Complex publication derivative resource content layout planning system driven by graph calculation

Abstract

The invention discloses a graph calculation-driven complex publication derivative resource content layout planning system, which belongs to the technical field of digital publication and intelligent media, and comprises a multi-modal data analysis module for analyzing heterogeneous data such as texts, images, tables and the like in publications into structured node features; a feature fusion module; a content relationship modeling module; a graph calculation driving layout optimization module; performing layout constraint modeling: converting a layout problem into a graph constraint optimization problem; a modeling optimization module; a multi-terminal adaptive rendering module; the layout generation engine is used for converting a graph calculation result into a visual layout scheme; and the interactive feedback module is used for feeding back user operation to the graph in real time to trigger layout reconstruction. According to the method, advanced technologies such as graph calculation, multi-modal fusion and constraint solution are deeply fused, the limitation that a traditional typesetting tool depends on a fixed template is broken through, and a content-driven one-stop solution is provided for scenes such as academic periodicals, electronic textbooks and digital reports.

Description

Technical Field

[0001] The present invention relates to the field of digital publishing and intelligent media technology, and in particular to a complex publication derivative resource content layout planning system driven by graph computing. Background Art

[0002] With the rapid development of digital publishing and multimedia technologies, the demand for derivative resources for complex publications is growing. These derivative resources, including supporting exercise banks, case libraries, knowledge graphs, interactive charts, and video tutorials, must be closely linked to the main publication content and form a structured layout. However, traditional layout planning relies on manual experience and is difficult to efficiently handle multi-dimensional content associations, dynamic updates, and personalized needs. Therefore, we propose a graph-based computing-driven content layout planning system for complex publication derivative resources to address this problem. Summary of the Invention

[0003] The purpose of the present invention is to provide a complex publication-derived resource content layout planning system driven by graph computing to solve the problems raised in the above-mentioned background technology.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A graph-driven system for content layout planning of complex publication-derived resources, including: Multimodal data parsing module, which parses heterogeneous data such as text, images, and tables in publications into structured node features; Feature fusion module, cross-modal alignment and unified encoding to form semantically consistent node vectors; The content relationship modeling module builds a network of relationships between the main publication and its derivative resources, forming a computable graph structure foundation; The graph computing-driven layout optimization module performs community detection on the content relationship graph based on the graph computing framework and clusters strongly related derivative resources; Layout constraint modeling: transforming the layout problem into a graph constraint optimization problem; Modeling and optimization module, which optimizes layout solutions through parameter tuning and constraint solving to ensure compliance with design rules and visual aesthetics; The multi-terminal adaptive rendering module dynamically adjusts layout parameters and rendering strategies based on device characteristics to ensure cross-terminal display consistency and interactive smoothness; Layout generation engine: converts graph calculation results into visual layout solutions; Interactive feedback module: User operations are fed back to the graph in real time, triggering layout reconstruction.

[0005] Preferably, the multimodal data parsing module includes a text parsing unit, an image parsing unit and a table parsing unit. The text parsing unit extracts the semantic features of the publication text based on the BERT model and identifies the logical structure of the content. The image parsing unit uses ResNet to extract the visual semantics of illustrations / charts and mark the associated positions of images and texts. The table parsing unit parses the relationship between rows and columns of the table through structured data extraction technology to generate interactive matrix nodes.

[0006] Preferably, the feature fusion module includes a cross-modal alignment unit, a feature dimensionality reduction unit and a node embedding unit. The cross-modal alignment unit uses a graph attention network to establish the associated edge weights between text paragraphs, images, and tables. The feature dimensionality reduction unit compresses high-dimensional heterogeneous features into a unified low-dimensional space through PCA or t-SNE algorithm to reduce computational complexity. The node embedding unit encodes the fused features into graph node vectors as input for subsequent graph calculations.

[0007] Preferably, the content relationship modeling module includes a node abstraction unit, an edge relationship modeling unit and a dynamic update unit. The node abstraction unit maps the main publication chapters and derivative resources into graph nodes and labels the node types. The edge relationship modeling unit calculates the correlation strength between nodes based on co-occurrence analysis and semantic similarity and constructs weighted edges. The dynamic update unit adds and deletes nodes and edges in real time according to user interactions, triggering local graph structure updates.

[0008] Preferably, the graph computing driven layout optimization module includes a community detection unit, a clustering optimization unit and a layout weight allocation unit. The community detection unit uses the Louvain algorithm to divide the graph nodes into communities and identify strongly associated clusters. The clustering optimization unit dynamically adjusts the community division threshold based on reinforcement learning to balance the clustering granularity and layout density. The layout weight allocation unit allocates layout priority according to the node centrality, and the core nodes occupy a larger display area.

[0009] Preferably, the layout constraint modeling module includes a hard constraint unit, a soft constraint unit and a constraint solving unit. The hard constraint unit defines rigid rules such as device restrictions, non-overlapping content, and node coordinate boundaries. The soft constraint unit models aesthetic rules and reading lines. The constraint solving unit calls mathematical programming tools to solve multi-objective optimization problems and generates a node coordinate matrix that meets the constraints.

[0010] Preferably, the modeling optimization module includes a parameter tuning unit, a conflict detection unit and an iterative optimization unit. The parameter tuning unit adjusts hyperparameters such as the community detection threshold and the edge weight calculation formula through A / B testing to optimize the rationality of the layout. The conflict detection unit identifies constraint violations, triggers alarms and records conflict types. The iterative optimization unit uses a simulated annealing algorithm to perform multiple rounds of fine-tuning on the initial layout to gradually approach the optimal solution.

[0011] Preferably, the multi-terminal adaptive rendering module includes a device perception unit, a progressive rendering unit and an interaction degradation unit. The device perception unit obtains the terminal screen size, resolution, and horizontal and vertical screen status, and dynamically switches the layout mode. The progressive rendering unit prioritizes rendering core nodes and delays loading derivative resources on demand. The interaction degradation unit simplifies the interaction effect on low-performance devices to ensure smoothness.

[0012] Preferably, the layout generation engine includes a coordinate mapping unit, a style matching unit and a dynamic adjustment unit. The coordinate mapping unit converts the node coordinate matrix output by the graph calculation into absolute positioning parameters. The style matching unit automatically assigns visual styles according to the node type. The dynamic adjustment unit responds to user zoom operations and recalculates node size and spacing in real time.

[0013] Preferably, the interactive feedback module includes an operation capture unit, an incremental update unit and a user preference analysis unit. The operation capture unit records user behaviors such as dragging, folding / expanding, and annotation, and converts them into graph structure change instructions. The incremental update unit uses a dynamic graph computing engine to locally update the affected subgraphs to avoid recalculation of the entire graph. The user preference analysis unit counts high-frequency operations and extracts preference rules to inject into the constraint model.

[0014] The beneficial effects of the present invention are: In the present invention, the graph computing-driven complex publication derivative resource content layout planning system deeply extracts the semantic and structural features of text, images, and tables through a multimodal data analysis module, and combines the cross-modal alignment technology of a feature fusion module to ensure the consistency of graphic and text content in the semantic space; the content relationship modeling module constructs a weighted association graph based on co-occurrence analysis and semantic similarity to accurately depict the inherent logical relationship between the main publication and the derivative resources; the graph computing-driven layout optimization module uses community detection and reinforcement learning to dynamically adjust the clustering granularity, intelligently aggregates strongly related content, and avoids the subjectivity and fragmentation problems of traditional manual typesetting; the layout constraint modeling module uses the dual guarantees of hard constraints and soft constraints to ensure that the generated solution meets technical standards and is visually friendly, ultimately achieving a high degree of unity between content logic and spatial layout; In the present invention, the graph computing-driven complex publication derivative resource content layout planning system, the multi-terminal adaptive rendering module captures screen size, resolution and other parameters in real time based on the device perception unit, and dynamically switches between grid layout, streaming layout and other modes to ensure display consistency of devices such as PCs, mobile terminals, and tablets; the progressive rendering unit prioritizes loading core content nodes and delays loading derivative resources on demand, reducing user waiting time and network load; the interactive degradation unit automatically simplifies animation effects in low-performance environments to ensure smooth operation; combined with the dynamic adjustment unit of the layout generation engine, user operations such as zooming and rotating can trigger intelligent recalculation of node size and spacing in real time to avoid content deformation or overflow; In the present invention, the graph computing-driven complex publication-derived resource content layout planning system automatically adjusts graph computing hyperparameters through the parameter tuning unit of the modeling optimization module, combines with the conflict detection unit to identify layout conflicts in real time and feeds back to the iterative optimization unit, and uses the simulated annealing algorithm to perform multiple rounds of fine-tuning on the plan, replacing the traditional lengthy process that relies on manual trial and error. The feature dimensionality reduction technology of the feature fusion module compresses high-dimensional heterogeneous data into a unified low-dimensional space, reducing the complexity of graph computing. The structured extraction capability of the multimodal data analysis module can reuse the analysis results of historical publications, reducing the consumption of repeated computing resources. In the present invention, the graph-computing-driven complex publication-derived resource content layout planning system records user actions such as dragging, folding, and annotation in real time through the operation capture unit of the interactive feedback module, converting them into graph structure weight adjustment instructions. The incremental update unit only recalculates local subgraphs to achieve a response in seconds. The user preference analysis unit extracts personalized rules through statistical high-frequency operations and dynamically injects them into the layout constraint model, allowing the system to gradually adapt to the user's unique needs. The style matching unit of the layout generation engine automatically assigns visual styles according to node types, combined with the real-time scaling adaptation of the dynamic adjustment unit, to meet the user's demand for refined control of visualization effects. In the present invention, the graph computing-driven complex publication-derived resource content layout planning system deeply integrates cutting-edge technologies such as graph computing, multimodal fusion, and constraint solving, breaking through the limitations of traditional typesetting tools that rely on fixed templates, and providing content-driven one-stop solutions for scenarios such as academic journals, e-textbooks, and digital reports; the multi-terminal adaptive rendering module supports offline edge computing, adapts to low-bandwidth environments through model lightweighting and caching mechanisms, and realizes cross-domain applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a system block diagram of the graph computing-driven complex publication-derived resource content layout planning system proposed in the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] Reference Figure 1 , a graph computing-driven complex publication-derived resource content layout planning system, including: Multimodal data parsing module, which parses heterogeneous data such as text, images, and tables in publications into structured node features; Feature fusion module, cross-modal alignment and unified encoding to form semantically consistent node vectors; The content relationship modeling module builds a network of relationships between the main publication and its derivative resources, forming a computable graph structure foundation; The graph computing-driven layout optimization module performs community detection on the content relationship graph based on the graph computing framework and clusters strongly related derivative resources; Layout constraint modeling: transforming the layout problem into a graph constraint optimization problem; Modeling and optimization module, which optimizes layout solutions through parameter tuning and constraint solving to ensure compliance with design rules and visual aesthetics; The multi-terminal adaptive rendering module dynamically adjusts layout parameters and rendering strategies based on device characteristics to ensure cross-terminal display consistency and interactive smoothness; Layout generation engine: converts graph calculation results into visual layout solutions; Interactive feedback module: User operations are fed back to the graph in real time, triggering layout reconstruction.

[0018] In this embodiment, the multimodal data parsing module includes a text parsing unit, an image parsing unit, and a table parsing unit. The text parsing unit extracts the semantic features of the publication text based on the BERT model and identifies the logical structure of the content. The image parsing unit uses ResNet to extract the visual semantics of illustrations / charts and mark the associated positions of images and texts. The table parsing unit uses structured data extraction technology to parse the relationship between rows and columns of the table and generate interactive matrix nodes.

[0019] In this embodiment, the feature fusion module includes a cross-modal alignment unit, a feature dimensionality reduction unit and a node embedding unit. The cross-modal alignment unit uses a graph attention network to establish the associated edge weights between text paragraphs, images, and tables. The feature dimensionality reduction unit compresses high-dimensional heterogeneous features into a unified low-dimensional space through PCA or t-SNE algorithm to reduce computational complexity. The node embedding unit encodes the fused features into graph node vectors as input for subsequent graph calculations.

[0020] In this embodiment, the content relationship modeling module includes a node abstraction unit, an edge relationship modeling unit and a dynamic update unit. The node abstraction unit maps the main publication chapters and derivative resources into graph nodes and labels the node types. The edge relationship modeling unit calculates the correlation strength between nodes based on co-occurrence analysis and semantic similarity and constructs weighted edges. The dynamic update unit adds and deletes nodes and edges in real time according to user interactions, triggering local graph structure updates.

[0021] In this embodiment, the graph computing-driven layout optimization module includes a community detection unit, a clustering optimization unit, and a layout weight allocation unit. The community detection unit uses the Louvain algorithm to divide graph nodes into communities and identify strongly associated clusters. The clustering optimization unit dynamically adjusts the community division threshold based on reinforcement learning to balance the clustering granularity and layout density. The layout weight allocation unit assigns layout priority based on node centrality, so that core nodes occupy a larger display area.

[0022] In this embodiment, the layout constraint modeling module includes a hard constraint unit, a soft constraint unit and a constraint solving unit. The hard constraint unit defines rigid rules such as device restrictions, non-overlapping content, and node coordinate boundaries. The soft constraint unit models aesthetic rules and reading routes. The constraint solving unit calls mathematical programming tools to solve multi-objective optimization problems and generate a node coordinate matrix that meets the constraints.

[0023] In this embodiment, the modeling optimization module includes a parameter tuning unit, a conflict detection unit and an iterative optimization unit. The parameter tuning unit adjusts hyperparameters such as the community detection threshold and the edge weight calculation formula through A / B testing to optimize the rationality of the layout. The conflict detection unit identifies constraint violations, triggers alarms and records conflict types. The iterative optimization unit uses a simulated annealing algorithm to perform multiple rounds of fine-tuning on the initial layout, gradually approaching the optimal solution.

[0024] In this embodiment, the multi-terminal adaptive rendering module includes a device perception unit, a progressive rendering unit and an interaction degradation unit. The device perception unit obtains the terminal screen size, resolution, and horizontal and vertical screen status, and dynamically switches the layout mode. The progressive rendering unit prioritizes rendering core nodes and delays loading derivative resources on demand. The interaction degradation unit simplifies the interaction effect on low-performance devices to ensure smoothness.

[0025] In this embodiment, the layout generation engine includes a coordinate mapping unit, a style matching unit and a dynamic adjustment unit. The coordinate mapping unit converts the node coordinate matrix output by the graph calculation into absolute positioning parameters. The style matching unit automatically assigns visual styles according to the node type. The dynamic adjustment unit responds to user zoom operations and recalculates node size and spacing in real time.

[0026] In this embodiment, the interactive feedback module includes an operation capture unit, an incremental update unit and a user preference analysis unit. The operation capture unit records user behaviors such as dragging, folding / expanding, and annotation, and converts them into graph structure change instructions. The incremental update unit uses a dynamic graph computing engine to locally update the affected subgraphs to avoid recalculation of the entire graph. The user preference analysis unit counts high-frequency operations and extracts preference rules to inject into the constraint model.

[0027] In this embodiment, the text parsing unit of the multimodal data parsing module extracts the semantic features of the publication text and identifies the logical structure of the content based on the BERT model. The image parsing unit uses ResNet to extract the visual semantics of illustrations or charts and mark the associated positions of images and texts. The table parsing unit uses structured data extraction technology to parse the row and column relationships of the table to generate interactive matrix nodes. The cross-modal alignment unit of the feature fusion module uses a graph attention network to establish the associated edge weights between text paragraphs, images and tables. The feature dimensionality reduction unit compresses high-dimensional heterogeneous features into a unified low-dimensional space through PCA or t-SNE algorithm to reduce computational complexity. The node embedding unit encodes the fused features into graph nodes. The vector is input to the content relationship modeling module; the node abstraction unit of the content relationship modeling module maps the main publication chapters and derivative resources into graph nodes and annotates the types; the edge relationship modeling unit calculates the correlation strength between nodes based on co-occurrence analysis and semantic similarity to construct weighted edges; the dynamic update unit adds and deletes nodes and edges in real time according to user interaction to trigger local graph structure updates; the community detection unit of the graph calculation driven layout optimization module uses the Louvain algorithm to divide the graph nodes into communities and identify strongly associated clusters; the clustering optimization unit dynamically adjusts the community division threshold based on reinforcement learning to balance the clustering granularity and layout density; the layout weight allocation unit allocates layout priorities according to the node centrality so that the core nodes occupy a larger display area; the layout approximation The hard constraint unit of the bundle modeling module defines rigid rules such as device restrictions, non-overlapping content, and node coordinate boundaries. The soft constraint unit models aesthetic rules and reading routes. The constraint solving unit uses mathematical programming tools to solve multi-objective optimization problems and generate a node coordinate matrix that meets the constraints. The parameter tuning unit of the modeling optimization module adjusts the community detection threshold and edge weight calculation formula through A / B testing to optimize the layout rationality. The conflict detection unit identifies constraint violations, triggers alarms, and records conflict types. The iterative optimization unit uses a simulated annealing algorithm to perform multiple rounds of fine-tuning on the initial layout to gradually approach the optimal solution. The device perception unit of the multi-terminal adaptive rendering module obtains the terminal screen size, resolution, and horizontal and vertical screen states to dynamically switch layout modes. The progressive rendering unit prioritizes rendering core nodes and delays loading of derivative resources on demand. The interaction degradation unit simplifies the interaction effect on low-performance devices to ensure smoothness. The coordinate mapping unit of the layout generation engine converts the node coordinate matrix into absolute positioning parameters. The style matching unit automatically assigns visual styles according to the node type. The dynamic adjustment unit responds to user zoom operations and recalculates the node size and spacing in real time. The operation capture unit of the interactive feedback module records the user's dragging, folding or expanding behavior and converts it into graph structure change instructions. The incremental update unit uses a dynamic graph calculation engine to locally update the affected subgraph to avoid recalculating the entire graph. The user preference analysis unit counts high-frequency operations to extract preference rules and injects them into the constraint model to form a closed-loop optimization process.

[0028] The above is a detailed introduction to the graph computing-driven complex publication derivative resource content layout planning system 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. A graph computing-driven system for content layout planning of complex publication derivative resources, characterized by: include: Multimodal data parsing module, which parses heterogeneous data such as text, images, and tables in publications into structured node features; Feature fusion module, cross-modal alignment and unified encoding to form semantically consistent node vectors; The content relationship modeling module builds a network of relationships between the main publication and its derivative resources, forming a computable graph structure foundation; The graph computing-driven layout optimization module performs community detection on the content relationship graph based on the graph computing framework and clusters strongly related derivative resources; Layout constraint modeling: transforming the layout problem into a graph constraint optimization problem; Modeling and optimization module, which optimizes layout solutions through parameter tuning and constraint solving to ensure compliance with design rules and visual aesthetics; The multi-terminal adaptive rendering module dynamically adjusts layout parameters and rendering strategies based on device characteristics to ensure cross-terminal display consistency and interactive smoothness; Layout generation engine: converts graph calculation results into visual layout solutions; Interactive feedback module: User operations are fed back to the graph in real time, triggering layout reconstruction.

2. The graph computing-driven complex publication derivative resource content layout planning system according to claim 1, characterized in that: The multimodal data parsing module includes a text parsing unit, an image parsing unit, and a table parsing unit. The text parsing unit extracts the semantic features of the publication text based on the BERT model and identifies the logical structure of the content. The image parsing unit uses ResNet to extract the visual semantics of illustrations / charts and mark the associated positions of images and texts. The table parsing unit uses structured data extraction technology to parse the relationship between table rows and columns and generate interactive matrix nodes.

3. The graph computing-driven complex publication derivative resource content layout planning system according to claim 1, characterized in that: The feature fusion module includes a cross-modal alignment unit, a feature dimensionality reduction unit and a node embedding unit. The cross-modal alignment unit uses a graph attention network to establish the associated edge weights between text paragraphs, images, and tables. The feature dimensionality reduction unit compresses high-dimensional heterogeneous features into a unified low-dimensional space through PCA or t-SNE algorithm to reduce computational complexity. The node embedding unit encodes the fused features into graph node vectors as input for subsequent graph calculations.

4. The graph computing-driven complex publication derivative resource content layout planning system according to claim 1, characterized in that: The content relationship modeling module includes a node abstraction unit, an edge relationship modeling unit and a dynamic update unit. The node abstraction unit maps the main publication chapters and derivative resources into graph nodes and labels the node types. The edge relationship modeling unit calculates the association strength between nodes based on co-occurrence analysis and semantic similarity and constructs weighted edges. The dynamic update unit adds and deletes nodes and edges in real time according to user interactions, triggering local graph structure updates.

5. The graph computing-driven complex publication derivative resource content layout planning system according to claim 1, characterized in that: The graph computing-driven layout optimization module includes a community detection unit, a clustering optimization unit, and a layout weight allocation unit. The community detection unit uses the Louvain algorithm to divide graph nodes into communities and identify strongly associated clusters. The clustering optimization unit dynamically adjusts the community division threshold based on reinforcement learning to balance clustering granularity and layout density. The layout weight allocation unit assigns layout priorities based on node centrality, so that core nodes occupy a larger display area.

6. The graph computing-driven complex publication derivative resource content layout planning system according to claim 1, characterized in that: The layout constraint modeling module includes a hard constraint unit, a soft constraint unit and a constraint solving unit. The hard constraint unit defines rigid rules such as device restrictions, non-overlapping content, and node coordinate boundaries. The soft constraint unit models aesthetic rules and reading routes. The constraint solving unit uses mathematical programming tools to solve multi-objective optimization problems and generate a node coordinate matrix that meets the constraints.

7. The graph computing-driven complex publication derivative resource content layout planning system according to claim 1, characterized in that: The modeling optimization module includes a parameter tuning unit, a conflict detection unit, and an iterative optimization unit. The parameter tuning unit adjusts hyperparameters such as the community detection threshold and the edge weight calculation formula through A / B testing to optimize the rationality of the layout. The conflict detection unit identifies constraint violations, triggers alarms, and records conflict types. The iterative optimization unit uses a simulated annealing algorithm to perform multiple rounds of fine-tuning on the initial layout, gradually approaching the optimal solution.

8. The graph computing-driven complex publication derivative resource content layout planning system according to claim 1, characterized in that: The multi-terminal adaptive rendering module includes a device perception unit, a progressive rendering unit and an interaction degradation unit. The device perception unit obtains the terminal screen size, resolution, and horizontal and vertical screen status, and dynamically switches the layout mode. The progressive rendering unit prioritizes rendering core nodes and delays loading derivative resources on demand. The interaction degradation unit simplifies the interaction effect on low-performance devices to ensure smoothness.

9. The graph computing-driven complex publication derivative resource content layout planning system according to claim 1, characterized in that: The layout generation engine includes a coordinate mapping unit, a style matching unit and a dynamic adjustment unit. The coordinate mapping unit converts the node coordinate matrix output by graph calculation into absolute positioning parameters. The style matching unit automatically assigns visual styles according to node types. The dynamic adjustment unit responds to user zoom operations and recalculates node size and spacing in real time.

10. The graph computing-driven complex publication derivative resource content layout planning system according to claim 1, characterized in that: The interactive feedback module includes an operation capture unit, an incremental update unit, and a user preference analysis unit. The operation capture unit records user behaviors such as dragging, folding / expanding, and annotation, and converts them into graph structure change instructions. The incremental update unit uses a dynamic graph computing engine to locally update the affected subgraphs to avoid recalculating the entire graph. The user preference analysis unit counts high-frequency operations and extracts preference rules to inject into the constraint model.

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