Brand plane creative design system based on AIGC

Abstract

The invention discloses an AI GC-based brand plane creative design system, and particularly relates to the technical field of automatic design systems, and the system is configured to be an intelligent processing module, a creative generation module, an intelligent analysis module, a user interaction module, a resource management module, a cooperative work module and a multi-modal output module. According to the brand plane creative design system based on the AI GC, all the modules are matched with one another to form a closed-loop process, manual intervention is reduced in the whole process from demand input to generation, optimization, cooperation and output, efficient standardization and personalized innovation of brand design are achieved, and meanwhile through historical material analysis and style migration, the brand plane creative design efficiency is improved. The consistency of new and old designs in brand tonality is ensured, and the problems of low efficiency, creativity limitation and multi-terminal adaptation in the traditional design are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of automated design systems, and particularly relates to a brand graphic creative design system based on AIGC. Background Art

[0002] With the rapid development of artificial intelligence technology, artificial intelligence-generated content (AIGC) has gradually become the core driving force for promoting innovation in various industries. AIGC is a technology that realizes automated content creation through deep learning models (such as generative adversarial networks GAN, diffusion models, etc.), and can efficiently generate multi-modal materials such as text, images, and videos, showing great potential in fields such as advertising design, film and television production, and game development.

[0003] In the field of brand graphic design, the traditional design process highly relies on the creative concept and manual operation of designers, and there are the following significant pain points:

[0004] Low efficiency: From requirement analysis, sketch concept to the final draft, it needs to go through multiple rounds of modification and cross-departmental communication, taking weeks or even months, and it is difficult to meet the rapidly iterative market demands.

[0005] Creative limitations: The design scheme is limited by the personal experience and aesthetic preferences of designers, it is difficult to break through the homogenization bottleneck, and the ability to integrate cross-domain elements is insufficient.

[0006] Difficult to adapt to multiple platforms: Brand design needs to be adapted to multiple terminals such as printed materials, social media, and e-commerce platforms. The traditional method needs to be repeatedly adjusted for different sizes and formats, with high costs.

[0007] In summary, developing an intelligent design system that can deeply integrate AIGC technology, accurately match the brand tone, support multi-modal creative generation and full-process collaborative management has become an urgent need to improve the efficiency and quality of brand design and reduce the creation cost. The present invention aims to solve the pain points of the traditional design mode by constructing a brand graphic creative design system based on AIGC. Summary of the Invention

[0008] The main purpose of the present invention is to provide a brand graphic creative design system based on AIGC, which can effectively solve the problems mentioned in the background art.

[0009] To achieve the above purpose, the technical solution adopted by the present invention is:

[0010] A brand graphic creative design system based on AIGC, including an intelligent processing module, and the system is configured to:

[0011] A creative generation module, connected to the intelligent processing module, constructs a multi-modal creative generation engine based on a generative adversarial network (GAN) for automatically generating brand graphic design schemes;

[0012] An intelligent analysis module, connected to the creative generation module, analyzes brand tone, industry characteristics, and user needs through a deep learning model, and generates design optimization suggestions;

[0013] A user interaction module, connected to the intelligent processing module, provides a visual editing interface and real-time interaction feedback, and supports multi-terminal collaborative operations;

[0014] A resource management module, connected to the creative generation module, constructs a dynamic brand asset library, and realizes intelligent retrieval and reuse of design materials;

[0015] A collaborative work module, connected to the user interaction module, supports cross-team real-time collaboration and version control;

[0016] A multi-modal output module, connected to the intelligent processing module, generates brand graphic design files adapted to multiple platforms.

[0017] Preferably, the creative generation module is configured to:

[0018] A cross-modal generation unit that fuses multi-source data such as text, images, and videos to generate an initial design scheme;

[0019] A style transfer unit that automatically adapts and innovates the brand visual style based on a pre-trained model.

[0020] Preferably, the intelligent analysis module is configured to:

[0021] A semantic understanding unit that parses the user requirement document through natural language processing (NLP) and extracts key design elements;

[0022] A visual evaluation unit that evaluates the aesthetic compliance and brand recognition of the design scheme based on a convolutional neural network (CNN).

[0023] Preferably, the user interaction module is configured to:

[0024] An intelligent canvas unit that supports drag-and-drop editing and automatic layout adjustment;

[0025] A real-time feedback unit that previews the actual application effect of the design scheme through augmented reality (AR) technology.

[0026] Preferably, the resource management module is configured to:

[0027] A dynamic tagging unit that automatically tags the attributes and application scenarios of design materials based on metadata;

[0028] An intelligent recommendation unit that pushes brand assets adapted to the current design requirements through a collaborative filtering algorithm.

[0029] Preferably, the collaborative working module is configured as follows:

[0030] Version tracing unit, which records all modification operations during the design process and supports historical version backtracking;

[0031] The rights management unit uses blockchain technology to realize copyright confirmation and usage traceability of design assets.

[0032] Preferably, the multimodal output module is configured as follows:

[0033] Format adaptation unit, automatically generates design files that meet the requirements of multiple terminals such as printing, digital media, and mobile terminals;

[0034] Dynamic expansion unit supports seamless connection with external interfaces such as advertising delivery systems and e-commerce platforms.

[0035] Preferably, the style transfer unit is connected to the resource management module, and the visual features of the brand's historical design works are extracted through a contrast learning algorithm to achieve consistent matching between the new design and the brand's tonality.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention forms a closed-loop process through the cooperation of various modules: from demand input to generation, optimization, collaboration, and output, human intervention is reduced throughout the process, and efficient standardization and personalized innovation of brand design are achieved. At the same time, through historical material analysis and style migration, the consistency of brand tonality between new and old designs is ensured, effectively solving the problems of inefficiency, creative limitations, and multi-terminal adaptation in traditional design.

[0038] 2. The present invention uses the creative generation module based on GAN and multimodal data fusion to quickly generate diverse design solutions, combined with the semantic understanding and visual evaluation of the intelligent analysis module to ensure that the solution is both in line with the brand tone and has aesthetic value. The intelligent canvas and AR real-time feedback of the user interaction module lower the operating threshold and intuitively present the design effect, while the resource management module efficiently reuses brand assets through dynamic tags and intelligent recommendations to avoid repeated creation. The collaborative work module uses version tracing and blockchain confirmation to ensure the security and traceability of team collaboration. At the same time, the multimodal output module automatically adapts to multi-platform formats and opens up external interfaces, greatly shortening the design implementation cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the overall process structure of the present invention;

[0040] Figure 2 It is a schematic diagram of the structure of the creative generation module of the present invention;

[0041] Figure 3Schematic diagram of the intelligent analysis module of the present invention;

[0042] Figure 4 Schematic diagram of the user interaction module of the present invention;

[0043] Figure 5 Schematic diagram of the resource management module of the present invention;

[0044] Figure 6 Schematic diagram of the collaborative work module of the present invention;

[0045] Figure 7 Schematic diagram of the multimodal output module of the present invention. Detailed implementation manners

[0046] To make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with the detailed implementation manners.

[0047] Example 1, as Figure 1 shown, a brand graphic creative design system based on AIGC includes an intelligent processing module, and the system is configured as:

[0048] A creative generation module, connected to the intelligent processing module, constructs a multimodal creative generation engine based on the generative adversarial network (GAN) for automatically generating brand graphic design schemes. Refer to Figure 2 , and the creative generation module is configured as:

[0049] The operation of the cross-modal generation unit can integrate multi-source data such as text, images, and videos (such as brand stories, competitor analysis, user behavior data), and generate an initial design scheme through the generative adversarial network (GAN).

[0050] Break through the single-modal limitation, achieve multi-dimensional creative integration, and improve the diversity and novelty of the design scheme.

[0051] A style transfer unit, based on a pre-trained model, automatically adapts to the brand visual style (such as colors, fonts, graphic elements), and extracts historical design features in combination with the contrast learning algorithm to ensure that the new design is consistent with the brand tone.

[0052] Reduce the time for designers to manually adjust the style, avoid brand visual fragmentation, and at the same time support style innovation.

[0053] An intelligent analysis module, connected to the creative generation module, analyzes the brand tone, industry characteristics and user needs through a deep learning model, and generates design optimization suggestions. Refer to Figure 3 , and the intelligent analysis module is configured as:

[0054] The semantic understanding unit parses user demand documents through natural language processing (NLP), extracts keywords (such as brand positioning, target audience, design elements), and converts them into structured data;

[0055] Accurately capture user needs, reduce deviations in understanding needs, and improve the matching degree of design solutions.

[0056] The visual evaluation unit can evaluate the aesthetic compliance (such as color contrast, composition rationality) and brand recognition (such as LOGO recognition, style consistency) of the design scheme based on CNN.

[0057] Automatically screen low-quality solutions, reduce manual review costs, and ensure that designs meet brand standards.

[0058] Feedback the analysis results to the creative generation module to optimize the generation strategy;

[0059] The user interaction module is connected to the intelligent processing module, provides a visual editing interface and real-time interactive feedback, and supports multi-terminal collaborative operations. Figure 4 , the user interaction module is configured as:

[0060] Smart canvas unit, which supports drag-and-drop editing, automatic layout adjustment (such as intelligently adapting element positions according to canvas size), and provides real-time preview;

[0061] The threshold for design operations is lowered, so non-professional users can also quickly adjust plans and improve editing efficiency.

[0062] The real-time feedback unit uses augmented reality (AR) technology to preview the actual application effect of the design solution, helping users to intuitively understand the design intent and reduce repeated modifications caused by imagination deviation.

[0063] The intelligent analysis module works in conjunction with the user interaction module to provide real-time optimization suggestions (such as adjusting font size and replacing colors).

[0064] The resource management module is connected with the creative generation module to build a dynamic brand asset library and realize intelligent retrieval and reuse of design materials. Figure 5 , the resource management module is configured as:

[0065] The dynamic label unit automatically annotates material attributes (such as "sense of technology", "holiday promotion", "mobile terminal adaptation") based on metadata and associates them with application scenarios. This enables accurate classification and rapid retrieval of materials, avoiding repeated creation.

[0066] The intelligent recommendation unit pushes brand assets that are suitable for current design needs through collaborative filtering algorithms.

[0067] The user interaction module can call the material library of the resource management module to provide intelligent recommendations (such as "similar background images").

[0068] The style transfer unit is connected to the resource management module, extracts the visual features of the brand's historical design works through a contrastive learning algorithm, and realizes the consistency matching between the new design and the brand tone.

[0069] The collaborative work module, connected to the user interaction module, supports cross-team real-time collaboration and version control. Refer to Figure 6 , and the collaborative work module is configured to:

[0070] The version tracing unit records all modification operations in the design process and supports historical version backtracking; it avoids file chaos caused by collaboration conflicts, facilitating accountability and inspiration reuse.

[0071] The permission management unit realizes the copyright confirmation and usage tracing of design assets through blockchain technology. It protects the intellectual property rights of designers, prevents piracy, and also supports commercial authorization tracking.

[0072] The user interaction module synchronizes the editing status with the collaborative work module to support multi-user real-time collaboration.

[0073] The multi-modal output module, connected to the intelligent processing module, generates brand graphic design files adapted to multiple platforms. Refer to Figure 7 , and the multi-modal output module is configured to:

[0074] The format adaptation unit automatically generates design files that meet the requirements of multiple terminals such as printing, digital media, and mobile devices; it avoids manual format adjustment and reduces cross-platform adaptation costs.

[0075] The dynamic expansion unit supports seamless docking with external interfaces such as advertising delivery systems and e-commerce platforms. It shortens the cycle from design to implementation and improves the marketing response speed.

[0076] The multi-modal output module can call the material library of the resource management module to ensure the integrity of the materials in the output file;

[0077] The multi-modal output module can also receive the evaluation results of the intelligent analysis module to optimize output parameters (such as image compression ratio).

[0078] Form a closed loop from creative generation to analysis, then editing and finally output: after the user inputs requirements, the system automatically generates solutions, evaluates and optimizes, provides AR previews, and outputs adapted to multiple platforms, reducing manual intervention.

[0079] The style transfer unit combines the analysis of historical materials in the resource management module to ensure the consistency between the new design and the brand tone; the visual evaluation unit further verifies compliance to avoid brand image fragmentation.

[0080] The collaborative work module supports real-time editing and version control. Combined with the AR preview of the user interaction module, it enables remote teams to quickly reach a consensus and reduce meeting costs.

[0081] The dynamic expansion unit is connected to external systems, enabling the design results to be directly used in scenarios such as advertising placement and e-commerce operation, improving the resource reuse rate.

[0082] The intelligent processing module, as the core center of the system, realizes the full-process intelligent design and resource integration by coordinating the interaction and data flow of each functional module. The intelligent processing module is directly connected to modules such as creative generation, intelligent analysis, and user interaction, and is responsible for overall coordinating the automated operation of the design process.

[0083] During the operation of this embodiment, through the collaborative operation of multiple modules, the efficiency, quality, and collaboration ability of brand graphic design have been significantly improved. The creative generation module, based on the fusion of GAN and multi-modal data, quickly generates diverse design schemes. Combined with the semantic understanding and visual evaluation of the intelligent analysis module, it ensures that the schemes not only conform to the brand tone but also have aesthetic value. The intelligent canvas and AR real-time feedback of the user interaction module lower the operation threshold and intuitively present the design effect, while the resource management module, through dynamic tagging and intelligent recommendation, efficiently reuses brand assets and avoids repeated creation. The collaborative work module, with the help of version tracing and blockchain confirmation of rights, guarantees the security and traceability of team collaboration. At the same time, the multi-modal output module automatically adapts to multi-platform formats and opens up external interfaces, significantly shortening the design implementation cycle. Each module forms a closed-loop process: from requirement input to generation, optimization, collaboration, and output, reducing manual intervention throughout the process, realizing the efficient standardization and personalized innovation of brand design. At the same time, through historical material analysis and style migration, it ensures the consistency of brand tone between new and old designs, effectively solving the problems of low efficiency, creative limitations, and multi-terminal adaptation in traditional design.

[0084] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A brand graphic creative design system based on AIGC, including an intelligent processing module, characterized in that: The system is configured to: A creative generation module, connected to the intelligent processing module, constructs a multi-modal creative generation engine based on the Generative Adversarial Network (GAN) for automatically generating brand graphic design solutions; An intelligent analysis module, connected to the creative generation module, analyzes brand tone, industry characteristics, and user needs through a deep learning model to generate design optimization suggestions; A user interaction module, connected to the intelligent processing module, provides a visual editing interface and real-time interaction feedback, and supports multi-terminal collaborative operations; A resource management module, connected to the creative generation module, constructs a dynamic brand asset library to realize intelligent retrieval and reuse of design materials; A collaborative work module, connected to the user interaction module, supports cross-team real-time collaboration and version control; A multi-modal output module, connected to the intelligent processing module, generates brand graphic design files adapted to multiple platforms.

2. The brand graphic creative design system based on AIGC according to claim 1, characterized in that: The creative generation module is configured to: A cross-modal generation unit that fuses multi-source data such as text, images, and videos to generate an initial design solution; A style transfer unit that automatically adapts and innovates the brand visual style based on a pre-trained model.

3. The brand graphic creative design system based on AIGC according to claim 1, wherein: The intelligent analysis module is configured to: A semantic understanding unit that parses the user requirements document through Natural Language Processing (NLP) to extract key design elements; A visual evaluation unit that evaluates the aesthetic compliance and brand recognition of the design solution based on a Convolutional Neural Network (CNN).

4. An AIGC-based brand graphic creative design system according to claim 1, characterized in that: The user interaction module is configured to: An intelligent canvas unit that supports drag-and-drop editing and automatic layout adjustment; A real-time feedback unit that previews the actual application effect of the design solution through Augmented Reality (AR) technology.

5. The brand graphic creative design system based on AIGC according to claim 1, characterized in that: The resource management module is configured to: A dynamic tagging unit that automatically tags the attributes and application scenarios of design materials based on metadata; An intelligent recommendation unit that pushes brand assets adapted to the current design requirements through a collaborative filtering algorithm.

6. The brand graphic creative design system based on AIGC according to claim 1, characterized in that: The collaborative work module is configured to: A version tracing unit that records all modification operations during the design process and supports historical version backtracking; A permission management unit that realizes copyright confirmation and usage tracing of design assets through blockchain technology.

7. An AIGC-based brand graphic creative design system according to claim 1, characterized in that: The multi-modal output module is configured to: A format adaptation unit that automatically generates design files meeting the requirements of multiple terminals such as printing, digital media, and mobile devices; A dynamic expansion unit that supports seamless docking with external interfaces such as advertising delivery systems and e-commerce platforms.

8. The brand graphic creative design system based on AIGC according to claim 1, wherein: The style transfer unit is connected to the resource management module, extracts the visual features of the brand's historical design works through a contrastive learning algorithm, and realizes the consistency matching between the new design and the brand tone.

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