Multi-specialty MBD tool set evaluation method and system based on 3DE platform

Abstract

The invention discloses a multi-specialty MBD tool set evaluation method and system based on a 3DE platform. The method comprises the steps of obtaining design data of a plurality of model sub-modules in an MBD tool set; running a first sample constructed by the plurality of model sub-modules based on the design data; obtaining feedback data of the plurality of model sub-modules and the first sample; and based on the feedback data, generating an evaluation result of the first sample. Through the method provided by the invention, the cooperation efficiency can be improved, the information island can be eliminated, the data format unification can be realized, the design accuracy and reliability can be enhanced, and the resource configuration can be optimized.

Description

Technical Field

[0001] The present invention belongs to the field of computer system engineering, and particularly relates to a method and system for evaluating a multi-disciplinary MBD tool set based on a 3DE platform. Background Art

[0002] In the field of modern engineering design and manufacturing, with the continuous improvement of product complexity and the increasing prevalence of multi-disciplinary collaboration, Model-Based Design (MBD) has become an important methodology. The MBD method 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 multiple technical challenges, especially in a multi-disciplinary collaboration environment. In response to these challenges, it is of great significance to establish a method for evaluating the common tool set of multi-disciplinary MBD based on a 3DE platform.

[0003] Modern products are usually composed of multiple subsystems and components, and each subsystem may be designed by different professional teams (such as mechanical, electrical, software, etc.). This complexity requires design teams not only to be able to design independently but also to collaborate throughout the design life cycle. The successful implementation of multi-disciplinary design depends on the effective management of the relationships between professional teams, which is a dynamic and complex process. Once a decision made in the initial stage of design is incorrect, adjustments in subsequent stages will be more difficult and may lead to a significant increase in project costs or even project failure.

[0004] With the diversification of data sources, different departments and professional teams use different tools and processes, resulting in the formation of information silos. The lack of information and inconsistencies between teams reduce the collaborative efficiency of design and lead to design delays and resource waste. Summary of the Invention

[0005] In view of the defects existing in the above-mentioned prior art, the present invention provides a method for evaluating a multi-disciplinary MBD tool set based on a 3DE platform, including the following steps:

[0006] Step S101: Obtain the design data of multiple model sub-modules in the MBD tool set;

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

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

[0009] Step S107: Generate an evaluation result of the first sample based on the feedback data.

[0010] The MBD toolset includes one or more of a structural model checking tool submodule, a piping model checking tool submodule, an electrical model checking tool submodule, and / or an outfitting model checking tool submodule.

[0011] Among them, data links are established between each model sub-module to share information in real time.

[0012] Wherein, the 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, second feedback data is generated after running the first sample.

[0015] The 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, which reflects the overall performance of the model; n represents the number of professional modules involved in the evaluation; C i Indicates the contribution coefficient of the i-th submodule, reflecting the importance of the submodule; R i represents the feedback value of the i-th submodule, reflecting its performance; φ i represents the adjustment coefficient of the i-th module, reflecting the quality of the module and its usage; W represents the weight constant, which is the basic influence on the overall design evaluation; m represents the number of feedback items; δ j represents the weight coefficient of the jth feedback item, reflecting the importance of the feedback; ψ(α j ) represents the function of the importance of the feedback item information, such as a Gaussian function; α j represents the value of the jth feedback item, σ is the standard deviation; χ(β j ) represents a complex filtering function used to filter unnecessary data; F represents the feedback value of the first sample run.

[0017] Wherein, the calculation is performed using the following formula:

[0018]

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

[0020]

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

[0022] Among them, the method further includes: generating design improvement suggestions, which are displayed in the comprehensive evaluation report in order of priority, and helping the design team quickly identify the main risks and improvement directions based on the feedback intensity and impact degree of each sub-module, so as to achieve precise optimization and improve the design effectiveness and innovation of the overall product.

[0023] The present invention also proposes a multi-disciplinary MBD toolset evaluation system based on a 3DE platform, including:

[0024] An input module, which is used to obtain the design data of multiple model sub-modules in the MBD toolset;

[0025] An operation module, which is used to run the first sample constructed by the multiple model sub-modules based on the design data;

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

[0027] An evaluation module, which is used to generate an evaluation result of 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 multi-disciplinary design teams, the toolset based on the 3DE platform can achieve rapid information sharing and real-time update. Designers can access and edit the latest design data at any time, without the need for frequent data transfer and format conversion, thus significantly saving time and improving collaboration efficiency.

[0030] Through a unified data interface and a standardized toolset, the information flow between different disciplines can be smoothly transmitted, effectively eliminating the information island phenomenon. The design team no longer faces the problems of data lag and inconsistency, ensuring that all team members have a common understanding of the project progress and status, and promoting in-depth cross-disciplinary cooperation.

[0031] This evaluation method 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 into 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 opinions and suggestions from all parties in a timely manner during the design process. This rapid feedback ability enables the team to quickly discover and correct defects in the design, reducing the cost and time of later modifications and improving the design quality.

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

[0034] Through the effective evaluation of the toolset, enterprises can better understand the usage effects of various tools and their applicability in real projects. This enables enterprises to optimize resource allocation, reasonably select tools, maximize return on investment, and reduce unnecessary expenditures.

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

[0036] By establishing an effective evaluation framework, the design team 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 methods, continuously improve the design process, and increase market adaptability. Brief Description of the Drawings

[0037] By referring to the following detailed description in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present disclosure will become readily understood. In the drawings, several embodiments of the present disclosure are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

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

[0039] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present 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 the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "the", and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. "Plural" generally includes at least two.

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

[0042] It should be understood that the term "and / or" used herein is merely a description of the relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Additionally, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship.

[0043] Depending on the context, the words "if" and "when" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0044] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a commodity or device comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the commodity or device comprising the said element.

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

[0046] Embodiment 1

[0047] As Figure 1 shown, the present invention discloses a method for evaluating a multi-disciplinary MBD toolset based on a 3DE platform, including the following steps:

[0048] Step S101, obtain the design data of 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 the feedback data of the multiple model sub-modules and the first sample;

[0051] Step S107: Generate an evaluation result for the first sample based on the feedback data.

[0052] Embodiment 2:

[0053] A method for evaluating a multi - specialty MBD toolset based on a 3DE platform proposed by the present invention includes the following steps:

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

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

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

[0057] Step S107: Generate an evaluation result for the first sample based on the feedback data.

[0058] Among them, the MBD toolset includes one or more of a structural model inspection tool sub - module, a pipeline model inspection tool sub - module, an electrical model inspection tool sub - module, and / or an outfitting model inspection tool sub - module.

[0059] Among them, the functions of the MBD toolset include structural plate inspection, profile end form inspection, part weight inspection, structural part name inspection; pipeline 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; pedestals, part name, quantity, material, weight) attribute inspection; outfitting model (outfitting parts, code, name, quantity, material (name / standard), weight; equipment, code, name, quantity, material (name / standard), weight) attribute inspection, etc., so as to ensure the requirements of single - data - source transfer in the factory, realize team collaborative work, improve design efficiency and quality, and ensure the consistency and integrity of design data.

[0060] Among them, a data link is established between each model sub - module (such as using API, database connection or middleware) to share information in real - time. A unified data format (such as JSON, XML) and standardized interfaces are used to achieve information inter - communication between tools.

[0061] Among them, the step S105 includes:

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

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

[0064] Among them, the step S107 includes: evaluating the first sample under the current design data based on the following formula:

[0065] Among them, 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, pipeline, electrical, and outfitting; C i represents the contribution coefficient of the i-th sub-module, reflecting the importance of this sub-module; R i represents the feedback value of the i-th sub-module, reflecting its performance; φ i represents the adjustment coefficient of the i-th module, reflecting the quality of the module and its usage; W represents the weight constant, which is the basic influence on the overall design evaluation; m represents the number of feedback items; δ j represents the weight coefficient of the j-th feedback item, reflecting the importance of the feedback; ψ(α j ) represents the function of the importance of the feedback item information, such as the Gaussian function; α j represents the value of the j-th feedback item, σ is the standard deviation; χ(β j ) represents the complex screening function, used to filter unnecessary data; F represents the feedback value of the first sample run.

[0066] Among them, is calculated using the following formula:

[0067]

[0068] Among them, the complex screening function is calculated using the following formula:

[0069]

[0070] Among them, where θ i is the feedback gain.

[0071] Among them, p represents the number of performance indicators, σ k is the standard deviation of the k-th performance indicator, γ k is the weight coefficient, indicating the degree of emphasis on volatility, Q k,q represents the result of the k-th performance indicator under the q-th run, and a represents the number of runs of the first sample.

[0072] In a certain embodiment, the contribution coefficient (C i ), the feedback value (R i ), and the weight coefficient of the feedback (δ j)Usually relies on statistical analysis, historical data, expert evaluation, and specific calculations of design-related parameters.

[0073] The contribution coefficient (C i ) represents the degree of contribution of the i-th professional sub-module to the overall design goal, which can usually be determined through the calculation of module performance indicators and expert evaluation. According to the functions and purposes of different sub-modules, a set of key performance indicators (KPIs) are set, such as strength, stability, cost, etc. By standardizing these indicators, the performance values of the sub-modules are obtained.

[0074] Invite experts in relevant fields to score each module to determine its importance in the design process, so as to calculate the contribution coefficient.

[0075] Suppose there are three professional sub-modules (structure, electrical, pipeline), and the KPIs and expert scores of 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] The feedback value (R i ) represents the performance feedback of the i-th sub-module during actual use, which can be obtained based on user evaluation, failure data, and improvement records. Collect the feedback information of users on each module, and calculate the user satisfaction using a questionnaire. Verify the faults and defects of the sub-module during actual operation and quantify them as feedback values.

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

[0080]

[0081]

[0082] The calculation of the feedback value can be obtained by the proportion of positive feedback numbers in the total feedback volume.

[0083] The weight coefficient of the feedback (δ j ) is used to adjust the influence degree of different feedback items on the comprehensive evaluation, which is usually determined according to the importance of the feedback items and their contributions in the overall design. According to the types and importance of the feedback items, different weights are assigned through expert evaluation or historical performance data analysis. Sort according to the influence of each feedback item on the design to determine the weight value.

[0084] Suppose the weight distribution of the feedback items is as follows:

[0085]

[0086] For example,

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

[0088] The 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] The number of feedback items m = 3;

[0092] Weight coefficient δ1 = 0.5.

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

[0094] In a certain 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.). Establish a list containing all possible accessible data sources, and clarify each type of data format and its source.

[0095] Use a data access module to support the upload and access of multiple file formats. This module should be able to identify the file type and select an appropriate parsing method according to the file format. Design a standardized data interface so that different data sources can submit data in a unified manner. The standardized interface helps to simplify system integration and improve data processing efficiency.

[0096] Use an automatic parsing module to achieve data parsing of files in different formats. This module should be able to extract key information from the design document and convert it into a unified format within the system.

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

[0098] For possible errors during the parsing process, design an error detection and reporting mechanism to quickly discover and solve problems to ensure the validity of the data.

[0099] Store the parsed data set in a database for subsequent querying and management. The database should be designed to support the storage of multiple data types and be able to flexibly handle different types of data requirements. Perform version control on the design documents to ensure that historical versions can be traced when the data is updated or modified, maintaining the transparency of information. Regularly check the consistency of the accessed data to ensure that the information in different professional modules corresponds to each other and detect possible conflicts or inconsistencies. During the data access and conversion process, perform data verification to ensure that the imported data conforms to the preset format and standards to maintain the high quality of the design information.

[0100] Among them, the method further includes: generating design improvement suggestions, which are displayed in the comprehensive evaluation report in order of priority. Based on the feedback intensity and impact degree of each sub-module, it helps the design team quickly identify the main risks and improvement directions, so as to achieve precise optimization and improve the design effectiveness and innovation of the overall product.

[0101] In a certain embodiment, quantitatively evaluate the feedback intensity of each sub-module, and collect various indicators related to design improvement, including: the functionality and usability of the design; the satisfaction of performance requirements; user experience and satisfaction; production cost, production efficiency, etc.

[0102] Summarize and analyze the collected feedback data, and compile a comprehensive evaluation report. The report should include the evaluation and analysis of the performance of each sub-module to help the team understand the performance and improvement space of different modules.

[0103] Rank the design improvement suggestions according to the feedback intensity and impact degree. This can be achieved through the following methods:

[0104] Calculate the impact degree of each feedback on the overall design or project, and evaluate its importance; or set weights for each feedback according to its importance and calculate the weighted score.

[0105] In the comprehensive evaluation report, display the design improvement suggestions in the form of a list, sorted from high to low priority. Each suggestion should include a brief description, listing its specific improvement direction and expected effect. Use charts (such as bar charts, heat maps, etc.) to visualize the feedback intensity and impact degree, so that the design team can intuitively identify which modules need to be prioritized.

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

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

[0108] According to the priority suggestions, formulate corresponding action plans, clarify the responsible persons, completion time limits, and required resources, so as to ensure the effective implementation of the suggestions.

[0109] After formulating the optimization plan, establish a corresponding evaluation mechanism to ensure that the improvement effect can be understood in real time during the implementation process and adjusted if necessary.

[0110] After implementing the improvement, continue to collect feedback from users and the team to form a continuous improvement cycle to ensure the continuous optimization of the product design.

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

[0112] Embodiment III

[0113] The present invention also provides a multi-disciplinary MBD toolset evaluation system based on a 3DE platform, including:

[0114] An input module for obtaining design data of multiple model sub-modules in the MBD toolset;

[0115] An operation module for running a first sample constructed by the multiple model sub-modules based on the design data;

[0116] A feedback module for obtaining feedback data of the multiple model sub-modules and the first sample;

[0117] An evaluation module for generating an evaluation result of the first sample based on the feedback data.

[0118] Embodiment IV

[0119] An embodiment of the present disclosure provides a non-volatile computer storage medium storing computer-executable instructions that can execute the method steps as described in the above embodiments.

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

[0121] The above computer-readable medium can be included in the above electronic device; or it can exist separately without being assembled into the electronic device.

[0122] The computer program code for performing the operations of the present disclosure can be written in one or more programming languages or combinations thereof. The above programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include 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, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

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

[0124] The units described in the embodiments of the present disclosure can be implemented in software or in hardware. Among them, the name of the unit does not constitute a limitation to the unit itself in some cases.

[0125] The above describes the preferred embodiments of the present invention, aiming to make the spirit of the present invention clearer and easier to understand, and is not intended to limit the present invention. Any modifications, substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope defined by the appended claims of the present invention.

Claims

1. A multi-disciplinary MBD toolset evaluation method based on the 3DE platform, characterized in that Including the following steps: Step S101: Obtain the design data of 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 the feedback data of the multiple model sub - modules and the first sample; Step S107: Generate the evaluation result of the first sample based on the feedback data.

2. The method according to claim 1, wherein The MBD toolset includes one or more of a structural model inspection tool sub - module, a pipeline model inspection tool sub - module, an electrical model inspection tool sub - module, and / or an outfitting model inspection tool sub - module.

3. The method according to claim 2, characterized in that Step S103 includes: Establish a data link between each model sub - module to share information in real - time.

4. The method according to claim 3, wherein Step S105 includes: Based on the design data, each sub - module generates first feedback data after completion of inspection; Based on the design data, second feedback data is generated after running the first sample.

5. The method according to claim 1, wherein Step S107 includes: Evaluating the first sample under the current design data based on the following formula: Among them, 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 represents the contribution coefficient of the i-th sub-module, reflecting the importance of this sub-module; R i represents the feedback value of the i-th sub-module, reflecting its performance; φ i represents the adjustment coefficient of the i-th module, reflecting the quality of the module and its usage; W represents the weight constant, which is the basic influence on the overall design evaluation; m represents the number of feedback items; δ j represents the weight coefficient of the j-th feedback item, reflecting the importance of the feedback; ψ(α j ) represents the function of the importance of the feedback item information; α j represents the value of the j-th feedback item, σ is the standard deviation; χ(β j ) represents the complex screening function, used to filter unnecessary data; F represents the feedback value of the first sample run.

6. The method according to claim 5, wherein The following formula is used for calculation:

7. The method according to claim 5, wherein The complex screening function is calculated using the following formula:

8. The method according to claim 1, wherein The method further includes: Establishing a multi - data - source access strategy to ensure the ability to access design documents in different formats and types, and performing data format conversion through an automatic parsing module.

9. The method according to claim 1, wherein The method further includes: Generating design improvement suggestions, and the design improvement suggestions are displayed in priority order in the comprehensive evaluation report.

10. A multi - professional MBD toolset evaluation system based on a 3DE platform, including: An input module, which is used to obtain the design data of multiple model sub - modules in the MBD toolset; A running module, which is used to run the first sample constructed by the multiple model sub - modules based on the design data; A feedback module, which is used to obtain the feedback data of the multiple model sub - modules and the first sample; An evaluation module, which is used to generate the evaluation result of the first sample based on the feedback data.

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