A multi-disciplinary MBD toolset evaluation method and system based on the 3DE platform

Abstract

This invention discloses a method and system for evaluating a multi-disciplinary MBD toolset based on a 3DE platform. The method includes: acquiring design data of multiple model sub-modules in the MBD toolset; running a first sample constructed by the multiple model sub-modules based on the design data; acquiring feedback data from the multiple model sub-modules and the first sample; and generating an evaluation result for the first sample based on the feedback data. The method provided by this invention can improve collaboration efficiency, eliminate information silos, achieve data format unification, enhance the accuracy and reliability of designs, and optimize resource allocation.

Description

Technical Field

[0001] This invention belongs to the field of computer systems engineering, and in particular relates to a multi-disciplinary MBD toolset evaluation method and system based on the 3DE platform. Background Technology

[0002] In modern engineering design and manufacturing, with the increasing complexity of products and the growing prevalence of multidisciplinary collaboration, Model-Based Design (MBD) has become an important methodology. MBD improves the efficiency and accuracy of the design process by integrating multiple aspects of a product (such as function, performance, manufacturing, and service) into a single model. However, the effective implementation of this method faces several technical challenges, especially in multidisciplinary collaborative environments. Therefore, establishing a common toolset evaluation method for multidisciplinary MBD based on the 3DE platform is of great significance in addressing these challenges.

[0003] Modern products typically consist of multiple subsystems and components, each potentially designed by different specialized teams (e.g., mechanical, electrical, software). This complexity demands that design teams not only design independently but also collaborate throughout the design lifecycle. Successful implementation of multidisciplinary design relies on the effective management of relationships between these teams, a dynamic and complex process. Incorrect decisions made in the early design stages make adjustments much more difficult in later phases and can lead to significantly increased project costs or even project failure.

[0004] With the diversification of data sources, different departments and professional teams use different tools and processes, leading to the formation of information silos. The lack and inconsistency of information between teams reduces the efficiency of collaborative design and causes design delays and waste of resources. Summary of the Invention

[0005] To address the shortcomings of the existing technology, this invention provides a multi-disciplinary MBD toolset evaluation method based on a 3DE platform, comprising the following steps:

[0006] Step S101: Obtain design data for multiple model sub-modules in the MBD toolset;

[0007] Step S103: Run the first sample constructed by the multiple model sub-modules based on the design data;

[0008] Step S105: Obtain feedback data from the multiple model sub-modules and the first sample;

[0009] Step S107: Based on the feedback data, generate the evaluation result of the first sample.

[0010] The MBD toolset includes one or more of the following: structural model inspection tool submodule, piping model inspection tool submodule, electrical model inspection tool submodule, and / or outfitting model inspection tool submodule.

[0011] Data links are established between the various model sub-modules to share information in real time.

[0012] Step S105 includes:

[0013] Based on the design data, each submodule generates first feedback data after completing the inspection;

[0014] Based on the design data, the second feedback data is generated after running the first sample.

[0015] Step S107 includes: evaluating the first sample under the current design data based on the following formula:

[0016] Where D represents the design evaluation index, reflecting the overall performance of the model; n represents the number of professional modules participating in the evaluation; C i R represents the contribution coefficient of the i-th submodule, reflecting the importance of that submodule; i φ represents the feedback value of the i-th submodule, reflecting its performance; i The adjustment coefficient for the i-th module represents the module's quality and usage; W represents the weighting constant, which is the fundamental influence on the overall design evaluation; m represents the number of feedback items; δ j ψ(α) represents the weight coefficient of the j-th feedback item, reflecting the importance of the feedback; j α represents a function that indicates the importance of feedback information, such as a Gaussian function; j χ(β) represents the value of the j-th feedback term, σ is the standard deviation; j ) represents a complex filtering function used to filter out unnecessary data; F represents the feedback value of the first sample run.

[0017] The calculation is performed using the following formula:

[0018]

[0019] The complex screening function is calculated using the following formula:

[0020]

[0021] The method further includes: establishing a multi-data source access strategy to ensure access to design documents of different formats and types, such as CAD drawings and 3D models, and performing data format conversion through an automatic parsing module to maintain the integrity and consistency of design information.

[0022] The method further includes generating design improvement suggestions, which are displayed in a priority order in the comprehensive evaluation report. Based on the feedback intensity and impact of each sub-module, the method helps the design team quickly identify major risks and improvement directions, thereby achieving precise optimization and improving the overall design effectiveness and innovation of the product.

[0023] This invention also proposes a multi-disciplinary MBD toolset evaluation system based on the 3DE platform, comprising:

[0024] The input module is used to acquire design data from multiple model sub-modules in the MBD toolset.

[0025] A running module, which is used to run the first sample constructed by the plurality of model sub-modules based on the design data;

[0026] A feedback module is used to obtain feedback data from the plurality of model sub-modules and the first sample;

[0027] An evaluation module is used to generate an evaluation result for the first sample based on the feedback data.

[0028] Compared with the prior art, the present invention has the following advantages:

[0029] By integrating the collaborative capabilities of multidisciplinary design teams, the toolset based on the 3DE platform enables rapid information sharing and real-time updates. Designers can access and edit the latest design data at any time without frequent data transfers and format conversions, thus significantly saving time and improving collaboration efficiency.

[0030] This method, through a unified data interface and standardized toolset, enables smooth information flow between different disciplines, effectively eliminating information silos. Design teams no longer face issues of data lag and inconsistency, ensuring all team members have a shared understanding of project progress and status, and promoting in-depth cross-disciplinary collaboration.

[0031] This evaluation methodology supports the integration and conversion of multiple data formats, enabling efficient processing of data from different disciplines and tools. By converting data in different formats to a unified standard, the data integration process is simplified, ensuring the integrity and consistency of the project.

[0032] By integrating a real-time feedback mechanism, the design team can obtain timely opinions and suggestions from all parties during the design process. This rapid feedback capability enables the team to quickly identify and correct design flaws, reducing the cost and time of later modifications and improving design quality.

[0033] By evaluating the performance and feasibility of the design model in real time, we can ensure that the design results meet functional and performance requirements, thereby reducing product defects caused by design deviations.

[0034] By effectively evaluating toolsets, companies can better understand the effectiveness of various tools and their applicability in real-world projects. This enables companies to optimize resource allocation, select tools rationally, maximize return on investment, and reduce unnecessary expenditures.

[0035] The 3DE platform-based evaluation methodology enables design teams to focus on high-value innovative activities. By reducing duplication of work and misunderstandings caused by information asymmetry, teams can better utilize their time and energy for innovative design, thereby improving the overall market competitiveness of the product.

[0036] By establishing an effective evaluation framework, design teams can continuously collect and analyze feedback data, providing a reference for tool selection and process optimization in subsequent projects. Enterprises can continuously enhance the effectiveness of their MBD (Design by Design) methodology, improve their design processes, and increase market adaptability. Attached Figure Description

[0037] The above and other objects, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the present disclosure are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein:

[0038] Figure 1 This is a flowchart illustrating a multi-disciplinary MBD toolset evaluation method based on a 3DE platform according to an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0040] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0041] It should be understood that although the terms first, second, third, etc., may be used to describe... in the embodiments of the present invention, these... should not be limited to these terms. These terms are only used to distinguish... For example, first... may also be referred to as second... without departing from the scope of the embodiments of the present invention, and similarly, second... may also be referred to as first...

[0042] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0043] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0044] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0045] The optional embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0046] Example 1

[0047] like Figure 1 As shown, this invention discloses a multi-professional MBD toolset evaluation method based on the 3DE platform, comprising the following steps:

[0048] Step S101: Obtain design data for multiple model sub-modules in the MBD toolset;

[0049] Step S103: Run the first sample constructed by the multiple model sub-modules based on the design data;

[0050] Step S105: Obtain feedback data from the multiple model sub-modules and the first sample;

[0051] Step S107: Based on the feedback data, generate the evaluation result of the first sample.

[0052] Example 2

[0053] This invention proposes a multi-disciplinary MBD toolset evaluation method based on a 3DE platform, comprising the following steps:

[0054] Step S101: Obtain design data for multiple model sub-modules in the MBD toolset;

[0055] Step S103: Run the first sample constructed by the multiple model sub-modules based on the design data;

[0056] Step S105: Obtain feedback data from the multiple model sub-modules and the first sample;

[0057] Step S107: Based on the feedback data, generate the evaluation result of the first sample.

[0058] The MBD toolset includes one or more of the following: structural model inspection tool submodule, piping model inspection tool submodule, electrical model inspection tool submodule, and / or outfitting model inspection tool submodule.

[0059] The MBD toolset includes functions such as structural plate inspection, profile end form inspection, part weight inspection, and structural part name inspection; piping model (pipe material, equipment code, equipment name, quantity, material, weight; pipe fittings, equipment code, equipment name, quantity, material; equipment, part name, quantity, material, weight) attribute inspection; electrical model (equipment electrical accessories, equipment code, equipment name, quantity, material, weight; supports and hangers, equipment code, equipment name, quantity, material; base, part name, quantity, material, weight) attribute inspection; outfitting model (outfitting components, code, name, quantity, material (name / standard), weight; equipment, code, name, quantity, material (name / standard), weight) attribute inspection, etc., thereby ensuring the requirements of a single data source for plant and institute, realizing team collaboration, improving design efficiency and quality, and ensuring the consistency and integrity of design data.

[0060] Within this framework, data links are established between the various model sub-modules (e.g., using APIs, database connections, or middleware) to share information in real time. A unified data format (e.g., JSON, XML) and standardized interfaces are used to enable information exchange between tools.

[0061] Step S105 includes:

[0062] Based on the design data, each submodule generates first feedback data after completing the inspection;

[0063] Based on the design data, the second feedback data is generated after running the first sample.

[0064] Step S107 includes: evaluating the first sample under the current design data based on the following formula:

[0065] Where D represents the design evaluation index, reflecting the overall performance of the model; n represents the number of professional modules participating in the evaluation, such as structure, piping, electrical, and outfitting; C i R represents the contribution coefficient of the i-th submodule, reflecting the importance of that submodule; i φ represents the feedback value of the i-th submodule, reflecting its performance; i The adjustment coefficient for the i-th module represents the module's quality and usage; W represents the weighting constant, which is the fundamental influence on the overall design evaluation; m represents the number of feedback items; δ j ψ(α) represents the weight coefficient of the j-th feedback item, reflecting the importance of the feedback; j α represents a function that indicates the importance of feedback information, such as a Gaussian function; j χ(β) represents the value of the j-th feedback term, σ is the standard deviation; j ) represents a complex filtering function used to filter out unnecessary data; F represents the feedback value of the first sample run.

[0066] The calculation is performed using the following formula:

[0067]

[0068] The complex screening function is calculated using the following formula:

[0069]

[0070] in, Where θ i For feedback gain.

[0071] in, p represents the number of performance metrics, σ k γ is the standard deviation of the k-th performance index. k Q is a weighting coefficient that indicates the degree of importance attached to volatility. k,q This represents the result of the k-th performance metric in the q-th run, where a represents the number of runs for the first sample.

[0072] In one embodiment, the contribution coefficient (C) i ), Feedback value (R) i ) and the feedback weighting coefficient (δ) jIt typically relies on statistical analysis, historical data, expert evaluation, and specific calculations of design-related parameters.

[0073] Contribution coefficient (C) i The value represents the contribution of the i-th specialized submodule to the overall design goal, which is typically determined through the calculation of module performance indicators and expert evaluation. Based on the functions and purposes of different submodules, a set of key performance indicators (KPIs) are set, such as strength, stability, and cost. By standardizing these indicators, the performance values ​​of the submodules are obtained.

[0074] Experts in relevant fields were invited to score each module to determine its importance in the design process, thereby calculating its contribution coefficient.

[0075] Assuming there are three professional sub-modules (structure, electrical, and piping), the KPIs and expert scores for each sub-module are as follows:

[0076]

[0077] When calculating the contribution coefficient, the standardized performance indicators and expert scores of each sub-module can be weighted to obtain the corresponding contribution coefficient.

[0078] Feedback value (R) i The expression represents the performance feedback of the i-th submodule during actual use, which can be obtained based on user evaluations, fault data, and improvement records. User feedback on each module is collected, and user satisfaction is calculated using questionnaires. Faults and defects of the submodule during actual operation are verified and quantified as feedback values.

[0079] Suppose the following information is obtained through user feedback and fault analysis:

[0080]

[0081]

[0082] The feedback value can be calculated by the proportion of positive feedback to the total feedback.

[0083] Feedback weighting coefficient (δ) j This is used to adjust the degree of influence of different feedback items on the overall evaluation, and is usually determined based on the importance of the feedback item and its contribution to the overall design. Different weights are assigned based on the type and importance of the feedback items, through expert evaluation or historical performance data analysis. The weight values ​​are then determined by ranking the impact of each feedback item on the design.

[0084] Assume the weights of the feedback items are assigned as follows:

[0085]

[0086] For example,

[0087] Assume we have the following hypothetical data to calculate the comprehensive design evaluation index D:

[0088] Number of modules n = 3;

[0089] Contribution coefficients C1 = 0.4, C2 = 0.35, C3 = 0.25;

[0090] Feedback values ​​R1 = 0.9375, R2 = 0.7692, R3 = 0.8000;

[0091] Number of feedback items m = 3;

[0092] The weighting coefficient δ1 = 0.5.

[0093] The method further includes: establishing a multi-data source access strategy to ensure access to design documents of different formats and types, such as CAD drawings and 3D models, and performing data format conversion through an automatic parsing module to maintain the integrity and consistency of design information.

[0094] In one embodiment, it is necessary to identify and classify potential data sources, which may include file types (such as CAD drawings, 3D models, documents, etc.) and data formats (such as DWG, DXF, IFC, STEP, OBJ, etc.). A list containing all possible data sources should be created, specifying each type of data format and its origin.

[0095] The data access module supports uploading and accessing various file formats. This module should be able to identify file types and select the appropriate parsing method based on the file format. A standardized data interface should be designed so that different data sources can submit data in a unified manner. A standardized interface helps simplify system integration and improve data processing efficiency.

[0096] An automatic parsing module is used to parse data from files of different formats. This module should be able to extract key information from design documents and convert it into a unified format used within the system.

[0097] Establish a data format conversion function to convert documents of different formats into a unified data model (such as XML, JSON, etc.) to ensure the integrity of design information during the transfer and processing process.

[0098] To address potential errors during the parsing process, an error detection and reporting mechanism is designed to quickly identify and resolve issues, ensuring the validity of the data.

[0099] The parsed data is centrally stored in a database for easy subsequent querying and management. The database should be designed to support multiple data types and flexibly handle different data needs. Version control should be implemented for design documents to ensure that historical versions can be traced when data is updated or modified, maintaining information transparency. Regular consistency checks should be performed on the imported data to ensure that information in different professional modules corresponds and to detect potential conflicts or inconsistencies. During data access and transformation, data validation should be performed to ensure that the imported data conforms to preset formats and standards, maintaining the high quality of design information.

[0100] The method further includes generating design improvement suggestions, which are displayed in a priority order in the comprehensive evaluation report. Based on the feedback intensity and impact of each sub-module, the method helps the design team quickly identify major risks and improvement directions, thereby achieving precise optimization and improving the overall design effectiveness and innovation of the product.

[0101] In one embodiment, the feedback intensity of each submodule is quantitatively evaluated, and various indicators related to design improvement are collected, including: the functionality and usability of the design; the fulfillment of performance requirements; user experience and satisfaction; production costs and production efficiency, etc.

[0102] The collected feedback data will be compiled and analyzed to create a comprehensive evaluation report. This report should include an assessment and analysis of the performance of each submodule, helping the team understand the performance of different modules and areas for improvement.

[0103] Prioritize design improvement suggestions based on the intensity and impact of feedback. This can be achieved through the following methods:

[0104] Calculate the impact of each feedback on the overall design or project and assess its importance; or assign a weight to each feedback based on its importance and calculate a weighted score.

[0105] The comprehensive evaluation report should present design improvement suggestions in a list format, ordered from highest to lowest priority. Each suggestion should include a brief description, outlining its specific areas for improvement and expected outcomes. Use charts (such as bar charts and heatmaps) to visualize the intensity and impact of feedback, enabling the design team to visually identify which modules require priority.

[0106] Attach risk assessment information to the priority list, indicating potential risk factors and their impact, to help the design team quickly identify major risks.

[0107] Analyze the correlation between each suggestion and the design goal, and clarify the degree of contribution of each improvement suggestion to the overall design goal.

[0108] Based on the priority recommendations, develop corresponding action plans, clarify the responsible persons, completion deadlines, and required resources, thereby ensuring that the recommendations can be effectively implemented.

[0109] After developing the optimization plan, a corresponding evaluation mechanism should be established to ensure that the improvement effect can be understood in real time during the implementation process and adjustments can be made when necessary.

[0110] After implementing improvements, we continue to collect feedback from users and the team to create a cycle of continuous improvement, ensuring that product design is constantly optimized.

[0111] The results and lessons learned from each improvement were recorded to provide a reference for the design improvement of subsequent projects.

[0112] Example 3

[0113] This invention also proposes a multi-disciplinary MBD toolset evaluation system based on the 3DE platform, comprising:

[0114] The input module is used to acquire design data from multiple model sub-modules in the MBD toolset.

[0115] A running module, which is used to run the first sample constructed by the plurality of model sub-modules based on the design data;

[0116] A feedback module is used to obtain feedback data from the plurality of model sub-modules and the first sample;

[0117] An evaluation module is used to generate an evaluation result for the first sample based on the feedback data.

[0118] Example 4

[0119] This disclosure provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0120] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0121] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0122] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (AN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0124] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0125] The preferred embodiments of the present invention have been described above to make the spirit of the present invention clearer and easier to understand, and are not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope summarized by the appended claims.

Claims

1. A multi-disciplinary MBD toolset evaluation method based on the 3DE platform, characterized in that, Includes the following steps: Step S101: Obtain design data for multiple model sub-modules in the MBD toolset; Step S103: Run the first sample constructed by the multiple model sub-modules based on the design data; Step S105: Obtain feedback data from the multiple model sub-modules and the first sample; Step S107: Based on the feedback data, generate the evaluation result of the first sample; Step S107 includes: evaluating the first sample under the current design data based on the following formula: Where D represents the design evaluation index, reflecting the overall performance of the model; n represents the number of professional modules participating in the evaluation; C i R represents the contribution coefficient of the i-th submodule, reflecting the importance of that submodule; i This represents the feedback value of the i-th submodule, reflecting its performance. i The adjustment coefficient for the i-th module represents the module's quality and usage; W represents the weighting constant, which is the fundamental influence on the overall design evaluation; m represents the number of feedback items; δ j ψ(α) represents the weight coefficient of the j-th feedback item, reflecting the importance of the feedback; j α represents a function indicating the importance of feedback information; j χ(β) represents the value of the j-th feedback term; j ) represents a complex filtering function used to filter out unnecessary data; F represents the feedback value of the first sample run.

2. The method as described in claim 1, characterized in that, The MBD toolset includes a structural model inspection tool submodule, a piping model inspection tool submodule, an electrical model inspection tool submodule, and an outfitting model inspection tool submodule.

3. The method as described in claim 2, characterized in that, Step S103 includes: establishing data links between each model submodule to share information in real time.

4. The method as described in claim 3, characterized in that, Step S105 includes: Based on the design data, each submodule generates first feedback data after completing the inspection; Based on the design data, the second feedback data is generated after running the first sample.

5. The method as described in claim 1, characterized in that, The function representing the importance of the feedback item information is calculated using the following formula: , where σ is the standard deviation.

6. The method as described in claim 1, characterized in that, The complex screening function is calculated using the following formula: 。 7. The method as described in claim 1, characterized in that, The method also includes: establishing a multi-data source access strategy to ensure access to design documents of different formats and types, and performing data format conversion through an automatic parsing module.

8. The method as described in claim 1, characterized in that, The method further includes generating design improvement suggestions, which are displayed in priority order in the comprehensive evaluation report.

9. A multi-disciplinary MBD toolset evaluation system based on a 3DE platform, using the method as described in any one of claims 1-8, comprising: An input module is used to acquire design data from multiple model sub-modules in the MBD toolset. A running module, which is used to run the first sample constructed by the plurality of model sub-modules based on the design data; A feedback module is used to obtain feedback data from the plurality of model sub-modules and the first sample; An evaluation module is used to generate an evaluation result for the first sample based on the feedback data.

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