Building decoration intelligent design system and method

Abstract

The invention relates to the technical field of building decoration design, in particular to a building decoration intelligent design system and method, and the system comprises an intelligent perception analysis module which is used for collecting spatial feature information and environment parameters; generating a fusion feature matrix based on the spatial feature information and the environmental parameters; the AI design engine module is in communication connection with the intelligent perception analysis module and is used for receiving the fusion feature matrix; generating a preliminary design scheme based on the fused feature matrix; the multi-dimensional optimization evaluation module is in communication connection with the AI design engine module and is used for performing functional, aesthetic and economic evaluation on the preliminary design scheme; generating an optimized design scheme based on the evaluation result; the immersive visual interaction module is in communication connection with the multi-dimensional optimization evaluation module and is used for receiving the optimized design scheme; generating a three-dimensional visual model of the optimized design scheme; an interactive design adjustment interface is provided, the design period can be remarkably shortened, and the labor cost is reduced.

Description

Technical Field

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

[0002] As people's living standards continue to improve, their requirements for living environments are also increasing. As an important means of improving living environments, architectural decoration design is gaining increasing attention. Traditional architectural decoration design relies primarily on the designer's experience and creativity. While this method can meet basic needs, it still has many shortcomings in terms of efficiency, accuracy, and personalization.

[0003] In recent years, with the advancement of computer technology, a wide range of architectural and interior design software has emerged. These software programs offer features such as 3D modeling and rendering, significantly improving design efficiency. However, current design software still suffers from numerous limitations. First, they lack intelligent spatial and environmental perception, making it incapable of comprehensively and accurately acquiring and analyzing the fundamental data required for design. Second, these programs typically offer only preset templates and a limited number of design elements, making it difficult to meet the personalized needs of users. Furthermore, existing software is limited in its ability to optimize designs, often requiring designers to make multiple manual adjustments to achieve satisfactory results.

[0004] Furthermore, current design methods and tools lack capabilities in collaborative design and version management. In large-scale projects, multiple designers working simultaneously often encounter issues such as information asynchrony and version conflicts, severely impacting work efficiency. Furthermore, existing design systems lack adaptive learning capabilities and are unable to draw on experience from previous projects, making it difficult to continuously improve design quality.

[0005] Finally, current design methods face challenges in cost control and material selection. Designers often need to manually calculate and adjust to ensure the design is within budget, which is not only time-consuming and labor-intensive but also prone to deviations. Summary of the Invention

[0006] In view of the above problems, there is an urgent need for an intelligent system that can comprehensively improve the efficiency and quality of architectural decoration design. The present invention aims to solve these problems existing in the prior art and provide an architectural decoration intelligent design system and method.

[0007] The present invention proposes an intelligent design system for architectural decoration, comprising:

[0008] Intelligent perception analysis module for:

[0009] Collect spatial feature information and environmental parameters;

[0010] generating a fusion feature matrix based on the spatial feature information and the environmental parameters;

[0011] The AI ​​design engine module is in communication with the intelligent perception analysis module and is used to:

[0012] receiving the fused feature matrix;

[0013] generating a preliminary design scheme based on the fusion feature matrix;

[0014] A multi-dimensional optimization and evaluation module is communicatively connected to the AI ​​design engine module and is used to:

[0015] Evaluate the functionality, aesthetics and economic feasibility of the preliminary design;

[0016] generating an optimized design solution based on the evaluation results;

[0017] The immersive visualization interaction module is in communication with the multi-dimensional optimization evaluation module and is used to:

[0018] receiving the optimized design solution;

[0019] Generating a three-dimensional visual model of the optimized design solution;

[0020] Provides an interactive design adjustment interface.

[0021] Preferably, the intelligent perception analysis module includes:

[0022] Spatial feature extraction unit, used for:

[0023] Collect point cloud data;

[0024] Based on the point cloud data, a spatial feature matrix is ​​generated by a topological persistence operator and a random projection matrix;

[0025] The environmental feature fusion unit is communicatively connected to the spatial feature extraction unit and is used to:

[0026] receiving the spatial feature matrix and environmental sensor data;

[0027] The spatial feature matrix and environmental sensor data are fused through a quantum convolutional neural network to generate a fused feature matrix.

[0028] Preferably, the AI ​​design engine module includes:

[0029] User requirements mapping unit, used to:

[0030] receiving the fused feature matrix;

[0031] Based on chaos theory and fractal geometry, a nonlinear mapping between user needs and environmental characteristics is established to generate a user needs matrix;

[0032] A design solution generating unit is communicatively connected to the user demand mapping unit and is configured to:

[0033] receiving the user demand matrix;

[0034] A generative adversarial network is constructed based on group theory and Lie algebra to generate preliminary design solutions.

[0035] Preferably, the multi-dimensional optimization evaluation module uses functional analysis and variational methods to perform optimization evaluation, including:

[0036] Objective function building block for:

[0037] Constructing an optimization objective function based on functional, aesthetic and economic indicators;

[0038] Constraint setting unit, used to:

[0039] Set constraints such as design space, material selection, and cost limits;

[0040] The optimization solving unit is in communication with the objective function building unit and the constraint condition setting unit, and is used to:

[0041] Based on the objective function and constraints, the optimal design solution is solved by variational method.

[0042] Preferably, the immersive visual interaction module includes:

[0043] 3D rendering unit for:

[0044] receiving the optimized design solution;

[0045] Use ray tracing technology to generate high-quality 3D rendered images;

[0046] A VR interaction unit is connected to the 3D rendering unit and is used to:

[0047] constructing a virtual reality scene based on the three-dimensional rendered image;

[0048] Provide immersive space roaming and real-time design adjustment capabilities;

[0049] An AR preview unit is connected to the 3D rendering unit and is configured to:

[0050] superimposing the three-dimensional rendered image into a real environment;

[0051] Provides real-time preview and gesture interaction functions on mobile devices.

[0052] As an advantage, it also includes:

[0053] A knowledge base module, in communication with the AI ​​design engine module, is configured to:

[0054] Store historical design cases and design rules;

[0055] Providing knowledge support for the AI ​​design engine module;

[0056] An adaptive learning module, in communication with the knowledge base module, is configured to:

[0057] Analyze user feedback and design effects;

[0058] The design knowledge and rules in the knowledge base module are updated.

[0059] As an advantage, it also includes:

[0060] The BIM integration module is in communication with the AI ​​design engine module and the multi-dimensional optimization evaluation module and is used to:

[0061] Converting the optimized design scheme into a BIM model;

[0062] Realize data interaction with building information model.

[0063] Preferably, the AI ​​design engine module further includes:

[0064] Style matching unit, used to:

[0065] Identify design styles based on deep learning algorithms;

[0066] Select matching design elements from the style element library;

[0067] The material recommendation unit is in communication with the style matching unit and is configured to:

[0068] Recommend suitable decorative materials based on the selected design style and environmental characteristics;

[0069] Optimize selection by considering material performance, cost and environmental indicators.

[0070] Preferably, the multi-dimensional optimization evaluation module further includes:

[0071] Collaborative design unit for:

[0072] Support multi-user remote collaborative design;

[0073] Synchronize design modifications and evaluation results in real time;

[0074] A version management unit, in communication with the collaborative design unit, is configured to:

[0075] Record the evolution of the design;

[0076] Supports comparison and rollback between different versions.

[0077] An intelligent design method for architectural decoration, based on the above system, comprises the following steps:

[0078] S1, collects spatial feature information and environmental parameters through the intelligent perception analysis module;

[0079] S2, based on the spatial feature information and environmental parameters, generating a spatial feature matrix by using a topological persistence operator and a random projection matrix;

[0080] S3, using a quantum convolutional neural network to fuse the spatial feature matrix and the environmental sensor data to generate a fused feature matrix;

[0081] S4, based on chaos theory and fractal geometry, establishes a nonlinear mapping between user needs and environmental characteristics to generate a user needs matrix;

[0082] S5, generating a preliminary design solution based on the user demand matrix using a generative adversarial network based on group theory and Lie algebra;

[0083] S6, through a multi-dimensional optimization evaluation module, using functional analysis and variational methods to evaluate the functionality, aesthetics and economy of the preliminary design scheme;

[0084] S7, generating an optimized design solution based on the evaluation results;

[0085] S8, generating a three-dimensional visualization model of the optimized design solution using an immersive visualization interaction module;

[0086] S9, provides an interactive design adjustment interface, allowing users to make real-time modifications;

[0087] S10, analyze user feedback and design effects through the adaptive learning module and update the design knowledge base.

[0088] Specifically, the beneficial effects of the present invention are mainly reflected in the following aspects:

[0089] From a macro perspective, this invention achieves comprehensive intelligence and automation in the architectural decoration design process. The system integrates the entire process, from spatial perception, demand analysis, solution generation, to optimization and evaluation, significantly improving design efficiency and quality. This system's advantages are particularly evident when handling complex and large-scale design projects, significantly shortening the design cycle and reducing labor costs.

[0090] In terms of system architecture, this invention adopts a modular design, with each functional module working closely together to form an efficient and flexible design ecosystem. The intelligent perception analysis module provides accurate basic data for subsequent design; the AI ​​design engine module generates personalized design solutions based on this data; the multi-dimensional optimization and evaluation module ensures that the design solution is optimized in multiple dimensions such as functionality, aesthetics, and economy; and the immersive visualization interaction module provides users with an intuitive and convenient design experience. This modular architecture not only improves the system's maintainability and scalability, but also lays the foundation for future functional upgrades and technological iterations.

[0091] From a technological innovation perspective, this invention employs cutting-edge algorithms and methods in multiple key areas. For example, it uses a topological persistence operator and a random projection matrix for spatial feature extraction, a quantum convolutional neural network for environmental feature fusion, and a generative adversarial network based on group theory and Lie algebra for design solution generation. These innovative algorithms not only improve system performance but also significantly expand its capabilities, enabling it to handle more complex and diverse design tasks.

[0092] This invention also excels in resolving technical contradictions. For example, personalized design and high efficiency often conflict with each other. However, through intelligent demand analysis and rapid solution generation, this system successfully achieves both personalized design and improved design efficiency. Similarly, this system achieves a balance between ensuring design quality and controlling costs through multi-dimensional optimization evaluation and intelligent material recommendation.

[0093] Another significant advantage of this system is its adaptive learning capabilities. By continuously analyzing user feedback and design results, the system continuously optimizes its design strategies and knowledge base, achieving continuous performance improvements. This increasing intelligence with use makes this system far more valuable over the long term than traditional design methods and tools.

[0094] In terms of collaborative design, the present invention effectively solves the information synchronization and version control issues in multi-person collaboration through the collaborative design unit and version management unit, greatly improving the efficiency and quality of team work. This is particularly important for the management of large and complex projects.

[0095] Finally, the invention's innovations in visualization and interaction also significantly enhance the user experience. Through immersive VR / AR technology, users can intuitively experience and adjust design solutions, which not only improves design accuracy but also enhances user engagement and satisfaction.

[0096] In summary, the intelligent architectural decoration design system and method of the present invention have achieved a qualitative leap in efficiency, quality, personalization, collaboration and other aspects, bringing revolutionary progress to the field of architectural decoration design and is expected to become a new standard and new paradigm for the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is a logic block diagram of the entire system of the present invention. DETAILED DESCRIPTION

[0098] See Figure 1 The present invention provides an intelligent architectural decoration design system, comprising an intelligent perception and analysis module 1, an AI design engine module 2, a multi-dimensional optimization and evaluation module 3, and an immersive visualization and interaction module 4. These modules work together to achieve intelligentization throughout the entire process, from spatial perception to final design solution.

[0099] The intelligent perception analysis module 1 is used to collect spatial feature information and environmental parameters and generate a fused feature matrix based on this information. Specifically, this module can collect geometric information of the building space using devices such as 3D laser scanners and depth cameras, while using various sensors to collect environmental parameters such as lighting, temperature and humidity, and acoustic characteristics. In a preferred embodiment, spatial feature information may include room dimensions, door and window locations, and wall structure, while environmental parameters may include natural light intensity, indoor temperature distribution, and noise levels.

[0100] Intelligent Perception Analysis Module 1 utilizes innovative mathematical methods to process and integrate this information. First, a spatial feature matrix is ​​generated using a topological persistence operator and a random projection matrix. This method effectively extracts and represents the geometric and topological features of the space, providing crucial foundational information for subsequent design processes.

[0101] Next, the intelligent perception analysis module 1 uses a quantum convolutional neural network to fuse the spatial feature matrix and environmental sensor data to generate a fused feature matrix. This quantum computing-based approach can more efficiently process high-dimensional data and capture the complex relationships between spatial features and environmental parameters.

[0102] The AI ​​design engine module 2 is in communication with the intelligent perception analysis module 1, and is configured to receive the fused feature matrix and generate a preliminary design solution based on it. The core of this module is a design generation system based on the latest artificial intelligence technology. In one embodiment, the AI ​​design engine module 2 first uses chaos theory and fractal geometry to establish a nonlinear mapping between user needs and environmental characteristics, generating a user needs matrix. This method can capture the complex nonlinear relationship between user needs and environmental characteristics, providing a foundation for generating personalized design solutions.

[0103] Next, the AI ​​Design Engine Module 2 uses a generative adversarial network based on group theory and Lie algebra to generate preliminary design proposals. This generative approach, based on advanced mathematical theory, can create more innovative and diverse design proposals.

[0104] The Multi-Dimensional Optimization Evaluation Module 3 communicates with the AI ​​Design Engine Module 2 and evaluates the functionality, aesthetics, and economics of the preliminary design proposal. Based on these evaluations, the module generates an optimized design. This module utilizes an innovative multi-objective optimization algorithm that simultaneously evaluates and optimizes across multiple dimensions. This approach comprehensively considers multiple design objectives and constraints, resulting in an optimized design that strikes a balance between functionality, aesthetics, and economics.

[0105] Through this innovative system architecture and algorithmic design, the intelligent architectural decoration design system of the present invention enables an efficient, personalized, and high-quality design process. The system not only accurately perceives and analyzes spatial characteristics and environmental parameters, but also generates design solutions that meet user needs based on this information, optimizing them across multiple dimensions. This approach significantly improves design efficiency while ensuring the quality and personalization of design solutions.

[0106] In practical applications, this system can be flexibly adjusted to the specific needs of architectural decoration projects. For example, for different types of architectural spaces (such as residential, office, and commercial spaces), the system can adjust its parameters and optimization targets to suit different design requirements. Furthermore, the system's adaptive learning capabilities enable it to continuously learn and improve from historical projects, further improving design quality and efficiency.

[0107] In summary, this invention provides an innovative intelligent architectural decoration design system and method. By combining advanced mathematical theory and artificial intelligence technology, it achieves intelligent control of the entire process, from spatial perception to final design solution. This not only greatly improves design efficiency but also opens up new possibilities for creating more personalized and high-quality architectural decoration designs.

[0108] The intelligent perception analysis module 1 of the present invention further includes a spatial feature extraction unit 11 and an environmental feature fusion unit 12. These two units work together to realize the conversion process from raw data to a fused feature matrix.

[0109] The spatial feature extraction unit 11 is primarily responsible for collecting point cloud data and generating a spatial feature matrix based on this data. In a preferred embodiment of the present invention, point cloud data collection can be accomplished using devices such as 3D laser scanners or depth cameras. These devices can quickly and accurately capture spatial geometric information, including room dimensions, wall structure, and door and window locations.

[0110] The spatial feature extraction unit 11 uses an innovative mathematical method to process these point cloud data. Specifically, it uses a topological persistence operator and a random projection matrix to generate a spatial feature matrix. This process can be expressed as follows:

[0111]

[0112] In this formula, S represents the generated spatial feature matrix, T is the topological persistence operator, P is the input point cloud data matrix, and R is the random projection matrix.

[0113] Suppose there is a 3D scan data of a room, point cloud data matrix It is an n×3 matrix, where each row represents the three-dimensional coordinates (x, y, z) of a point. For example:

[0114]

[0115] Topological persistence operator It can be calculated by the following steps:

[0116] 1. Calculate the Betti number β k (P):

[0117] For a one-dimensional structure (such as the walls of a room), β0 represents the number of connected components, and β1 represents the number of loops. Suppose the room has two walls and a doorway, then β0 = 1 (the entire room is a connected component) and β1 = 1 (the doorway forms a loop).

[0118] 2. Compute the topological persistence operator:

[0119]

[0120] In this example, assume that only β0 and β1 are considered:

[0121]

[0122] 3. The random projection matrix R can be a 3×2 matrix used to project the three-dimensional point cloud data onto a two-dimensional plane:

[0123]

[0124] 4. Calculate the spatial feature matrix S:

[0125]

[0126] In this example, since Therefore S will also be a zero matrix.

[0127] When designing a home, the system can analyze a 3D model of the room to determine the best furniture layout. For example, based on the room’s geometry and the location of doors and windows, the system can suggest the best place to place a sofa, ensuring it doesn’t block a path or windows.

[0128] Topological persistence operator The calculation method is as follows:

[0129]

[0130] Here, β j (P) is the k-th order Betti number, which reflects the topological characteristics of the point cloud data. Through this method, the system of the present invention can effectively extract and represent the geometric and topological characteristics of the space, providing important basic information for the subsequent design process.

[0131] The environmental feature fusion unit 12 is in communication with the spatial feature extraction unit 11. Its primary task is to receive the spatial feature matrix and environmental sensor data and fuse these two pieces of information. In one embodiment of the present invention, the environmental sensor data may include parameters such as light intensity, temperature and humidity, and acoustic characteristics. These parameters are crucial for architectural design because they directly impact the comfort and functionality of a space.

[0132] The environmental feature fusion unit 12 uses a quantum convolutional neural network to achieve data fusion. The advantage of this method is that it can process high-dimensional data and capture the complex relationship between spatial features and environmental parameters. The fusion process can be expressed as follows:

[0133]

[0134] Here, E is the fused environment feature matrix, Q represents the quantum convolution operation, is the environmental sensor data matrix. The specific definition of the quantum convolution operation is as follows:

[0135]

[0136] In this formula, U is the quantum gate operation, By using this quantum computing-based approach, the system of the present invention can more efficiently process and fuse complex spatial and environmental data.

[0137] Suppose there is a room's light intensity data, the environmental sensor data matrix is an m×1 vector representing the light intensity values ​​at different locations. For example:

[0138]

[0139] Hypothesis space feature matrix It has been calculated that it is an n×2 matrix (two-dimensional data after random projection):

[0140]

[0141] Quantum convolution operation It can be calculated by the following steps:

[0142] 1. Tensor Product

[0143]

[0144] 2. Quantum Gate Operation Assumptions is a simple rotation matrix:

[0145]

[0146] Where θ = π / 4 (45 degree rotation).

[0147] 3. Calculate the fusion feature matrix E:

[0148]

[0149] When designing an office, the system can optimize the layout of the lighting system based on the distribution of natural light. For example, if the natural light in a certain area is weak, the system can recommend adding artificial light to ensure that the entire office area can get enough light.

[0150] The AI ​​design engine module 2 of the present invention further includes a user demand mapping unit 21 and a design solution generating unit 22. These two units work together to realize the conversion process from user demand to preliminary design solution.

[0151] The primary function of the user needs mapping unit 21 is to receive the fused feature matrix and, based on this information, establish a nonlinear mapping between user needs and environmental features. In a preferred embodiment of the present invention, this mapping process utilizes methods based on chaos theory and fractal geometry. This method is advantageous in that it can capture the complex nonlinear relationship between user needs and environmental features, thereby generating a more accurate user needs matrix.

[0152] The process of user needs mapping can be expressed by the following formula:

[0153]

[0154] Here, N represents the generated user demand matrix, is the fractal mapping function, E is the input fusion feature matrix, and λ is the control parameter. The specific definition of the fractal mapping function is as follows:

[0155]

[0156] Through this method, the system of the present invention can more accurately understand and express the user's design requirements, providing important input for subsequent design solution generation.

[0157] Consider a home theater design project where a user wants to install a high-end audio system in the room. The environment feature matrix E contains information about the room's acoustic characteristics, dimensions, and other aspects. For example:

[0158]

[0159] Fractal mapping function It can be calculated by the following steps:

[0160] 1. Select the control parameter λ:

[0161] Assume that λ=0.8, which means that the user has higher requirements for acoustic effects.

[0162] 2. Calculate the user demand matrix N:

[0163]

[0164] In practical applications, a finite number of iterations is usually used, for example, n = 10:

[0165] N=λE(1-E) 10 ,

[0166] In home theater design, the system can recommend a suitable audio system configuration based on the user's preferences (such as the type of action movies they like to watch) and the acoustic characteristics of the room to ensure the best sound effects.

[0167] The design solution generation unit 22 is in communication with the user requirement mapping unit 21. Its main task is to receive the user requirement matrix and generate a preliminary design solution based on this information. In one embodiment of the present invention, the design solution generation adopts a generative adversarial network based on group theory and Lie algebra. The advantage of this method is that it can generate more innovative and diverse design solutions. The generation process can be expressed as follows:

[0168]

[0169] Here, D is the generated design matrix, G is the generator function, N is the input user demand matrix, and Z is the random noise matrix. is a Lie group element, X iis a Lie algebra basis. The specific definition of the generator function G is as follows:

[0170] G(NZ)=σ(W g ·[N,Z]+b g ),

[0171] In this formula, σ is the activation function, W g and b g Through this generation method based on advanced mathematical theory, the system of the present invention can create more innovative and diverse design solutions to meet the personalized needs of different users.

[0172] Assume there is a commercial space design project, the user needs matrix Contains the customer's brand image and market positioning information. The random noise matrix Z is an m×k matrix used to introduce diversity. For example:

[0173]

[0174] The generator function G(N,Z) can be calculated by the following steps:

[0175] 1. Weights and biases W g and b g :

[0176] Assume W g is a k×n matrix, b g is an n×1 vector.

[0177]

[0178] 2. Activation function σ:

[0179] Use the ReLU activation function σ(x)=max(0,x).

[0180] 3. Calculate the generator output:

[0181] G(NZ)=σ(W g ·[N,Z]+b g ),

[0182] 4. Lie group elements Assume α i =1,X i is a set of basis vectors representing different transformation directions.

[0183] In commercial space design, the system can generate multiple design options based on the client's brand image and market positioning, showcasing different decoration styles and layouts. For example, the system can generate a modern minimalist design option and a European classical style design option for the client to choose from.

[0184] The multi-dimensional optimization evaluation module 3 of the present invention uses functional analysis and variational methods for optimization evaluation. This method can simultaneously evaluate and optimize across multiple dimensions, including functionality, aesthetics, and economics. This module includes an objective function construction unit 31, a constraint setting unit 32, and an optimization solution unit 33.

[0185] The primary task of the objective function construction unit 31 is to construct an optimization objective function based on functional, aesthetic, and economic indicators. In a preferred embodiment of the present invention, functional indicators may include space utilization and pedestrian flow efficiency; aesthetic indicators may include color harmony and design balance; and economic indicators may include material cost and construction difficulty. These indicators are integrated into a comprehensive objective function to achieve multi-objective optimization.

[0186] Constraint setting unit 32 is responsible for setting constraints such as design space, material selection, and cost limits. These constraints ensure that the generated design solution is feasible and meets practical design requirements and limitations. For example, in one embodiment of the present invention, constraints may include room size restrictions, budget caps, and requirements for the use of specific materials.

[0187] The optimization solution unit 33 is in communication with the objective function construction unit 31 and the constraint condition setting unit 32. Its main task is to solve the optimal design solution based on the objective function and the constraints through the variational method. This process can be expressed as follows:

[0188]

[0189] here, It is the optimized design. is the Lagrangian function, and Ω is the design space. The specific definition of the Lagrangian function is as follows:

[0190]

[0191] In this formula, f(D) is the objective function, g1, g2, and g3 are constraint functions, and λ1, λ2, and λ3 are Lagrange multipliers. This approach allows the system to comprehensively consider multiple design objectives and constraints, resulting in an optimized design solution that strikes a balance between functionality, aesthetics, and cost-effectiveness.

[0192] Suppose there is a high-end restaurant design project, the preliminary design plan Contains information such as the restaurant's layout and decoration style. Objective function f(D), constraint function g1(D), and g3(x) represent cost, aesthetics, and practicality, respectively. For example:

[0193] f(D)=0.5D1+0.3D2,

[0194]

[0195]

[0196] g3(x)=x 2 ,

[0197] Lagrange multipliers λ1, λ2, and λ3 are used to adjust the importance of each objective. For example:

[0198]

[0199] Optimization process:

[0200] By solving Find the optimal design solution

[0201] In high-end restaurant design, the system can optimize the restaurant's layout and decoration while meeting the budget and improving the customer experience. For example, the system can propose a design solution that is both beautiful and practical based on the restaurant's budget constraints, while ensuring the restaurant's functionality and comfort.

[0202] The immersive visualization interaction module 4 of the present invention further includes a 3D rendering unit 41, a VR interaction unit 42, and an AR preview unit 43. These units work together to provide users with a rich visual experience and interaction mode, making the display and adjustment of design solutions more intuitive and convenient.

[0203] The primary function of the 3D rendering unit 41 is to receive the optimized design solution and generate a high-quality three-dimensional rendered image using ray tracing technology. In one embodiment of the present invention, ray tracing technology can simulate the propagation of light in space, generating images with realistic lighting effects such as reflection, refraction, and shadows. This method can produce extremely realistic rendering effects, allowing users to more intuitively experience the visual effects of the design solution.

[0204] The VR interaction unit 42 is in communication with the 3D rendering unit 41. Its primary task is to construct a virtual reality scene based on the 3D rendered image, providing immersive spatial navigation and real-time design adjustments. In a preferred embodiment of the present invention, users can use VR devices to enter the virtual design space, observe design details from different angles and distances, and even make real-time design modifications, such as changing wall color or adjusting furniture positions. This interactive method significantly enhances the user experience and makes the design process more intuitive and efficient.

[0205] The AR preview unit 43 is also in communication with the 3D rendering unit 41. Its main function is to overlay the 3D rendered image onto the real environment and provide real-time mobile preview and gesture interaction. In one embodiment of the present invention, users can overlay virtual design plans onto the real space using a mobile device such as a smartphone or tablet. This augmented reality technology allows users to intuitively experience the effects of the design plan in the real space, greatly improving the visualization of the design plan and the user's decision-making efficiency.

[0206] Through this innovative visualization and interaction approach, the system of the present invention not only improves the presentation quality of design solutions but also enhances user participation in the design process, thereby better meeting user needs and improving satisfaction with the design solutions. The system of the present invention also includes a knowledge base module 5 and an adaptive learning module 6, which work together to achieve continuous learning and performance optimization of the system.

[0207] The knowledge base module 5 is in communication with the AI ​​design engine module 2. Its primary function is to store historical design cases and design rules, providing knowledge support for the AI ​​design engine module 2. In a preferred embodiment of the present invention, the knowledge base module 5 utilizes a distributed storage architecture, enabling efficient management and retrieval of large amounts of design data. This data includes, but is not limited to, past design plans, material libraries, and design specifications.

[0208] A key feature of the Knowledge Base module 5 is its dynamic updating capability. As new design cases are added, the knowledge base automatically updates and optimizes its content. For example, when a new design solution is approved and implemented by a user, its key features and parameters are extracted and added to the knowledge base. This mechanism ensures that the knowledge base always contains the latest and most relevant design information.

[0209] Adaptive learning module 6 is in communication with knowledge base module 5. Its primary task is to analyze user feedback and design results and update the design knowledge and rules in knowledge base module 5 accordingly. In one embodiment of the present invention, adaptive learning module 6 employs a reinforcement learning algorithm to continuously optimize the system's design strategy based on user feedback and actual design results.

[0210] Specifically, Adaptive Learning Module 6 collects user feedback on generated design solutions, including satisfaction ratings and specific modification suggestions. It also analyzes the actual performance of implemented design solutions, such as space utilization and user comfort. Based on this information, Adaptive Learning Module 6 adjusts the design parameters and weights in AI Design Engine Module 2 to improve the quality of future design solutions.

[0211] Preferably, the adaptive learning module 6 also has cross-project learning capabilities. It can extract common knowledge from different types of design projects and apply this knowledge to new design tasks. For example, certain principles learned from office space design may be applied to home design, thus achieving knowledge transfer and reuse.

[0212] The system of the present invention also includes a BIM integration module 7, which is in communication with the AI ​​design engine module 2 and the multi-dimensional optimization and evaluation module 3. The main function of the BIM integration module 7 is to convert the optimized design scheme into a BIM (Building Information Model) model to achieve data interaction with the building information model.

[0213] In a preferred embodiment of the present invention, the BIM integration module 7 utilizes semantic mapping technology to automatically convert system-generated design solutions into a standard BIM format. This process involves not only the conversion of geometric information but also the mapping of non-geometric data such as material properties, cost information, and construction procedures. For example, a system-generated wall design will not only be represented in the BIM model with the correct geometric shape, but also include detailed information such as material type, thickness, and sound insulation performance.

[0214] Another important function of the BIM integration module 7 is bidirectional data flow. It not only converts system-generated design solutions into BIM models but also extracts information from existing BIM models for use in the system's design process. This bidirectional interaction significantly enhances the compatibility and interoperability of the system with other architectural design tools.

[0215] Preferably, the BIM integration module 7 also supports version control and collaborative design. When multiple designers work on a project at the same time, the BIM integration module 7 can manage different versions of the design and coordinate the modifications of each designer to ensure the consistency and integrity of the design.

[0216] The AI ​​design engine module 2 of the present invention also includes a style matching unit 23 and a material recommendation unit 24. These two units work together to further improve the personalization and practicality of the design solution.

[0217] The main function of the style matching unit 23 is to identify design styles based on a deep learning algorithm and select matching design elements from a library of style elements. In one embodiment of the present invention, the style matching unit 23 uses a convolutional neural network (CNN) to analyze and identify different design styles. This neural network has been trained on a large number of design cases and can accurately identify various common design styles, such as modern minimalist, Nordic, and traditional Chinese.

[0218] The workflow of the style matching unit 23 can be described as follows: First, it analyzes the reference image or text description provided by the user to extract key stylistic features. Then, it searches the system's stylistic element library for matching elements, which may include specific color combinations, furniture types, decorative items, and so on. Finally, it integrates these matching elements into the design plan to ensure overall stylistic consistency.

[0219] Preferably, the style matching unit 23 also has the ability to blend styles. When the user's needs involve a mixture of multiple styles, it can intelligently balance the elements of different styles to create a unique and harmonious design effect.

[0220] Material recommendation unit 24 is in communication with style matching unit 23. Its primary task is to recommend suitable decorative materials based on the selected design style and environmental characteristics. In a preferred embodiment of the present invention, material recommendation unit 24 utilizes a multi-criteria decision-making approach, comprehensively considering material performance, cost, and environmental performance indicators to optimize material selection.

[0221] Specifically, the material recommendation unit 24 first determines the range of materials that match the design style based on the output of the style matching unit 23. It then considers environmental characteristics (such as indoor temperature, humidity, and lighting conditions) and user needs (such as budget constraints and environmental requirements) to select the most suitable material combination. During this process, the material recommendation unit 24 weighs various factors, such as material durability, maintenance costs, and environmental friendliness, to ensure that the recommended materials are not only aesthetically pleasing, but also practical and sustainable.

[0222] Preferably, the material recommendation unit 24 also has an intelligent substitution function. When an ideal material is unavailable due to high cost or supply issues, it can automatically recommend an alternative material with similar performance and appearance, thereby ensuring the feasibility and flexibility of the design solution.

[0223] Through the collaborative work of style matching unit 23 and material recommendation unit 24, the system of the present invention can generate design solutions that both meet the user's aesthetic needs and are practical and feasible, greatly improving the personalization and practicality of the design. The multi-dimensional optimization and evaluation module 3 of the present invention also includes a collaborative design unit 34 and a version management unit 35. These two units work together to greatly enhance the system's collaborative capabilities and design efficiency.

[0224] The primary function of the collaborative design unit 34 is to support multi-user remote collaborative design and achieve real-time synchronization of design modifications and evaluation results. In a preferred embodiment of the present invention, the collaborative design unit 34 utilizes distributed real-time collaboration technology, allowing multiple designers to work on the same project simultaneously, regardless of their location.

[0225] Specifically, the collaborative design unit 34 provides a shared virtual design space. When one designer makes changes to a design, those changes are reflected in real time across all participants' interfaces. For example, one designer might be adjusting the living room layout while another is simultaneously selecting kitchen finishes. The collaborative design unit 34 ensures that these operations are seamlessly integrated, avoiding conflicts and maintaining overall design consistency.

[0226] Preferably, the collaborative design unit 34 also features intelligent conflict resolution. When multiple designers make conflicting modifications to the same element, the system automatically detects and alerts the relevant personnel, providing conflict resolution suggestions. This significantly reduces potential errors and confusion during the collaborative process.

[0227] The version management unit 35 is in communication with the collaborative design unit 34. Its main task is to record the evolution history of the design solution and support comparison and rollback between different versions. In one embodiment of the present invention, the version management unit 35 adopts a graph-based version control system, which can efficiently manage complex design modification histories.

[0228] The workflow of the version management unit 35 can be described as follows: Whenever a significant design change occurs, the system automatically creates a new version. Each version not only contains the complete design data but also records metadata such as the specific content of the change, the designer who made the change, and the reason for the change. This allows the design team to easily track the evolution of the design and understand the reasons behind each decision.

[0229] Preferably, the version management unit 35 also provides powerful comparison and rollback capabilities. Designers can intuitively compare the differences between any two versions, including visual comparison of 3D models. If a modification is found to have caused a problem, the designer can easily roll back to the previous version or undo only the specific modification without affecting other completed work.

[0230] Through the collaborative work of the collaborative design unit 34 and the version management unit 35, the system of the present invention greatly improves the efficiency and flexibility of team collaboration, and also provides a powerful tracking and management tool for the design process.

[0231] Finally, the present invention also provides an intelligent design method for architectural decoration, which closely corresponds to the above-mentioned system and realizes the intelligence of the entire process from data collection to the generation of the final design scheme.

[0232] The method first collects spatial feature information and environmental parameters through the intelligent perception analysis module 1 (step S1). In a preferred embodiment of the present invention, this step may involve using a 3D laser scanner to obtain spatial geometric information, while simultaneously collecting environmental data such as lighting, temperature and humidity through various sensors.

[0233] Next, the method generates a spatial feature matrix using a topological persistence operator and a random projection matrix (step S2).

[0234] Then, the method uses a quantum convolutional neural network to fuse the spatial feature matrix and the environmental sensor data to generate a fused feature matrix (step S3).

[0235] Next, the method establishes a nonlinear mapping between user needs and environmental characteristics based on chaos theory and fractal geometry, and generates a user needs matrix (step S4).

[0236] Then, the method uses a generative adversarial network based on group theory and Lie algebra to generate a preliminary design solution based on the user demand matrix (step S5).

[0237] Next, the method uses functional analysis and variational methods to evaluate the functionality, aesthetics, and economic efficiency of the preliminary design through the multi-dimensional optimization and evaluation module 3 (step S6). Based on the evaluation results, the method generates an optimized design (step S7). Then, the immersive visualization and interaction module 4 generates a three-dimensional visualization model of the optimized design (step S8).

[0238] The method also provides an interactive design adjustment interface to support users to make real-time modifications (step S9). Finally, the adaptive learning module 6 analyzes user feedback and design effects and updates the design knowledge base (step S10).

[0239] This method, developed by the present invention, intelligently implements the entire process from data collection to final design solution generation, significantly improving the efficiency and quality of architectural decoration design. This method not only generates high-quality initial designs but also continuously optimizes them based on user feedback, achieving intelligent and personalized design processes.

[0240] In order to verify the superiority of the present invention, a typical architectural decoration design scenario was selected for simulation testing. Specifically, a three-bedroom, two-living room house with an area of ​​120 square meters was selected as the test object, and a comprehensive interior decoration design was required for it.

[0241] Example 1 employed the intelligent architectural decoration design system and method of the present invention. Comparative Example 1 employed traditional manual design methods, with experienced designers completing the entire design process. Comparative Example 2 employed commercially available architectural decoration design software, which has basic 3D modeling and rendering capabilities but lacks intelligent optimization and adaptive learning capabilities.

[0242] The test conditions are as follows:

[0243] 1. Space Information: 120 square meters, three-bedroom, two-living room residence, including a living room, dining room, master bedroom, second bedroom, study, kitchen and two bathrooms.

[0244] 2. User requirements: modern minimalist style, focusing on functionality and comfort, with a budget within 300,000 yuan.

[0245] 3. Environmental parameters: Good natural lighting conditions, located in the city center, sound insulation needs to be considered.

[0246] 4. Design cycle: The entire process from preliminary plan to final design is required to be completed within 2 weeks.

[0247] The following five key indicators were selected to evaluate the quality and efficiency of the design solutions:

[0248] 1. Design completion time: the time required from receiving the design task to generating the final solution.

[0249] 2. Number of solution iterations: The number of modifications and optimizations required to reach the final solution.

[0250] 3. Space utilization: the ratio of effectively used space to total area.

[0251] 4. User satisfaction: A comprehensive score based on a questionnaire (out of 100 points).

[0252] 5. Budget control accuracy: the percentage deviation between actual cost and budget.

[0253] The testing methods for these indicators are as follows:

[0254] 1. Design completion time: record the actual time from project start to finalization of the plan.

[0255] 2. Number of solution iterations: Count the number of modifications and optimizations during the design process.

[0256] 3. Space utilization: Calculate the ratio of effective use space to total area through 3D model.

[0257] 4. User satisfaction: Invite 50 potential users to rate the final solution and calculate the average score.

[0258] 5. Budget control accuracy: Calculate the actual cost based on the final plan and compare it with the budget.

[0259] The test results are shown in the following table:

[0260]

[0261] The test results show that the intelligent design system for architectural decoration of the present invention has significant advantages in various indicators:

[0262] 1. Design Completion Time: The system completed the design in just three days, 75% faster than manual design and 62.5% faster than conventional software. This fully demonstrates the efficiency of the system, whose intelligent algorithm can quickly generate and optimize design solutions.

[0263] 2. Number of solution iterations: The proposed method achieved the final solution in just five iterations, three fewer than manual design and five fewer than conventional software. This demonstrates that the proposed multi-dimensional optimization and evaluation module more accurately captures user needs and generates higher-quality initial solutions.

[0264] 3. Space Utilization: This method achieved a 92% space utilization rate, significantly higher than the other two methods. This demonstrates that the AI ​​design engine of this method can more intelligently plan space layouts and fully utilize every inch of space.

[0265] 4. User Satisfaction: This method received a high score of 92, far exceeding the other two methods. This reflects that the system of this invention can better understand and meet user needs, and the generated design solutions are more in line with users' aesthetic and functional needs.

[0266] 5. Budget Control Precision: The budget deviation of this invention is only 2.5%, which is very precise. This is due to the system's intelligent material recommendation and cost optimization functions, which can ensure design quality while strictly controlling costs.

[0267] These test results fully demonstrate the superiority of this invention. It not only significantly improves design efficiency and shortens design cycles, but also generates higher-quality designs that better meet user needs. The significant improvements in space utilization and user satisfaction demonstrate the system's powerful ability to understand user needs and optimize spatial layouts.

[0268] Furthermore, the system's superior performance is also reflected in its adaptive learning capabilities. While this single test case cannot directly reflect this, it is foreseeable that as the system handles more projects, its performance will continue to improve, and the quality of design solutions will continue to rise – an advantage that traditional methods and standard software lack.

[0269] In summary, the intelligent architectural decoration design system and method of the present invention are significantly superior to existing technologies in terms of efficiency, quality, and user satisfaction, bringing revolutionary progress to the field of architectural decoration design.

[0270] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent design system for architectural decoration, characterized in that: include: Intelligent perception analysis module for: Collect spatial feature information and environmental parameters; generating a fusion feature matrix based on the spatial feature information and the environmental parameters; The AI ​​design engine module is in communication with the intelligent perception analysis module and is used to: receiving the fused feature matrix; generating a preliminary design scheme based on the fusion feature matrix; A multi-dimensional optimization and evaluation module is communicatively connected to the AI ​​design engine module and is used to: Evaluate the functionality, aesthetics and economic feasibility of the preliminary design; generating an optimized design solution based on the evaluation results; The immersive visualization interaction module is communicatively connected to the multi-dimensional optimization evaluation module and is used to: receiving the optimized design solution; Generating a three-dimensional visual model of the optimized design solution; Provides an interactive design adjustment interface.

2. The intelligent building decoration design system according to claim 1, characterized in that: The intelligent perception analysis module includes: Spatial feature extraction unit, used for: Collect point cloud data; Based on the point cloud data, a spatial feature matrix is ​​generated by a topological persistence operator and a random projection matrix; The environmental feature fusion unit is communicatively connected to the spatial feature extraction unit and is used to: receiving the spatial feature matrix and environmental sensor data; The spatial feature matrix and environmental sensor data are fused through a quantum convolutional neural network to generate a fused feature matrix.

3. The intelligent building decoration design system according to claim 2, characterized in that: The AI ​​design engine module includes: User requirements mapping unit, used to: receiving the fused feature matrix; Based on chaos theory and fractal geometry, a nonlinear mapping between user needs and environmental characteristics is established to generate a user needs matrix; A design solution generating unit is communicatively connected to the user demand mapping unit and is configured to: receiving the user demand matrix; A generative adversarial network is constructed based on group theory and Lie algebra to generate preliminary design solutions.

4. The intelligent building decoration design system according to claim 1, characterized in that: The multi-dimensional optimization evaluation module uses functional analysis and variational methods to perform optimization evaluation, including: Objective function building block for: Constructing an optimization objective function based on functional, aesthetic and economic indicators; Constraint setting unit, used to: Set constraints such as design space, material selection, and cost limits; The optimization solving unit is in communication with the objective function building unit and the constraint condition setting unit, and is used to: Based on the objective function and constraints, the optimal design solution is solved by variational method.

5. The intelligent building decoration design system according to claim 1, characterized in that: The immersive visualization interaction module includes: 3D rendering unit for: receiving the optimized design solution; Use ray tracing technology to generate high-quality 3D rendered images; A VR interaction unit is connected to the 3D rendering unit and is used to: constructing a virtual reality scene based on the three-dimensional rendered image; Provide immersive space roaming and real-time design adjustment capabilities; An AR preview unit is connected to the 3D rendering unit and is configured to: superimposing the three-dimensional rendered image into a real environment; Provides real-time preview and gesture interaction functions on mobile devices.

6. The intelligent building decoration design system according to claim 1, characterized in that: Also includes: A knowledge base module, in communication with the AI ​​design engine module, is configured to: Store historical design cases and design rules; Providing knowledge support for the AI ​​design engine module; An adaptive learning module, in communication with the knowledge base module, is configured to: Analyze user feedback and design effects; The design knowledge and rules in the knowledge base module are updated.

7. The intelligent building decoration design system according to claim 1, characterized in that: Also includes: The BIM integration module is in communication with the AI ​​design engine module and the multi-dimensional optimization evaluation module and is used to: Converting the optimized design scheme into a BIM model; Realize data interaction with building information model.

8. The intelligent building decoration design system according to claim 1, characterized in that: The AI ​​design engine module also includes: Style matching unit, used to: Identify design styles based on deep learning algorithms; Select matching design elements from the style element library; The material recommendation unit is in communication with the style matching unit and is configured to: Recommend suitable decorative materials based on the selected design style and environmental characteristics; Optimize selection by considering material performance, cost and environmental indicators.

9. The intelligent building decoration design system according to claim 1, characterized in that: The multi-dimensional optimization evaluation module also includes: Collaborative design unit for: Support multi-user remote collaborative design; Synchronize design modifications and evaluation results in real time; A version management unit, in communication with the collaborative design unit, configured to: Record the evolution of the design; Supports comparison and rollback between different versions.

10. An intelligent design method for architectural decoration, based on the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1, collects spatial feature information and environmental parameters through the intelligent perception analysis module; S2, based on the spatial feature information and environmental parameters, generating a spatial feature matrix by using a topological persistence operator and a random projection matrix; S3, using a quantum convolutional neural network to fuse the spatial feature matrix and the environmental sensor data to generate a fused feature matrix; S4, based on chaos theory and fractal geometry, establishes a nonlinear mapping between user needs and environmental characteristics to generate a user needs matrix; S5, generating a preliminary design solution based on the user demand matrix using a generative adversarial network based on group theory and Lie algebra; S6, through a multi-dimensional optimization evaluation module, using functional analysis and variational methods to evaluate the functionality, aesthetics and economy of the preliminary design scheme; S7, generating an optimized design solution based on the evaluation results; S8, using an immersive visualization interaction module to generate a three-dimensional visualization model of the optimized design solution; S9, provides an interactive design adjustment interface, allowing users to make real-time modifications; S10, analyze user feedback and design effects through the adaptive learning module and update the design knowledge base.

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