a combination of CAD transformation parameters. Define the fitness function Evaluate the quality of each individual transformation: ;in: is the weight coefficient (can be optimized experimentally). Minimize HSK and maximize CRDI to ensure that the model retains more information after conversion. Calculate the fitness values ​​of all individuals and sort them. HSK is the surface deviation abnormality index, and the value range of HSK is [0,1].

[0050] The mutation rate of individuals with large gradients is reduced to avoid drastic fluctuations; the mutation rate of individuals with small gradients is increased to enhance exploration; 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 gradients are used to adjust the mutation rate, and individual genes are randomly perturbed to ensure search diversity.

[0051] Repeat the process of calculating fitness → selection → crossover → mutation until the conversion quality converges: ;in: is the convergence threshold, which is set to 0.001 or adjusted dynamically according to the error change rate. When the conversion quality changes of 10 consecutive generations are less than ϵ, 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 fitness change tends to be stable, the algorithm stops optimizing.

[0052] 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.

[0053] After collaborative design is completed, the system supports exporting the final design results into a variety of 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, native CAD formats (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.

[0054] In addition, the system can seamlessly connect to the PDM (Product Data Management) / PLM (Product Lifecycle Management) system to achieve functions such as design data version control, authority management, change tracking, and approval process management to ensure the traceability and collaborative efficiency of the product development process. Through API 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), achieving full lifecycle data management from conceptual design to production and manufacturing, and improving product R&D efficiency and data security.

[0055] Example 2, please refer to Figure 2 As shown, the collaborative design system based on the CAD platform described in this embodiment includes a format parsing module, a parameter conversion analysis module, a data conversion quality assessment module, a low-quality conversion data optimization module and a design result 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.

[0056] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0057] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0058] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0059] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present 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 ; Calculate the bidirectional Hausdorff distance, marked 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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