Method and system for generating 3D building block models based on deep learning architecture

Abstract

The present invention includes a method and system for generating 3D building block models based on deep learning architecture, aiming to solve the problems of monotonous building block play and difficulty in independently designing and constructing models. The method of the present invention includes the following steps: obtaining model generation prompt information input by users; generating a three-dimensional building block model through deep learning architecture based on the model generation prompt information; validating the generated building block model to ensure model stability and connectivity; converting the generated model data into a file format convenient for viewing and construction. The present invention can convert any user ideas or designs into detailed 3D building block models, enabling users to create unique building block construction works, whether complex architectural models or personalized creative designs, significantly enhancing the flexibility and creativity of building block play.

Description

Method and System for Generating 3D Building Block Models Based on Deep Learning ArchitectureTechnical Field

[0001] The present invention relates to the technical field of building block model generation, particularly to a method and system for generating 3D building block models based on deep learning architecture.Background Art

[0002] Existing building block play methods are primarily based on manual design, which cannot meet users' needs for rapid and flexible independent design and construction of models. Manual design is inefficient, and the stability and rationality of models are difficult to guarantee. Furthermore, existing methods have limited enhancement in education and entertainment aspects, failing to fully stimulate users' creativity and spatial ability. Therefore, there is an urgent need for a simple and intuitive method that can generate new 3D building block models based on existing building block data through deep learning architecture, solving the problems of monotonous building block play and difficulty in independently designing and constructing models, while enhancing the educational and entertainment value of building blocks.Technical Problem

[0003] The present invention provides a method and system for generating 3D building block models based on deep learning architecture to solve the problems of monotonous building block play and difficulty in independently designing and constructing models.Technical Solution

[0004] The present invention provides a method for generating 3D building block models based on deep learning architecture, comprising:

[0005] Obtaining model generation prompt information input by users;

[0006] Generating a three-dimensional building block model through deep learning architecture based on the model generation prompt information;

[0007] Validating the generated building block model to ensure model stability and connectivity;

[0008] Converting the generated model data into a file format convenient for viewing and construction.

[0009] According to the method provided by the present invention, obtaining model generation prompt information specifically includes:

[0010] Users describing model states through text, voice, image materials, or drawn graphics;

[0011] Processing the user-described model state information to generate prompt information.

[0012] According to the method provided by the present invention, generating a three-dimensional building block model through deep learning architecture specifically includes:

[0013] Using an attention mechanism-based deep learning model to extract features from user input prompt information;

[0014] Generating a three-dimensional building block model based on feature extraction results, and continuously optimizing the generated model through deep learning architecture.

[0015] According to the method provided by the present invention, validating the generated building block model specifically includes:

[0016] Validating geometric features and connection relationships of the model to ensure correct alignment of geometric features and connection relationships between components;

[0017] Validating model stability and connectivity according to predefined rules.

[0018] According to the method provided by the present invention, converting the generated model data into a file format convenient for viewing and construction specifically includes:

[0019] Writing information of each building block in the model to a file, recording color, dimensions, and position of each building block;

[0020] Ensuring file format compatibility with existing building block model construction software for convenient viewing and construction.

[0021] The present invention also provides a system for generating 3D building block models based on deep learning architecture, comprising:

[0022] Information acquisition module, for obtaining model generation prompt information input by users;

[0023] Deep learning transformation module, for generating a three-dimensional building block model through deep learning architecture based on the model generation prompt information;

[0024] Validation module, for validating geometric features and connection relationships of the generated building block model;

[0025] Conversion module, for converting the generated model data into a file format convenient for viewing and construction.

[0026] The present invention also provides a computer program product, including computer programs, which when executed by a processor, implements any of the above-mentioned methods for generating 3D building block models based on deep learning architecture.Advantageous Effects

[0027] The method and system for generating 3D building block models based on deep learning architecture provided by the present invention, after obtaining user input generation prompt information, generates building block three-dimensional models through deep learning architecture, validates the models, and finally converts the model data into a file format convenient for viewing and construction. It can convert any user ideas or designs into detailed 3D building block models, enabling users to create unique building block construction works, whether complex architectural models or personalized creative designs. The generated models have structural rationality and stability, ensuring smooth construction process for users.Description of Drawings

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention.

[0029] Figure 1 is a schematic diagram of the building block data structure in the method for generating 3D building block models based on deep learning architecture provided by the present invention;

[0030] Figure 2 is a flowchart of loading building block data in the method for generating 3D building block models based on deep learning architecture provided by the present invention;

[0031] Figure 3 is a flowchart of generating building block models based on deep learning architecture in the method provided by the present invention;

[0032] Figure 4 is a flowchart of converting generated models into a file format convenient for viewing and construction in the method provided by the present invention;

[0033] Figure 5 is a flowchart of validating generated building block models in the method provided by the present invention.Best Mode

[0034] To make the objectives, technical solutions, and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings.Mode for Invention

[0035] Define building block data structure:

[0036] The data structure of building blocks includes the following information:

[0037] Dimensions:length, width, and height of the building block.

[0038] Color:color of the building block.

[0039] Features of each face:number of studs and holes on each face.

[0040] This structure can comprehensively describe the basic features of building blocks, facilitating subsequent data loading and model generation.

[0041] Load building block data:

[0042] Read data from files containing building block information and create corresponding building block objects. The data files record detailed information of all building blocks, ensuring data completeness and consistency. When loading data, parse the data files and store each building block's data in building block objects for subsequent processing and combination.

[0043] Generate building block models:

[0044] Use attention mechanism-based deep learning architecture to generate new building block models, including the following steps:

[0045] Feature extraction:Extract geometric features and connection relationships from building block data to form feature vectors.

[0046] Model training:Use attention mechanism-based deep learning architecture to train feature vectors, learning connection and combination rules between building blocks.

[0047] Decode and load building block data:Apply the trained model to new input data, generating new building block models through decoding.

[0048] Generate models:Based on the trained deep learning architecture, generate new building block models. This process is similar to sentence generation in natural language processing, generating building block models with structural rationality and stability by learning combination relationships between building blocks.

[0049] Validate generated building block models:

[0050] Validate geometric features and connection relationships of the generated building block models, ensuring correct alignment of geometric features and connection relationships between components, and ensuring model stability and connectivity. Validate model rationality and stability according to predefined rules.

[0051] Convert to file format convenient for viewing and construction:

[0052] Convert the generated building block models into a file format convenient for viewing and construction in relevant software. The conversion process includes the following steps:

[0053] Generate files: Write information of each building block in the model to files, recording color, dimensions, and position of each building block.

[0054] Ensure compatibility: Ensure file format compatibility with existing building block model construction software for convenient viewing and construction.Industrial Applicability

[0055] The present invention has broad industrial applicability in the fields of toy manufacturing, education, and entertainment. It can be widely used in building block toy design, educational tools development, and creative design platforms, providing an intelligent solution for generating 3D building block models.Free Text of Sequence listing

[0056] Not applicable.

Claims

1. A method for generating 3D building block models based on deep learning architecture, characterized in that the method comprises the following steps:obtaining model generation prompt information input by users;generating a three-dimensional building block model through deep learning architecture based on the model generation prompt information;validating the generated building block model to ensure model stability and connectivity;converting the generated model data into a file format convenient for viewing and construction.

2. The method for generating 3D building block models based on deep learning architecture according to claim 1, characterized in that the obtaining model generation prompt information comprises:users describing model states through text, voice, image materials, or drawn graphics;processing the user-described model state information to generate prompt information.

3. The method for generating 3D building block models based on deep learning architecture according to claim 1, characterized in that the generating a three-dimensional building block model through deep learning architecture comprises:using an attention mechanism-based deep learning model to extract features from user input prompt information;generating a three-dimensional building block model based on feature extraction results, and continuously optimizing the generated model through deep learning architecture.

4. The method for generating 3D building block models based on deep learning architecture according to claim 1, characterized in that the validating the generated building block model comprises:validating geometric features and connection relationships of the model to ensure correct alignment of geometric features and connection relationships between components;validating model stability and connectivity according to predefined rules.

5. The method for generating 3D building block models based on deep learning architecture according to claim 1, characterized in that the converting the generated model data into a file format convenient for viewing and construction comprises:writing information of each building block in the model to a file, recording color, dimensions, and position of each building block;ensuring file format compatibility with existing building block model construction software for convenient viewing and construction.

6. A system for generating 3D building block models based on deep learning architecture, characterized in that the system comprises:an information acquisition module for obtaining model generation prompt information input by users;a deep learning transformation module for generating a three-dimensional building block model through deep learning architecture based on the model generation prompt information;a validation module for validating geometric features and connection relationships of the generated building block model;a conversion module for converting the generated model data into a file format convenient for viewing and construction.

7. The system for generating 3D building block models based on deep learning architecture according to claim 6, characterized in that the information acquisition module comprises:users describing model states through text, voice, image materials, or drawn graphics;processing the user-described model state information to generate prompt information.

8. The system for generating 3D building block models based on deep learning architecture according to claim 6, characterized in that the deep learning transformation module comprises:using an attention mechanism-based deep learning model to extract features from user input prompt information;generating a three-dimensional building block model based on feature extraction results, and continuously optimizing the generated model through deep learning architecture.

9. The system for generating 3D building block models based on deep learning architecture according to claim 6, characterized in that the validation module comprises:validating geometric features and connection relationships of the model to ensure correct alignment of geometric features and connection relationships between components;validating model stability and connectivity according to predefined rules.

10. The system for generating 3D building block models based on deep learning architecture according to claim 6, characterized in that the conversion module comprises:writing information of each building block in the model to a file, recording color, dimensions, and position of each building block;ensuring file format compatibility with existing building block model construction software for convenient viewing and construction.

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