Cloud architecture CAD (Computer Aided Design) and CAE (Computer Aided Engineering) integrated cooperation method, equipment and medium

Abstract

The embodiment of the invention discloses a cloud architecture CAD (Computer Aided Design) and CAE (Computer Aided Engineering) integrated collaboration method, equipment and a medium, relates to the technical field of collaboration design, and is used for solving the problems of low collaboration efficiency, insufficient knowledge reuse rate and the like in the prior art. The method is applied to a CAD and CAE integrated cooperative system built on a cloud architecture, the system comprises a client and a server, and the method comprises the steps that modeling processing is conducted on a model creating instruction of the client through a preset geometric modeling engine of the server, and a current CAD model file is obtained; inputting the data into a preset CAE (Computer Aided Engineering) pre-processing and post-processing module for processing to generate a finite element model; performing simulation solution on the finite element model based on a preset multi-solver in a CAE simulation module to obtain a finite element result; and according to a preset data conversion service module of the CAE simulation module, performing data analysis processing on the finite element result, and organizing an analysis result into visual data to display so as to feed back and optimize CAD modeling.

Description

Technical Field

[0001] This specification relates to the field of collaborative design technology, and in particular to an integrated collaborative method, device and medium for cloud architecture CAD and CAE. Background Technology

[0002] With the deepening of digital transformation in the manufacturing industry, Computer-Aided Design (CAD) and Computer-Aided Engineering Analysis (CAE) have become crucial links in the product development process. However, the inherent defects of traditional localized deployment architectures severely restrict the release of their technological potential: limited system scalability is manifested in the rigidity of local hardware resources, making it impossible to flexibly expand computing power as needed; low collaboration capabilities are reflected in the reliance on physical media or local area network transmission for cross-departmental collaboration, making it difficult for remote teams to synchronize design changes and simulation results in real time. Therefore, the collaborative processing of CAD and CAE is an important aspect of the current design field.

[0003] Current technologies suffer from software silos, preventing direct communication between CAD geometric models and CAE simulation parameters due to heterogeneous data formats. Design data must be manually converted or transferred via intermediate files, leading to feature loss and error accumulation. Secondly, the traditional CAE preprocessing process is independent of the CAD environment. This fragmented workflow forces the traditional sequential development model to complete CAD modeling before transferring it to CAE for analysis. Each design modification necessitates repeating the model export, parameter redefinition, and simulation restart process. Furthermore, the lack of intelligent correlation between simulation results and CAD models creates a knowledge gap, preventing CAE analysis from effectively feeding back into the CAD modeling stage. Experience relies on manual transfer, resulting in a disconnect between design specifications and simulation requirements. Summary of the Invention

[0004] To address the aforementioned technical problems, this specification provides one or more embodiments of an integrated collaborative method, device, and medium for cloud-based CAD and CAE architectures.

[0005] One or more embodiments of this specification employ the following technical solutions: This specification provides one or more embodiments of an integrated collaborative method for cloud-based CAD and CAE, applied to an integrated collaborative system for CAD and CAE built on a cloud architecture. The system includes a client integrating a CAD modeling module and pre-built CAE pre- and post-processing modules, and a server integrating a database and a CAE simulation module. The method includes: Obtain the model creation instruction uploaded by the CAD modeling module of the client, and use the preset geometric modeling engine in the database of the server to model the model creation instruction to obtain the current CAD model file; The current CAD model file is input into the client's preset CAE pre- and post-processing module via the cloud data transmission channel; The current CAD model file is processed by the preset CAE pre- and post-processing modules to generate the finite element model corresponding to the current CAD model file; Based on the preset multiple solvers in the CAE simulation module on the server side, the finite element model is simulated and solved to obtain finite element results; Based on the preset data conversion service module in the CAE simulation module, the finite element results are processed by data parsing, and the parsing results are organized into visual data for visualization display, so as to adjust the modeling process based on the feedback of the visualization display.

[0006] Optionally, in one or more embodiments of this specification, before inputting the current CAD model file into the client's preset CAE pre- and post-processing module, the method further includes: Based on the historical simulation data and pre-defined domain requirements information on the server side, multiple simulation scenario templates for the initial CAE pre- and post-processing modules are determined; wherein, the multiple simulation scenario templates include the execution order of each CAE pre- and post-processing function and the parameters of each CAE pre- and post-processing function. Based on historical simulation project data, the existing associations between each CAD feature and CAE analysis feature are determined. Based on the existing associations and the current custom associations, a mapping rule between the CAD features and the CAE analysis feature is constructed. Based on the aforementioned multi-type simulation scenario templates and the aforementioned mapping rules, the initial CAE pre- and post-processing modules are configured automatically. The configured pre-configured CAE pre- and post-processing modules are embedded into the modeling environment of the cloud architecture CAD, and a data channel between the cloud architecture CAD and the pre-configured CAE pre- and post-processing modules is established based on the pre-configured data transmission environment.

[0007] Optionally, in one or more embodiments of this specification, the current CAD model file is processed according to the preset CAE pre- and post-processing module to generate the finite element model corresponding to the current CAD model file, specifically including: Based on the aforementioned multi-type simulation scenario templates, the execution order of each CAE pre- and post-processing function is determined. Based on the execution order, each CAE preprocessing operation is performed sequentially on the current CAD model file to obtain the finite element feature information corresponding to the current CAD model file; wherein, the finite element feature information includes: the mesh model and physical properties of the current CAD model file.

[0008] The finite element feature information is encapsulated and transformed based on preset mapping rules to obtain a finite element model corresponding to the current CAD model file.

[0009] Optionally, in one or more embodiments of this specification, before simulating and solving the finite element model based on the preset multiple solvers in the CAE simulation module on the server side to obtain the finite element results, the method further includes: The current CAD model file is processed by the preset CAE pre- and post-processing modules to generate the finite element model corresponding to the current CAD model file; Alternatively, based on the integrated collaborative system of CAD and CAE, the initial finite element model file exported by the relevant CAE software can be obtained; wherein, the initial finite element model file includes: bdf, inp, cdb and k format files; The initial finite element model file is parsed and its format is converted to obtain the finite element model.

[0010] Optionally, in one or more embodiments of this specification, the finite element model is simulated and solved based on the pre-built multiple solvers in the server-side CAE simulation module to obtain finite element results, specifically including: The simulation scenario based on the finite element model is matched with a list of preset multi-solvers to obtain a preset multi-solver corresponding to the finite element model. Based on the solver type of the preset multi-solver, the finite element model is format-converted to obtain the input data of the finite element model; The specified solver corresponding to the input data is determined based on the real-time load of the server where each preset multi-solver is located. Based on a pre-set hot update strategy, it is determined whether to update the version of the specified solver so that the input data can be input into the corresponding specified solver to perform simulation calculations and obtain finite element results.

[0011] Optionally, in one or more embodiments of this specification, determining whether to update the specified solver based on a preset hot update strategy specifically includes: The solver code of a specified solver is periodically solved using a pre-built version control tool to compare the solver code with the corresponding latest version of the solver code; Based on the comparison results, determine whether to update the specified solver; If a version update is performed, the new version solver corresponding to the specified solver will be deployed in the test environment for version testing. The new version solver that passes the test will be pushed out based on the canary release process, and feedback information of the new version solver will be obtained. If it is determined based on the feedback information that a full update of the new version of the solver should be performed, then the task execution status of the specified solver is determined. Based on the task execution status, the update process node of the specified solver is determined, and the new version of the solver is started at the update process node to realize the version update of the specified solver.

[0012] Optionally, in one or more embodiments of this specification, the finite element results are parsed based on the preset data conversion service module in the CAE simulation module, and the parsing results are organized into visual data for visualization display, so as to adjust the modeling process based on the feedback from the visualization display. Specifically, this includes: Based on the preset data conversion service module in the CAE simulation module, parallel data parsing is performed on each of the finite element results to extract key analysis data based on preset indicators. The key analysis data is normalized, and the processed key analysis data is rendered based on a preset display method to obtain visualized data for visualization display; Based on the visualization, the current CAD model file and the preset CAE pre- and post-processing modules are adjusted and optimized.

[0013] Optionally, in one or more embodiments of this specification, obtaining the model creation instruction uploaded by the CAD modeling module of the client, and then processing the model creation instruction through the preset geometric modeling engine in the database of the server to obtain the current CAD model file, specifically includes: Obtain the instruction type of the model creation instruction; wherein, the instruction type includes: creation type and import type; If the instruction type is determined to be a creation type, then the model creation instruction is parsed to determine the creation model type and initial parameters corresponding to the model creation instruction; The modeling engine in the pre-built geometric modeling engine is invoked to perform modeling analysis on the model type and initial parameters based on the modeling engine, and to generate a geometric CAD model. Obtain the design constraint data and feature parameter data corresponding to the geometric CAD model to generate the current CAD model file corresponding to the geometric CAD model; If the instruction type is determined to be an import type, the model creation instruction is parsed to determine the storage information of the existing CAD geometric model file, and the existing CAD geometric model file is imported as the current CAD model file based on the storage information.

[0014] This specification provides one or more embodiments of an integrated collaborative device for cloud-based CAD and CAE, the device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform any of the methods described above.

[0015] This specification provides one or more embodiments of a non-volatile computer storage medium storing computer-executable instructions, the computer-executable instructions being configured to execute any of the methods described above.

[0016] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: By incorporating pre- and post-processing modules for CAE, seamless integration between geometric modeling and simulation analysis is achieved, eliminating data transfer barriers and resolving the issue of data feature loss during traditional workflows. This seamless transition from geometric modeling to simulation pre-processing allows CAE analysis content to effectively feed back into the CAD modeling stage. Based on pre-built multiple solvers on the server side, the system can flexibly call different solvers to meet the needs of multiphysics coupled simulations. It significantly accelerates the solution process for large-scale complex problems while supporting dynamic expansion of computing resources, ensuring efficient and stable simulation performance. Automated analytical processing of finite element results enables rapid extraction of key indicators and generation of structured reports, reducing manual data processing time. The visualization module presents the analytical results in interactive charts, cloud maps, or animations, supporting multi-terminal access, facilitating intuitive user understanding of simulation results and promoting collaborative optimization. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating an integrated collaborative method for cloud-based CAD and CAE, provided as an embodiment of this specification; Figure 2 A schematic diagram of the architecture of an integrated collaborative system for cloud-based CAD and CAE provided in the embodiments of this specification; Figure 3 A system flowchart illustrating an integrated collaborative method for cloud-based CAD and CAE, provided as an embodiment of this specification; Figure 4 This specification provides a flowchart illustrating a visual processing method as described in an embodiment. Figure 5 A schematic diagram of the structure of an integrated collaborative device for cloud-based CAD and CAE provided in the embodiments of this specification; Figure 6 This is a schematic diagram of the structure of a non-volatile storage medium provided in the embodiments of this specification. Detailed Implementation

[0018] This specification provides an integrated collaborative method, device, and medium for cloud-based CAD and CAE.

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0020] like Figure 1 As shown in the figure, this specification provides a flowchart illustrating an integrated collaborative method for cloud-based CAD and CAE architectures. Figure 1 As can be seen, one or more embodiments of this specification describe an integrated collaborative method for cloud-based CAD and CAE, which is applicable to, for example... Figure 2 The diagram shows a cloud-based architecture centered around 3D CAD, comprising both client-side and server-side functionalities. Figure 2As can be seen, the client integrates CAD modeling and CAE pre- and post-processing functions. The CAD modeling function boasts comprehensive design capabilities to meet diverse modeling needs. CAE pre- and post-processing, catering to multiple disciplines such as structure, fluid dynamics, and thermal engineering, implements a series of professional CAE functions, including: Geometric inspection and repair: accurately checking and effectively repairing geometric and topological problems in the model to ensure model quality; Meshing: supporting 2D and 3D mesh generation, ensuring smooth display even for mesh models with millions of meshes, providing a reliable foundation for subsequent analysis; Material parameter settings: leveraging a rich material library, users can easily select materials, view detailed material information, and perform operations such as saving and searching for materials, facilitating the management and retrieval of material data; Working condition settings: allowing users to reasonably set construction conditions according to actual needs; Calculation and result interpretation: covering the calculation process and providing professional analysis of the results to help users accurately understand the analysis results. The server-side consists of two main parts: a database and CAE simulation. The database includes: a geometric modeling database, primarily responsible for CAD modeling services. Through a self-developed 3D CAD geometric modeling engine, it comprehensively supports multiple modules such as geometric modeling, part design, sketching, data exchange, engineering drawing generation, and assembly, providing solid data support for CAD design; and a finite element model database, an extension of the geometric modeling database. Building upon CAD data, it further incorporates simulation-related data such as attribute models, boundary conditions, and mesh models, enriching the data dimensions and meeting the data needs of CAE analysis. The CAE simulation includes: a multi-solver integration service, integrating mainstream solvers, domestically produced solvers, and open-source solvers, providing diverse solution options for different types of simulation analysis and improving the applicability and accuracy of the solutions; and a data conversion service, providing comprehensive data conversion functions, covering conversion between simulation data formats, data parsing of finite element models, simulation result data parsing, and visualization services for simulation data. This streamlines data flow and enables efficient data utilization at different stages and in different applications. The aforementioned cloud-based CAD / CAE integrated collaborative system overcomes the limitations of traditional CAD / CAE workflow separation, constructing an integrated system with cloud-based 3D CAD as its core. It achieves a closed-loop iterative R&D system from design to simulation, resolving issues of design data transmission feature loss and workflow fragmentation. Furthermore, it employs an advanced 3D cloud architecture, enabling full-function access from the browser, significantly reducing terminal device configuration requirements. Subsequent incremental hot update technology allows for functional iteration and vulnerability patching without user awareness, changing the complex local installation and deployment, and difficult upgrade and maintenance of traditional software, thus improving software operation and maintenance efficiency and economy. The methods implemented in this application based on the aforementioned system architecture include: S101: Obtain the model creation instruction uploaded by the CAD modeling module of the client, and process the model creation instruction through the preset geometric modeling engine in the database of the server to obtain the current CAD model file.

[0021] When a user initiates a model creation command through the client's CAD modeling module, the server-side pre-built geometric modeling engine processes the command to obtain the current CAD model file. This involves creating a new model or importing an existing CAD geometric model for parametric modeling or geometric adjustments. Then, cloud-based 3D CAD is used for CAD modeling, and the current CAD model file is obtained upon completion.

[0022] Specifically, in one or more embodiments of this specification, obtaining the model creation instructions uploaded by the CAD modeling module of the client, and then processing the model creation instructions through the preset geometric modeling engine in the database of the server to obtain the current CAD model file, specifically includes: First, the instruction type of the model creation command is obtained. Instruction types include: creation type and import type. That is, the user-uploaded model creation command is first classified as either a creation type or an import type. This can be understood as being achieved by parsing the operation identifier in the command metadata. If the instruction type is determined to be a creation type, the command is parsed to determine the corresponding model type and initial parameters. The modeling engine in the pre-built geometric modeling engine is then invoked to perform modeling analysis on the model type and initial parameters, generating a geometric CAD model. Then, the design constraint data and feature parameter data corresponding to the geometric CAD model are obtained to generate the current CAD model file. If the instruction type is determined to be an import type, the command is parsed to determine the storage information of the existing CAD geometric model file, and the existing CAD geometric model file is imported as the current CAD model file based on this storage information. Throughout this process, whether the model is created or imported, the final generated current CAD model file contains comprehensive data. These data can directly support subsequent finite element analysis, collaborative design, and other processes, avoiding the loss of model data during transmission and achieving seamless integration from modeling to downstream applications.

[0023] Furthermore, in one feasible embodiment, the model creation instruction uploaded by the client's CAD modeling module is obtained, and the model creation instruction is processed by a pre-built geometric modeling engine in the database on the server side to obtain the current CAD model file, specifically including: Obtain the instruction type of the model creation instruction; wherein, the instruction type includes: creation type and import type; if the instruction type is determined to be creation type, parse the model creation instruction to determine the creation model type and initial parameters corresponding to the model creation instruction; parse the model creation instruction to obtain the creation model type and initial parameters corresponding to the model creation instruction; determine whether the model creation instruction contains a collaborative modeling identifier, and if so, determine whether there is a concurrent operation conflict based on the collaborative modeling identifier; if so, cache the model creation instruction in a preset instruction queue to wait for execution; start the model creation instruction and obtain historical modeling data of the same project as the model creation instruction. The process involves: calling the modeling engine in the pre-built geometric modeling engine to generate an initial geometric CAD model based on the model type and the initial parameters; supplementing the initial geometric CAD model with implicit design constraints based on the historical design constraints corresponding to the historical modeling data to obtain a geometric CAD model; acquiring the design constraint data and feature parameter data corresponding to the geometric CAD model to generate the current CAD model file corresponding to the geometric CAD model; if the instruction type is determined to be an import type, parsing the model creation instruction, determining the storage information of the existing CAD geometric model file, and importing the existing CAD geometric model file as the current CAD model file based on the storage information.

[0024] After obtaining the model creation instruction, the process first determines whether it is a creation type or an import type. The instruction is parsed to obtain the model type, initial parameters, and whether it contains a collaborative modeling identifier. If a collaborative modeling identifier is present, it checks for concurrent operation conflicts: if a conflict exists, the instruction is cached in a pre-defined queue for execution; otherwise, the instruction is initiated. By identifying the collaborative modeling identifier and detecting concurrent conflicts, design chaos caused by multiple people operating the same model simultaneously is avoided, while the queue caching mechanism ensures the orderly execution of instructions and improves the reliability of team collaborative modeling. Then, after the model creation instruction is initiated, historical modeling data from the same project is retrieved, and the modeling engine of the pre-defined geometric modeling engine is invoked. The initial geometric CAD model is generated by combining the model type and initial parameters. By introducing historical modeling data from the same project during model creation, implicit constraints are supplemented based on historical design constraints, ensuring the new model remains consistent with the project's past design logic, reducing the oversight of manually supplementing constraints, and improving the standardization of model design. Then, based on the historical design constraints in the historical modeling data, implicit design constraints are supplemented to the initial model, forming a complete geometric CAD model. Finally, design constraint data and feature parameter data are extracted to generate the current CAD model file.

[0025] S102: Input the current CAD model file into the client's preset CAE pre- and post-processing module through the cloud data transmission channel.

[0026] To address the issues of data transmission feature loss and process fragmentation in traditional workflows, this embodiment of the application embeds CAE preprocessing functions during the CAD modeling stage to achieve seamless integration of geometric modeling and simulation analysis. In this embodiment, the current CAD model file is input into the pre-built CAE pre- and post-processing module of the cloud architecture CAD, thereby efficiently processing the current CAD model file through the pre-built CAE pre- and post-processing module.

[0027] Furthermore, in one or more embodiments of this specification, before inputting the current CAD model file into the client's preset CAE pre- and post-processing module via a cloud data transmission channel, the method further includes: Based on historical simulation data and pre-defined domain requirements from the server, multiple simulation scenario templates for the initial CAE pre- and post-processing modules are constructed. These templates clarify the execution order and corresponding parameter composition of each CAE pre- and post-processing function under different simulation scenarios, providing a standardized workflow framework for subsequent processing. Using historical simulation project data, existing relationships between CAD features and CAE analysis features are determined. Combined with current custom relationships, mapping rules between the two are formed, achieving accurate conversion from CAD model features to CAE analysis features, thereby obtaining the mapping rules for constructing the CAD features and CAE analysis features. Using the obtained simulation scenario templates as workflow guides and the CAD-CAE feature mapping rules as the conversion basis, the initial CAE pre- and post-processing modules are automatically configured to adapt to current simulation requirements. Then, the configured pre-defined CAE pre- and post-processing modules are embedded into the cloud-based CAD modeling environment. Simultaneously, relying on the pre-defined data transmission environment, a data channel is established between the cloud-based CAD and this module to ensure smooth data transmission. Then, the pre-configured CAE pre- and post-processing modules are used to process the current CAD model file, and finally generate the finite element model corresponding to the CAD model file, laying the foundation for subsequent simulation analysis.

[0028] This process clarifies the processing flow and parameters through simulation-based scenario templates, avoiding repetitive manual settings. Automated configuration further reduces manual operations, allowing CAE pre- and post-processing modules to quickly adapt to requirements, significantly improving processing efficiency while ensuring standardization across different scenarios. Secondly, by constructing mapping rules, CAD features and CAE analysis features are closely linked, resolving the issue of inaccurate feature conversion and ensuring accuracy when converting CAD models to finite element models, reducing errors caused by feature mismatches. Furthermore, embedding the CAE pre- and post-processing modules into a cloud-based CAD modeling environment and establishing a data channel enables modeling and CAE pre- and post-processing to operate within the same environment, avoiding the cumbersome transfer of models between different software. The data channel also ensures real-time and smooth data transmission, improving the overall workflow continuity.

[0029] Furthermore, regarding the process of using historical simulation project data to determine the existing relationships between various CAD features and CAE analysis features, and then combining this with current custom relationships to form mapping rules between the two, it's important to note that when combining existing and custom relationships, it's necessary to consider whether there are any conflicts between these two types of relationships. If the custom relationship and the basic rules do not conflict, then it can be directly added to the mapping rule library. However, if conflicts exist, such as the same CAD feature corresponding to different CAE features, then conflicting rules can be marked based on the principle of prioritizing custom relationships, and conflict logs can be recorded for subsequent optimization. This includes adding scene tags to each mapping rule to avoid misuse across scenes.

[0030] S103: The current CAD model file is processed according to the preset CAE pre- and post-processing modules to generate the finite element model corresponding to the current CAD model file.

[0031] like Figure 3 As shown, after the current CAD model file is input into the preset CAE pre- and post-processing module, the preset CAE pre- and post-processing module processes the current CAD model file to generate the corresponding finite element model. Specifically, in one or more embodiments of this specification, the process of processing the current CAD model file according to the preset CAE pre- and post-processing module to generate the corresponding finite element model includes the following steps: First, based on multiple simulation scenario templates, the execution order of each CAE pre- and post-processing function is determined. It should be noted that CAE pre-processing operations include geometric inspection and repair, mesh generation, and material parameter settings. Then, according to the execution order, each CAE pre-processing operation is performed on the current CAD model file sequentially to obtain the finite element feature information corresponding to the current CAD model file. The finite element feature information includes the network model and physical properties of the current CAD model file. For example, in a certain application scenario, a geometric inspection is first performed on the current CAD model file to identify and repair geometric defects and topological errors. Then, based on preset simulation requirements, key geometric features are automatically extracted and simulation priorities are marked. Default material properties and boundary conditions are assigned to the model according to the material library and working condition library. Meshing is automatically performed based on geometric features to generate a 2D or 3D mesh model. Then, physical properties are added to the mesh model according to the material properties and boundary conditions. Finally, the finite element feature information is encapsulated and transformed using preset mapping rules to obtain the finite element model corresponding to the current CAD model file. This finite element model is then stored in the finite element model database (extended database) and associated with the corresponding current CAD model file.

[0032] This process determines the CAE preprocessing execution order based on multiple simulation scenario templates, avoiding the randomness of manual judgment in the operation process, ensuring the consistency of preprocessing steps under different scenarios, and significantly reducing manual intervention. It solves the problem of repetitive and time-consuming work in traditional preprocessing, significantly improving processing efficiency. By encapsulating and transforming finite element feature information through preset mapping rules, it ensures that the geometric features and design intent of the CAD model can be accurately transferred to the finite element model. Storing the finite element model in an extended database and associating it with the corresponding CAD model achieves centralized management of model data, avoiding model version confusion or data silos.

[0033] S104: Based on the preset multiple solvers in the CAE simulation module on the server side, the finite element model is simulated and solved to obtain finite element results.

[0034] To support multiple solver types, allowing users to freely choose the appropriate solver based on their actual needs and scenarios, this specification addresses the diverse requirements of different projects regarding simulation accuracy, computational resources, and cost control, thereby improving the system's applicability in complex engineering scenarios. In this embodiment, a pre-built multi-solver on the server side is used to simulate and solve the finite element model, obtaining finite element results. Through multi-solver integration and automatic data conversion, seamless integration of structural, electromagnetic, and fluid simulations is achieved. This eliminates the need for developers to frequently switch platforms or manually process data, significantly reducing the time cost of multi-disciplinary joint simulation and greatly improving the efficiency of complex engineering problem analysis. It should also be noted that the pre-built multi-solver within the CAE simulation module of this cloud-based integrated CAD and CAE system supports multiple solver types, covering mainstream commercial, domestically developed, and open-source products. Users can freely choose the appropriate solver based on their actual needs and scenarios, meeting the diverse requirements of different projects regarding simulation accuracy, computational resources, and cost control, thus improving the system's applicability in complex engineering scenarios.

[0035] Furthermore, due to the need for manual or third-party data format conversion between platforms in traditional CAD / CAE systems during multidisciplinary joint simulations, it is crucial to avoid data loss and error accumulation caused by manual conversion, as well as the cumbersome process of transferring intermediate files, and to ensure accurate and efficient data transmission between different solvers. For example... Figure 3 As shown, in one or more embodiments of this specification, before simulating and solving the finite element model based on a pre-built multi-solver on the server side to obtain the finite element results, the method further includes the following process: like Figure 3 As shown, based on a pre-built multi-solver on the server side, the finite element model is simulated and solved. Before obtaining the finite element results, the data model document that can be sent to the solver for solving the finite element model is obtained. The solver can receive this data model document as follows: Figure 3The data source is twofold. First, the finite element model corresponding to the current CAD model file is generated after processing the current CAD model file using the pre-configured CAE pre- and post-processing modules, as described above. Second, it bypasses the CAD and CAE pre-processing modules of the integrated CAD and CAE collaborative system, directly uploading an initial finite element model file generated and exported by relevant CAE software. It should be noted that the initial finite element model file includes .bdf, .inp, .cdb, and .k format files. The initial finite element model file then undergoes data parsing and format conversion to obtain the finite element model. The data parsing and format conversion steps can be automatically executed based on built-in system rules, thus avoiding problems such as format misjudgment and missing parameters that may occur during manual operation. Furthermore, it should be noted that the integrated CAD and CAE collaborative system, by supporting bidirectional conversion and reuse of multiple data formats and by constructing a unified data conversion and management mechanism, completely breaks down data fusion barriers, achieving efficient utilization and cross-platform sharing of historical data. This changes the current situation of data loss and format incompatibility during traditional data conversion processes, achieving full system compatibility. Furthermore, in a feasible embodiment, the data parsing and format conversion stage may include: the system performing format feature recognition on uploaded initial finite element model files such as bdf, inp, cdb, k, etc., and automatically determining the file type by matching a preset format feature library, providing a basis for subsequent parsing rule calls. Then, it can call the corresponding data parsing engine based on the syntax rules and data organization logic of different format files to extract key structured data such as geometric information, element information, material properties, boundary conditions, and load information from the initial finite element model file, forming an intermediate data structure. The extracted structured data is then validated, including data integrity verification, data validity verification, and data consistency verification. Abnormal data detected is automatically cleaned and corrected, thereby mapping the cleaned structured data to a standardized data format recognizable by the system according to a unified data standard. This achieves a unified expression of finite element model data from different sources and in different formats, laying the foundation for subsequent solver calls.

[0036] Specifically, in one or more embodiments of this specification, the finite element model is simulated and solved based on a pre-built multi-solver on the server side to obtain finite element results, specifically including the following steps: The simulation scenario based on the finite element model is matched with a list of pre-defined multi-solvers to obtain the pre-defined multi-solvers corresponding to the finite element model. Then, according to the solver type of the pre-defined multi-solvers, the finite element model is format-converted to obtain the input data for the finite element model. Different types of solvers have specific requirements for the format of the input data; for example, some solvers require specific formats of mesh data, material parameter data, etc. The finite element model is converted according to the format standard corresponding to the matched solver type to make it input data that the solver can recognize and process. Then, in order to select a solver on a server with suitable load as the designated solver for this simulation calculation, the designated solver corresponding to the input data is determined based on the real-time load of the servers hosting each pre-defined multi-solver. Finally, based on a pre-defined hot update strategy, it is determined whether to update the version of the designated solver so that the input data can be input into the corresponding designated solver to perform the simulation calculation and obtain the finite element results.

[0037] This process matches the simulation scenario of the finite element model with a pre-set list of multiple solvers, accurately selecting the solver suitable for the current simulation requirements from the available solvers in the system. This avoids simulation failures or result deviations caused by incompatibility between solver functionality and the scenario. For the matched solver type, the finite element model format is converted, adhering to the solver's specific data format standards, ensuring that the converted input data can be directly recognized by the solver. This step avoids data reading errors caused by format incompatibility, eliminates the tedious manual format adjustment, and ensures that core data such as mesh and physical properties are not lost during conversion, providing a reliable data foundation for simulation calculations. Based on the real-time load of the servers hosting each solver, specific solvers are selected, prioritizing tasks for servers with lower loads and avoiding computational delays caused by server overload. This dynamic allocation mechanism achieves rational utilization of server resources, reduces simulation task queuing time, and significantly shortens the overall computation cycle, especially when multiple tasks are running in parallel. By employing a pre-defined hot update strategy, the specified solver can be updated to the latest version when needed, without interrupting the task. This ensures that the solver incorporates the latest algorithm optimizations, bug fixes, and functional enhancements. The updated solver reduces computational errors caused by defects in the older version and better adapts to the solution requirements of complex models, ultimately improving the accuracy and reliability of finite element results.

[0038] Furthermore, in one or more embodiments of this specification, updating the version of a specified solver based on a preset hot update strategy specifically includes: First, the solver code of a specified solver is periodically solved using a pre-built version control tool to compare it with the corresponding latest version solver code. Based on the comparison results, it is determined whether to update the specified solver. If an update is required, the new version solver is deployed in a test environment for testing. The tested new version solver is then pushed out via a canary release process, and feedback is obtained. If a full update is required based on the feedback, the task execution status of the specified solver is determined. Based on the task execution status, the update process node is determined, and the new version solver is started at the update process node, thus achieving the version update. In this application scenario, the integration of multiple solvers is implemented based on a cloud architecture, deploying multiple solvers on the server side, including mainstream solvers, domestically produced solvers, and open-source solvers, to meet the needs of different users. Then, the client provides simulation preprocessing functions, allowing users to specify the required solver after completing model building and parameter settings. The system automatically generates the corresponding input file based on the solver selected by the user and transmits it to the server. Upon receiving the input file, the server invokes the appropriate solver to perform the solution. To ensure real-time updates and compatibility of the solver, the system supports hot-update functionality, automatically updating the solver version without affecting normal user operation. Specifically, version control tools such as Git are used to manage the solver code. During updates, the new solver version is first deployed to a test environment for comprehensive testing, including functional, compatibility, and performance testing. Only after the new version passes all tests is it gradually pushed to a subset of users through a canary release approach to collect feedback and ensure no issues before a full update. During the update process, process replacement technology seamlessly switches to the new solver version after the old solver task is completed, ensuring task continuity.

[0039] S105: Based on the preset data conversion service module in the CAE simulation module, perform data parsing processing on the finite element results, and organize the parsing results into visual data for visualization display, so as to adjust the modeling processing based on the feedback of the visualization display.

[0040] To facilitate adjustments and optimizations to the CAD model and CAE settings based on the visualization results, and to iteratively complete the entire design-simulation-optimization process, this embodiment of the application, after obtaining the finite element results based on step S104, performs data parsing processing on the finite element results and organizes the parsing results into visual data for visualization display. Based on... Figure 3It can be seen that, in addition to simulation and solution based on finite element results, simulation result files can also be imported, and after data analysis and data visualization processing, they can be visualized and displayed to adjust CAD modeling and CAE simulation based on the feedback from the visualization display.

[0041] Specifically, in one or more embodiments of this specification, the finite element results are subjected to data parsing processing, and the parsing results are organized into visual data for visualization display, specifically including: For analysis scenarios involving multiple operating conditions and batches of simulation results, to shorten the overall analysis time, a pre-built data conversion service module within the CAE simulation module is used to perform parallel data parsing on each finite element result. This allows for the extraction of key analytical data based on pre-defined indicators. This process, using pre-defined indicators for data extraction, avoids interference from invalid information, ensuring that the acquired key analytical data directly serves the analysis objective and reducing redundant work in subsequent processing. Furthermore, the key analytical data can be normalized, eliminating differences in the original data format and magnitude. This allows key analytical data from different sources or with different physical quantities to be compared on the same visualization dimension. The processed key analytical data is then rendered using a pre-defined display method to obtain visualized data for visualization. That is, as... Figure 4 The finite element results shown are in binary format and require processing using a self-developed simulation data parser. First, the data is parsed, then organized into visual JSON data for visualization on the integrated CAD / CAE platform. Based on the visualization results, the CAD model and CAE settings can be adjusted and optimized, iteratively completing the closed-loop process from design and simulation to optimization.

[0042] like Figure 5 As shown in the diagram, this specification provides a structural schematic of an integrated collaborative device for cloud-based CAD and CAE architecture. Figure 5 As can be seen, in one or more embodiments of this specification, an integrated collaborative device for cloud-based CAD and CAE has an integrated collaborative system for CAD and CAE built on a cloud architecture. The system includes a client integrating a CAD modeling module and a pre-built CAE pre- and post-processing module, and a server integrating a database and a CAE simulation module. The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform any of the methods described above.

[0043] like Figure 6As shown in the diagram, this specification provides a schematic diagram of the structure of a non-volatile storage medium. Figure 6 As can be seen, in one or more embodiments of this specification, a non-volatile storage medium stores computer-executable instructions 601, which has an integrated collaborative system for CAD and CAE built on a cloud architecture. The system includes a client integrating a CAD modeling module and a pre-built CAE pre- and post-processing module, and a server integrating a database and a CAE simulation module. The computer-executable instructions 601 are capable of executing any of the methods described above.

[0044] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0045] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0046] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for integrated collaboration between cloud-based CAD and CAE, characterized in that, An integrated collaborative system for CAD and CAE built on a cloud architecture is provided. The system includes a client integrating a CAD modeling module and pre-built CAE pre- and post-processing modules, and a server integrating a database and a CAE simulation module. The method includes: Obtain the model creation instruction uploaded by the CAD modeling module of the client, and use the preset geometric modeling engine in the database of the server to model the model creation instruction to obtain the current CAD model file; The current CAD model file is input into the client's preset CAE pre- and post-processing module via the cloud data transmission channel; The current CAD model file is processed by the preset CAE pre- and post-processing modules to generate the finite element model corresponding to the current CAD model file; Based on the preset multiple solvers in the CAE simulation module on the server side, the finite element model is simulated and solved to obtain finite element results; Based on the preset data conversion service module in the CAE simulation module, the finite element results are processed by data parsing, and the parsing results are organized into visual data for visualization display, so as to adjust the modeling process based on the feedback of the visualization display.

2. The integrated collaborative method for cloud-based CAD and CAE architecture according to claim 1, characterized in that, Before inputting the current CAD model file into the client's preset CAE pre- and post-processing module, the method further includes: Based on the historical simulation data and pre-defined domain requirements information on the server side, multiple simulation scenario templates for the initial CAE pre- and post-processing modules are determined; wherein, the multiple simulation scenario templates include the execution order of each CAE pre- and post-processing function and the parameters of each CAE pre- and post-processing function. Based on historical simulation project data, the existing associations between each CAD feature and CAE analysis feature are determined. Based on the existing associations and the current custom associations, a mapping rule between the CAD features and the CAE analysis feature is constructed. Based on the aforementioned multi-type simulation scenario templates and the aforementioned mapping rules, the initial CAE pre- and post-processing modules are configured automatically. The configured pre-configured CAE pre- and post-processing modules are embedded into the modeling environment of the cloud architecture CAD, and a data channel between the cloud architecture CAD and the pre-configured CAE pre- and post-processing modules is established based on the pre-configured data transmission environment.

3. The integrated collaborative method for cloud-based CAD and CAE architecture according to claim 2, characterized in that, The current CAD model file is processed by the pre-set CAE pre- and post-processing modules to generate the corresponding finite element model, specifically including: Based on the aforementioned multi-type simulation scenario templates, the execution order of each CAE pre- and post-processing function is determined. Based on the execution order, each CAE preprocessing operation is performed sequentially on the current CAD model file to obtain the finite element feature information corresponding to the current CAD model file; wherein, the finite element feature information includes: the mesh model and physical properties of the current CAD model file; The finite element feature information is encapsulated and transformed based on preset mapping rules to obtain a finite element model corresponding to the current CAD model file.

4. The integrated collaborative method for cloud-based CAD and CAE according to claim 3, characterized in that, Before obtaining the finite element results by simulating and solving the finite element model based on the preset multiple solvers in the CAE simulation module on the server side, the method further includes: The current CAD model file is processed by the preset CAE pre- and post-processing modules to generate the finite element model corresponding to the current CAD model file; Alternatively, based on the integrated collaborative system of CAD and CAE, the initial finite element model file exported by the relevant CAE software can be obtained; wherein, the initial finite element model file includes: bdf, inp, cdb and k format files; The initial finite element model file is parsed and its format is converted to obtain the finite element model.

5. The integrated collaborative method for cloud-based CAD and CAE according to claim 1, characterized in that, Based on the pre-built multiple solvers in the CAE simulation module on the server side, the finite element model is simulated and solved to obtain finite element results, specifically including: The simulation scenario based on the finite element model is matched with a list of preset multi-solvers to obtain a preset multi-solver corresponding to the finite element model. Based on the solver type of the preset multi-solver, the finite element model is format-converted to obtain the input data of the finite element model; The specified solver corresponding to the input data is determined based on the real-time load of the server where each preset multi-solver is located. Based on a pre-set hot update strategy, it is determined whether to update the version of the specified solver so that the input data can be input into the corresponding specified solver to perform simulation calculations and obtain finite element results.

6. The integrated collaborative method for cloud-based CAD and CAE according to claim 5, characterized in that, Determining whether to update the specified solver based on a pre-defined hot update strategy specifically includes: The solver code of a specified solver is periodically solved using a pre-built version control tool to compare the solver code with the corresponding latest version of the solver code; Based on the comparison results, determine whether to update the specified solver; If a version update is performed, the new version solver corresponding to the specified solver will be deployed in the test environment for version testing. The new version solver that passes the test will be pushed out based on the canary release process, and feedback information of the new version solver will be obtained. If it is determined based on the feedback information that a full update of the new version of the solver should be performed, then the task execution status of the specified solver is determined. Based on the task execution status, the update process node of the specified solver is determined, and the new version of the solver is started at the update process node to realize the version update of the specified solver.

7. The integrated collaborative method for cloud-based CAD and CAE according to claim 1, characterized in that, Based on the pre-built data conversion service module in the CAE simulation module, the finite element results are processed through data parsing, and the parsing results are organized into visual data for visualization. The modeling process is then adjusted based on feedback from the visualization display. Specifically, this includes: Based on the preset data conversion service module in the CAE simulation module, parallel data parsing is performed on each of the finite element results to extract key analysis data based on preset indicators. The key analysis data is normalized, and the processed key analysis data is rendered based on a preset display method to obtain visualized data for visualization display; Based on the visualization, the current CAD model file and the preset CAE pre- and post-processing modules are adjusted and optimized.

8. The integrated collaborative method for cloud-based CAD and CAE architecture according to claim 1, characterized in that, Obtain the model creation instruction uploaded by the CAD modeling module of the client, and process the model creation instruction through the preset geometric modeling engine in the database of the server to obtain the current CAD model file, specifically including: Obtain the instruction type of the model creation instruction; wherein, the instruction type includes: creation type and import type; If the instruction type is determined to be a creation type, then the model creation instruction is parsed to determine the creation model type and initial parameters corresponding to the model creation instruction; The modeling engine in the pre-built geometric modeling engine is invoked to perform modeling analysis on the model type and initial parameters based on the modeling engine, and to generate a geometric CAD model. Obtain the design constraint data and feature parameter data corresponding to the geometric CAD model to generate the current CAD model file corresponding to the geometric CAD model; If the instruction type is determined to be an import type, the model creation instruction is parsed to determine the storage information of the existing CAD geometric model file, and the existing CAD geometric model file is imported as the current CAD model file based on the storage information.

9. An integrated collaborative device for cloud-based CAD and CAE, characterized in that, The system comprises an integrated collaborative CAD and CAE system built on a cloud architecture. The system includes a client integrating a CAD modeling module and pre-built CAE pre- and post-processing modules, and a server integrating a database and CAE simulation module. The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-8.

10. A non-volatile storage medium storing computer-executable instructions, characterized in that, An integrated collaborative system for CAD and CAE built on a cloud architecture is provided. The system includes a client that integrates a CAD modeling module and a pre-built CAE pre- and post-processing module, and a server that integrates a database and a CAE simulation module. The computer-executable instructions are capable of executing the method described in any one of claims 1-8.

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