Home decoration design drawing generation method, device and equipment
By obtaining and processing user home decoration design requirements data, identifying key design elements and generating feature vectors, and using trained design drawing generation models, the problem of low efficiency of traditional home decoration design is solved, and fast, efficient and personalized design drawing generation is achieved. , meet market demand.
Patent Information
- Application Number
- CN202411231741.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-04
- Publication Date
- 2024-12-27
AI Technical Summary
The traditional home decoration design process takes a long time and is costly, and cannot meet the market's demand for fast, efficient and personalized design, and the design efficiency is inefficient.
By obtaining user home decoration design requirements data, extracting design feature quantity, generating home decoration design drawings, and using the model trained by the historical design training set to generate design drawings, including obtaining user demand data, preprocessing, identifying key design elements, creating feature vectors and inputting Design drawings generate models to generate design drawings.
It realizes fast, efficient and personalized home decoration design, improves design efficiency, meets market demands, and can automatically generate design solutions based on user preferences and needs, reducing the time and energy investment of manual design.
Smart Images
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Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of computer information processing, and in particular to a method, device and equipment for generating home decoration design drawings. Background Art
[0002] The traditional home decoration design process often requires designers to invest a lot of time and energy in multiple steps such as customer demand analysis, solution conception, sketching, 3D modeling, etc., which is not only time-consuming but also costly. Manual design by designers can no longer meet the market's demand for fast, efficient, and personalized design, and the design efficiency is low. Summary of the invention
[0003] The present invention provides a method, device and equipment for generating a home decoration design drawing, so as to solve the problem that the traditional manual home decoration design is inefficient and cannot meet the personalized design requirements.
[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:
[0005] A method for generating a home decoration design drawing, comprising:
[0006] Obtain user home decoration design demand data;
[0007] Obtaining design feature quantities according to the user's home decoration design demand data;
[0008] A home decoration design drawing is generated according to the design feature quantity.
[0009] Optionally, extracting design feature quantities according to the user's home decoration design demand data includes:
[0010] Extracting the user's home decoration design demand data to obtain design preference data and reference drawings; the user's home decoration design demand data includes a text description and a design sketch input by the user;
[0011] Identify the reference image to obtain key design elements;
[0012] A design feature quantity is obtained according to the design preference data and the key design elements.
[0013] Optionally, extracting the user's home decoration design demand data to obtain design preference data and reference images includes:
[0014] Preprocessing the user's home decoration design demand data to remove invalid, repeated or erroneous information;
[0015] The text description and design sketches in the preprocessed data are extracted to obtain design preference data and reference drawings.
[0016] Optionally, the reference diagram is identified to obtain key design elements, including:
[0017] Split the reference image into multiple regions;
[0018] Specific objects or patterns in each area are individually identified to reveal key design elements.
[0019] Optionally, obtaining a design feature quantity according to the design preference data and the key design element includes:
[0020] Creating a feature vector based on the design preference data and the key design elements;
[0021] The feature vector is normalized to obtain the design feature quantity.
[0022] Optionally, generating a home decoration design drawing according to the design feature quantity includes:
[0023] The design feature quantity is input into a home decoration design drawing generation model to generate a home decoration design drawing; wherein the home decoration design drawing generation model is obtained by training a preset model through a historical design training set.
[0024] Optionally, the home decoration design drawing generation model obtained by training a preset model with a historical design training set includes:
[0025] Obtain historical design training sets;
[0026] Insert decomposition layers at preset positions in a preset model;
[0027] The parameters of the decomposition layer are updated using the historical design training set to obtain the home decoration design drawing generation model.
[0028] The present invention also provides a device for generating a home decoration design drawing, comprising:
[0029] The acquisition module is used to obtain the user's home decoration design demand data;
[0030] The processing module is used to obtain design feature quantities according to the user's home decoration design demand data; and generate a home decoration design drawing according to the design feature quantities.
[0031] The present invention also provides a computing device, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed.
[0032] The present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.
[0033] The above solution of the present invention includes at least the following beneficial effects:
[0034] The above solution of the present invention obtains user home decoration design demand data; obtains design feature quantities according to the user home decoration design demand data; and generates home decoration design drawings according to the design feature quantities. The solution of the present invention can meet the market demand for fast, efficient and personalized design and improve design efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] Figure 1 is a flow chart of a method for generating a home decoration design drawing provided by an embodiment of the present invention;
[0036] Figure 2 It is a module schematic diagram of a device for generating a home decoration design drawing provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0037] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.
[0038] like Figure 1 As shown, an embodiment of the present invention provides a method for generating a home decoration design drawing, comprising:
[0039] Step 11, obtaining user home decoration design demand data;
[0040] Step 12, obtaining design feature quantities according to the user's home decoration design demand data;
[0041] Step 13, generating a home decoration design drawing according to the design feature quantity.
[0042] In this embodiment, based on the demand data input by the user, including but not limited to style preference, color preference, space layout preference and budget information, the user's home decoration design demand data is analyzed to identify the user's needs and preferences; based on the analysis results, at least one set of home decoration design plan drawings is automatically generated, and each plan includes three-dimensional space layout, furniture placement, color matching and decorative elements.
[0043] The solution of this embodiment can transform abstract ideas and concepts into visual design drawings. Design drawings can be generated by providing text descriptions and adjusted and optimized based on feedback. This can help people break through traditional thinking frameworks, discover new design directions and element combinations, and gradually improve home improvement plans to make them more in line with their expectations and needs, bringing novel ideas and inspiration to home improvement design.
[0044] In an optional embodiment of the present invention, step 12 may include:
[0045] Step 121, extracting the user's home decoration design demand data to obtain design preference data and reference drawings; the user's home decoration design demand data includes a text description and a design sketch input by the user;
[0046] Step 122, identifying the reference image to obtain key design elements;
[0047] Step 123: obtaining design feature quantities according to the design preference data and the key design elements.
[0048] In this embodiment, the user can input text description and design sketch according to their own design needs. The text description can be decoration style, decoration show style, functional purpose, furniture layout, color matching, decoration material, budget and other contents; the input design sketch can be wireframe, hand-drawn picture, stereogram, photo or any other image containing visual information.
[0049] The system identifies keywords related to the design content in the text description to form design preference data. The design elements in the design sketch are extracted to obtain the design reference drawing.
[0050] The design preference data and design reference drawings are used as inputs of the home decoration design drawing generation model, and multiple home decoration design plans are automatically generated through the home decoration design drawing generation model. Each plan includes three-dimensional space layout, furniture placement, color matching and decorative elements.
[0051] In an optional embodiment of the present invention, step 121 may include:
[0052] Step 1211, pre-processing the user's home decoration design demand data to remove invalid, repeated or erroneous information;
[0053] Step 1212, extracting text descriptions and design sketches from the preprocessed data to obtain design preference data and reference drawings.
[0054] In this embodiment, the data is first preprocessed to clean and denoise the data to remove invalid, repeated or erroneous information, including removing noise in text descriptions, such as punctuation marks, special characters, irrelevant spaces, etc., and performing operations such as denoising, adjusting contrast, cropping and scaling on images.
[0055] Furthermore, feature extraction is performed on the preprocessed data, including feature extraction on the text description and feature extraction on the reference image.
[0056] Specifically, break down the text described in words into words, phrases, or other meaningful elements, remove common words that contribute little to the meaning of the text (such as "of", "is", etc.), reduce the stem or word form to its basic form, identify the part of speech of each word, such as noun, verb, adjective, etc., and identify specific entities in the text, such as names, locations, etc.
[0057] Based on conditions such as the frequency of word occurrence and the importance of words in the document collection, convert the text into a numerical vector. Map words to vectors in a high-dimensional space so that semantically similar words are close in the vector space, thereby expressing the context relationship. This vector can capture the semantic information of words, phrases, or sentences in the text data.
[0058] Convert the text into a vector through the formula: TF-IDF(t,d) = TF(t,d) × IDF(t).
[0059] Among them, IDF(t,d) is the frequency of the word t in the text d, IDF(t) is the inverse document frequency of t, and TF is the term frequency, which is the number of times a word appears in a document. This number is usually normalized (usually by dividing the term frequency by the total number of words in the document) to prevent it from biasing towards long documents. The inverse document frequency is a measure of how common a word is. It is obtained by dividing the total number of documents in the corpus by the number of documents containing the word, and then taking the logarithm of the resulting quotient.
[0060] After the text is converted into a vector numerical form, it is possible to determine which category the design style of the home improvement belongs to through the vector, and extract structured information from the text, such as space design, furniture placement, etc.
[0061] In an optional embodiment of the present invention, step 122 may include:
[0062] Step 1221, divide the reference figure into multiple regions;
[0063] Step 1222, separately identify specific objects or patterns in each region to obtain key design elements.
[0064] In this embodiment, when identifying the reference figure, it is necessary to divide the reference figure into several different and non-overlapping regions, which are usually defined based on features such as color, texture, and shape in the image. The purpose of image segmentation is to simplify image analysis so that each region can represent a relatively independent and clearly defined object or pattern.
[0065] After the image is successfully segmented into multiple regions, the next step is to identify each region independently. Useful features are extracted from each segmented region, which may include color histograms, shape descriptors, texture features, edge information, etc. This step involves the use of image recognition technology, such as convolutional neural network (CNN) deep learning models, to identify specific objects or patterns in each region. It is able to identify key features in the region, such as shape, color, texture, etc., and match them with preset templates or entries in the database to determine the specific content of each region.
[0066] By independently identifying specific objects or patterns in each area, key design elements can be further extracted. These elements may include color matching, pattern style, layout structure, font selection, etc., which are essential to the aesthetics, functionality and user experience of home decoration design. The extracted key design elements can not only be used to analyze the characteristics and advantages and disadvantages of existing designs, but also serve as a source of inspiration or reference for subsequent design creation.
[0067] In an optional embodiment of the present invention, step 123 may include:
[0068] Step 1231, creating a feature vector according to the design preference data and the key design elements;
[0069] Step 1232, normalize the feature vector to obtain the designed feature quantity.
[0070] In this embodiment, the design preference data and key design elements obtained in step 121 and step 122 are combined into a feature vector, and each dimension of the feature vector corresponds to a feature, and its value is a quantitative representation of the feature. Specifically, a series of quantifiable features can be defined. For example, for color preference, the color code or the position of the color on the color wheel can be extracted as a feature; for shape preference, the geometric properties of the shape (such as aspect ratio, curvature, etc.) can be extracted as a feature. For key design elements, their quantified values are directly used as features, such as size (length, width, height), material number, etc.
[0071] Since different features may have different dimensions and value ranges (for example, color codes may be integers between 0 and 255, while dimensions may be floating point numbers between a few centimeters and a few meters), directly using these raw values for model training may cause some features to have too large or too small weights in the model. Therefore, normalization is required to scale all features to the same scale.
[0072] By formula:
[0073]
[0074] Among them, Xnorm is the normalized value, X is the original data value, and X min is the minimum value in the data set, X max is the maximum value in the data set. The feature value is scaled to between a given minimum and maximum value. The template range specified in this embodiment is 0 to 1.
[0075] In an optional embodiment of the present invention, step 13 may include:
[0076] The design feature quantity is input into a home decoration design drawing generation model to generate a home decoration design drawing; wherein the home decoration design drawing generation model is obtained by training a preset model through a historical design training set.
[0077] In this embodiment, the design feature quantity obtained in step 12 is input into the home decoration design drawing generation model, and the home decoration design drawing generation model analyzes the space size, style preference, color preference, functional requirements, budget range and other data in the design feature quantity.
[0078] Among them, the space dimensions include the length, width, and height of the room, the position and dimensions of doors and windows, and the dimensions of special space requirements (such as wardrobes, storage rooms, etc.).
[0079] Style preference: the home decoration style that the customer likes, such as modern simplicity, Chinese style, European style, Nordic style, etc.
[0080] Color preference: the main color, auxiliary color and decorative color preferred by the customer. Functional requirements: the specific usage functions of each space, such as cooking needs in the kitchen and storage needs in the bedroom.
[0081] Budget range: The customer's acceptable renovation budget, which will affect the material selection and design plan.
[0082] According to the design feature quantity, a 3D model of the room is established, and the corresponding materials, textures and colors are determined according to the customer's style preferences and color preferences. These elements will be used for subsequent scene layout and rendering. According to the space size and functional requirements, appropriate furniture is selected in the room for layout. The selection of furniture should consider whether its size, style and function are coordinated with the overall design. Decorations are added to the room to enhance the atmosphere and beauty of the room. At the same time, lighting elements are added according to lighting requirements, including main lights, auxiliary lights and decorative lights, etc., to form a 3D interior design model diagram of the room decoration design.
[0083] Furthermore, the 3D interior design model is rendered, and parameters such as light source, shadow, material, etc. are adjusted during rendering to generate interior design renderings. Then, image processing software (such as Photoshop) is used to post-process the rendered renderings. By adjusting parameters such as color, contrast, brightness, etc., the renderings can better meet the expectations and requirements of customers.
[0084] The completed interior design renderings are shown to the client and can be modified and improved in real time based on the client’s feedback and suggestions.
[0085] In an optional embodiment of the present invention, the home decoration design drawing generation model obtained by training a preset model with a historical design training set includes:
[0086] Obtain historical design training sets;
[0087] Insert decomposition layers at preset positions in a preset model;
[0088] The parameters of the decomposition layer are updated using the historical design training set to obtain the home decoration design drawing generation model.
[0089] In this embodiment, a series of data covering images, materials, design styles, and budgets are collected to build a diverse and comprehensive database as a training set for subsequent training models. Specifically, high-quality, multi-angle, clean-background images are collected, and the image size is processed to 512x512 or 768x768. To add labels to the training images, you can use automatic annotation combined with manual screening to ensure the accuracy of the labels and simulate the content of the user's text description.
[0090] The preset training model uses the Stable Diffusion image generation model, inserts a low-rank matrix decomposition layer into the Transformer block of the Stable Diffusion, and uses a random Gaussian distribution to initialize the parameters of the decomposition layer to ensure that the decomposition layer has zero effect on the model output when training starts. In this embodiment, the decomposition layer can use the Lora layer.
[0091] Use the training set to train the decomposition layer. During the training process, the parameters of the basic model are fixed and only the parameters of the decomposition layer are updated. Configure the training parameters, including model name, output directory, dataset name, resolution, batch size, learning rate, maximum number of training steps, etc., and adjust the weight of the decomposition layer to control its influence on the generated image.
[0092] By adjusting the parameters, the loss function Minimize, at this time the difference between the generated image and the actual image is the smallest, the model training is completed, and the home decoration design drawing generation model is obtained.
[0093] The method for generating a home decoration design drawing described in the above embodiment of the present invention can generate a design drawing by providing a text description, and adjust and optimize it according to feedback. This method can help people break through the traditional thinking framework, discover new design directions and element combinations, and gradually improve the home decoration plan to make it more in line with their expectations and needs; the traditional home decoration design process usually takes a lot of time and energy. Through this method, a preliminary design plan can be quickly generated, reducing people's investment in the initial stage. In this way, people can focus more on subsequent adjustments and improvements, save time and energy, and improve efficiency; everyone has different preferences and styles for home decoration. This method can generate a design drawing in a customized manner based on personal text descriptions and preferences. In this way, everyone can get a home decoration plan that suits their preferences and meets personalized needs; this method can transform abstract ideas and concepts into visual design drawings. This is particularly meaningful for non-professionals who may not be familiar with design terms and tools. Through the images generated by this method, people can have a clearer understanding of the appearance and layout of the design and make better decisions.
[0094] The embodiment of the present invention further provides a home decoration design drawing generating device 20, comprising:
[0095] The acquisition module 21 is used to acquire the user's home decoration design demand data;
[0096] The processing module 22 is used to obtain design feature quantities according to the user's home decoration design demand data; and generate a home decoration design drawing according to the design feature quantities.
[0097] Optionally, extracting design feature quantities according to the user's home decoration design demand data includes:
[0098] Extracting the user's home decoration design demand data to obtain design preference data and reference drawings; the user's home decoration design demand data includes a text description and a design sketch input by the user;
[0099] Identify the reference image to obtain key design elements;
[0100] A design feature quantity is obtained according to the design preference data and the key design elements.
[0101] Optionally, extracting the user's home decoration design demand data to obtain design preference data and reference images includes:
[0102] Preprocessing the user's home decoration design demand data to remove invalid, repeated or erroneous information;
[0103] The text description and design sketches in the preprocessed data are extracted to obtain design preference data and reference drawings.
[0104] Optionally, the reference diagram is identified to obtain key design elements, including:
[0105] Split the reference image into multiple regions;
[0106] Specific objects or patterns in each area are individually identified to reveal key design elements.
[0107] Optionally, obtaining a design feature quantity according to the design preference data and the key design element includes:
[0108] Creating a feature vector based on the design preference data and the key design elements;
[0109] The feature vector is normalized to obtain the design feature quantity.
[0110] Optionally, generating a home decoration design drawing according to the design feature quantity includes:
[0111] The design feature quantity is input into a home decoration design drawing generation model to generate a home decoration design drawing; wherein the home decoration design drawing generation model is obtained by training a preset model through a historical design training set.
[0112] Optionally, the home decoration design drawing generation model obtained by training a preset model with a historical design training set includes:
[0113] Obtain historical design training sets;
[0114] Insert decomposition layers at preset positions in a preset model;
[0115] The parameters of the decomposition layer are updated using the historical design training set to obtain the home decoration design drawing generation model. It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiment are applicable to the embodiment of the device and can achieve the same technical effect.
[0116] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the method described in the above embodiment is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.
[0117] In an embodiment of the present invention, a computer-readable storage medium is further provided, which stores instructions, and when the instructions are executed on a computer, the computer executes the method described in the above embodiment. All implementations in the above method embodiment are applicable to this embodiment, and can also achieve the same technical effect.
[0118] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0120] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0121] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0122] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0123] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.
[0124] In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.
[0125] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.
[0126] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.
Claims
1. A method for generating a home decoration design drawing, characterized in that: include: Obtain user home decoration design demand data; Obtaining design feature quantities according to the user's home decoration design demand data; A home decoration design drawing is generated according to the design feature quantity.
2. The method for generating a home decoration design drawing according to claim 1, characterized in that: According to the user's home decoration design demand data, design feature quantities are extracted, including: Extracting the user's home decoration design demand data to obtain design preference data and reference drawings; the user's home decoration design demand data includes a text description and a design sketch input by the user; Identify the reference image to obtain key design elements; A design feature quantity is obtained according to the design preference data and the key design elements.
3. The method for generating a home decoration design drawing according to claim 2, characterized in that: Extracting the user's home decoration design demand data to obtain design preference data and reference diagrams, including: Preprocessing the user's home decoration design demand data to remove invalid, repeated or erroneous information; The text description and design sketches in the preprocessed data are extracted to obtain design preference data and reference drawings.
4. The method for generating a home decoration design drawing according to claim 2, characterized in that: The reference diagram is identified to obtain key design elements, including: Split the reference image into multiple regions; Specific objects or patterns in each area are individually identified to reveal key design elements.
5. The method for generating a home decoration design drawing according to claim 1, characterized in that: According to the design preference data and the key design elements, a design feature quantity is obtained, including: Creating a feature vector based on the design preference data and the key design elements; The feature vector is normalized to obtain the design feature quantity.
6. The method for generating a home decoration design drawing according to claim 1, characterized in that: Generating a home decoration design drawing according to the design feature quantity includes: The design feature quantity is input into a home decoration design drawing generation model to generate a home decoration design drawing; wherein the home decoration design drawing generation model is obtained by training a preset model through a historical design training set.
7. The method for generating a home decoration design drawing according to claim 6, characterized in that: The home decoration design drawing generation model obtained by training the preset model with the historical design training set includes: Obtain historical design training sets; Insert decomposition layers at preset positions in a preset model; The parameters of the decomposition layer are updated using the historical design training set to obtain the home decoration design drawing generation model.
8. A device for generating a home decoration design drawing, characterized in that: include: The acquisition module is used to obtain the user's home decoration design demand data; A processing module, used to obtain design feature quantities according to the user's home decoration design demand data; A home decoration design drawing is generated according to the design feature quantity.
9. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is performed.
10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.