AI indoor decoration design optimization system based on BIM technology

Abstract

The invention provides an AI indoor decoration design optimization system based on a BIM technology, and relates to the technical field of data processing, and the AI indoor decoration design optimization system comprises a graphic design module, a BIM modeling module, a BIM analysis module, a BIM design optimization module and a user interaction module. The plane design module is used for acquiring a decoration design drawing of an indoor space of a user; the BIM modeling module is used for converting the two-dimensional decoration design drawing into a three-dimensional BIM model; the BIM analysis module is used for carrying out analysis based on a BIM model and automatically generating various decoration design schemes; the BIM design optimization module is used for optimizing an intentional decoration design scheme selected by the user from the multiple decoration design schemes; and the user interaction module is used for displaying the optimized decoration design scheme. According to the method, various decoration design schemes can be automatically generated for customers to select, manual modeling does not need to be depended on decoration designers, the time cost is reduced, and the indoor decoration design efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an AI interior decoration design optimization system based on BIM technology. Background Art

[0002] Interior decoration design is something people often encounter in life. Decoration design is not just about decorating the space, but also focuses on the overall layout of the space, the selection of materials, the matching of colors and the use of light to meet the living and psychological needs of the residents or users.

[0003] BIM (Building Information Modeling) is a digital 3D model technology widely used in the fields of architecture, engineering and construction (AEC). BIM technology integrates multi-dimensional information and digitizes all stages of a building project (from design, construction to operation and maintenance) to achieve higher efficiency, accuracy and collaboration.

[0004] At present, although there is technology for interior decoration design based on BIM models, interior decoration design solutions often rely on decoration designers to carry out design planning, manually perform 3D modeling of the room, and add materials, lamps, furniture and other contents to the 3D model to form a complete interior decoration design solution. There is still a lack of technology for intelligent and complete decoration design solutions. The time cost of manual modeling and design is high, and the efficiency of interior decoration design is low. At the same time, under the manual design mode, it is difficult for designers to generate a variety of different styles or layouts for customers to choose from in a short period of time. Usually, only a few alternative solutions can be provided. The lack of diversity and selection space in the solution generation method limits customers' choices and the satisfaction of personalized needs. Summary of the invention

[0005] In order to solve the technical problems in the prior art that the design planning relies on decoration designers and the rooms are manually 3D modeled, which results in high time cost and low efficiency of interior decoration design; in the manual design mode, it is difficult for designers to generate a variety of different styles or layouts for customers to choose from in a short time, and usually only a few alternatives can be provided. The scheme generation method lacking diversity and selection space limits the customer's choice and the satisfaction of personalized needs, the present invention provides an AI interior decoration design optimization system based on BIM technology.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] A first aspect of an embodiment of the present invention provides an AI interior decoration design optimization system based on BIM technology, including:

[0008] Graphic design module, used to obtain the decoration design drawings of the user's indoor space;

[0009] A BIM modeling module, used to convert the two-dimensional decoration design drawing into a three-dimensional BIM model;

[0010] A BIM analysis module, used to analyze based on the BIM model and automatically generate a variety of decoration design solutions;

[0011] BIM design optimization module, used to optimize the intended decoration design scheme selected by the user from a variety of decoration design schemes;

[0012] The user interaction module is used to provide an interactive interface between the system and the user and display the optimized decoration design plan.

[0013] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0014] In the present invention, the two-dimensional decoration design drawing can be automatically converted into a three-dimensional BIM model, and analysis can be performed based on the BIM model to automatically generate a variety of decoration design schemes for customers to choose from, so as to meet the personalized needs of customers. The intended decoration design scheme selected by the user from a variety of decoration design schemes can be optimized without relying on manual modeling by decoration designers, thus reducing time costs and improving the efficiency of interior decoration design. The environmental friendliness and resource sustainability of materials can also be considered in the optimization process, thus facilitating the realization of green buildings and providing users with a healthier and more environmentally friendly indoor environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A structural schematic diagram of an AI interior decoration design optimization system based on BIM technology provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0018] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0019] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0020] Reference Manual Attached Figure 1 , which shows a flow chart of an AI interior decoration design optimization system based on BIM technology provided in an embodiment of the present invention.

[0021] An embodiment of the present invention provides an AI interior decoration design optimization system based on BIM technology, including: a graphic design module, a BIM modeling module, a BIM analysis module, a BIM design optimization module and a user interaction module.

[0022] The graphic design module is used to obtain the decoration design drawings of the user's interior space.

[0023] Specifically, designers or users can add interior decoration design drawings by uploading them.

[0024] BIM modeling module is used to convert two-dimensional decoration design drawings into three-dimensional BIM models.

[0025] In a possible implementation, the BIM modeling module is specifically used to perform S201 to S204:

[0026] S201: Based on the digital image technology of convolutional neural network, identify the walls, doors and windows in the two-dimensional decoration design drawings.

[0027] Among them, Convolutional Neural Network (CNN) is a deep learning model specifically used to process image and video data. CNN extracts local features in images through convolution operations, and has significant advantages in computational efficiency and recognition accuracy. It is the core technology in computer vision, image recognition, target detection and other fields.

[0028] Optionally, S201 specifically includes:

[0029] In the input layer of the convolutional neural network, input the decoration design drawing.

[0030] In the convolutional layer of the convolutional neural network, the graphic features in the decoration design picture are extracted.

[0031] In the pooling layer of the convolutional neural network, the extracted line features are subjected to dimensionality reduction.

[0032] In the fully connected layer of the convolutional neural network, the features in the convolutional layer and the pooling layer are summarized.

[0033] In the classification layer of the convolutional neural network, walls, doors and windows in the decoration design drawings are identified.

[0034] In the present invention, CNN-based image recognition technology provides an accurate and efficient solution for the automated processing of two-dimensional decoration drawings. Through feature extraction by the convolution layer, dimensionality reduction by the pooling layer, aggregation by the fully connected layer, and recognition by the classification layer, the solution can accurately and efficiently identify the wall, door, and window information in the drawings, providing accurate data support for subsequent three-dimensional modeling and optimization.

[0035] S202: Determine room size information based on wall, door and window recognition results.

[0036] Optionally, S202 specifically includes: determining the closed area of ​​the decoration design drawing based on the recognition results of the wall and the door in combination with the connectivity detection algorithm, and determining the size information of each room.

[0037] Among them, connected component labeling is an image processing technology that can mark different connected areas in an image as independent areas. Applying the connectivity detection algorithm, the closed area in the image, that is, the outline of the room, can be identified.

[0038] Specifically, the recognition results of walls, doors and other elements can be binarized to generate a binary image containing the outlines of walls and doors. In the case of thin wall lines, an appropriate expansion operation can be performed to make the wall area more obvious, thereby improving the accuracy of subsequent connectivity detection. Using the binary image of walls and doors, a connectivity detection algorithm (for example, Flood Fill algorithm or DFS / BFS traversal) is used to identify all closed spatial areas. The algorithm will traverse the pixels in the image one by one and mark each connected area, excluding unclosed spaces. When detecting closed areas, the location of the door needs to be processed as an interrupted area of ​​the wall connection to ensure that the location of the door does not affect the closure detection, so as to correctly identify the boundary of the room. The closed area obtained by the connectivity detection algorithm is the outline of the room. According to the boundary points of the closed area, the length, width and area of ​​the room are calculated. The pixel values ​​in the image can be converted into actual sizes through the BIM scale to obtain the accurate room area.

[0039] In the present invention, by determining the closed area based on the recognition results of walls and doors and combining the connectivity detection algorithm, the boundary and size information of the room can be obtained quickly and accurately. This method not only realizes efficient and standardized data processing, but also improves the intelligence level of the design process, laying a solid foundation for subsequent BIM modeling, decoration design and construction management.

[0040] S203: Generate a three-dimensional BIM model based on the identified room size information, walls, doors and windows.

[0041] Specifically, the basic three-dimensional structure of each room is generated based on the room size information, including the length, width and height of the room. The ground can usually be regarded as the XY plane, and the room height extends along the Z axis. The space of each floor of the room is first generated, and then stacked layer by layer to build a complete building structure. According to the plane position and thickness of the wall, the wall outline is generated along the boundary of the room. Using the standard building floor height or specific room height information, the wall is extended from the ground to the top surface to form a three-dimensional wall. In the BIM model, each wall is given attributes such as material, color and thickness for use in the subsequent design and construction stages. For example, it can be specified as a load-bearing wall, a soundproof wall or a decorative wall. Then, according to the position of the door in the recognition result, the door opening is accurately located at the corresponding position of the wall. Ensure that the position and size of the door are consistent with the annotation of the floor plan. Generate the three-dimensional geometry of the door, including the door frame and door leaf, based on the width, height and thickness information of the door. In the BIM software, set the opening and closing direction and angle of the door to meet the requirements in the architectural design drawings. For example, specify it as an inward opening, outward opening or sliding door. Assign material, color, model and other attribute information to the door for reference in later construction and maintenance. According to the identified window position and size, add the window opening to the corresponding wall to ensure that the size and position of the window conform to the design drawings. Generate a 3D model of the window according to the width, height and depth of the window, including the window frame and glass part. The thickness and material of the window frame can be set according to actual needs. Specify the orientation and opening method of the window (such as casement, sliding, fixed, etc.) in the BIM model to meet the functional and ventilation requirements. Define the window's material, transparency, thermal insulation performance and other attributes to provide data support for subsequent lighting and energy consumption analysis. According to the room's size information, add the floor component and set the material, texture and thickness for the floor. For example, select materials such as wooden floor, tile or carpet. Generate a ceiling at the upper position of the room height to close the upper boundary of the room, and set the material and lamp opening position and other attributes. Assign real building materials and texture effects to each part of the BIM model to ensure the realism of the 3D model. You can choose different materials such as concrete, brick, plaster, glass, wood, etc. According to the functional requirements of the design plan, furniture and fixtures (such as kitchen and bathroom equipment, lamps) are added to the BIM model to make it closer to the actual use needs. The interior decoration elements of the room, such as curtains and lighting fixtures, can be further refined to improve the integrity and visualization of the model.

[0042] After generating the three-dimensional BIM model, the detection function of the BIM software is used to check whether there are problems such as space overlap and structural conflict in the model to ensure the accuracy and constructability of the model.

[0043] In the present invention, a three-dimensional BIM model that meets the design requirements can be automatically generated by using the identification information of walls, doors and windows. The model not only has the three-dimensional spatial structure of the building, but also contains rich material, size and attribute information, providing data support for subsequent design optimization, energy consumption analysis and construction.

[0044] S204: Determine room function information.

[0045] Optionally, S204 specifically includes:

[0046] When there are text annotations in the decoration design drawings, OCR technology is used to determine the functional information of each room.

[0047] Among them, OCR (Optical Character Recognition) technology is a technology used to recognize text in images and convert them into editable text. OCR extracts character information from scanned documents, photos or other images through image processing and pattern recognition, thereby digitizing the text content.

[0048] In the absence of text annotations on the decoration design drawings, feature matching technology is used to determine the functional information of each room.

[0049] Specifically, images of common room function symbols (such as sinks, beds, sofas, toilets, bathtubs, etc.) are collected and sorted to establish a feature template library, where each symbol represents a room function. Afterwards, the decoration design drawings are denoised and binarized to make the contrast between the room symbols and the background more obvious, which is convenient for feature extraction. Methods such as Canny edge detection are used to extract the edges of objects in the room, highlight the outline of the symbols, and reduce the interference of the background. Algorithms such as SIFT (scale-invariant feature transform), SURF or ORB are used to detect key points in the room symbol area to extract the features of the symbols. Descriptors (such as feature vectors) are generated for each key point, and the local features of the symbols are converted into numerical representations to facilitate subsequent matching with the template library. The extracted room symbol features are matched with the feature templates in the template library. Algorithms such as FLANN (fast approximate nearest neighbor) or BFMatcher (brute force matching) are used to compare the feature symbols in the drawings with the symbols in the template library one by one. Each matching result is scored, and the higher the score, the more consistent the template is with the symbol in the drawing. By setting the scoring threshold, the matches that meet the requirements are screened out to avoid misidentification.

[0050] In the present invention, the room function information is automatically identified by using OCR and feature matching technology, which can achieve fast, efficient and accurate function labeling and improve the intelligence level of the design and modeling process. Through the automatic recognition technology, not only can the work efficiency and data accuracy be improved, but also the entire decoration design process can be made more flexible, intelligent and user-friendly, providing a solid data foundation for the construction and subsequent application of BIM models.

[0051] The BIM analysis module is used to analyze based on the BIM model and automatically generate a variety of decoration design solutions.

[0052] In a possible implementation, the BIM analysis module is specifically used to perform S301 to S303:

[0053] S301: Analyze the room dimensions and room functional attributes of the BIM model.

[0054] S302: Obtain the decoration style preferred by the user.

[0055] S303: Based on the decoration style, room size and room functional attributes, a variety of decoration design solutions are automatically generated based on artificial neural networks.

[0056] Among them, Artificial Neural Network (ANN) is a computational model that simulates the neural network of the human brain and is widely used in image recognition, speech recognition, natural language processing and other fields. Neural networks imitate the learning and processing methods of the human brain, gradually learning the characteristics and patterns of data, thereby completing tasks such as classification and prediction.

[0057] Optionally, S303 specifically includes:

[0058] The decoration characteristic vector is determined according to the decoration style, room size and room functional attributes.

[0059] In the input layer of the artificial neural network, the decoration feature vector is input.

[0060] Optionally, the decoration characteristic vector includes: room length, width, height, room function, material preference, style preference, and color preference.

[0061] In the hidden layer of the artificial neural network, the input state vector is weighted and summed through each hidden layer neuron to obtain the hidden state vector:

[0062]

[0063] Among them, y j represents the hidden state of the jth hidden layer neuron output, σ1 represents the hidden layer activation function, W jrepresents the weight vector of the jth hidden layer neuron, T represents the transpose operation, X represents the input state vector, and b j represents the bias term of the jth hidden layer neuron, ω ij represents the weight of the i-th state parameter in the j-th hidden layer neuron, x i represents the i-th state parameter value, and n represents the total number of state parameters.

[0064] In the matching layer of the artificial neural network, the recommendation index of each decoration design scheme is calculated according to the hidden state vector:

[0065]

[0066] Among them, ρ i represents the recommendation index of the i-th decoration design scheme, σ2 represents the output layer activation function, ω ij represents the connection weight between the jth hidden layer neuron and the output node represented by the i-th decoration design scheme, b i represents the bias term of the output node represented by the i-th decoration design scheme, and m represents the total number of hidden layer neurons.

[0067] In the output layer of the artificial neural network, a preset number of decoration design plans with high recommendation index rankings are output.

[0068] It should be noted that by using the characteristic vector input and hidden state vector calculation of artificial neural networks, the system can efficiently and automatically generate and recommend a variety of personalized decoration design solutions. This method not only improves the efficiency and intelligence level of design, but also meets the personalized needs of users, improves user satisfaction, and provides strong data support and convenient operation experience for the entire design to construction process.

[0069] In this invention, the automated design method based on the BIM analysis module generates design solutions through data analysis and artificial intelligence, making the decoration design more efficient, accurate and intelligent. It not only provides users with a variety of design options, but also meets users' needs for personalization, functionality and sustainability, and provides solid data support and technical guarantees for design, construction and post-management.

[0070] The BIM design optimization module is used to optimize the intended decoration design scheme selected by the user from a variety of decoration design schemes.

[0071] In a possible implementation, the BIM design optimization module is specifically used to execute S401 and S402:

[0072] S401: Obtaining a desired decoration design scheme selected by a user from among multiple decoration design schemes.

[0073] S402: With the goal of reducing construction energy consumption, improving light transmittance, thermal comfort and decoration style fit, an optimization objective function is constructed:

[0074] f(θ)=-λ1E+λ2T+λ3PMV+λ4S

[0075] Among them, f represents the optimization objective function, θ represents the combination of decoration design parameters, E represents construction energy consumption, T represents light transmittance, PMV represents the predicted mean voting index, which is used to evaluate thermal comfort, S represents the degree of fit of decoration style, λ1 represents the weight coefficient of construction energy consumption, λ2 represents the weight coefficient of light transmittance, λ3 represents the weight coefficient of thermal comfort, and λ4 represents the weight coefficient of the degree of fit of decoration style.

[0076] Among them, those skilled in the art can set the weight coefficient λ1 of construction energy consumption, the weight coefficient λ2 of transmittance, the weight coefficient λ3 of thermal comfort and the weight coefficient λ4 of decoration style compatibility according to actual conditions, and the present invention does not limit this.

[0077] Among them, the decoration design parameters include: windows, doors, furniture styles and placement, wall materials, color matching, lighting design, etc.

[0078] It should be noted that by combining the weight coefficients of the four indicators of construction energy consumption, light transmittance, thermal comfort and style fit, a balance can be achieved between different design requirements. The optimization objective function can improve the practicality and comfort of the space while meeting the aesthetic needs of users.

[0079]

[0080] Where E represents the construction energy consumption, m i represents the construction area of ​​the i-th decoration construction link, c i It represents the energy consumption per unit area of ​​the ith decoration construction link, and N represents the total number of decoration construction links.

[0081]

[0082] Where, T represents the transmittance, a j represents the area of ​​the jth light-transmitting region, t j represents the transmittance of the jth light-transmitting area, and M represents the total number of light-transmitting areas.

[0083] PMV=(0.303×e -0.036×M +0.028)×L

[0084] L=(MW)-0.00305×[5733-6.99×(MW)-P a ]

[0085] -0.42×(MW-58.15)-0.000017×(5867-P a )

[0086] -0.0014×M×(34-T a )-3.96×10 -8 ×f d ×[(T cl +273) 4 -(T r +273) 4 ]

[0087] -f cl ×h c ×(T cl -T a )

[0088] Where PMV stands for predicted mean voting index, which is used to evaluate thermal comfort, e stands for natural constant, M stands for metabolic rate, W stands for external work, and P a represents the water vapor partial pressure, T a Indicates the air temperature, T r represents the radiation temperature, f cl Indicates the clothing surface coefficient, h c represents the convective heat transfer coefficient, T cl Indicates the surface temperature of the garment.

[0089]

[0090] Among them, S represents the degree of fit of the decoration style, ω k represents the weight coefficient of the kth design feature, F k represents the kth design feature value of the decoration design scheme, P k represents the kth design feature value of the user's preferred style, max represents a larger value, and K represents the total number of design features.

[0091] S403: Optimizing the details of the intended decoration design scheme through a genetic algorithm according to the optimization objective function.

[0092] Among them, Genetic Algorithm (GA) is an optimization algorithm that simulates the natural evolution process. It optimizes a set of candidate solutions through operations such as "selection", "crossover" and "mutation" to approach or reach the global optimal solution to the problem.

[0093] Specifically, the optimization objective function can be used as the fitness function of the genetic algorithm.

[0094] Initialize the population Q1, which includes multiple individuals X, each of which represents a feasible decoration design parameter combination θ.

[0095] Calculate the fitness value of the fitness function of each individual in the initial population Q1.

[0096] An elite selection strategy is adopted to remove the 20% individuals with the lowest fitness values ​​to form a new population Q2.

[0097] Perform a crossover operation on population Q2, randomly select two individuals from population H2 as parents, generate a random number, and compare the random number with the crossover probability p e Compare the size, if the random number is less than the crossover probability p e , then the parent is cross-operated to generate new individuals to form a new population Q3. The generation of new individuals is as follows:

[0098] Y1=rand×X1+(1-rand)×X2

[0099] Y2=rand×X2+(1-rand)×X1

[0100] Among them, Y1 and Y2 represent new individuals, X1 represents the first parent, X2 represents the second parent, and rand represents a random number between 0 and 1.

[0101] Perform a mutation operation on population Q3, randomly select an individual from population Q3 as the parent, generate a random number, and compare the random number with the mutation probability p m Compare the size, if the random number is less than the mutation probability p m , then the parent is mutated to generate new individuals to form a new population Q4. The generation of new individuals is as follows:

[0102]

[0103] Among them, Y3 represents the new individual, X3 represents the parent, and X max represents the individual with the largest fitness value, X min represents the individual with the smallest fitness value, and rand represents a random number between 0 and 1.

[0104] Repeat the above steps and iterate until the preset number of iterations is reached. The solution with the largest fitness value is output as the optimal decoration design parameter combination, and the details of the intended decoration design are optimized.

[0105] In the present invention, through the combination of the optimization objective function and genetic algorithm in the BIM design optimization module, the system can achieve comprehensive optimization in many aspects such as construction energy consumption, light transmittance, thermal comfort and style fit. This approach not only makes the design more scientific, accurate and efficient, but also meets personalized needs, reduces construction costs, improves user satisfaction and comfort, and achieves higher quality and more environmentally friendly decoration design solutions.

[0106] The user interaction module is used to provide an interactive interface between the system and the user and display the optimized decoration design plan.

[0107] In a possible implementation, the user interaction module is specifically used to: display the optimized decoration design plan and the corresponding estimated cost and estimated decoration time.

[0108] In the present invention, the optimized decoration design plan, estimated cost and estimated duration are displayed in the user interaction module, which helps users make more scientific choices and plans, and improves their sense of participation and satisfaction. At the same time, this information increases the professionalism, transparency and practicality of the system, helps users find a balance between budget, time and effect, and achieves a higher quality decoration experience.

[0109] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0110] In the present invention, the two-dimensional decoration design drawing can be automatically converted into a three-dimensional BIM model, and analysis can be performed based on the BIM model to automatically generate a variety of decoration design schemes for customers to choose from, so as to meet the personalized needs of customers. The intended decoration design scheme selected by the user from a variety of decoration design schemes can be optimized without relying on manual modeling by decoration designers, thus reducing time costs and improving the efficiency of interior decoration design. The environmental friendliness and resource sustainability of materials can also be considered in the optimization process, thus facilitating the realization of green buildings and providing users with a healthier and more environmentally friendly indoor environment.

[0111] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An AI interior decoration design optimization system based on BIM technology, characterized in that: include: Graphic design module, used to obtain the decoration design drawings of the user's indoor space; A BIM modeling module, used to convert the two-dimensional decoration design drawing into a three-dimensional BIM model; A BIM analysis module, used to analyze based on the BIM model and automatically generate a variety of decoration design solutions; BIM design optimization module, used to optimize the intended decoration design scheme selected by the user from a variety of decoration design schemes; The user interaction module is used to provide an interactive interface between the system and the user and display the optimized decoration design plan.

2. The AI ​​interior decoration design optimization system based on BIM technology according to claim 1 is characterized in that: The BIM modeling module is specifically used for: S201: using digital image technology based on convolutional neural networks to identify walls, doors and windows in the two-dimensional decoration design drawing; S202: Determine room size information based on wall, door and window recognition results; S203: Generate a three-dimensional BIM model based on the identified room size information, walls, doors, and windows; S204: Determine room function information.

3. The AI ​​interior decoration design optimization system based on BIM technology according to claim 2 is characterized in that: The S201 specifically includes: In the input layer of the convolutional neural network, input the decoration design drawing; In the convolutional layer of the convolutional neural network, the graphic features in the decoration design picture are extracted; In the pooling layer of the convolutional neural network, the extracted line features are subjected to dimensionality reduction processing; In the fully connected layer of the convolutional neural network, the features in the convolutional layer and the pooling layer are summarized; In the classification layer of the convolutional neural network, walls, doors and windows in the decoration design drawings are identified.

4. The AI ​​interior decoration design optimization system based on BIM technology according to claim 2 is characterized in that: The S202 is specifically as follows: Based on the recognition results of walls and doors, the connectivity detection algorithm is used to determine the closed areas of the decoration design drawings and the size information of each room.

5. The AI ​​interior decoration design optimization system based on BIM technology according to claim 2 is characterized in that: The S204 specifically includes: When there are text annotations on the decoration design drawings, use OCR technology to determine the functional information of each room; In the absence of text annotations on the decoration design drawings, feature matching technology is used to determine the functional information of each room.

6. The AI ​​interior decoration design optimization system based on BIM technology according to claim 1 is characterized in that: The BIM analysis module is specifically used for: S301: Analyze the room size and room functional attributes of the BIM model; S302: Acquire the decoration style preferred by the user; S303: Automatically generate a plurality of decoration design solutions based on the artificial neural network according to the decoration style, the room size and the room functional attributes.

7. The AI ​​interior decoration design optimization system based on BIM technology according to claim 6 is characterized in that: The S303 specifically includes: Determining a decoration characteristic vector according to the decoration style, the room size and the room functional attributes; In the input layer of the artificial neural network, the decoration characteristic vector is input; In the hidden layer of the artificial neural network, the input state vector is weighted and summed through each hidden layer neuron to obtain the hidden state vector; In the matching layer of the artificial neural network, the recommendation index of each decoration design scheme is calculated according to the hidden state vector; In the output layer of the artificial neural network, a preset number of decoration design plans with high recommendation index rankings are output.

8. The AI ​​interior decoration design optimization system based on BIM technology according to claim 6 is characterized in that: The decoration characteristic vector includes: room length, width, height, room function, material preference, style preference, and color preference.

9. The AI ​​interior decoration design optimization system based on BIM technology according to claim 1 is characterized in that: The BIM design optimization module is specifically used for: S401: Obtaining a desired decoration design scheme selected by a user from among multiple decoration design schemes; S402: Construct an optimization objective function with the goal of reducing construction energy consumption, improving light transmittance, thermal comfort and decoration style compatibility; S403: Optimizing the details of the intended decoration design scheme through a genetic algorithm according to the optimization objective function.

10. The AI ​​interior decoration design optimization system based on BIM technology according to claim 1 is characterized in that: The user interaction module is specifically used for: Display the optimized decoration design plan and the corresponding estimated cost and estimated decoration time.

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