Online resource text and image layout system based on natural language processing

Abstract

The invention discloses an online resource text and image layout system based on natural language processing, and belongs to the technical field of natural language processing. An image semantic understanding module; a layout generation and optimization module; an interactive adjustment module; a multi-modal content fusion module; the aesthetic evaluation and compliance verification module is used for automatically evaluating the aesthetic degree and compliance of the generated layout; and the dynamic response type adaptation module is used for adjusting the layout in real time according to the sizes of terminal equipment and a screen, ensuring the cross-platform consistency, generating a multi-resolution layout scheme based on a Flexbox response type rule, and dynamically adjusting the element stacking sequence, the personalized recommendation module and the real-time cooperation and version management module through reinforcement learning. And the edge calculation module improves the efficiency of large-scale layout generation, supports an offline or low-delay scene, compresses a CNN model by using a knowledge distillation or quantification technology, and adapts to edge equipment. The invention also discloses a method.

Description

Technical Field

[0001] The present invention relates to the field of natural language processing technology, and in particular to an online resource text and image layout system based on natural language processing. Background Art

[0002] Currently, the online resource text and image layout system based on natural language processing is in a stage of rapid technological breakthroughs and market demand growth. On the one hand, natural language processing technology can accurately parse user spoken instructions through pre-trained models such as BERT and GPT, and combine computer vision technology to achieve semantic matching of images and texts. Then, through rule engines and reinforcement learning algorithms, it automatically generates layouts that conform to aesthetic principles, and supports adaptive adaptation on multiple platforms such as PC and mobile terminals. On the other hand, the market demand for low-threshold typesetting tools has increased significantly: non-professional users are in urgent need of intelligent solutions to replace manual adjustments, and professional designers also hope to use AI to liberate repetitive work and focus on creative design. However, the technology still faces challenges such as complex instruction understanding, real-time adaptation of dynamic content, and data privacy. The current graphic and text typesetting of online resources has the following problems:

[0003] High dependence on manual labor: Layouts require manual adjustments by designers, which is time-consuming and costly;

[0004] Template solidification: Pre-set templates cannot adapt to dynamic content requirements;

[0005] Semantic fragmentation: The logical relationship between text and image is not analyzed by the system, resulting in low image-text matching;

[0006] High interaction threshold: It is difficult for non-professional users to achieve personalized typesetting through code or complex tools;

[0007] Therefore, we propose an online resource text and image layout system based on natural language processing to solve this problem. Summary of the Invention

[0008] The purpose of the present invention is to provide an online resource text and image layout system based on natural language processing to solve the problems raised in the above background technology.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] Online resource text and image layout system based on natural language processing, including:

[0011] The natural language parsing module converts user-entered text instructions into machine-executable layout parameters. It uses a custom layout vocabulary to identify layout keywords and extract constraints such as position, size, and alignment. It also uses an intent classification model to distinguish between functional and decorative instructions.

[0012] The image semantic understanding module analyzes the uploaded image content, extracts semantic tags and associates them with text. It generates image feature vectors based on the CLIP model, identifies the main objects and scene types, and calculates the relevance of images and text based on text keywords to ensure consistent layout logic.

[0013] The layout generation and optimization module automatically generates layout solutions based on analysis results, taking into account aesthetics and readability. The rule engine generates initial layouts based on design principles, and uses LayoutGAN or reinforcement learning to optimize element spacing and alignment to avoid visual crowding, dynamically balancing text density, image prominence, and responsive adaptation.

[0014] The interactive adjustment module allows users to modify layouts in real time and obtain intelligent suggestions. It provides a drag-and-drop interface where users can manually adjust element positions, and the system automatically updates semantic constraints.

[0015] The multimodal content fusion module unifies data such as text semantics, image features, and user preferences into a unified model to generate a comprehensive basis for layout decisions. It uses contrastive learning to align the semantic spaces of text and images to ensure the relevance of text and images, and dynamically adjusts the weights of text, images, and user historical preferences in layout decisions.

[0016] Aesthetics assessment and compliance verification module, which automatically evaluates the aesthetics and compliance of the generated layout;

[0017] The dynamic responsive adaptation module adjusts the layout in real time according to the terminal device and screen size to ensure cross-platform consistency. It generates multi-resolution layout solutions based on the responsive rules of Flexbox and dynamically adjusts the element stacking order through reinforcement learning.

[0018] The personalized recommendation module learns from historical user operation data to provide personalized layout suggestions, analyzes the layout preferences of similar user groups, recommends frequently selected solutions, records user operations such as dragging and zooming, and optimizes the layout decision model;

[0019] The real-time collaboration and version management module supports multi-user collaborative editing, retains historical versions for backtracking, records user operations based on a conflict-free data structure, and supports real-time synchronization among multiple users.

[0020] The edge computing module improves the efficiency of large-scale layout generation, supports offline or low-latency scenarios, uses knowledge distillation or quantization technology to compress CNN models, and adapts to edge devices.

[0021] Preferably, the natural language parsing module includes an instruction classification unit, a constraint extraction unit and a layout vocabulary library. The instruction classification unit uses an intent classification model to distinguish instruction types. The constraint extraction unit parses parameters such as position, size, alignment, etc. based on a custom layout vocabulary library. The layout vocabulary library stores an industry term mapping table.

[0022] Preferably, the image semantic understanding module includes a CLIP feature extraction unit, a subject recognition unit and a scene classification unit. The CLIP feature extraction unit generates an image feature vector based on the CLIP model and calculates the image-text correlation. The subject recognition unit locates the main object in the image through the target detection model. The scene classification unit identifies the image scene type to adapt the layout style.

[0023] Preferably, the layout generation and optimization module includes a rule engine unit, a GAN optimization unit and a reinforcement learning unit. The rule engine unit generates an initial layout plan based on design principles. The GAN optimization unit uses LayoutGAN to generate diversified candidate layouts and screen the optimal solution. The reinforcement learning unit dynamically adjusts the element spacing and alignment with readability, aesthetics and responsive adaptation as optimization goals.

[0024] Preferably, the interactive adjustment module includes a drag editing unit and an intelligent suggestion unit. The drag editing unit provides a visual interface to support users to manually adjust the position and size of elements. The intelligent suggestion unit generates optimization suggestions based on the real-time layout status.

[0025] Preferably, the multimodal content fusion module includes a semantic alignment unit and a weight allocation unit. The semantic alignment unit aligns text keywords and image visual focus through comparative learning to enhance the relevance between text and images. The weight allocation unit dynamically adjusts the influence weights of text, images and user preferences on layout decisions according to content type.

[0026] Preferably, the aesthetic evaluation and compliance verification module includes an aesthetic scoring unit and a compliance checking unit. The aesthetic scoring unit evaluates the color contrast, visual balance and other indicators of the layout through a pre-trained aesthetic model, and the compliance checking unit verifies whether the layout complies with brand specifications and barrier-free design standards.

[0027] Preferably, the dynamic responsive adaptation module includes a grid generation unit and a breakpoint optimization unit. The grid generation unit generates a multi-resolution responsive layout based on Flexbox or CSS Grid rules, and the breakpoint optimization unit dynamically adjusts the element stacking order and proportional distribution under different screen sizes through reinforcement learning.

[0028] Preferably, the personalized recommendation module includes a user behavior analysis unit and a collaborative filtering unit. The user behavior analysis unit records user historical operations and extracts preference features. The collaborative filtering unit recommends high-frequency solutions based on the layout selection of similar user groups.

[0029] Preferably, the real-time collaboration and version management module includes an operation synchronization unit and a version comparison unit. The operation synchronization unit adopts the CRDT algorithm to achieve conflict-free synchronization of real-time editing by multiple people, and the version comparison unit visualizes historical version differences through the Diff algorithm.

[0030] Preferably, the edge computing module includes a model lightweight unit and a local cache unit. The model lightweight unit compresses the NLP and image models through knowledge distillation or quantization technology to adapt to the computing power of the edge device, and the local cache unit pre-stores high-frequency layout templates to support offline rapid generation.

[0031] The beneficial effects of the present invention are:

[0032] 1. In the present invention, the online resource text and image layout system based on natural language processing realizes content-driven intelligent layout generation through the deep collaboration of the natural language parsing module and the image semantic understanding module. The system can automatically parse the text instructions entered by the user and the uploaded visual materials, accurately extract keywords, image subjects and scene features, and ensure the logical association between text descriptions and visual elements through cross-modal semantic alignment technology, thereby generating a layout solution with consistent themes and clear structure. This process completely avoids the problem of disconnection between text and images caused by traditional tools relying on manual experience, significantly improves content creation efficiency, and is particularly suitable for e-commerce, news and other fields that require high-frequency content updates;

[0033] 2. In the present invention, the online resource text and image layout system based on natural language processing achieves adaptive layout output across terminal devices through the linkage of a dynamic responsive adaptation module and a layout generation and optimization module. The system can dynamically adjust element proportions, stacking order, and spacing parameters based on the terminal screen size and resolution characteristics, while optimizing the visual hierarchy by combining aesthetic rules and user reading habits. This technology not only solves the pain point of traditional responsive design requiring multiple manual adjustments, but also ensures a consistent experience from desktop to mobile, significantly reducing the cost of multi-platform adaptation.

[0034] 3. In the present invention, the online resource text and image layout system based on natural language processing achieves highly personalized layout decisions through the synergy of the multimodal content fusion module and the personalized recommendation module. The system can comprehensively analyze user historical operation data, group preference characteristics, and real-time interactive feedback, dynamically adjust the weight distribution of text, images, and decorative elements, and generate layout suggestions that suit user habits. For example, it can automatically increase the white space ratio for users who prefer a simple style, or prioritize the enlargement of core product images for marketing scenarios, thereby significantly improving user acceptance and satisfaction with the layout plan;

[0035] 4. In this invention, the online resource text and image layout system based on natural language processing achieves automated output of professional-grade design quality through the dual guarantees of aesthetic evaluation and compliance verification modules. The system has built-in aesthetic evaluation models such as visual balance and color harmony, which can automatically identify and correct defects such as crowded typesetting and low-contrast color matching. At the same time, it strictly adheres to barrier-free design standards to ensure text readability and color-blind friendliness. This mechanism enables non-professional users to quickly generate high-quality layouts that meet brand standards and possess artistic quality.

[0036] 5. In the present invention, the online resource text and image layout system based on natural language processing achieves efficient and stable services in high-concurrency scenarios through the integration of edge computing modules and real-time collaboration modules. The system uses model lightweight technology to optimize computing resource usage, supports rapid generation of layout solutions on low-computing power devices, and realizes seamless collaboration of multiple people in real-time editing through distributed synchronization algorithms. This capability makes it widely applicable to scenarios such as online education and advertising that require instant feedback and team collaboration, significantly improving the flexibility and reliability of large-scale content production; BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a system block diagram of the online resource text and image layout system based on natural language processing proposed by the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0039] Reference Figure 1 , an online resource text and image layout system based on natural language processing, including:

[0040] The natural language parsing module converts user-entered text instructions into machine-executable layout parameters. It uses a custom layout vocabulary to identify layout keywords and extract constraints such as position, size, and alignment. It also uses an intent classification model to distinguish between functional and decorative instructions.

[0041] The image semantic understanding module analyzes the uploaded image content, extracts semantic tags and associates them with text. It generates image feature vectors based on the CLIP model, identifies the main objects and scene types, and calculates the relevance of images and text based on text keywords to ensure consistent layout logic.

[0042] The layout generation and optimization module automatically generates layout solutions based on analysis results, taking into account aesthetics and readability. The rule engine generates initial layouts based on design principles, and uses LayoutGAN or reinforcement learning to optimize element spacing and alignment to avoid visual crowding, dynamically balancing text density, image prominence, and responsive adaptation.

[0043] The interactive adjustment module allows users to modify layouts in real time and obtain intelligent suggestions. It provides a drag-and-drop interface where users can manually adjust element positions, and the system automatically updates semantic constraints.

[0044] The multimodal content fusion module unifies data such as text semantics, image features, and user preferences into a unified model to generate a comprehensive basis for layout decisions. It uses contrastive learning to align the semantic spaces of text and images to ensure the relevance of text and images, and dynamically adjusts the weights of text, images, and user historical preferences in layout decisions.

[0045] Aesthetics assessment and compliance verification module, which automatically evaluates the aesthetics and compliance of the generated layout;

[0046] The dynamic responsive adaptation module adjusts the layout in real time according to the terminal device and screen size to ensure cross-platform consistency. It generates multi-resolution layout solutions based on the responsive rules of Flexbox and dynamically adjusts the element stacking order through reinforcement learning.

[0047] The personalized recommendation module learns from historical user operation data to provide personalized layout suggestions, analyzes the layout preferences of similar user groups, recommends frequently selected solutions, records user operations such as dragging and zooming, and optimizes the layout decision model;

[0048] The real-time collaboration and version management module supports multi-user collaborative editing, retains historical versions for backtracking, records user operations based on a conflict-free data structure, and supports real-time synchronization among multiple users.

[0049] The edge computing module improves the efficiency of large-scale layout generation, supports offline or low-latency scenarios, uses knowledge distillation or quantization technology to compress CNN models, and adapts to edge devices.

[0050] In this embodiment, the natural language parsing module includes an instruction classification unit, a constraint extraction unit and a layout vocabulary library. The instruction classification unit uses an intent classification model to distinguish instruction types. The constraint extraction unit parses parameters such as position, size, alignment, etc. based on a custom layout vocabulary library. The layout vocabulary library stores an industry term mapping table.

[0051] In this embodiment, the image semantic understanding module includes a CLIP feature extraction unit, a subject recognition unit, and a scene classification unit. The CLIP feature extraction unit generates an image feature vector based on the CLIP model and calculates the image-text correlation. The subject recognition unit locates the main object in the image through the target detection model. The scene classification unit identifies the image scene type to adapt the layout style.

[0052] In this embodiment, the layout generation and optimization module includes a rule engine unit, a GAN optimization unit, and a reinforcement learning unit. The rule engine unit generates an initial layout plan based on design principles. The GAN optimization unit uses LayoutGAN to generate diverse candidate layouts and screen the optimal solution. The reinforcement learning unit dynamically adjusts element spacing and alignment with readability, aesthetics, and responsive adaptation as optimization goals.

[0053] In this embodiment, the interactive adjustment module includes a drag editing unit and an intelligent suggestion unit. The drag editing unit provides a visual interface to support users to manually adjust the position and size of elements. The intelligent suggestion unit generates optimization suggestions based on the real-time layout status.

[0054] In this embodiment, the multimodal content fusion module includes a semantic alignment unit and a weight allocation unit. The semantic alignment unit aligns text keywords and image visual focus through comparative learning to enhance the relevance between text and images. The weight allocation unit dynamically adjusts the influence weights of text, images and user preferences on layout decisions according to content type.

[0055] In this embodiment, the aesthetic evaluation and compliance verification module includes an aesthetic scoring unit and a compliance checking unit. The aesthetic scoring unit evaluates the color contrast, visual balance and other indicators of the layout through a pre-trained aesthetic model, and the compliance checking unit verifies whether the layout complies with brand specifications and barrier-free design standards.

[0056] In this embodiment, the dynamic responsive adaptation module includes a grid generation unit and a breakpoint optimization unit. The grid generation unit generates a multi-resolution responsive layout based on Flexbox or CSS Grid rules, and the breakpoint optimization unit dynamically adjusts the element stacking order and proportional distribution under different screen sizes through reinforcement learning.

[0057] In this embodiment, the personalized recommendation module includes a user behavior analysis unit and a collaborative filtering unit. The user behavior analysis unit records user historical operations and extracts preference features. The collaborative filtering unit recommends high-frequency solutions based on the layout selection of similar user groups. The real-time collaboration and version management module includes an operation synchronization unit and a version comparison unit. The operation synchronization unit uses the CRDT algorithm to achieve conflict-free synchronization of real-time editing by multiple people. The version comparison unit visualizes historical version differences through the Diff algorithm. The edge computing module includes a model lightweight unit and a local cache unit. The model lightweight unit compresses NLP and image models through knowledge distillation or quantization technology to adapt to the computing power of edge devices. The local cache unit pre-stores high-frequency layout templates to support offline rapid generation.

[0058] In this embodiment, the deep collaboration between the natural language parsing module and the image semantic understanding module enables content-driven intelligent layout generation. The system can automatically parse user-entered text instructions and uploaded visual materials, accurately extract keywords, image subjects, and scene features, and ensure the logical connection between text descriptions and visual elements through cross-modal semantic alignment technology, thereby generating a layout solution with consistent themes and clear structure. This process completely avoids the problem of disconnection between text and images caused by traditional tools relying on manual experience, significantly improving content creation efficiency. It is particularly suitable for e-commerce and news fields that require frequent content updates. By linking the dynamic responsive adaptation module with the layout generation and optimization module, adaptive layout output is achieved across terminal devices. The system can dynamically adjust element proportions, stacking order, and spacing parameters based on terminal screen size and resolution characteristics, while optimizing visual hierarchy by combining aesthetic rules and user reading habits. This technology not only solves the pain point of traditional responsive design that requires multiple manual adjustments, but also ensures a consistent experience from desktop to mobile, significantly reducing the cost of multi-platform adaptation. Through the synergy of the multimodal content fusion module and the personalized recommendation module, highly personalized layout decisions are achieved. The system comprehensively analyzes user historical operation data, group preference characteristics, and real-time interactive feedback, dynamically adjusts the weighting of text, images, and decorative elements, and generates layout suggestions that suit user habits. For example, it automatically increases the white space ratio for users who prefer a simple style, or prioritizes the enlargement of core product images for marketing scenarios, thereby significantly improving user acceptance and satisfaction with the layout plan.

[0059] Through the dual safeguards of aesthetic assessment and compliance verification modules, automated output of professional-grade design quality is achieved. The system's built-in aesthetic evaluation models, such as visual balance and color harmony, automatically identify and correct flaws such as crowded layouts and low-contrast color schemes. While strictly adhering to accessibility standards, it ensures text readability and color-blind friendliness. This mechanism enables even non-professional users to quickly generate high-quality, artistic layouts that adhere to brand specifications.

[0060] The above is a detailed introduction to the online resource text and image layout system based on natural language processing provided by the present invention. Specific embodiments are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. An online resource text and image layout system based on natural language processing, characterized by: include: The natural language parsing module converts user-entered text instructions into machine-executable layout parameters. It uses a custom layout vocabulary to identify layout keywords and extract constraints such as position, size, and alignment. It also uses an intent classification model to distinguish between functional and decorative instructions. The image semantic understanding module analyzes the uploaded image content, extracts semantic tags and associates them with text. It generates image feature vectors based on the CLIP model, identifies the main objects and scene types, and calculates the relevance of images and text based on text keywords to ensure consistent layout logic. The layout generation and optimization module automatically generates layout solutions based on analysis results, taking into account aesthetics and readability. The rule engine generates initial layouts based on design principles, and uses LayoutGAN or reinforcement learning to optimize element spacing and alignment to avoid visual crowding, dynamically balancing text density, image prominence, and responsive adaptation. The interactive adjustment module allows users to modify layouts in real time and obtain intelligent suggestions. It provides a drag-and-drop interface where users can manually adjust element positions, and the system automatically updates semantic constraints. The multimodal content fusion module unifies data such as text semantics, image features, and user preferences into a unified model to generate a comprehensive basis for layout decisions. It uses contrastive learning to align the semantic spaces of text and images to ensure the relevance of text and images, and dynamically adjusts the weights of text, images, and user historical preferences in layout decisions. Aesthetics assessment and compliance verification module, which automatically evaluates the aesthetics and compliance of the generated layout; A dynamic responsive adaptation module adjusts the layout in real time based on the terminal device and screen size to ensure cross-platform consistency. It generates multi-resolution layout solutions based on Flexbox's responsive rules and dynamically adjusts the stacking order of elements through reinforcement learning. The personalized recommendation module learns from historical user operation data to provide personalized layout suggestions, analyzes the layout preferences of similar user groups, recommends frequently selected solutions, records user operations such as dragging and zooming, and optimizes the layout decision model; Real-time collaboration and version management module supports multi-user collaborative editing, retains historical versions for backtracking, records user operations based on conflict-free data structures, and supports real-time synchronization among multiple users; The edge computing module improves the efficiency of large-scale layout generation, supports offline or low-latency scenarios, uses knowledge distillation or quantization technology to compress CNN models, and adapts to edge devices.

2. The online resource text and image layout system based on natural language processing according to claim 1, characterized in that: The natural language parsing module includes an instruction classification unit, a constraint extraction unit and a layout vocabulary library. The instruction classification unit uses an intent classification model to distinguish instruction types. The constraint extraction unit parses parameters such as position, size, alignment, etc. based on a custom layout vocabulary library. The layout vocabulary library stores an industry term mapping table.

3. The online resource text and image layout system based on natural language processing according to claim 1, characterized in that: The image semantic understanding module includes a CLIP feature extraction unit, a subject recognition unit, and a scene classification unit. The CLIP feature extraction unit generates an image feature vector based on the CLIP model and calculates the image-text correlation. The subject recognition unit locates the main object in the image through the target detection model. The scene classification unit identifies the image scene type to adapt the layout style.

4. The online resource text and image layout system based on natural language processing according to claim 1, characterized in that: The layout generation and optimization module includes a rule engine unit, a GAN optimization unit, and a reinforcement learning unit. The rule engine unit generates an initial layout plan based on design principles. The GAN optimization unit uses LayoutGAN to generate diverse candidate layouts and screen the optimal solution. The reinforcement learning unit dynamically adjusts element spacing and alignment with readability, aesthetics, and responsive adaptation as optimization goals.

5. The online resource text and image layout system based on natural language processing according to claim 1, characterized in that: The interactive adjustment module includes a drag-and-drop editing unit and an intelligent suggestion unit. The drag-and-drop editing unit provides a visual interface to support users to manually adjust the position and size of elements. The intelligent suggestion unit generates optimization suggestions based on the real-time layout status.

6. The online resource text and image layout system based on natural language processing according to claim 1, characterized in that: The multimodal content fusion module includes a semantic alignment unit and a weight allocation unit. The semantic alignment unit aligns text keywords and image visual focus through comparative learning to enhance the relevance between text and images. The weight allocation unit dynamically adjusts the influence weights of text, images and user preferences on layout decisions according to content type.

7. The online resource text and image layout system based on natural language processing according to claim 1, characterized in that: The aesthetic evaluation and compliance verification module includes an aesthetic scoring unit and a compliance checking unit. The aesthetic scoring unit evaluates the color contrast, visual balance and other indicators of the layout through a pre-trained aesthetic model, and the compliance checking unit verifies whether the layout complies with brand specifications and barrier-free design standards.

8. The online resource text and image layout system based on natural language processing according to claim 1, characterized in that: The dynamic responsive adaptation module includes a grid generation unit and a breakpoint optimization unit. The grid generation unit generates a multi-resolution responsive layout based on Flexbox or CSS Grid rules, and the breakpoint optimization unit dynamically adjusts the element stacking order and proportional distribution under different screen sizes through reinforcement learning.

9. The online resource text and image layout system based on natural language processing according to claim 1, characterized in that: The personalized recommendation module includes a user behavior analysis unit and a collaborative filtering unit. The user behavior analysis unit records user historical operations and extracts preference features. The collaborative filtering unit selects and recommends high-frequency solutions based on the layout of similar user groups.

10. The online resource text and image layout system based on natural language processing according to claim 1, characterized in that: The real-time collaboration and version management module includes an operation synchronization unit and a version comparison unit. The operation synchronization unit uses the CRDT algorithm to achieve conflict-free synchronization of real-time editing by multiple people, and the version comparison unit uses the Diff algorithm to visualize historical version differences. The edge computing module includes a model lightweight unit and a local cache unit. The model lightweight unit compresses the NLP and image models through knowledge distillation or quantization technology to adapt to the computing power of the edge device. The local cache unit pre-stores high-frequency layout templates to support offline rapid generation.

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