Interior design method and interior design system

Abstract

The invention relates to the technical field of interior design, and discloses an interior design method and an interior design system.The interior design method comprises the following steps: S1, multi-source data acquisition: acquiring spatial size, layout, illumination and user preference; s2, data cleaning: preprocessing data, removing redundancy and unifying formats; s3, intelligent analysis: generating a design scheme by machine learning and optimizing the layout; s4, personalized generation: automatically generating design and alternative schemes; s5, interactive optimization: user adjustment and system real-time optimization and update; s6, performing three-dimensional preview: performing virtual display, and supporting material and illumination adjustment; and S7, performing budget calculation: automatically calculating materials and cost, and generating a report. By intelligently analyzing the user preferences, the spatial features and the market trend, the design scheme meeting the user requirements is automatically generated, the design generation speed is increased, the workload of designers is greatly reduced, and the design efficiency and precision are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of interior design, in particular to an interior design method and an interior design system. Background Art

[0002] As an important part of architectural decoration, interior design is gaining more and more attention with the improvement of modern quality of life and people's increasing demand for personalized space. As an indispensable part of architectural decoration, interior design not only directly affects the beauty and functionality of the space, but also affects people's daily life experience and living comfort. With the development of social economy and the improvement of modern living standards, people's requirements for living environment are no longer limited to basic functional requirements, but pay more attention to the personalization and aesthetic expression of space. Whether it is residential, office or commercial space, design style, space layout, color matching and other factors have become important factors affecting the overall atmosphere and user experience; in recent years, the demand for personalized space design has been growing, and people hope to show their personal taste, lifestyle and pursuit of a comfortable environment through a unique design language. As this trend continues to deepen, interior design is no longer just an expression of aesthetics, but also a multi-dimensional art that integrates functionality, culture and emotional appeal. In this context, the complexity of interior design is also increasing. Designers need to consider more factors, such as how to maximize functionality in a limited space, how to incorporate environmental protection concepts into the design, and how to balance the fashion sense of the design with the actual needs of the residents. In general, with the improvement of people's living standards and the intensification of personalized needs, interior design has gradually transformed from a simple decorative function to an important service industry that can comprehensively improve the quality of life.

[0003] However, traditional interior design methods usually rely on the designer's experience and hand-drawn design plans, which is not only time-consuming and labor-intensive, but also difficult to quickly respond to users' personalized needs and changing market trends. In addition, when communicating with users, traditional design methods often display design effects through two-dimensional floor plans, making it difficult for users to intuitively understand the actual layout and material matching of the space, which may lead to design plans that do not meet user expectations, thereby increasing the difficulty and cost of later modifications.

[0004] In the existing technology, some interior design software simplifies the design process through computer-aided design technology, but these software usually only provide basic drawing functions, lack the ability to generate intelligent design solutions and automatically optimize, and cannot automatically adjust the design solutions according to the user's real-time feedback and personalized needs. At the same time, the existing system has limited performance in the visualization of design solutions, usually only providing static three-dimensional previews, unable to display the effects of design adjustments in real time, and users lack intuitive feedback when making design decisions.

[0005] In addition, budget control is a very important part of interior design, but in the traditional design process, budget calculation relies on the designer's experience and estimation, which often cannot accurately reflect the cost of materials, furniture and labor required for the project. This inaccurate budget management can easily lead to cost overruns in the later stages of the project, bringing unnecessary financial burdens to users and affecting the smooth progress of the project. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an interior design method and an interior design system, which solve the problems of low design efficiency, insufficient personalized response, unintuitive design feedback and inaccurate budget in the traditional interior design process.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an interior design method, comprising the following steps:

[0008] S1. Multi-source data collection: Obtain basic data related to indoor space through CAD drawings, 3D scanning equipment, user input and social media platforms, including space dimensions, room layout, wall height, lighting conditions and user preference information;

[0009] S2. Data cleaning and integration: pre-process the collected multi-source data, remove redundant information, correct erroneous data, and integrate the collected data into an interior design data set through data standardization and format unification;

[0010] S3, Intelligent Data Analysis: Use pre-trained machine learning algorithms to analyze the integrated data, identify potential design solutions and optimize space layout based on space characteristics, user preferences and design trends;

[0011] S4, Personalized design generation: Automatically generate design solutions based on intelligent analysis results, and provide multiple design alternatives based on user preferences;

[0012] S5. Interactive optimization of design solutions: Allow designers or users to interactively adjust the generated preliminary design solutions. The system will update the design parameters in real time based on the user's modification feedback and generate an optimized design solution.

[0013] S6, 3D visualization preview: The built-in 3D rendering engine generates a 3D virtual display of the design plan. Users can view the design plan from different angles and perform operations such as material replacement, lighting simulation, and space detail magnification.

[0014] S7. Budget calculation: Based on the selection of materials, furniture and decorations in the design plan, the required material quantity, furniture cost and construction budget are automatically calculated, and a detailed budget report and product material list are generated.

[0015] Preferably, in step S3, the interior design scheme is evaluated and selected by the following optimization algorithm:

[0016]

[0017] Among them, C optimal represents the optimal design solution, f space (C) is the spatial adaptability function of the design solution, f aesthetic (C) is the aesthetic value function f budget (C) is the budget matching function, w 1 、w 2 、w 3 is the weight of each indicator.

[0018] Preferably, in step S6, the three-dimensional rendering improves the realism of the three-dimensional rendering effect through ray tracing technology, and supports users to adjust the furniture layout, color and material selection in real time during the preview process.

[0019] Preferably, in step S7, the total budget is calculated using the following formula:

[0020]

[0021] Among them, B is the total budget, A is i is the area or quantity of the i-th material or furniture, P i is the unit price of the ith material or furniture, and n is the total number of materials or furniture involved in the design.

[0022] Preferably, the step S4 further comprises the following steps:

[0023] a. Based on the spatial functional requirements and preference data provided by the user, the collected design elements are feature analyzed to generate a preliminary set of design parameters, which includes furniture layout, color scheme, material selection and lighting configuration;

[0024] b. Verify and screen the initially generated design parameter set through the pre-trained design rule library to ensure that the generated design scheme meets the spatial functionality, aesthetic value and user needs;

[0025] c. Generate multiple personalized design alternatives based on the design parameters screened out by the design rule library, wherein the design alternatives include different furniture layouts, color combinations, materials and lighting solutions;

[0026] d. Evaluate the generated design alternatives based on criteria including space utilization, visual aesthetics, functionality and budget matching, and generate the final recommended design solution;

[0027] e. The recommended design plan is presented to the user, allowing the user to further modify the various design parameters in the design plan, including furniture arrangement, material selection and lighting effects. The system updates the design parameters in real time based on the user's modification feedback and generates an optimized design plan.

[0028] Interior design system, including:

[0029] Multi-source data collection module, used to collect basic data of indoor space and user preference information through CAD drawings, 3D scanning equipment, user questionnaires and social media platforms;

[0030] Data cleaning and integration module, used to clean, denoise and standardize the collected data to build a unified interior design data set;

[0031] Intelligent data analysis module, which uses machine learning algorithms to analyze the integrated data, generate design solutions based on spatial features and user preferences, and optimize the solutions;

[0032] Personalized design generation module, which is used to automatically generate personalized design suggestions including color matching, material selection, furniture layout and lighting design based on the analysis results;

[0033] Design scheme optimization module supports users to adjust the generated preliminary design scheme. The system adjusts the design parameters and optimizes the design scheme in real time based on user feedback;

[0034] 3D visualization module, with built-in 3D rendering engine, is used to generate 3D display of design solutions, allowing users to view design effects from different angles and perform interactive operations, including material replacement and light simulation, and dynamically modify room size, dimensions and angles according to the designed room type;

[0035] The budget calculation module is used to automatically calculate the floor and wall areas and required materials according to the current design plan, and generate a budget report and product bill of materials.

[0036] Preferably, the multi-source data acquisition module updates the design trend information according to the preference data published by users on the social media platform, and applies it to the design solution generation process.

[0037] Preferably, the intelligent data analysis module uses a decision tree machine learning algorithm to analyze the spatial layout, design elements and user preferences, and the analysis includes the following steps:

[0038] a. Classify space characteristics and design requirements based on the collected space data and user preference information;

[0039] b. Build a decision tree model and divide the space layout, color matching, material selection and lighting design layer by layer by using design elements as nodes;

[0040] c. Evaluate different design solutions based on the analysis results of the decision tree model and select the best solution based on user needs;

[0041] d. Generate personalized design suggestions and provide optimization recommendations.

[0042] Preferably, the design scheme optimization module evaluates and selects the optimized design scheme by the following formula:

[0043]

[0044] Among them, L optimal is the optimal design solution, g i (L) represents each objective function in the design scheme, w i is the weight of each objective function and n is the number of parameters in the design.

[0045] Preferably, the three-dimensional visualization module generates a highly realistic three-dimensional rendering effect through real-time ray tracing technology, and the generation and interaction of the three-dimensional rendering effect includes the following steps:

[0046] a. Obtain the three-dimensional spatial data of the design plan, including the size of the room, the layout of the furniture, and the material information of the walls and floors;

[0047] b. Use ray tracing technology to simulate the reflection, refraction and scattering effects of natural and artificial light sources in the room to generate real-time 3D rendering images;

[0048] c. Provide a user interaction interface through which users can operate the 3D scene, including:

[0049] S11. Adjust the placement of furniture, change the spatial position of furniture by dragging, and update its position in real time in the 3D preview;

[0050] S22. Change the material. Change the material of the wall, floor and furniture by selecting the menu, and immediately present the changed material effect in the 3D rendering;

[0051] S33. Modify the lighting settings. Users can adjust the type, brightness and position of the light source. The system will update the indoor lighting effects in real time based on the ray tracing algorithm and display the changed light distribution and shadow effects.

[0052] d. The user-adjusted design plan is dynamically generated in the 3D rendering image. The user can view the adjustment results immediately and fine-tune the design parameters again through the interactive interface until the design plan meets the needs.

[0053] The present invention provides an interior design method and an interior design system, which have the following beneficial effects:

[0054] 1. The present invention integrates multi-source data collection, machine learning algorithms and personalized design generation modules to intelligently analyze user preferences, spatial features and market trends, thereby automatically generating design solutions that meet user needs. Compared with traditional manual design methods, the system is optimized through intelligent algorithms such as decision trees, which not only speeds up the design generation speed, but also can adjust and iterate the design according to real-time user feedback to ensure that each adjusted solution is more in line with the user's personalized needs, thereby greatly reducing the designer's workload and improving design efficiency and accuracy. It is particularly suitable for large-scale and diversified design scenarios.

[0055] 2. The present invention uses real-time ray tracing technology to simulate the dynamic changes of light in the design space and generate highly realistic three-dimensional rendering effects. Users can adjust furniture layout, materials and lighting settings in real time in the three-dimensional interface. The system instantly updates and displays the adjusted design effects, thereby effectively improving user decision-making efficiency and satisfaction, while reducing the trial and error costs in the design and ensuring that the design solution is closely matched with actual needs.

[0056] 3. The present invention uses a budget calculation module to automatically calculate the usage of floors, walls, furniture and materials in the design plan, and combines real-time market prices to generate accurate budget reports and product material lists. In addition, it can dynamically update the budget while adjusting the design to ensure the economy of the design plan, thereby improving cost transparency and accuracy, helping users to better carry out financial planning and project management, and avoiding cost overruns or adjustment risks due to inaccurate budgets in the later stages of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flow chart of the method steps of the present invention;

[0058] Figure 2 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0060] Embodiment 1:

[0061] Please refer to the attached Figure 1 , an embodiment of the present invention provides an indoor design method, comprising the following steps:

[0062] S1. Multi-source data collection: Obtain basic data related to indoor space through CAD drawings, 3D scanning equipment, user input and social media platforms, including space dimensions, room layout, wall height, lighting conditions and user preference information;

[0063] S2. Data cleaning and integration: pre-process the collected multi-source data, remove redundant information, correct erroneous data, and integrate the collected data into an interior design data set through data standardization and format unification;

[0064] S3, Intelligent Data Analysis: Use pre-trained machine learning algorithms to analyze the integrated data, identify potential design solutions and optimize space layout based on space characteristics, user preferences and design trends;

[0065] S4, Personalized design generation: Automatically generate design solutions based on intelligent analysis results, and provide multiple design alternatives based on user preferences;

[0066] S5. Interactive optimization of design solutions: Allow designers or users to interactively adjust the generated preliminary design solutions. The system will update the design parameters in real time based on the user's modification feedback and generate an optimized design solution.

[0067] S6, 3D visualization preview: The built-in 3D rendering engine generates a 3D virtual display of the design plan. Users can view the design plan from different angles and perform operations such as material replacement, lighting simulation, and space detail magnification.

[0068] S7. Budget calculation: According to the selection of materials, furniture and decorations in the design plan, the required material quantity, furniture cost and construction budget are automatically calculated, and a detailed budget report and product material list are generated;

[0069] In step S3, the interior design scheme is evaluated and selected through the following optimization algorithm:

[0070]

[0071] Among them, C optimal represents the optimal design solution, f space (C) is the spatial adaptability function of the design solution, f aesthetic (C) is the aesthetic value function f budget (C) is the budget matching function, w 1 、w 2 、w 3 is the weight of each indicator;

[0072] In step S6, 3D rendering uses ray tracing technology to improve the realism of 3D rendering effects, allowing users to adjust furniture layout, color and material selection in real time during the preview process;

[0073] In step S7, the total budget is calculated using the following formula:

[0074]

[0075] Among them, B is the total budget, A is i is the area or quantity of the i-th material or furniture, P i is the unit price of the i-th material or furniture, and n is the total number of materials or furniture involved in the design plan;

[0076] In step S4, the following steps are further included:

[0077] a. Based on the spatial functional requirements and preference data provided by the user, the collected design elements are analyzed for characteristics to generate a preliminary set of design parameters, including furniture layout, color scheme, material selection and lighting configuration;

[0078] b. Verify and screen the initially generated design parameter set through the pre-trained design rule library to ensure that the generated design scheme meets the spatial functionality, aesthetic value and user needs;

[0079] c. Generate multiple personalized design alternatives based on the design parameters selected by the design rule library, including different furniture layouts, color combinations, materials and lighting solutions;

[0080] d. Evaluate the generated design alternatives based on criteria including space utilization, visual aesthetics, functionality and budget matching, and generate the final recommended design solution;

[0081] e. The recommended design plan is presented to the user, allowing the user to further modify the various design parameters in the design plan, including furniture arrangement, material selection and lighting effects. The system updates the design parameters in real time based on the user's modification feedback and generates an optimized design plan.

[0082] Specifically, in this embodiment, an intelligent data processing method for interior design is provided, which can effectively improve the efficiency and accuracy of interior design through steps such as multi-source data collection, intelligent data analysis, personalized design generation, three-dimensional visual preview, and budget calculation. First, the system obtains the basic data of the indoor space through CAD drawings, three-dimensional scanning equipment, user input, and social media platforms, including space dimensions, room layout, wall height, lighting conditions, and user preference information. Next, data cleaning and integration are performed to remove redundant information, correct erroneous data, and ensure data consistency and accuracy; in this embodiment, the intelligent data analysis step uses a pre-trained machine learning algorithm, such as a neural network or a decision tree model, according to the following formula Among them, C optimal represents the optimal design solution, f space (C) is the spatial adaptability function of the design solution, f aesthetic (C) is the aesthetic value function f budget (C) is the budget matching function, w 1 、w 2 、w 3 is the weight of each indicator, so as to optimize the design plan through this formula to ensure that the generated design strikes a balance between spatial layout, aesthetic effect and budget; in the personalized design generation step, the system automatically generates multiple design alternatives based on the analysis results, and the content of the plan covers furniture layout, color matching, material selection and lighting design. Users can make real-time adjustments to the design plan through the interactive interface, and the system dynamically optimizes the design parameters based on user feedback and generates updated design plans; in this embodiment, the three-dimensional visualization preview step generates a highly realistic three-dimensional rendering effect through ray tracing technology. Users can view the design plan from different angles and perform material replacement, lighting simulation and detail magnification operations. In the budget calculation step, the system calculates the budget based on the materials and furniture quantity selected in the design plan using the following formula:

[0083]

[0084] Among them, B is the total budget, A is i is the area or quantity of the i-th material or furniture, P i is the unit price of the i-th material or furniture, and n is the total number of materials or furniture involved in the design plan. The system generates a budget report and a product bill of materials to ensure that users can understand the project cost in a timely manner.

[0085] Embodiment 2:

[0086] Please refer to the attached Figure 2 , interior design system, including:

[0087] Multi-source data collection module, used to collect basic data of indoor space and user preference information through CAD drawings, 3D scanning equipment, user questionnaires and social media platforms;

[0088] Data cleaning and integration module, used to clean, denoise and standardize the collected data to build a unified interior design data set;

[0089] Intelligent data analysis module, which uses machine learning algorithms to analyze the integrated data, generate design solutions based on spatial features and user preferences, and optimize the solutions;

[0090] Personalized design generation module, which is used to automatically generate personalized design suggestions including color matching, material selection, furniture layout and lighting design based on the analysis results;

[0091] Design scheme optimization module supports users to adjust the generated preliminary design scheme. The system adjusts the design parameters and optimizes the design scheme in real time based on user feedback;

[0092] 3D visualization module, with built-in 3D rendering engine, is used to generate 3D display of design solutions, allowing users to view design effects from different angles and perform interactive operations, including material replacement and light simulation, and dynamically modify room size, dimensions and angles according to the designed room type;

[0093] Budget calculation module, used to automatically calculate the floor and wall area and required materials according to the current design plan, and generate a budget report and product material list;

[0094] The multi-source data acquisition module updates design trend information based on the preference data posted by users on social media platforms and applies it to the design solution generation process;

[0095] The intelligent data analysis module uses a decision tree machine learning algorithm to analyze the space layout, design elements, and user preferences. The analysis includes the following steps:

[0096] a. Classify space characteristics and design requirements based on the collected space data and user preference information;

[0097] b. Build a decision tree model and divide the space layout, color matching, material selection and lighting design layer by layer by using design elements as nodes;

[0098] c. Evaluate different design solutions based on the analysis results of the decision tree model and select the best solution based on user needs;

[0099] d. Generate personalized design suggestions and provide optimization suggestions;

[0100] The design scheme optimization module evaluates and selects the optimal design scheme through the following formula:

[0101]

[0102] Among them, L optimal is the optimal design solution, g i (L) represents each objective function in the design scheme, w i is the weight of each objective function, n is the number of parameters in the design;

[0103] The 3D visualization module generates highly realistic 3D rendering effects through real-time ray tracing technology. The generation and interaction of 3D rendering effects include the following steps:

[0104] a. Obtain the three-dimensional spatial data of the design plan, including the size of the room, the layout of the furniture, and the material information of the walls and floors;

[0105] b. Use ray tracing technology to simulate the reflection, refraction and scattering effects of natural and artificial light sources in the room to generate real-time 3D rendering images;

[0106] c. Provide a user interaction interface through which users can operate the 3D scene, including:

[0107] S11. Adjust the placement of furniture, change the spatial position of furniture by dragging, and update its position in real time in the 3D preview;

[0108] S22. Change the material. Change the material of the wall, floor and furniture by selecting the menu, and immediately present the changed material effect in the 3D rendering;

[0109] S33. Modify the lighting settings. Users can adjust the type, brightness and position of the light source. The system will update the indoor lighting effects in real time based on the ray tracing algorithm and display the changed light distribution and shadow effects.

[0110] d. The user-adjusted design plan is dynamically generated in the 3D rendering image. The user can view the adjustment results immediately and fine-tune the design parameters again through the interactive interface until the design plan meets the needs.

[0111] Specifically, in this embodiment, an intelligent data processing system for interior design is provided, which is composed of a multi-source data acquisition module, a data cleaning and integration module, an intelligent data analysis module, a personalized design generation module, a design scheme optimization module, a three-dimensional visualization module and a budget calculation module. Through the collaboration of these modules, the system can provide users with efficient interior design services; in this embodiment, the multi-source data acquisition module includes a data input unit, a user preference acquisition unit and a market trend acquisition unit, which are respectively used to collect basic data of indoor space, personalized preference information of users and market design trend data from CAD drawings, three-dimensional scanning equipment and social media platforms. Through the data cleaning unit and data standardization unit of the data cleaning and integration module, the system cleans and integrates the collected data to ensure the accuracy and consistency of the data; the intelligent data analysis module includes a feature extraction unit and an algorithm application unit, the feature extraction unit extracts key features from indoor space data and user preference information, and the algorithm application unit uses a machine learning algorithm (such as a decision tree model) to analyze the data. The specific process of the decision tree algorithm includes: building a decision tree model based on spatial features and design requirements, and analyzing the design elements by dividing them layer by layer. Finally, the system outputs personalized design suggestions and generates multiple design alternatives through the design parameter generation unit; in this embodiment, the design scheme optimization module includes a user interaction unit and a design feedback optimization unit. The user interaction unit allows the user to modify the preliminary design scheme through the interactive interface. The design feedback optimization unit automatically optimizes the design scheme according to the user's adjustment and ensures the functionality and aesthetic value of the design result; the three-dimensional visualization module generates highly realistic three-dimensional rendering effects through real-time ray tracing technology. The specific steps include: 1. Acquire the three-dimensional spatial data of the design scheme and construct a three-dimensional scene; 2. Use the ray tracing algorithm to simulate the reflection, refraction and scattering effects of light in the scene, and render the three-dimensional scene in real time; 3. Provide an interactive interface, and the user can perform design operations by adjusting the furniture position, changing the material, modifying the lighting, etc., and instantly view the updated three-dimensional effect; the budget calculation module in this embodiment is based on the selection of materials, furniture and decorations in the current design scheme through the formula: Calculate total budget, generate budget reports and product bills of materials to ensure users can control the cost of their designs.

[0112] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An interior design method, characterized in that The following steps are involved: S1. Multi-source data collection: Obtain basic data related to indoor space through CAD drawings, 3D scanning equipment, user input and social media platforms, including space dimensions, room layout, wall height, lighting conditions and user preference information; S2. Data cleaning and integration: pre-process the collected multi-source data, remove redundant information, correct erroneous data, and integrate the collected data into an interior design data set through data standardization and format unification; S3, Intelligent Data Analysis: Use pre-trained machine learning algorithms to analyze the integrated data, identify potential design solutions and optimize space layout based on space characteristics, user preferences and design trends; S4, Personalized design generation: Automatically generate design solutions based on intelligent analysis results, and provide multiple design alternatives based on user preferences; S5. Interactive optimization of design solutions: Allow designers or users to interactively adjust the generated preliminary design solutions. The system will update the design parameters in real time based on the user's modification feedback and generate an optimized design solution. S6, 3D visualization preview: The built-in 3D rendering engine generates a 3D virtual display of the design plan. Users can view the design plan from different angles and perform operations such as material replacement, lighting simulation, and space detail magnification. S7. Budget calculation: Based on the selection of materials, furniture and decorations in the design plan, the required material quantity, furniture cost and construction budget are automatically calculated, and a detailed budget report and product material list are generated.

2. The interior design method according to claim 1, characterized in that: In step S3, the interior design scheme is evaluated and selected through the following optimization algorithm: Among them, C optimal represents the optimal design solution, f space (C) is the spatial adaptability function of the design solution, f aesthetic (C) is the aesthetic value function f budget (C) is the budget matching function, w1, w2, and w3 are the weights of each indicator.

3. The interior design method according to claim 1, characterized in that: In the step S6, the three-dimensional rendering improves the realism of the three-dimensional rendering effect through ray tracing technology, and supports users to adjust the furniture layout, color and material selection in real time during the preview process.

4. The interior design method according to claim 1, characterized in that: In step S7, the total budget is calculated using the following formula: Among them, B is the total budget, A is i is the area or quantity of the i-th material or furniture, P i is the unit price of the ith material or furniture, and n is the total number of materials or furniture involved in the design.

5. The interior design method according to claim 1, characterized in that: The step S4 further comprises the following steps: a. Based on the spatial functional requirements and preference data provided by the user, the collected design elements are feature analyzed to generate a preliminary set of design parameters, which includes furniture layout, color scheme, material selection and lighting configuration; b. Verify and screen the initially generated design parameter set through the pre-trained design rule library to ensure that the generated design scheme meets the spatial functionality, aesthetic value and user needs; c. Generate multiple personalized design alternatives based on the design parameters screened out by the design rule library, wherein the design alternatives include different furniture layouts, color combinations, materials and lighting solutions; d. Evaluate the generated design alternatives based on criteria including space utilization, visual aesthetics, functionality and budget matching, and generate the final recommended design solution; e. The recommended design plan is presented to the user, allowing the user to further modify the various design parameters in the design plan, including furniture arrangement, material selection and lighting effects. The system updates the design parameters in real time based on the user's modification feedback and generates an optimized design plan.

6. An interior design system according to the interior design method according to any one of claims 1 to 5, characterized in that: include: Multi-source data collection module, used to collect basic data of indoor space and user preference information through CAD drawings, 3D scanning equipment, user questionnaires and social media platforms; Data cleaning and integration module, used to clean, denoise and standardize the collected data to build a unified interior design data set; Intelligent data analysis module, which uses machine learning algorithms to analyze the integrated data, generate design solutions based on spatial features and user preferences, and optimize the solutions; Personalized design generation module, which is used to automatically generate personalized design suggestions including color matching, material selection, furniture layout and lighting design based on the analysis results; Design scheme optimization module supports users to adjust the generated preliminary design scheme. The system adjusts the design parameters and optimizes the design scheme in real time based on user feedback; 3D visualization module, with built-in 3D rendering engine, is used to generate 3D display of design solutions, allowing users to view design effects from different angles and perform interactive operations, including material replacement and light simulation, and dynamically modify room size, dimensions and angles according to the designed room type; The budget calculation module is used to automatically calculate the floor and wall areas and required materials according to the current design plan, and generate a budget report and product bill of materials.

7. The interior design system according to claim 6, characterized in that: The multi-source data acquisition module updates the design trend information according to the preference data published by users on the social media platform, and applies it to the design solution generation process.

8. The interior design system according to claim 6, characterized in that: The intelligent data analysis module uses a decision tree machine learning algorithm to analyze the space layout, design elements and user preferences, and the analysis includes the following steps: a. Classify space characteristics and design requirements based on the collected space data and user preference information; b. Build a decision tree model and divide the space layout, color matching, material selection and lighting design layer by layer by using design elements as nodes; c. Evaluate different design solutions based on the analysis results of the decision tree model and select the best solution based on user needs; d. Generate personalized design suggestions and provide optimization recommendations.

9. The interior design system according to claim 6, characterized in that: The design scheme optimization module evaluates and selects the optimized design scheme through the following formula: Among them, L optimal is the optimal design solution, g i (L) represents each objective function in the design scheme, w i is the weight of each objective function and n is the number of parameters in the design.

10. The interior design system according to claim 6, characterized in that: The three-dimensional visualization module generates a highly realistic three-dimensional rendering effect through real-time ray tracing technology. The generation and interaction of the three-dimensional rendering effect includes the following steps: a. Obtain the three-dimensional spatial data of the design plan, including the size of the room, the layout of the furniture, and the material information of the walls and floors; b. Use ray tracing technology to simulate the reflection, refraction and scattering effects of natural and artificial light sources in the room to generate real-time 3D rendering images; c. Provide a user interaction interface through which users can operate the 3D scene, including: S11. Adjust the placement of furniture, change the spatial position of furniture by dragging, and update its position in real time in the 3D preview; S22. Change the material. Change the material of the wall, floor and furniture by selecting the menu, and immediately present the changed material effect in the 3D rendering; S33. Modify the lighting settings. Users can adjust the type, brightness and position of the light source. The system will update the indoor lighting effects in real time based on the ray tracing algorithm and display the changed light distribution and shadow effects. d. The user-adjusted design plan is dynamically generated in the 3D rendering image. The user can view the adjustment results immediately and fine-tune the design parameters again through the interactive interface until the design plan meets the needs.

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