Personalized interior design method based on user floor plan

Abstract

The present invention relates to a personalized interior design method based on a user's floor plan, comprising the following steps: analyzing the behavioral data of a target user to obtain the target user's preference parameters; performing vector conversion on the floor plan to obtain vector space relationship feature data; inputting the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network for space layout to generate an initial furniture layout plan; screening the initial furniture layout plan to obtain a screened furniture layout plan; matching and designing the matching parameters with the screened furniture layout plan to obtain a matching design plan, and initially visualizing the matching design plan in a preset interactive interface to generate a three-dimensional visualization rendering. This method solves the technical problem that traditional interior design services are often costly and time-consuming, making them unaffordable for ordinary consumers, leading to a large imbalance between supply and demand in the market.

Description

Technical Field

[0001] The present invention relates to the technical field of interior design, and in particular to a personalized interior design method based on a user's floor plan. Background Art

[0002] With the acceleration of urbanization, people's demands for living environments are increasing, going beyond basic living needs to include personalization and comfort. However, traditional interior design services are often costly and time-consuming, making them unaffordable for the average consumer. This has led to a significant imbalance between supply and demand in the market. On the one hand, consumers have a strong demand for high-quality, personalized interior design; on the other hand, existing design service models are unable to effectively meet these demands, especially when it comes to quickly responding to users' personalized preferences.

[0003] Furthermore, in the traditional interior design process, designers often need to spend considerable time and effort communicating with clients to understand their specific needs and preferences. This process is not only inefficient but also prone to information miscommunication, which in turn affects the final design results. The development of information technology, particularly the application of big data and artificial intelligence, has opened up new possibilities for addressing these issues. By analyzing user behavior data to predict preferences, design accuracy and service efficiency can be significantly improved, making personalized interior design more convenient and accessible.

[0004] While leveraging advanced technologies for personalized interior design offers numerous advantages, practical applications still face challenges. For example, ensuring that recommendation algorithms accurately capture users' true needs, rather than merely superficial preferences, balancing design creativity with user expectations, and protecting user privacy pose challenges. Failure to effectively address these issues will directly impact the user experience and market acceptance of personalized interior design systems. Therefore, exploring design approaches that both meet users' personalized needs and safeguard their privacy has become a key research direction. Summary of the Invention

[0005] The main purpose of the present invention is to provide a personalized interior design method based on the user's floor plan, which solves the technical problem that traditional interior design services are often costly and time-consuming, which are unaffordable for ordinary consumers and lead to a large imbalance between supply and demand in the market.

[0006] To achieve the above object, the present invention provides a personalized interior design method based on a user's floor plan, comprising the following steps:

[0007] Analyze the target user's behavior data through a preset recommendation algorithm to obtain the target user's preference parameters; wherein the preference parameters include spatial function arrangement parameters and matching parameters;

[0008] Obtaining a house plan uploaded by a target user, and performing vector conversion on the house plan using a plan image vectorization technique to obtain vector spatial relationship feature data;

[0009] Inputting the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network to perform space layout and generate an initial furniture layout plan;

[0010] Screening the initial furniture layout scheme using preset hard design rules to obtain a screened furniture layout scheme;

[0011] Performing matching design on the matching parameters and the screened furniture layout plan to obtain a matching design plan, and initially visualizing the matching design plan on a preset interactive interface;

[0012] It is detected in real time whether the target user performs interface interaction operations on the interactive interface. If so, the matching design scheme targeted by the interface interaction operation is used as the target scheme, and the target scheme is edited and rendered according to the interface interaction operation through a preset AI editing system to obtain a three-dimensional visualization effect diagram corresponding to the house floor plan.

[0013] Furthermore, the behavior data includes furniture browsing history and input text data. The target user's behavior data is analyzed by a preset recommendation algorithm to obtain the target user's preference parameters, including:

[0014] By using a preset recommendation algorithm, the target user's furniture product browsing history and input text data are analyzed to obtain user-product interaction information; wherein the user-product interaction information is the target user's browsing time, number of clicks, and collection status of different furniture products;

[0015] Performing cluster analysis on the user-product interaction information to obtain a target user's preferred furniture style feature vector;

[0016] Analyzing the co-occurrence relationship of furniture products in the user-product interaction information to obtain a furniture matching pattern vector;

[0017] Analyzing the furniture product description text in the user-product interaction information, and extracting spatial function keywords in the furniture product description text to obtain a spatial function keyword vector;

[0018] Constructing a target user preference matrix based on the preferred furniture style feature vector, the furniture matching pattern vector, and the space function keyword vector;

[0019] Inputting the target user preference matrix into a preset Bayesian inference algorithm to obtain the target user's preference probability distribution;

[0020] The furniture selection behavior of the target user is predicted based on the preference probability distribution to obtain the preference parameters of the target user; wherein the preference parameters of the target user include space function arrangement parameters and matching parameters.

[0021] Furthermore, the floor plan image vectorization technology is used to convert the floor plan into a vector to obtain vector space relationship feature data, including:

[0022] Identifying and marking each functional area in the house plan to obtain functional area marking data;

[0023] Preprocessing the house plan using a plan image vectorization technology to obtain a preprocessed image;

[0024] Extracting contour lines from the preprocessed image to obtain a contour line image, and classifying and attribute-labeling the contour lines in the contour line image based on the functional area annotation data to obtain a contour line image with functional area information;

[0025] Converting the outline line image with the functional area information into a vector graphic to obtain vector graphic data;

[0026] Perform spatial relationship analysis on the vector graphics data to obtain vector spatial relationship feature data.

[0027] Furthermore, the vector space relationship feature data and the space function arrangement parameters are input into a preset graph neural network to perform space layout optimization and generate an initial furniture layout plan, including:

[0028] Performing spatial feature extraction on the vector spatial relationship feature data to obtain extracted spatial features; wherein the extracted spatial features include area, shape, window positions, and door opening direction information of the house plan;

[0029] Classify and process the spatial functional arrangement parameters to obtain functional area division information; wherein the functional area division information includes area requirements and relative position requirements of different functional areas;

[0030] Constructing a spatial layout relationship diagram based on the spatial feature vector and functional area division information;

[0031] Perform feature propagation and fusion on the spatial layout relationship graph based on a multi-scale graph neural network to obtain a fused feature graph;

[0032] Inputting the fused feature graph into a preset graph neural network to perform furniture layout exploration and space layout optimization to obtain candidate furniture layout solutions;

[0033] The candidate furniture layout plans are evaluated and screened for rationality to obtain an initial furniture layout plan.

[0034] Furthermore, the initial furniture layout scheme is screened using preset hard design rules to obtain a screened furniture layout scheme, including:

[0035] Performing spatial relationship analysis on the initial furniture layout plan using a spatial topology analysis algorithm to obtain a spatial topology relationship diagram between the furniture; wherein the spatial topology relationship diagram includes relative positions, distances, and connectivity between the furniture;

[0036] Based on ergonomic principles and the spatial topological relationship diagram, an ergonomic evaluation is performed on the initial furniture layout plan to obtain an ergonomic scoring matrix;

[0037] The initial furniture layout solution is optimized and calculated based on the ergonomic scoring matrix using a multi-objective optimization algorithm to obtain an ergonomic layout solution set; wherein the ergonomic layout solution set is a set of candidate layout solutions that meet ergonomic requirements;

[0038] Dividing the ergonomic layout solution set into functional areas to obtain a functional area semantic map;

[0039] By using preset hard design rules, the functional area semantic graph is subjected to rule constraint checking to obtain a set of ergonomic layout solutions after constraint checking;

[0040] The ergonomic layout scheme set after the constraint check is comprehensively evaluated and screened by a multi-criteria decision analysis method to obtain a screened furniture layout scheme.

[0041] Furthermore, matching and designing the matching parameters and the screened furniture layout scheme to obtain a matching design scheme includes:

[0042] Performing feature deconstruction on the matching parameters and the screened furniture layout scheme respectively, and obtaining a deconstructed parameter set and a layout element set;

[0043] Performing semantic encoding on the deconstructed parameter set to obtain an encoded semantic vector;

[0044] Performing spatial relationship encoding on the layout element set to obtain a spatial relationship vector;

[0045] Based on the attention mechanism network and matching algorithm, the correlation between the encoded semantic vector and the spatial relationship vector is calculated to obtain the matching weight matrix;

[0046] Reorganize the furniture layout plan after screening based on the matching weight matrix to obtain a layout reorganization plan;

[0047] The layout reorganization scheme is checked and adjusted for style consistency to obtain a matching design scheme, and the matching design scheme is initially visualized on a preset interactive interface.

[0048] Furthermore, the target plan is edited and rendered according to the interface interaction operation through a preset AI editing system to obtain a three-dimensional visualization effect diagram corresponding to the house plan, including:

[0049] Monitor the target user's interface interaction operations on the interactive interface in real time to obtain an operation instruction sequence of the target user on the matching design solution; wherein the operation instruction sequence includes recording the user's clicks, drags, and zooms;

[0050] Using a multi-objective optimization algorithm, adjusting the parameters in the target solution based on the operation instruction sequence to obtain adjusted design parameters and an adjusted design solution corresponding to the adjusted design parameters; wherein the parameters in the target solution include furniture layout parameters, color scheme parameters, and material parameters;

[0051] Performing spatial mapping on the adjusted design scheme using a preset spatial mapping algorithm to obtain a three-dimensional spatial layout model;

[0052] Performing material and lighting rendering on the three-dimensional space layout model to obtain a three-dimensional space layout model with material texture and lighting effects;

[0053] Through virtual reality technology, a three-dimensional visualization effect diagram is generated based on a three-dimensional space layout model with material texture and lighting effects.

[0054] The present invention also provides a personalized interior design system based on a user's floor plan, comprising:

[0055] The recommendation module is used to analyze the target user's behavior data through a preset recommendation algorithm to obtain the target user's preference parameters; wherein the preference parameters include spatial function arrangement parameters and matching parameters;

[0056] An acquisition module is used to acquire a house plan uploaded by a target user and perform vector conversion on the house plan using a plan image vectorization technique to obtain vector space relationship feature data;

[0057] Inputting the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network to perform space layout and generate an initial furniture layout plan;

[0058] A screening module, configured to screen the initial furniture layout scheme using preset hard design rules to obtain a screened furniture layout scheme;

[0059] a matching module, configured to match the matching parameters with the screened furniture layout scheme to obtain a matching design scheme, and to initially visualize the matching design scheme on a preset interactive interface;

[0060] The editing module is used to detect in real time whether the target user performs interface interaction operations on the interactive interface. If so, the matching design scheme targeted by the interface interaction operation is used as the target scheme, and the target scheme is edited and rendered according to the interface interaction operation through a preset AI editing system to obtain a three-dimensional visualization effect diagram corresponding to the house floor plan.

[0061] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0062] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0063] The personalized interior design method based on user floor plans provided by the present invention comprises the following steps: analyzing the behavior data of target users through a preset recommendation algorithm to obtain the preference parameters of the target users; wherein the preference parameters include space function arrangement parameters and matching parameters; obtaining the house floor plan uploaded by the target user, and performing vector conversion on the house floor plan using floor plan image vectorization technology to obtain vector space relationship feature data; inputting the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network for space layout to generate an initial furniture layout plan; screening the initial furniture layout plan through preset design hard rules to obtain a screened furniture layout plan; matching the matching parameters and the screened furniture layout plan to obtain a matching design plan, and The matching design scheme is initially visualized on a preset interactive interface; it is detected in real time whether the target user is performing an interface interaction operation on the interactive interface. If so, the matching design scheme targeted by the interface interaction operation is used as the target scheme, and the target scheme is edited and rendered according to the interface interaction operation through a preset AI editing system to obtain a three-dimensional visualization effect diagram corresponding to the house floor plan. This solves the technical problem that traditional interior design services are often costly and time-consuming, which is unaffordable for ordinary consumers and leads to a large imbalance between supply and demand in the market. It realizes the optimization of spatial layout using advanced floor plan image vectorization technology and graph neural networks, which can not only generate a reasonable furniture layout plan based on the user's actual floor plan, but also further screen out the optimal solution through preset design hard rules. More importantly, it allows users to adjust the design plan directly on the interactive interface and view the adjusted three-dimensional visualization effect diagram in real time, which greatly enhances the beneficial effects of user participation and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 1 is a schematic diagram of the steps of a personalized interior design method based on a user's floor plan in one embodiment of the present invention;

[0065] Figure 2 This is a structural block diagram of a personalized interior design system based on a user's floor plan in one embodiment of the present invention;

[0066] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0067] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0069] like Figure 1 As shown, Figure 1 This is a schematic diagram of the steps of a personalized interior design method based on a user's floor plan in one embodiment of the present invention;

[0070] In one embodiment of the present invention, a personalized interior design method based on a user's floor plan is provided, comprising the following steps:

[0071] Step S1: Analyze the target user's behavior data through a preset recommendation algorithm to obtain the target user's preference parameters; wherein the preference parameters include spatial function arrangement parameters and matching parameters.

[0072] Specifically, this personalized interior design method first uses a pre-set recommendation algorithm to analyze the target user's behavioral data, which serves as a key starting point for the entire design process. This "behavioral data" can include the user's online browsing preferences, past purchase history, and interactions on social media. This data can reflect the user's specific preferences for interior design. For example, if a user frequently browses modern minimalist home decor projects, the system will identify this style as a likely preference. Next, based on this behavioral data, the recommendation algorithm calculates and extracts the target user's preference parameters. These preference parameters are divided into two parts: "spatial layout parameters," which describe the user's functional requirements for different rooms or areas, such as whether they want an open or enclosed kitchen or a workspace in the bedroom; and "matching parameters," which refer to the user's personal preferences for colors, materials, and decorative elements, such as a preference for cool or warm tones, or for wooden or metal furniture. To achieve this, the system may utilize a machine learning model trained on a large, labeled dataset to accurately capture the correlation between user preferences and specific design elements, thereby providing unique design recommendations for each user. For example, in an actual application scenario, when a user uploads their home floor plan and authorizes the system to access their online behavior data, the system can quickly generate an interior design plan that conforms to the user's living habits and reflects their personal aesthetic preferences.

[0073] Step S2: obtaining the house floor plan uploaded by the target user, and performing vector conversion on the house floor plan using floor plan image vectorization technology to obtain vector space relationship feature data.

[0074] Specifically, in personalized interior design methods, obtaining floor plans uploaded by target users is a crucial step, as it directly determines the foundation for subsequent design work. Once a user uploads their floor plan via an app or website, the system immediately processes it. The "floor plan image vectorization technology" mentioned here refers to the process of converting a user-uploaded floor plan, which may be in an image format (such as JPEG or PNG), into a vector graphic. Vector graphics, composed of geometric elements such as points, lines, and surfaces, can more accurately represent spatial structure and dimensional information, are not restricted by resolution, and are therefore easier to edit and calculate. During this conversion process, the system automatically identifies and extracts key elements such as room boundaries and door and window locations, then converts this information into vector data, forming so-called "vector spatial relationship feature data." For example, suppose a user wishes to design a modern living room layout for their new home. They first upload a floor plan of their home to the design platform. Upon receiving this floor plan, the platform immediately initiates vectorization processing. During this process, the system not only identifies the outlines of each room, but also accurately marks the location and size of doors and windows, and can even distinguish between different wall materials, such as load-bearing walls and partition walls. This allows the system to obtain detailed vector spatial relationship feature data, which not only contains the basic structural information of the room but also provides accurate data support for subsequent spatial layout planning and furniture placement. Through this processing, designers or AI systems can more efficiently design spatial layouts, ensuring that the design plan not only meets the user's actual needs but also fully utilizes every inch of space to achieve the best design effect.

[0075] Step S3: Input the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network to perform space layout and generate an initial furniture layout plan.

[0076] Specifically, after the vectorization of the floor plan is complete, the next step is to input the vector spatial relationship feature data and the spatial functional arrangement parameters into a pre-set graph neural network for deeper spatial layout analysis and optimization. This process aims to generate the most appropriate initial furniture layout plan based on the user-provided floor plan information and specific functional requirements using advanced machine learning algorithms. Graph neural networks are deep learning models particularly well-suited for processing graph-structured data. They can effectively capture the complex relationships between nodes. For the house design application in this example, this can be understood as being able to effectively understand and optimize the spatial relationships and functional allocation between various parts of a room. For example, continuing with the user's living room design, the vectorized floor plan has been converted into vector spatial relationship feature data containing information such as all room boundaries, door and window locations, and wall types. The user also clearly stated their requirements for the living room space, such as sufficient relaxation area, TV viewing area, and possible office nook, which constitute the spatial functional arrangement parameters. When this data is fed into a pre-set graph neural network, the network comprehensively considers factors such as the shape, size, and orientation of each room, while also incorporating the user's specific functional requirements. By simulating multiple layout possibilities, it ultimately outputs one or more optimal initial furniture layout solutions. These solutions not only meet the user's functional needs but also maximize space utilization and aesthetics, providing an intuitive and practical design reference. In this way, even non-professional users can easily obtain professional-level interior design solutions, significantly improving the efficiency and satisfaction of the entire design process.

[0077] Step S4: screening the initial furniture layout schemes using preset hard design rules to obtain screened furniture layout schemes.

[0078] Specifically, after generating initial furniture layout plans, to ensure their rationality and practicality, they are further screened using pre-set hard design rules. These "hard design rules" refer to a series of standards based on fundamental interior design principles and ergonomics, used to evaluate the feasibility and comfort of furniture layout plans. These rules may include, but are not limited to, minimum spacing requirements between furniture, barrier-free access areas in front of doors and windows, and safe distances between electrical outlets and switches. The goal is to ensure that the design is not only aesthetically pleasing but also safe and practical. For example, let's assume the graph neural network has generated several different initial furniture layout plans. The system then automatically applies pre-set hard design rules to evaluate each plan. For example, one rule might ensure a minimum distance of 3 meters between the sofa and the TV for a comfortable viewing experience, while another requires that all main aisles be at least 90 cm wide to ensure smooth movement for family members. By applying these rules, the system can eliminate layout plans that do not meet the standards, such as a plan where the distance between the sofa and coffee table is too small, or where the TV cabinet is positioned in a way that blocks access to the balcony. After this round of screening, the system ultimately retains furniture layouts that meet both the user's individual needs and basic design specifications. These selected furniture layouts not only better suit the user's actual lifestyle, but also ensure the safety and convenience of the home environment. This screening process not only improves the quality of design solutions, but also enhances user trust and satisfaction with the final design results.

[0079] Step S5: matching and designing the matching parameters and the screened furniture layout scheme to obtain a matching design scheme, and initially visualizing the matching design scheme on a preset interactive interface.

[0080] Specifically, after completing the furniture layout screening, the next step is to match the matching parameters with the selected furniture layout plans to generate a final matching design. Here, "matching parameters" refer to the user's specific preferences in terms of color, material, and decorative style, while the "selected furniture layout plan" is a layout plan that has been verified to meet functional and safety requirements based on hard design rules. By combining these two, the system can provide users with a design solution that both meets their individual needs and is practical. For example, in a living room design scenario, suppose the user prefers a modern, minimalist style, prefers white and gray as primary colors, and enjoys the natural feel of wooden furniture. After obtaining these matching parameters, the system combines them with the previously screened furniture layout plans to automatically select furniture styles, colors, and materials that match the user's style preferences, such as a white sofa paired with a gray rug, and a wooden coffee table and TV cabinet. The system also considers decorative elements such as paintings and lighting to ensure overall style consistency and coordination. After the matching design is completed, the system provides an initial visual display of the resulting matching design on a pre-set interactive interface. This means users can visualize the design of their living room in a virtual three-dimensional environment, including details like furniture placement, color matching, and material texture, allowing them to gain an intuitive feel. If users have any comments or suggestions on the initial design, they can make immediate adjustments through the interactive interface, such as replacing a sofa with a different style or changing the wall color. The system will update the design in real time based on user feedback until the user is satisfied. This interactive design process not only increases design flexibility and user engagement, but also ensures that the final design fully meets user expectations.

[0081] Step S6: Detect in real time whether the target user performs interface interaction operations on the interactive interface. If so, use the matching design scheme targeted by the interface interaction operation as the target scheme, and use a preset AI editing system to edit and render the target scheme according to the interface interaction operation to obtain a three-dimensional visualization effect diagram corresponding to the house floor plan.

[0082] Specifically, during the personalized interior design process, the system detects in real time whether the target user has performed any interactive actions on the pre-set interface. This step is a key part of user participation in the design process, allowing users to directly participate in adjusting and refining the design plan. Once a user interaction is detected, the system immediately selects the matching design plan as the target plan. Using the pre-set AI editing system, the target plan is edited and rendered accordingly based on the user's actions, ultimately generating an updated 3D visualization. For example, in the aforementioned living room design example, the user first viewed the system-generated living room design plan through the interactive interface, including furniture placement, color matching, and decorative elements. Suppose the user finds the sofa's color a bit dull and wishes to liven up the space by adding some color. The user then selects the sofa on the interactive interface and chooses a dark blue from the color options. The system detects this action in real time and immediately selects the current design plan as the target plan, invoking the pre-set AI editing system for editing. Based on the user's new color selection, the AI ​​editing system recalculates the color coordination between the sofa and other furniture, walls, flooring, and other elements to ensure a harmonious and unified overall visual effect. At the same time, the system will render the new design and generate the latest 3D visualization, allowing users to immediately see the actual effect of the adjustments. If the user is still not satisfied with the new plan, they can continue to make other adjustments through the interactive interface, such as changing to a different style of coffee table or adding wall decorations. The system will respond and update in real time in the same way. This real-time interactive method not only greatly enhances user participation, but also makes the design process more flexible and efficient, ensuring that the final design plan can meet the user's personalized needs to the greatest extent possible.

[0083] In a specific embodiment, the behavior data includes furniture browsing history and input text data. The target user's behavior data is analyzed by a preset recommendation algorithm to obtain the target user's preference parameters, including:

[0084] By using a preset recommendation algorithm, the target user's furniture product browsing history and input text data are analyzed to obtain user-product interaction information; wherein the user-product interaction information is the target user's browsing time, number of clicks, and collection status of different furniture products;

[0085] Performing cluster analysis on the user-product interaction information to obtain a target user's preferred furniture style feature vector;

[0086] Analyzing the co-occurrence relationship of furniture products in the user-product interaction information to obtain a furniture matching pattern vector;

[0087] Analyzing the furniture product description text in the user-product interaction information, and extracting spatial function keywords in the furniture product description text to obtain a spatial function keyword vector;

[0088] Constructing a target user preference matrix based on the preferred furniture style feature vector, the furniture matching pattern vector, and the space function keyword vector;

[0089] Inputting the target user preference matrix into a preset Bayesian inference algorithm to obtain the target user's preference probability distribution;

[0090] The furniture selection behavior of the target user is predicted based on the preference probability distribution to obtain the preference parameters of the target user; wherein the preference parameters of the target user include space function arrangement parameters and matching parameters.

[0091] Specifically, in personalized interior design solutions, to more accurately meet the needs of target users, the system collects and analyzes their behavioral data. This data primarily includes furniture browsing history and user-entered text data. This data enables the system to gain a deeper understanding of the user's furniture preferences, thereby providing furniture recommendations and services that better suit their preferences. Specifically, the system first uses a pre-set recommendation algorithm to conduct an in-depth analysis of the target user's furniture product browsing history and text input data, aiming to obtain information about the interaction between the user and the products. This user-product interaction information encompasses various aspects, such as the target user's browsing time, number of clicks, and favorite status for different furniture products. For example, if a user spends an extended period of time on a page featuring a modern minimalist sofa, repeatedly clicks to zoom in and view details, or even adds it to their favorites, this indicates a high level of interest in that sofa, and perhaps even in the modern minimalist furniture style as a whole. The system then performs cluster analysis on this user-product interaction information to extract a feature vector representing the target user's preferred furniture style. Cluster analysis groups users with similar browsing behaviors and preferences. Through comparative analysis, it can identify which furniture styles the target user prefers, such as Scandinavian, Industrial, or Classic. During this process, the system might discover that the target user spends more time browsing modern minimalist furniture and has a higher click-through rate, thus determining that their preferred furniture style feature vector leans towards modern minimalist. Simultaneously, the system also conducts in-depth analysis of the co-occurrence relationships between furniture items in user-item interaction information to obtain a furniture matching pattern vector. This analysis primarily aims to understand which furniture items have a high co-occurrence probability—that is, which related furniture items users tend to view or purchase when browsing or purchasing a particular item. For example, the system might discover that users browsing sofas also frequently view matching coffee tables, rugs, and other items, suggesting a particular matching preference when purchasing furniture. By analyzing these co-occurrence relationships, the system can better understand users' matching preferences and recommend more harmonious furniture combinations. Furthermore, the system further analyzes the furniture item description text in user-item interaction information to extract spatial function keywords and construct a spatial function keyword vector. This step is to capture the user's interest in specific spatial functions, such as office areas, leisure areas, or children's play areas. For example, if a user frequently searches for products containing keywords such as "bookshelf" and "workbench", this may mean that the user is looking for furniture suitable for setting up an office area at home. Through such analysis, the system can more accurately understand the user's spatial needs and provide users with more targeted suggestions. Based on the preferred furniture style feature vector, furniture matching pattern vector, and spatial function keyword vector obtained above, the system will construct a comprehensive target user preference matrix.This matrix not only reflects the user's preferences for furniture styles and matching patterns, but also encompasses their needs for specific spatial functions. It represents a comprehensive, multi-dimensional dataset showcasing the user's furniture selection preferences. The system then inputs this preference matrix into a pre-set Bayesian inference algorithm to calculate a probability distribution of preferences for the target user. The Bayesian inference algorithm here probabilistically models the user's various preference parameters, predicting which style, matching pattern, or spatial layout the user will be most inclined to choose in the future. Finally, based on this probability distribution, the system predicts the target user's furniture selection behavior, ultimately deriving the target user's preference parameters, including but not limited to spatial functional layout and matching parameters. These parameters directly guide subsequent furniture recommendations and service optimization, ensuring that the provided solutions best align with the user's personal preferences and actual needs. For example, if the system predicts that a user has a strong preference for modern minimalist furniture and a functional layout for their home office space, then when recommending furniture solutions to that user, it will prioritize modern minimalist furniture, with a particular emphasis on the design of the office area, thereby enhancing user satisfaction and user experience. Through such a systematic and personalized analysis process, not only can the accuracy of furniture recommendations be effectively improved, but user participation and satisfaction can also be significantly enhanced, bringing users a more intimate and efficient home design service experience.

[0092] In a specific embodiment, the use of plan image vectorization technology to convert the house plan into a vector to obtain vector space relationship feature data includes:

[0093] Identifying and marking each functional area in the house plan to obtain functional area marking data;

[0094] Preprocessing the house plan using a plan image vectorization technology to obtain a preprocessed image;

[0095] Extracting contour lines from the preprocessed image to obtain a contour line image, and classifying and attribute-labeling the contour lines in the contour line image based on the functional area annotation data to obtain a contour line image with functional area information;

[0096] Converting the outline line image with the functional area information into a vector graphic to obtain vector graphic data;

[0097] Perform spatial relationship analysis on the vector graphics data to obtain vector spatial relationship feature data.

[0098] Specifically, in the personalized interior design process, using floor plan image vectorization technology to convert floor plans into vector data is a key step in achieving efficient and accurate design. This process not only converts user-uploaded floor plans into computer-processable vector data, but also extracts detailed vector spatial relationship feature data, providing a solid foundation for subsequent design optimization. Specifically, this step involves several closely linked operations. First, the functional areas in the manually or automatically uploaded floor plan are identified and labeled to obtain functional area annotation data. This step is crucial because it helps the system understand the purpose of different areas in the floor plan, such as the living room, bedroom, and kitchen, providing a basis for subsequent spatial layout design. For example, after a user uploads a floor plan containing multiple rooms, the system will automatically or through manual user input identify and annotate the names and functions of each room, ensuring that each area is accurately identified. Next, the system preprocesses the floor plan using floor plan image vectorization technology to produce a preprocessed image. The primary purpose of preprocessing is to remove interfering factors such as noise and blur, making the image clearer and easier to process. This stage may involve various techniques, such as image denoising, contrast adjustment, and edge enhancement, to ensure more accurate subsequent contour line extraction. Taking a living room design as an example, the preprocessed floor plan will appear cleaner and clearer, laying a solid foundation for the next steps. After preprocessing, the system enters the next crucial stage: extracting contour lines from the preprocessed image to generate a contour line image. This process involves applying edge detection algorithms. By detecting areas of significant pixel value variation within the image, the system accurately delineates the contour lines of key elements, such as room boundaries and door and window locations. Based on this, the system also classifies and attributes the contour lines in the contour line image based on previously acquired functional area annotation data, generating a contour line image with functional area information. For example, the system identifies which lines in the contour line image represent the living room boundary and which lines represent bedroom doors and windows. This step is crucial for subsequent spatial relationship analysis. Next, the system converts the contour line image with functional area information into vector graphics to generate vector graphics data. The process of vectorization is to convert elements such as lines and shapes in an image into mathematical geometric objects such as points, lines, and surfaces. These objects not only have precise coordinate information, but can also be infinitely scaled without distortion. Through this conversion, the system can more flexibly adjust and optimize the spatial layout, while also facilitating subsequent data processing and storage. For example, the boundary of a living room in a vector graphic will no longer be a string of pixels, but a line composed of a series of coordinate points that can be easily edited and modified. Finally, the system will perform spatial relationship analysis on the generated vector graphics data to extract vector spatial relationship feature data.This analysis process involves analyzing multiple aspects, including spatial topology and geometric relationships, to fully understand the relative positions and connections between various areas within a house. For example, the system identifies whether there is a direct doorway between the living room and dining room, and the distance between the bedroom and bathroom. This information is crucial for designing appropriate furniture layouts and circulation planning. By analyzing these spatial relationships, the system can provide users with more scientific and practical design suggestions, ensuring that the design is both aesthetically pleasing and meets practical needs. In summary, by utilizing floor plan image vectorization technology to convert floor plans into vectors and extracting vector spatial relationship feature data, the entire design process not only significantly improves design efficiency and accuracy, but also better meets the user's personalized needs. For example, in the aforementioned living room design example, through this series of technical means, the system not only accurately identifies the specific location and shape of the living room, but also understands in detail the user's functional requirements for the living room, such as the need for ample relaxation areas and TV viewing areas. This allows the user to provide a design solution that both meets their personal preferences and is practical. This process fully demonstrates the advanced and practical nature of personalized interior design methods.

[0099] In a specific embodiment, the step of inputting the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network to perform space layout optimization and generate an initial furniture layout plan includes:

[0100] Performing spatial feature extraction on the vector spatial relationship feature data to obtain extracted spatial features; wherein the extracted spatial features include area, shape, window positions, and door opening direction information of the house plan;

[0101] Classify and process the spatial functional arrangement parameters to obtain functional area division information; wherein the functional area division information includes area requirements and relative position requirements of different functional areas;

[0102] Constructing a spatial layout relationship diagram based on the spatial feature vector and functional area division information;

[0103] Perform feature propagation and fusion on the spatial layout relationship graph based on a multi-scale graph neural network to obtain a fused feature graph;

[0104] Inputting the fused feature graph into a preset graph neural network to perform furniture layout exploration and space layout optimization to obtain candidate furniture layout solutions;

[0105] The candidate furniture layout plans are evaluated and screened for rationality to obtain an initial furniture layout plan.

[0106] Specifically, in the personalized interior design method, inputting vector spatial relationship feature data and spatial function arrangement parameters into a pre-set graph neural network for spatial layout optimization is a core step in generating an initial furniture layout plan. This process not only relies on advanced graph neural network technology but also requires meticulous processing of the input data to ensure that the resulting layout plan not only meets the user's functional requirements, but also achieves good space utilization efficiency and aesthetics. First, the system extracts spatial features from the vector spatial relationship feature data to obtain extracted spatial features. The goal of this stage is to extract information from the vector graphics data that is useful for spatial layout optimization, including but not limited to the floor plan's area, shape, window location, and door opening direction. For example, when processing a user-uploaded living room floor plan, the system accurately measures the total area of ​​the living room, identifies the specific location and number of windows, and the door opening direction. This information is crucial for subsequent furniture layout design. Through these extracted spatial features, the system can better understand the basic structure and characteristics of the space, laying the foundation for the optimal placement of furniture. Next, the system classifies the spatial function arrangement parameters to obtain functional area division information. The spatial functional layout parameters reflect the user's specific requirements for different functional areas, such as the required area and relative location of each room. For example, a user may desire a living room that is not only spacious enough to accommodate a large gathering but also has a reading nook near a window. Based on these user requirements, the system divides the living room into different functional areas, such as an entertainment area, a rest area, and a reading area, and specifies the approximate area and ideal location of each area. This classification process ensures that subsequent layout optimization fully considers the user's functional needs and avoids inconvenience caused by irrational spatial division. Based on the extracted spatial features and functional area division information, the system constructs a spatial layout relationship graph. A spatial layout relationship graph is a graph-structured data structure in which each node represents a functional area, and edges indicate the connections and relative positions between different functional areas. For example, in the living room design, the system creates multiple nodes in the graph, corresponding to the entertainment area, rest area, and reading area. The system then determines the connection method and distance between these nodes based on the user's needs and the actual space conditions. By constructing the spatial layout relationship graph, the system can more intuitively represent the logical relationship between the various functional areas, providing a clear framework for subsequent spatial layout optimization. The system then propagates and fuses features from the spatial layout relationship graph using a multi-scale graph neural network (GNN) to produce a fused feature graph. GNNs are powerful tools for processing graph-structured data, capturing both local and global relationships between nodes at different scales to generate richer and more accurate feature representations.In the living room design example, the system utilizes a multi-scale graph neural network to capture the specific features of each functional area at the micro level, such as furniture size and shape, while also understanding the interactions between different areas at the macro level, such as the transition space between the entertainment area and the rest area. Through feature propagation and fusion, the system generates a fused feature map that not only contains basic information about each functional area but also reflects the complex relationships between them. Next, the system inputs the fused feature map into a pre-set graph neural network for furniture layout exploration and spatial layout optimization to generate candidate furniture layouts. This stage involves exploring multiple possible furniture layouts through simulation and calculation based on the fused feature map. The graph neural network intelligently selects appropriate furniture based on the characteristics of each functional area and user needs, and determines its optimal placement within the space. For example, in a living room design, the system might try placing the sofa near a window to maximize natural light, while also placing a TV cabinet opposite the sofa to create an ideal viewing area. Through continuous iteration and optimization, the system generates multiple candidate furniture layouts for subsequent evaluation and selection. Finally, the system evaluates and screens the candidate furniture layouts for plausibility to determine the initial furniture layout. The rationality assessment is primarily based on two aspects: hard design rules and user experience. Hard design rules, such as minimum spacing between furniture and barrier-free access areas in front of doors and windows, ensure the feasibility of the design plan. User experience focuses on the design's aesthetics, comfort, and practicality, ensuring that the design meets the user's actual needs. For example, when evaluating candidate living room designs, the system checks whether each plan meets basic design specifications, such as the appropriate distance between the sofa and the TV. It also considers the user's individual needs, such as whether the reading nook is quiet and comfortable. After comprehensive evaluation, the system ultimately selects one or several optimal plans as the initial furniture layout for further review and adjustment by the user. In summary, by inputting vector spatial relationship feature data and spatial function arrangement parameters into a pre-set graph neural network for spatial layout optimization, the entire design process not only efficiently generates furniture layout plans that meet user needs, but also ensures the plan's rationality and aesthetics. This process fully demonstrates the intelligent and technical advantages of personalized interior design methods, providing users with a more convenient and high-quality design experience.

[0107] In a specific embodiment, screening the initial furniture layout scheme using preset hard design rules to obtain a screened furniture layout scheme includes:

[0108] Performing spatial relationship analysis on the initial furniture layout plan using a spatial topology analysis algorithm to obtain a spatial topology relationship diagram between the furniture; wherein the spatial topology relationship diagram includes relative positions, distances, and connectivity between the furniture;

[0109] Based on ergonomic principles and the spatial topological relationship diagram, an ergonomic evaluation is performed on the initial furniture layout plan to obtain an ergonomic scoring matrix;

[0110] The initial furniture layout solution is optimized and calculated based on the ergonomic scoring matrix using a multi-objective optimization algorithm to obtain an ergonomic layout solution set; wherein the ergonomic layout solution set is a set of candidate layout solutions that meet ergonomic requirements;

[0111] Dividing the ergonomic layout solution set into functional areas to obtain a functional area semantic map;

[0112] By using preset hard design rules, the functional area semantic graph is subjected to rule constraint checking to obtain a set of ergonomic layout solutions after constraint checking;

[0113] The ergonomic layout scheme set after the constraint check is comprehensively evaluated and screened by a multi-criteria decision analysis method to obtain a screened furniture layout scheme.

[0114] Specifically, in the personalized interior design process, screening initial furniture layout plans using pre-set hard-coded design rules is a crucial step in ensuring that the design is both ergonomically sound and meets practical requirements. This process involves multiple steps, from analyzing spatial topology relationships to comprehensively evaluating and screening the final plan, each of which aims to improve the rationality of the design and user satisfaction. First, the system uses a spatial topology analysis algorithm to analyze the spatial relationships of the initial furniture layout plan, generating a spatial topology diagram of the furniture. This spatial topology diagram details information such as the relative positions, distances, and connectivity between furniture pieces, providing foundational data for subsequent evaluation and optimization. For example, in a living room design, the system analyzes the distance between the sofa and the TV, the relative position of the coffee table and the sofa, and the access paths from the door to each piece of furniture. Through these analyses, the system comprehensively understands the spatial relationships of the furniture layout, providing accurate data support for subsequent evaluation. Next, based on ergonomic principles, the system uses the generated spatial topology diagram to conduct an ergonomic evaluation of the initial furniture layout plan, generating an ergonomic scoring matrix. The ergonomic scoring matrix reflects the scores of each furniture layout solution in terms of human comfort, accessibility, and safety. For example, the system assesses whether the visual distance between the sofa and the TV is appropriate, whether the coffee table is at a convenient height, and whether the access paths from the door to each piece of furniture are unobstructed. Through these assessments, the system quantifies the ergonomic performance of each solution, providing a basis for subsequent optimization. Based on the generated ergonomic scoring matrix, the system optimizes the initial furniture layout solution using a multi-objective optimization algorithm to generate an ergonomic layout solution set. The multi-objective optimization algorithm finds the optimal balance between multiple optimization objectives, ensuring that the resulting layout solution not only meets ergonomic requirements but also balances aesthetics and practicality. For example, the system may generate multiple layout solutions, each with different performance in terms of visual distance, access paths, and furniture spacing, but all meeting basic ergonomic requirements. These solutions constitute the ergonomic layout solution set, providing a rich selection for subsequent screening. The system then divides the ergonomic layout solution set into functional areas and generates a functional area semantic map. The functional area semantic map not only identifies the specific location of each functional area but also clarifies the function and purpose of each area. For example, in a living room design, the system divides the living room into entertainment, rest, and reading areas, and annotates the primary furniture and functions of each area in a functional area semantic map. This functional area division ensures that each layout plan meets both ergonomic requirements and the user's functional needs. Next, the system applies pre-set hard design rules to the functional area semantic map, performing a constraint check and generating a set of ergonomic layout plans.Hard design rules include, but are not limited to, minimum spacing between furniture, barrier-free access areas in front of doors and windows, and safe distances between electrical outlets and switches. These rules are intended to ensure the feasibility and safety of design solutions. For example, the system checks that the distance between the sofa and coffee table in each layout plan is greater than 60 cm, and that the path from the door to each piece of furniture is wider than 90 cm. Only solutions that pass these rule-constraint checks proceed to the next stage of evaluation and screening. Finally, the system uses a multi-criteria decision analysis method to comprehensively evaluate and screen the ergonomic layout solutions after the constraint check, ultimately determining the selected furniture layout solutions. This multi-criteria decision analysis method comprehensively considers multiple evaluation criteria, such as ergonomic score, space utilization, and aesthetics, to assign a score to each solution. For example, the system comprehensively scores each solution based on user needs and preferences, ultimately selecting the top-scoring solutions as the selected furniture layout solutions. These solutions not only meet ergonomic requirements but also maximize the user's functional needs and aesthetic preferences. In summary, the process of screening initial furniture layout plans using pre-set hard-coded design rules not only ensures the rationality and safety of design solutions, but also significantly improves user satisfaction and user experience. For example, in the aforementioned living room design example, through this series of steps, the system not only generated multiple ergonomic layout options but also provided users with the most optimized design options through comprehensive evaluation and screening. This process fully demonstrates the scientific and intelligent nature of personalized interior design methods, providing users with more intimate and efficient design services.

[0115] In a specific embodiment, matching the matching parameters with the screened furniture layout scheme to obtain a matching design scheme includes:

[0116] Performing feature deconstruction on the matching parameters and the screened furniture layout scheme respectively, and obtaining a deconstructed parameter set and a layout element set;

[0117] Performing semantic encoding on the deconstructed parameter set to obtain an encoded semantic vector;

[0118] Performing spatial relationship encoding on the layout element set to obtain a spatial relationship vector;

[0119] Based on the attention mechanism network and matching algorithm, the correlation between the encoded semantic vector and the spatial relationship vector is calculated to obtain the matching weight matrix;

[0120] Reorganize the furniture layout plan after screening based on the matching weight matrix to obtain a layout reorganization plan;

[0121] The layout reorganization scheme is checked and adjusted for style consistency to obtain a matching design scheme, and the matching design scheme is initially visualized on a preset interactive interface.

[0122] Specifically, in the personalized interior design process, matching matching parameters with selected furniture layouts is a key step in generating the final matching design. This process requires not only an in-depth analysis of user preferences and existing layouts, but also the use of advanced computational methods to ensure that the final design not only meets the user's personalized needs, but also exhibits a high degree of stylistic consistency and practicality. First, the system performs feature deconstruction on the matching parameters and selected furniture layouts, resulting in a deconstructed parameter set and a layout element set. Matching parameters include user preferences for color, material, and decorative style, while selected furniture layouts contain optimized spatial layout information. For example, in a living room design, a user's matching parameters might include a preference for modern minimalist style, a preference for white and gray as primary colors, and a preference for wooden furniture. The system deconstructs these parameters into specific information such as color codes and material categories, forming a deconstructed parameter set. Simultaneously, the system deconstructs each piece of furniture and its location information in the selected furniture layouts into a layout element set, such as the position of the sofa or the size of the coffee table. Next, the system performs semantic encoding on the deconstructed parameter set, generating an encoded semantic vector. The purpose of semantic encoding is to convert user preference parameters into a computer-processable form for subsequent calculations and matching. For example, the system encodes "modern minimalist style" as a specific vector, "white" and "gray" as color vectors, and "wooden furniture" as a material vector. Through these encodings, the system can transform the user's abstract preferences into concrete numerical representations, providing a foundation for subsequent matching calculations. Simultaneously, the system performs spatial relationship encoding on the layout element set, generating a spatial relationship vector. Spatial relationship encoding aims to capture the spatial structural information of the furniture layout, such as the relative position, distance, and connectivity between furniture. For example, the system encodes the distance between the sofa and the TV as a numerical value, and the relative position of the coffee table and the sofa as a vector. Through these encodings, the system can convert the spatial features of the layout into vector form, providing data support for subsequent correlation calculations. Based on the attention mechanism network and matching algorithm, the system performs correlation calculations on the encoded semantic vectors and spatial relationship vectors to obtain a matching weight matrix. The attention mechanism network dynamically focuses on key features of user preferences and layout plans, thereby improving the accuracy and rationality of matching. For example, the system calculates the similarity between the semantic vector of a user's preference for a modern minimalist style and the spatial relationship vectors of furniture in the living room, such as the sofa and coffee table, to generate a matching weight matrix. Each element in this matrix represents the degree of match between the user's preference and a piece of furniture or area in the layout plan, with higher values ​​indicating a better match. Based on this matching weight matrix, the system reorganizes the selected furniture layout plan to produce a reorganized layout plan.The purpose of layout reorganization is to adjust the placement and matching of furniture based on user preferences to generate a design that better meets their needs. For example, if the matching weight matrix indicates that a user prefers a white sofa with a gray carpet, the system will adjust the sofa color in the living room to white and the carpet color to gray. Through this reorganization, the system can generate a layout that not only meets the user's preferences but also maintains high consistency. Finally, the system will perform a style consistency check and make adjustments to the reorganized layout to ensure that the final design is highly consistent in terms of color, material, and decorative style. The purpose of this style consistency check is to avoid a mix of styles within the design and ensure the overall coordination and aesthetics of the design. For example, the system will check whether the color, material, and style of the furniture in the adjusted layout are consistent. If not, the system will make further adjustments until the optimal effect is achieved. After the style consistency check is complete, the system will initially visualize the final matching design on a pre-set interactive interface, allowing users to intuitively see the design results. If users have any comments or suggestions on the initial design, they can make adjustments through the interactive interface. The system will update the design in real time based on their feedback until the user is satisfied. In summary, by matching matching parameters with selected furniture layout options, the entire design process not only accurately reflects the user's personalized needs, but also ensures a high degree of consistency and practicality in terms of style and functionality. For example, in the aforementioned living room design example, through this series of steps, the system not only generated a design that met the user's modern minimalist style preferences, but also ensured the overall coordination and aesthetics of the design through style consistency checks and adjustments. This process fully demonstrates the intelligent and technical advantages of personalized interior design methods, providing users with a more efficient and satisfying home design experience.

[0123] In a specific embodiment, a preset AI editing system is used to edit and render the target plan according to the interface interaction operation to obtain a three-dimensional visualization effect diagram corresponding to the house plan, including:

[0124] Monitor the target user's interface interaction operations on the interactive interface in real time to obtain an operation instruction sequence of the target user on the matching design solution; wherein the operation instruction sequence includes recording the user's clicks, drags, and zooms;

[0125] Using a multi-objective optimization algorithm, adjusting the parameters in the target solution based on the operation instruction sequence to obtain adjusted design parameters and an adjusted design solution corresponding to the adjusted design parameters; wherein the parameters in the target solution include furniture layout parameters, color scheme parameters, and material parameters;

[0126] Performing spatial mapping on the adjusted design scheme using a preset spatial mapping algorithm to obtain a three-dimensional spatial layout model;

[0127] Performing material and lighting rendering on the three-dimensional space layout model to obtain a three-dimensional space layout model with material texture and lighting effects;

[0128] Through virtual reality technology, a three-dimensional visualization effect diagram is generated based on a three-dimensional space layout model with material texture and lighting effects.

[0129] Specifically, within the personalized interior design process, a pre-set AI editing system edits and renders the target solution based on the target user's interactions on the interface. This is a crucial step in enabling real-time user participation and immediate viewing of design results. This process requires not only real-time monitoring of user actions but also the generation of 3D visualizations with material textures and lighting effects through multi-objective optimization and spatial mapping algorithms to ensure the accuracy and aesthetics of the design solution. First, the system monitors the target user's interactions on the interface in real time to capture the target user's action sequences for matching design solutions. These action sequences include user actions such as clicking, dragging, and zooming on the interface. For example, when a user clicks and drags a furniture icon to a new location, or resizes the furniture using zoom gestures, the system records these actions in real time. These action sequences not only reflect the user's immediate needs but also provide a basis for subsequent design adjustments. Next, the system uses a multi-objective optimization algorithm to adjust the parameters of the target solution based on these action sequences, generating the adjusted design parameters and the corresponding adjusted design solution. The parameters in the target design include furniture layout parameters, color scheme parameters, and material parameters. For example, if a user drags a sofa from one corner of the living room to another, the system adjusts the sofa's layout parameters based on the user's instructions, ensuring that the new position both meets the user's intent and maintains the rationality of the spatial layout. Furthermore, if the user changes the wall color or chooses a different flooring material, the system adjusts the color scheme and material parameters accordingly to ensure the overall coordination of the design. Using a preset spatial mapping algorithm, the system spatially maps the adjusted design to produce a 3D spatial layout model. This spatial mapping algorithm converts a 2D floor plan into a 3D spatial model, ensuring that each piece of furniture has the correct position, size, and shape in 3D space. For example, in a living room design, the system maps the adjusted sofa position and coffee table size into 3D space, generating a complete 3D spatial layout model. This model not only includes the specific positions of the furniture but also structural information such as the room's walls, doors, and windows, providing a foundation for subsequent rendering. The system then applies material and lighting rendering to the 3D spatial layout model, resulting in a 3D spatial layout model with textures and lighting effects. The purpose of material rendering is to add realistic textures to elements like furniture and walls, such as the wood grain of wooden furniture or the fabric texture of a fabric sofa. Lighting rendering simulates the effects of different light sources within a room, such as natural light entering through windows or light emitted from chandeliers or wall sconces. Through these renderings, the system generates a highly realistic 3D spatial layout model, allowing users to intuitively experience the actual effects of their design solutions.Finally, the system uses virtual reality technology to generate a 3D visualization based on the 3D spatial layout model with textures and lighting effects. Virtual reality technology provides an immersive experience for users, allowing them to "walk" and "observe" the virtual environment through VR devices, fully examining every detail of the design. For example, users can walk around the virtual living room, closely observing the material and color of the sofa, feeling the softness of the lighting, and even simulating natural lighting effects at different times of day to ensure that the design performs as expected in various environments. In summary, the pre-set AI editing system edits and renders the target design based on the user's interface interactions. This entire process not only responds to user actions in real time but also generates highly realistic 3D visualizations, significantly enhancing user engagement and satisfaction. For example, in the aforementioned living room design example, users can adjust the furniture layout and color scheme in real time with simple clicks and drags, and preview the final result in a 3D environment using virtual reality technology. This process not only simplifies the design process but also provides users with a more intuitive and flexible design experience, ensuring that the final design fully meets the user's personalized needs and aesthetic preferences.

[0130] The above describes the personalized interior design method based on the user's floor plan in the embodiment of the present invention. The following describes the personalized interior design system based on the user's floor plan in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a personalized interior design system based on a user's floor plan includes:

[0131] The recommendation module 21 is used to analyze the target user's behavior data through a preset recommendation algorithm to obtain the target user's preference parameters; wherein the preference parameters include spatial function arrangement parameters and matching parameters;

[0132] An acquisition module 22 is used to acquire a house plan uploaded by a target user and perform vector conversion on the house plan using a plan image vectorization technique to obtain vector space relationship feature data;

[0133] A generation module 23 is configured to input the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network to perform space layout and generate an initial furniture layout plan;

[0134] A screening module 24 is configured to screen the initial furniture layout scheme using preset hard design rules to obtain a screened furniture layout scheme;

[0135] A matching module 25 is configured to perform matching design on the matching parameters and the screened furniture layout scheme to obtain a matching design scheme, and to initially visualize the matching design scheme on a preset interactive interface;

[0136] The editing module 26 is used to detect in real time whether the target user performs interface interaction operations on the interactive interface. If so, the matching design scheme targeted by the interface interaction operation is used as the target scheme, and the target scheme is edited and rendered according to the interface interaction operation through a preset AI editing system to obtain a three-dimensional visualization effect diagram corresponding to the house floor plan.

[0137] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0138] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0139] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0140] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0141] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0142] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. 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, apparatus, article, or method comprising the element.

[0143] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A personalized interior design method based on a user's floor plan, characterized in that: The following steps are involved: Analyze the target user's behavior data through a preset recommendation algorithm to obtain the target user's preference parameters; wherein the preference parameters include spatial function arrangement parameters and matching parameters; Obtaining a house plan uploaded by a target user, and performing vector conversion on the house plan using a plan image vectorization technique to obtain vector spatial relationship feature data; Inputting the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network to perform space layout and generate an initial furniture layout plan; Screening the initial furniture layout scheme using preset hard design rules to obtain a screened furniture layout scheme; Performing matching design on the matching parameters and the screened furniture layout plan to obtain a matching design plan, and initially visualizing the matching design plan on a preset interactive interface; Detect in real time whether the target user performs an interface interaction operation on the interactive interface. If so, use the matching design scheme targeted by the interface interaction operation as the target scheme, and use a preset AI editing system to edit and render the target scheme according to the interface interaction operation to obtain a three-dimensional visualization effect diagram corresponding to the house floor plan; The behavior data includes furniture browsing records and input text data. The target user's behavior data is analyzed by a preset recommendation algorithm to obtain the target user's preference parameters, including: By using a preset recommendation algorithm, the target user's furniture product browsing history and input text data are analyzed to obtain user-product interaction information; wherein the user-product interaction information is the target user's browsing time, number of clicks, and collection status of different furniture products; Performing cluster analysis on the user-product interaction information to obtain a target user's preferred furniture style feature vector; Analyzing the co-occurrence relationship of furniture products in the user-product interaction information to obtain a furniture matching pattern vector; Analyzing the furniture product description text in the user-product interaction information, and extracting spatial function keywords in the furniture product description text to obtain a spatial function keyword vector; Constructing a target user preference matrix based on the preferred furniture style feature vector, the furniture matching pattern vector, and the space function keyword vector; Inputting the target user preference matrix into a preset Bayesian inference algorithm to obtain the target user's preference probability distribution; The furniture selection behavior of the target user is predicted based on the preference probability distribution to obtain the preference parameters of the target user; wherein the preference parameters of the target user include space function arrangement parameters and matching parameters.

2. The personalized interior design method based on user floor plan according to claim 1, characterized in that: The method of converting the house plan into a vector using the plan image vectorization technology to obtain vector space relationship feature data includes: Identifying and marking each functional area in the house plan to obtain functional area marking data; Preprocessing the house plan using a plan image vectorization technology to obtain a preprocessed image; Extracting contour lines from the preprocessed image to obtain a contour line image, and classifying and attribute-labeling the contour lines in the contour line image based on the functional area annotation data to obtain a contour line image with functional area information; Converting the outline line image with the functional area information into a vector graphic to obtain vector graphic data; Perform spatial relationship analysis on the vector graphics data to obtain vector spatial relationship feature data.

3. The personalized interior design method based on user floor plan according to claim 1, characterized in that: The step of inputting the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network to perform space layout optimization and generate an initial furniture layout plan includes: Performing spatial feature extraction on the vector spatial relationship feature data to obtain extracted spatial features; wherein the extracted spatial features include area, shape, window positions, and door opening direction information of the house plan; Classify and process the spatial functional arrangement parameters to obtain functional area division information; wherein the functional area division information includes area requirements and relative position requirements of different functional areas; Based on the extracted spatial features and functional area division information, a spatial layout relationship diagram is constructed; Perform feature propagation and fusion on the spatial layout relationship graph based on a multi-scale graph neural network to obtain a fused feature graph; Inputting the fused feature graph into a preset graph neural network to perform furniture layout exploration and space layout optimization to obtain candidate furniture layout solutions; The candidate furniture layout plans are evaluated and screened for rationality to obtain an initial furniture layout plan.

4. The personalized interior design method based on user floor plan according to claim 1, characterized in that: The initial furniture layout scheme is screened using preset hard design rules to obtain a screened furniture layout scheme, including: Performing spatial relationship analysis on the initial furniture layout plan using a spatial topology analysis algorithm to obtain a spatial topology relationship diagram between the furniture; wherein the spatial topology relationship diagram includes relative positions, distances, and connectivity between the furniture; Based on ergonomic principles and the spatial topological relationship diagram, an ergonomic evaluation is performed on the initial furniture layout plan to obtain an ergonomic scoring matrix; The initial furniture layout solution is optimized and calculated based on the ergonomic scoring matrix using a multi-objective optimization algorithm to obtain an ergonomic layout solution set; wherein the ergonomic layout solution set is a set of candidate layout solutions that meet ergonomic requirements; Dividing the ergonomic layout solution set into functional areas to obtain a functional area semantic map; By using preset hard design rules, the functional area semantic graph is subjected to rule constraint checking to obtain a set of ergonomic layout solutions after constraint checking; The ergonomic layout scheme set after the constraint check is comprehensively evaluated and screened by a multi-criteria decision analysis method to obtain a screened furniture layout scheme.

5. The personalized interior design method based on user floor plan according to claim 1, characterized in that: The matching design is performed on the matching parameters and the screened furniture layout scheme to obtain a matching design scheme, including: Performing feature deconstruction on the matching parameters and the screened furniture layout scheme respectively, and obtaining a deconstructed parameter set and a layout element set; Performing semantic encoding on the deconstructed parameter set to obtain an encoded semantic vector; Performing spatial relationship encoding on the layout element set to obtain a spatial relationship vector; Based on the attention mechanism network and matching algorithm, the correlation between the encoded semantic vector and the spatial relationship vector is calculated to obtain the matching weight matrix; Reorganize the furniture layout plan after screening based on the matching weight matrix to obtain a layout reorganization plan; The layout reorganization scheme is checked and adjusted for style consistency to obtain a matching design scheme, and the matching design scheme is initially visualized on a preset interactive interface.

6. The personalized interior design method based on user floor plan according to claim 1, characterized in that: Through the preset AI editing system, the target plan is edited and rendered according to the interface interaction operation to obtain a three-dimensional visualization effect diagram corresponding to the house plan, including: Monitor the target user's interface interaction operations on the interactive interface in real time to obtain an operation instruction sequence of the target user on the matching design solution; wherein the operation instruction sequence includes recording the user's clicks, drags, and zooms; Using a multi-objective optimization algorithm, adjusting the parameters in the target solution based on the operation instruction sequence to obtain adjusted design parameters and an adjusted design solution corresponding to the adjusted design parameters; wherein the parameters in the target solution include furniture layout parameters, color scheme parameters, and material parameters; Performing spatial mapping on the adjusted design scheme using a preset spatial mapping algorithm to obtain a three-dimensional spatial layout model; Performing material and lighting rendering on the three-dimensional space layout model to obtain a three-dimensional space layout model with material texture and lighting effects; Through virtual reality technology, a three-dimensional visualization effect diagram is generated based on a three-dimensional space layout model with material texture and lighting effects.

7. A personalized interior design system based on user floor plans, characterized by: include: The recommendation module is used to analyze the target user's behavior data through a preset recommendation algorithm to obtain the target user's preference parameters; wherein the preference parameters include spatial function arrangement parameters and matching parameters; An acquisition module is used to acquire a house plan uploaded by a target user and perform vector conversion on the house plan using a plan image vectorization technique to obtain vector space relationship feature data; Inputting the vector space relationship feature data and the space function arrangement parameters into a preset graph neural network to perform space layout and generate an initial furniture layout plan; A screening module, configured to screen the initial furniture layout scheme using preset hard design rules to obtain a screened furniture layout scheme; a matching module, configured to match the matching parameters with the screened furniture layout scheme to obtain a matching design scheme, and to initially visualize the matching design scheme on a preset interactive interface; An editing module is configured to detect in real time whether the target user performs an interface interaction operation on the interactive interface. If so, the matching design scheme targeted by the interface interaction operation is used as the target scheme, and a preset AI editing system is used to edit and render the target scheme according to the interface interaction operation to obtain a three-dimensional visualization effect diagram corresponding to the house floor plan; The behavior data includes furniture browsing records and input text data. The target user's behavior data is analyzed by a preset recommendation algorithm to obtain the target user's preference parameters, including: By using a preset recommendation algorithm, the target user's furniture product browsing history and input text data are analyzed to obtain user-product interaction information; wherein the user-product interaction information is the target user's browsing time, number of clicks, and collection status of different furniture products; Performing cluster analysis on the user-product interaction information to obtain a target user's preferred furniture style feature vector; Analyzing the co-occurrence relationship of furniture products in the user-product interaction information to obtain a furniture matching pattern vector; Analyzing the furniture product description text in the user-product interaction information, and extracting spatial function keywords in the furniture product description text to obtain a spatial function keyword vector; Constructing a target user preference matrix based on the preferred furniture style feature vector, the furniture matching pattern vector, and the space function keyword vector; Inputting the target user preference matrix into a preset Bayesian inference algorithm to obtain the target user's preference probability distribution; The furniture selection behavior of the target user is predicted based on the preference probability distribution to obtain the preference parameters of the target user; wherein the preference parameters of the target user include space function arrangement parameters and matching parameters.

8. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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