Computer-aided design method for automatically generating and reflecting design intention

Abstract

The invention relates to the technical field of computer-aided design, and discloses a computer-aided design method for automatically generating and reflecting a design intention, and the method comprises the steps: carrying out the cooperative work of a design intention understanding module, a design knowledge base module, a design scheme generation module and a design scheme optimization module; the intelligent analysis of the design intention of the user and the generation of the automatic design scheme are realized. The design intention understanding module adopts natural language processing and machine learning technologies to accurately extract user requirements; the design knowledge base module stores a large number of design rules and parameters and provides reference for design; the design scheme generation module automatically creates a preliminary design scheme according to user requirements and knowledge base information; and the design scheme optimization module performs performance improvement on the design scheme through an optimization algorithm. The method improves the design efficiency, reduces the number of design iterations, reduces the manpower and time cost, is suitable for various engineering design fields, and has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided design, and in particular to a computer-aided design method for automatically generating a document reflecting a design intention. Background Art

[0002] In today's design world, computer-aided design (CAD) technology has become a vital tool for designers and engineers in their daily work. While traditional CAD systems offer powerful drawing and modeling capabilities, achieving the designer's design intent often requires extensive manual manipulation and parameter adjustments. This approach is not only time-consuming and labor-intensive, but also prone to errors, especially in complex design projects.

[0003] Existing CAD systems have the following limitations in processing design intent:

[0004] Manual operation dependence: Designers need to operate the software through cumbersome mouse clicks and keyboard input, which limits the speed and efficiency of design.

[0005] Lack of intelligent understanding: Existing CAD systems usually lack a deep understanding of the designer's intent and are unable to automatically generate design solutions that are consistent with the designer's intent.

[0006] Low design iteration efficiency: During the design iteration process, designers need to repeat many of the same operations, which is not only inefficient but also easily leads to design deviations.

[0007] Imperfect data management: The large amount of data generated during the design process lacks effective management, resulting in difficulties in data retrieval, sharing and updating.

[0008] Poor collaboration: In team collaboration, it is difficult to integrate design data between different designers, which affects the consistency of design and collaborative efficiency.

[0009] To overcome these challenges, researchers and developers are constantly searching for new methods and technologies to automate the expression and efficient management of design intent. These technologies include, but are not limited to, artificial intelligence (AI), machine learning, natural language processing (NLP), data mining, and intelligent algorithms. While these technologies have made progress in certain areas, their application in CAD systems remains limited, particularly in the automated generation of designs that reflect design intent.

[0010] Therefore, the present invention aims to provide a computer-aided design method for automatically generating a design that reflects the design intention. By integrating advanced data management modules and intelligent analysis tools, the method can better understand the designer's intention and automatically generate design solutions that meet these intentions, thereby improving design efficiency and quality, shortening the design cycle, and reducing design costs. Summary of the Invention

[0011] (1) Technical problems solved

[0012] In view of the deficiencies of the prior art, the present invention provides a computer-aided design method for automatically generating a design that reflects the design intent, which solves the problems raised in the above-mentioned background technology.

[0013] (2) Technical solution

[0014] To achieve the above objectives, the present invention provides the following technical solution: a computer-aided design method for automatically generating a design that reflects design intent, the method comprising the following steps:

[0015] Step 1: Enter the graphical user interface module to input design requirements and receive design requirements input by the user, including design goals, design constraints and design preference information;

[0016] Step 2: Enter the design intent understanding module and use natural language processing technology to analyze the design requirements entered by the user and extract design intent keywords;

[0017] Step 3: Enter the design knowledge base module and the knowledge retrieval and matching module, retrieve relevant design knowledge from the preset design knowledge base based on the design intent keywords, and build a design knowledge base for the current design task;

[0018] Step 4: Enter the design solution generation module and automatically generate a design solution based on the design knowledge base using genetic algorithm and particle swarm optimization algorithm;

[0019] Step 5: Enter the design scheme evaluation module and evaluate the generated multiple design schemes based on the design goals, design constraints, and design preferences to select the scheme that meets the design intent. The fuzzy membership function in the fuzzy comprehensive evaluation method is as follows:

[0020] μ_A(x)=(xa) / (ba), for a trapezoidal fuzzy set A, where a and b are the boundaries of the fuzzy set;

[0021] Step 6: Enter the design scheme optimization module and further optimize the selected design schemes through iterative optimization algorithms to make them more consistent with the design intent;

[0022] Step 7. Enter the result output module, output the design results, and output the final optimized design scheme in the form of graphics and models for user reference. Through the feedback and iteration module, iteratively optimize the design scheme based on the feedback, and use the data management module to manage all data generated during the design process in real time.

[0023] Preferably, the feedback and iteration module is used to receive user feedback on the design solution, and iteratively optimize the design solution according to the feedback until the user's design intention is met;

[0024] The data management module is used to manage all data generated during the design process, including user input, design solutions and evaluation results, to ensure data security and traceability.

[0025] Preferably, the design intent understanding module includes a natural language processing engine, a keyword extractor, a semantic analyzer, a design intent converter, a pattern recognizer, and an anomaly detector;

[0026] The natural language processing engine is used to analyze text data input by users, including design requirements, goals and constraints, using syntax analysis, semantic understanding and sentiment analysis techniques;

[0027] The keyword extractor is used to identify and extract key design intent keywords and phrases from the text input by the user;

[0028] The semantic analyzer is used to understand the relationship between keywords and contextual meanings, and to construct a semantic network to represent design intent;

[0029] The design intent converter is used to convert the extracted semantic information into design parameters and rules so that the system can use this information to generate a design solution;

[0030] The pattern recognizer is used to identify common design patterns and templates in user input so as to quickly generate design solutions that meet specific styles and standards;

[0031] The anomaly detector is used to detect inconsistencies and errors in user input, providing feedback to correct the understanding of design intent.

[0032] Preferably, the knowledge retrieval and matching module includes a query interface, a query parser, a keyword indexer and a similarity calculation engine;

[0033] The query interface is used to provide a user interface, allowing users to input or select design requirements, keywords and parameters to initiate a knowledge retrieval request;

[0034] The query parser is used to analyze the query input by the user and convert it into a form that the system can understand and process;

[0035] The keyword indexer is used to create and maintain an index of keywords to corresponding content in the knowledge base, so as to quickly locate relevant knowledge points;

[0036] The similarity calculation engine is used to evaluate the similarity between user queries and entries in the knowledge base.

[0037] Preferably, the design solution generation module includes a design initializer, a design rule applicator, a parameter optimizer, a design template library, a template matcher, a design synthesizer, a feasibility analyzer, a design iterator, a multi-solution generator, a user interaction component, a design document generator and a design verifier;

[0038] The design initializer is used to initialize design parameters and basic framework according to the requirements input by the user and the information retrieved from the knowledge base;

[0039] The design rule applicator is used to apply design rules and constraints to the initialized design parameters to ensure that the design solution meets the specified requirements;

[0040] The parameter optimizer is used to adjust and optimize design parameters to improve the performance and compliance of the design solution;

[0041] The design template library is used to store a series of design templates for quickly generating the basic structure of the design solution;

[0042] The template matcher selects the design template that best matches the user's needs as the basis for the design solution;

[0043] The design synthesizer is used to combine different design elements and components into a complete design solution.

[0044] Preferably, the feasibility analyzer is used to evaluate the technical feasibility and cost-effectiveness of the design scheme; the design iterator is used to iteratively improve the design scheme based on feedback and evaluation results; and the multi-scheme generator is used to generate design schemes for users to choose from in order to explore different design possibilities.

[0045] Preferably, the user interaction component is used to provide an interface for users to interact with the generated design scheme, allowing users to provide feedback and guidance; the design document generator is used to automatically generate relevant documents of the design scheme, including design instructions, drawings and material lists; the design verifier is used to verify whether the design scheme meets all design rules and user requirements.

[0046] Preferably, the method comprises the following steps:

[0047] (1) Input design requirements: Receive design requirements input by users, including design goals, design constraints, and design preference information;

[0048] (2) Design intent understanding: Using natural language processing technology, the design requirements input by the user are analyzed and the design intent keywords are extracted;

[0049] (3) Design knowledge base construction: According to the design intention keywords, relevant design knowledge is retrieved from the preset design knowledge base to build a design knowledge base for the current design task;

[0050] (4) Design scheme generation: Based on the design knowledge base, the genetic algorithm and particle swarm optimization algorithm are used to automatically generate design schemes. The crossover operation formula in the genetic algorithm is as follows:

[0051] Single-point crossover: C = (A1, ..., Ap, Bp+1, ..., BN), where A and B are parent individuals, C is the offspring individual, and p is the crossover point;

[0052] Two-point intersection: C = (A1, ..., Ap, Bp+1, ..., Bq, Aq+1, ..., AN), where p and q are intersection points;

[0053] The speed update formula in the particle swarm optimization algorithm is:

[0054] v_i(t+1)=w*v_i(t)+c1*r1*(pbest_i-x_i(t))+c2*r2*(gbest-x_i(t))

[0055] Where v_i(t) is the velocity of particle i at time t, x_i(t) is the position of particle i at time t, pbest_i is the individual optimal position of particle i, gbest is the global optimal position, w is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers in the interval [0,1].

[0056] (5) Design scheme evaluation: Based on the design objectives, design constraints, and design preferences, multiple design schemes are evaluated to select the scheme that meets the design intent. The fuzzy membership function in the fuzzy comprehensive evaluation method is as follows:

[0057] μ_A(x)=(xa) / (ba), for a trapezoidal fuzzy set A, where a and b are the boundaries of the fuzzy set;

[0058] (6) Design optimization: For the selected design schemes, further optimize the design schemes through iterative optimization algorithms to make them more consistent with the design intent;

[0059] (7) Output design results: The final optimized design scheme is output in the form of graphics and models for user reference.

[0060] Preferably, the design knowledge base includes design rules, design cases and design templates. In the design solution generation step, a multi-objective optimization algorithm is adopted to simultaneously consider design goals, design constraints and design preferences.

[0061] Preferably, in the design scheme evaluation step, fuzzy comprehensive evaluation method and hierarchical analysis method are adopted, and in the design scheme optimization step, simulated annealing algorithm and ant colony optimization algorithm are adopted.

[0062] (3) Beneficial effects

[0063] Compared with the prior art, the present invention provides a computer-aided design method for automatically generating a design that reflects the design intent, which has the following beneficial effects:

[0064] 1. Improve design efficiency: This invention significantly reduces the manual workload of designers by automatically processing design intent and generating design solutions, thereby greatly improving design efficiency.

[0065] 2. Shorten the design cycle: The automated design process can quickly respond to design changes, shortening the design cycle from concept to finished product.

[0066] 3. Reduce error rate: By accurately understanding design intent and automatically applying design rules, human errors are reduced and the accuracy of design solutions is improved.

[0067] 4. Improve design quality: The present invention can optimize the scheme based on a rich design knowledge base, thereby improving the quality of the final design scheme.

[0068] 5. Enhance design innovation capabilities: Automated design methods can provide designers with more time and space to explore innovative designs, thereby enhancing design innovation capabilities.

[0069] 6. Reduce costs: Reduce the number of design iterations and manpower input, reduce design costs, and improve the economic benefits of the project.

[0070] 7. Improved user satisfaction: By continuously collecting user feedback and performing design iterations, the present invention can better meet user needs and improve user satisfaction.

[0071] 8. Easy to iterate and optimize: The feedback and iteration module provided by the present invention enables the design solution to be quickly adjusted based on user feedback, facilitating continuous optimization of the design.

[0072] 9. Cross-domain application: The present invention is applicable to a variety of engineering design fields, has wide applicability, and can provide support for design work in different industries.

[0073] 10. Promote teamwork: By providing effective communication and collaboration tools, the present invention promotes cooperation within the design team and with clients, thereby improving the overall work efficiency of the team. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1It is a schematic diagram of the overall system architecture of the present invention. DETAILED DESCRIPTION

[0075] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0076] A computer-aided design method for automatically generating a design that reflects design intent, the method comprising the following steps:

[0077] Step 1: Enter the graphical user interface module to input design requirements and receive design requirements input by the user, including design goals, design constraints and design preference information;

[0078] Step 2: Enter the design intent understanding module and use natural language processing technology to analyze the design requirements entered by the user and extract design intent keywords;

[0079] Step 3: Enter the design knowledge base module and the knowledge retrieval and matching module, retrieve relevant design knowledge from the preset design knowledge base based on the design intent keywords, and build a design knowledge base for the current design task;

[0080] Step 4: Enter the design solution generation module and automatically generate a design solution based on the design knowledge base using genetic algorithm and particle swarm optimization algorithm;

[0081] Step 5: Enter the design scheme evaluation module and evaluate the generated multiple design schemes based on the design goals, design constraints, and design preferences to select the scheme that meets the design intent;

[0082] Step 6: Enter the design scheme optimization module and further optimize the selected design schemes through iterative optimization algorithms to make them more consistent with the design intent;

[0083] Step 7: Enter the result output module to output the design results and the final optimized design scheme in the form of graphics and models for user reference. Then, through the feedback and iteration module, the design scheme is iteratively optimized based on the feedback. The data management module manages all data generated during the design process in real time.

[0084] The feedback and iteration module is used to receive user feedback on the design plan and iteratively optimize the design plan based on the feedback until it meets the user's design intent. The data management module is used to manage all data generated during the design process, including user input, design plans, and evaluation results, to ensure data security and traceability.

[0085] The design intent understanding module includes a natural language processing engine, a keyword extractor, a semantic analyzer, a design intent converter, a pattern recognizer, and an anomaly detector;

[0086] The natural language processing engine is used to analyze text data entered by users, including design requirements, goals, and constraints, using grammatical analysis, semantic understanding, and sentiment analysis techniques;

[0087] The keyword extractor is used to identify and extract key design intent keywords and phrases from the text input by the user;

[0088] The semantic analyzer is used to understand the relationship between keywords and contextual meaning, and build a semantic network to express design intent;

[0089] The design intent converter is used to convert the extracted semantic information into design parameters and rules so that the system can use this information to generate design solutions;

[0090] Pattern recognizers are used to identify common design patterns and templates in user input to quickly generate design solutions that meet specific styles and standards;

[0091] Anomaly detectors are used to detect inconsistencies and errors in user input, providing feedback to correct the understanding of design intent;

[0092] The knowledge retrieval and matching module includes a query interface, a query parser, a keyword indexer, and a similarity calculation engine;

[0093] The query interface is used to provide a user interface that allows users to input or select design requirements, keywords and parameters to initiate knowledge retrieval requests;

[0094] The query parser is used to analyze the query input by the user and convert it into a form that the system can understand and process;

[0095] The keyword indexer is used to create and maintain an index of keywords to the corresponding content in the knowledge base, so as to quickly locate relevant knowledge points;

[0096] The similarity calculation engine is used to evaluate the similarity between user queries and entries in the knowledge base;

[0097] The design solution generation module includes a design initializer, a design rule applicator, a parameter optimizer, a design template library, a template matcher, a design synthesizer, a feasibility analyzer, a design iterator, a multi-solution generator, a user interaction component, a design document generator, and a design validator;

[0098] The design initializer is used to initialize the design parameters and basic framework according to the requirements input by the user and the information retrieved from the knowledge base;

[0099] The design rule applicator is used to apply design rules and constraints to the initialized design parameters to ensure that the design scheme meets the specified requirements;

[0100] Parameter optimizer is used to adjust and optimize design parameters to improve the performance and compliance of the design solution;

[0101] The design template library is used to store a series of design templates and is used to quickly generate the basic structure of the design scheme;

[0102] The template matcher selects the design template that best matches the user's needs as the basis for the design solution;

[0103] Design synthesizer is used to combine different design elements and components into a complete design solution;

[0104] Feasibility Analyzer is used to evaluate the technical feasibility and cost-effectiveness of design solutions;

[0105] Design iterators are used to iteratively improve design solutions based on feedback and evaluation results;

[0106] The multi-scheme generator is used to generate design schemes for users to choose from to explore different design possibilities;

[0107] The user interaction component is used to provide an interface for users to interact with the generated design solutions, allowing users to provide feedback and guidance;

[0108] The design document generator is used to automatically generate relevant documents of the design plan, including design instructions, drawings and material lists;

[0109] The design validator is used to verify whether the design solution meets all design rules and user requirements; Specific embodiment:

[0111] The following is a specific example of implementing the present invention, which is used to illustrate the operation process and functions of the present invention.

[0112] Example 1:

[0113] Design intent input module: Designers input design intent through a graphical interface or natural language, for example: "Design a car body with efficient aerodynamic characteristics."

[0114] Intent Parsing Engine: This engine uses natural language processing technology to parse the designer's input and extract key design parameters and constraints, such as "efficient aerodynamic characteristics" and "car body."

[0115] Knowledge base retrieval: The system accesses the built-in design knowledge base to retrieve aerodynamic-related body design rules, parameters, and previous successful design cases.

[0116] Design Scheme Generation: Based on the analyzed design intent and retrieved knowledge, the system automatically generates multiple preliminary design schemes, including different body shapes, lines, and aerodynamic elements.

[0117] Design Evaluation and Optimization: Using computer simulation and evaluation tools, preliminary design solutions are tested for aerodynamic performance. The system iteratively optimizes the design based on the test results.

[0118] User feedback loop: Designers review the solutions generated by the system and provide feedback. The system further adjusts the design based on the feedback.

[0119] Final solution determination: After multiple iterations and optimizations, the system generates one or more final design solutions that meet the design intent.

[0120] Output and presentation: The final design is output in the form of a 3D model and detailed engineering drawings for designers to further refine or directly use in production.

[0121] The specific steps are as follows:

[0122] Step 1: The designer enters the design intent into the CAD system, and the system records and prepares for analysis.

[0123] Step 2: The intent parsing engine identifies the key elements of the design intent and converts them into quantifiable design parameters.

[0124] Step 3: The system extracts relevant design rules and parameters from the knowledge base and prepares them for generating design solutions.

[0125] Step 4: The system automatically generates multiple design schemes, each of which contains a different combination of design elements.

[0126] Step 5: Evaluate the aerodynamic performance of each solution through computer simulation and optimize based on the evaluation results.

[0127] Step 6: The designer reviews the plan and makes suggestions for improvement. The system adjusts the design plan based on the suggestions.

[0128] Step 7: Repeat steps 5 and 6 until you achieve a satisfactory design effect.

[0129] Step 8: The system outputs the final design plan, and the designer can choose to use the plan for further design work or direct production.

[0130] Through the above embodiments, the present invention provides a computer-aided design method that can automatically generate a design that reflects the design intent, thereby effectively improving the automation level and design quality of the design.

[0131] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0132] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A computer-aided design method for automatically generating a design that reflects design intent, characterized by: The method comprises the following steps: Step 1: Enter the graphical user interface module to input design requirements and receive design requirements input by the user, including design goals, design constraints and design preference information; Step 2: Enter the design intent understanding module and use natural language processing technology to analyze the design requirements entered by the user and extract design intent keywords; Step 3: Enter the design knowledge base module and the knowledge retrieval and matching module, retrieve relevant design knowledge from the preset design knowledge base based on the design intent keywords, and build a design knowledge base for the current design task; Step 4: Enter the design solution generation module and automatically generate a design solution based on the design knowledge base using genetic algorithm and particle swarm optimization algorithm; Step 5: Enter the design scheme evaluation module and evaluate the generated multiple design schemes based on the design goals, design constraints, and design preferences to select the scheme that meets the design intent; Step 6: Enter the design scheme optimization module and further optimize the selected design schemes through iterative optimization algorithms to make them more consistent with the design intent; Step 7. Enter the result output module, output the design results, and output the final optimized design scheme in the form of graphics and models for user reference. Through the feedback and iteration module, iteratively optimize the design scheme based on the feedback, and use the data management module to manage all data generated during the design process in real time.

2. A computer-aided design method for automatically generating a design that reflects design intent according to claim 1, characterized in that: The feedback and iteration module is used to receive user feedback on the design solution and iteratively optimize the design solution based on the feedback until the user's design intention is met.

3. The computer-aided design method for automatically generating a design that reflects design intent according to claim 2, characterized in that: The data management module is used to manage all data generated during the design process, including user input, design solutions and evaluation results, to ensure data security and traceability.

4. A computer-aided design method for automatically generating a design that reflects design intent according to claim 3, characterized in that: The design intent understanding module includes a natural language processing engine, a keyword extractor, a semantic analyzer, a design intent converter, a pattern recognizer and an anomaly detector; The natural language processing engine is used to analyze text data input by users, including design requirements, goals and constraints, using syntax analysis, semantic understanding and sentiment analysis techniques; The keyword extractor is used to identify and extract key design intent keywords and phrases from the text input by the user; The semantic analyzer is used to understand the relationship between keywords and contextual meanings, and to construct a semantic network to represent design intent; The design intent converter is used to convert the extracted semantic information into design parameters and rules so that the system can use this information to generate a design solution; The pattern recognizer is used to identify common design patterns and templates in user input so as to quickly generate design solutions that meet specific styles and standards; The anomaly detector is used to detect inconsistencies and errors in user input, providing feedback to correct the understanding of design intent.

5. The computer-aided design method for automatically generating a design that reflects design intent according to claim 4, characterized in that: The knowledge retrieval and matching module includes a query interface, a query parser, a keyword indexer and a similarity calculation engine; The query interface is used to provide a user interface, allowing users to input or select design requirements, keywords and parameters to initiate a knowledge retrieval request; The query parser is used to analyze the query input by the user and convert it into a form that the system can understand and process; The keyword indexer is used to create and maintain an index of keywords to corresponding content in the knowledge base, so as to quickly locate relevant knowledge points; The similarity calculation engine is used to evaluate the similarity between user queries and entries in the knowledge base.

6. The computer-aided design method for automatically generating a design that reflects design intent according to claim 5, characterized in that: The design solution generation module includes a design initializer, a design rule applicator, a parameter optimizer, a design template library, a template matcher, a design synthesizer, a feasibility analyzer, a design iterator, a multi-solution generator, a user interaction component, a design document generator and a design validator; The design initializer is used to initialize design parameters and basic framework according to the requirements input by the user and the information retrieved from the knowledge base; The design rule applicator is used to apply design rules and constraints to the initialized design parameters to ensure that the design solution meets the specified requirements; The parameter optimizer is used to adjust and optimize design parameters to improve the performance and compliance of the design solution; The design template library is used to store a series of design templates for quickly generating the basic structure of the design solution; The template matcher selects the design template that best matches the user's needs as the basis for the design solution; The design synthesizer is used to combine different design elements and components into a complete design solution.

7. A computer-aided design method for automatically generating a design that reflects design intent according to claim 6, characterized in that: The feasibility analyzer is used to evaluate the technical feasibility and cost-effectiveness of the design solution; The design iterator is used to iteratively improve the design scheme based on feedback and evaluation results; the multi-scheme generator is used to generate design schemes for users to choose from to explore different design possibilities. The crossover operation formula in the genetic algorithm in step 4 is as follows: Single-point crossover: C = (A1, ..., Ap, Bp+1, ..., BN), where A and B are parent individuals, C is the offspring individual, and p is the crossover point; Two-point intersection: C = (A1, ..., Ap, Bp+1, ..., Bq, Aq+1, ..., AN), where p and q are intersection points; The speed update formula in the particle swarm optimization algorithm is: v_i(t+1)=w*v_i(t)+c1*r1*(pbest_i-x_i(t))+c2*r2*(gbest-x_i(t))where vi_i(t) is the velocity of particle i at time t, x_i(t) is the position of particle i at time t, pbest_i is the individual optimal position of particle i, gbest is the global optimal position, w is the inertia weight, c1 and c2 are learning factors, and r1 and r2 are random numbers in the interval [0,1].

8. The computer-aided design method for automatically generating a design that reflects design intent according to claim 7, characterized in that: The user interaction component is used to provide an interface for users to interact with the generated design solutions, allowing users to provide feedback and guidance; The design document generator is used to automatically generate relevant documents of the design solution, including design instructions, drawings and material lists; the design verifier is used to verify whether the design solution meets all design rules and user requirements.

9. The computer-aided design method for automatically generating a design that reflects design intent according to claim 8, characterized in that: The design knowledge base includes design rules, design cases and design templates. In the design solution generation step, a multi-objective optimization algorithm is adopted to simultaneously consider design goals, design constraints and design preferences.

10. The computer-aided design method for automatically generating a design that reflects design intent according to claim 9, characterized in that: In the design scheme evaluation step, fuzzy comprehensive evaluation method and hierarchical analysis method are adopted, and in the design scheme optimization step, simulated annealing algorithm and ant colony optimization algorithm are adopted.

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