A ppt generation system and method based on structured content understanding and intelligent layout design

Abstract

The application relates to the technical field of intelligent document generation and automatic demonstration design, in particular to a PPT generation system and method based on structured content understanding and intelligent layout design, which comprises a structured content understanding and outline planning module, an intelligent layout decision module, a page content generation module, a visual rendering engine and a multi-format export module, and is provided with a multi-modal content enhancement module, a system management and scheduling module, a version management and re-export auxiliary module; based on the theme description and reference materials input by a user, structured outline data capable of being independently edited is generated, a layout is matched and adapted according to the page type and content density characteristics, standard page content is generated through a multi-dimensional consistency control strategy, and after the rendering is completed by fusing a theme system, the editable export of a multi-format file is realized. The application solves the problems of the existing PPT generation tool, such as imprecise content logic, poor layout adaptability and insufficient reusability, and greatly improves the PPT production efficiency and generation quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent document generation and automated presentation design technology, specifically a PPT generation system and method based on structured content understanding and intelligent layout design. Background Technology

[0002] Most mainstream PPT generation tools on the market today rely on fixed templates for simple text filling and format conversion, resulting in several technical shortcomings. In terms of content processing, existing tools cannot perform in-depth, structured analysis of user-input topic descriptions and reference materials, making it difficult to construct logically clear content hierarchies and presentation frameworks. The generated content often suffers from issues such as a lack of focus, incomplete arguments, and confused logical progression, failing to meet the rigorous content requirements of professional scenarios.

[0003] At the layout design level, the existing tools' layout matching mechanism lacks flexibility, and can only achieve a simple correspondence between templates and pages. It cannot dynamically adapt according to the page content type and information density characteristics, which easily leads to problems such as content overflow, layout imbalance, and inconsistent visual rhythm. Users still need to invest a lot of time in manual layout adjustments, and it has failed to truly achieve full automation of the PPT production process.

[0004] In summary, existing technologies cannot simultaneously address the professionalism of PPT content, format adaptability, ease of operation, and system stability, making it difficult to meet users' core needs for high-quality, high-efficiency automated PPT generation in various professional scenarios. These technological bottlenecks urgently need to be overcome. Summary of the Invention

[0005] The purpose of this invention is to provide a PPT generation system and method based on structured content understanding and intelligent layout design, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A PPT generation system based on structured content understanding and intelligent layout design includes: The structured content understanding and outline planning module is configured to respond to user input of topic descriptions and optional reference materials. It generates structured outline data containing chapter levels, page type identifiers, and key page text through semantic parsing, and persistently stores and visualizes the structured outline data as an editable intermediate result independent of the final presentation file. The intelligent layout decision module is configured to traverse each page in the structured outline data, extract the page type metadata of the page and calculate its content density characteristics, and determine the target layout that matches the page type and content density characteristics from the layout template library based on the preset layout matching rules. The page content generation module is configured to take the page key text in the structured outline data as input, call the trained content generation model, and follow a multi-dimensional consistency control strategy including terminology consistency, argument completeness, and length balance to generate the title, main text key points, and auxiliary explanatory text content for each page. The visual rendering engine is configured to acquire a preset theme system, which includes at least font hierarchy specifications, color palettes, and component style definitions. It integrates and renders the target layout, generated text content, and theme system to generate a visual page that corresponds one-to-one with each page in the structured outline data. The multi-format export module is configured to batch convert all visual pages into multiple delivery formats, including at least PPTX, PDF, and HTML, while preserving the editable properties of text layers and vector component layers in each delivery format during the export process.

[0007] As a preferred option, a multimodal content enhancement module is also included, configured as follows: Based on the semantic vector of the current page, the system retrieves matching image or icon resources from the preset material library or third-party material interface. After performing intelligent cropping, subject detection and composition adaptation on the retrieved material resources, the system embeds them into the specified placeholders of the visual page and records the material source identifier and license type information in the page metadata for generating a material source list during export. Additionally, it identifies implicit structured data relationships from key text on the page or receives tabular data directly input by the user, maps the identified or received data to a predefined basic chart type or structure diagram type data structure, calls the chart rendering engine to generate vector chart graphics, and inserts the chart graphics as editable objects into the target layout area of ​​the visualization page.

[0008] As a preferred option, a system governance and scheduling module is also included, configured as follows: After the structured outline data is generated, an outline structure consistency check is performed, including chapter completeness, logical progression, and page number control compliance. After the visualization page is generated, a visual consistency check is performed, including cross-page theme element reuse consistency, white space ratio, and component alignment status. When any check fails, the corresponding content adjustment strategy or layout adjustment strategy is triggered and adjustment suggestions are pushed to the front-end interaction layer. Furthermore, the page content generation task, visual rendering task, and multi-format export task are separated into mutually decoupled asynchronous execution units. A status tracking and progress reporting mechanism is established for each asynchronous execution unit, and the generated visual page previews are pushed page by page in a streaming manner. Failure retry and degradation strategies are set for model calls, rendering compositing, and file export, and state snapshots are recorded in case of exceptions to support breakpoint recovery.

[0009] As a preferred solution, a version management and re-export module is also included, configured as follows: Record the theme system configuration snapshot, structured outline data version identifier, and export parameters for each export operation; Receive user re-export instructions, and based on the same structured outline data version, call different historical theme system configuration snapshots or newly selected theme systems to batch regenerate and export multi-version delivery packages.

[0010] A PPT generation method based on structured content understanding and intelligent layout design, executed by a PPT generation system based on structured content understanding and intelligent layout design, includes the following steps: In response to the user's input of a topic description and optional reference materials, the system generates structured outline data containing chapter levels, page type identifiers, and key page text through semantic parsing. The structured outline data is then persistently stored and visualized as an editable intermediate result independent of the final presentation file. Iterate through each page in the structured outline data, extract the page type metadata of the page and calculate its content density characteristics, and determine the target layout that matches the page type and content density characteristics from the layout template library based on the preset layout matching rules. Using the page key text in the structured outline data as input, the trained content generation model is invoked, and a multi-dimensional consistency control strategy including terminology consistency, argument completeness, and length balance is followed to generate the title, main text key points, and auxiliary explanatory text content for each page. Obtain the preset theme system, which includes at least font hierarchy specifications, color palettes, and component style definitions. Then, integrate and render the target layout, generated text content, and theme system to generate a visual page that corresponds one-to-one with each page in the structured outline data. Batch convert all visual pages into multiple delivery formats, including at least PPTX, PDF, and HTML, while preserving the editable properties of text layers and vector component layers in each delivery format during the export process.

[0011] As a preferred embodiment, the method further includes: In response to the user's command to switch the theme system, without re-generating the page content, the fonts, colors and component styles of all current visualization pages are replaced in batches with the style definitions corresponding to the target theme system, and the presentation of the new theme version is re-rendered and generated. Additionally, based on the semantic vector of the current page, the system retrieves matching image or icon resources, performs intelligent cropping, subject detection, and composition adaptation on the retrieved resources, embeds them into specified placeholders on the visual page, and records the source identifier and license type information of the materials in the page metadata.

[0012] As can be seen from the technical solution provided by the present invention above, the PPT generation system and method based on structured content understanding and intelligent layout design provided by the present invention have the following beneficial effects: Significantly improves the efficiency of the entire PPT production process, building an automated closed loop from topic input to multi-format file delivery. Users can complete standardized presentation production without professional design skills, effectively reducing the time cost and operational threshold of manual production. Strengthen the logic and standardization of the presentation content, construct a clear content hierarchy and presentation logic through structured content understanding technology, and rely on multi-dimensional consistency control strategies to ensure that the content terminology is consistent, the arguments are complete, and the length is balanced, thereby avoiding logical confusion and content omissions that are prone to occur in manual production. Achieve precise adaptation between layout and content attributes, complete intelligent layout matching based on page type and content density characteristics, optimize layout selection by combining presentation rhythm control strategy, and support one-click batch switching of theme system, taking into account the professionalism, consistency and personalization needs of visual presentation. Enhance the editability and reusability of presentations by persistently managing structured outline data as an independent and editable intermediate result. Support multiple theme configuration snapshot retention and batch re-export. During the export of multiple formats, the editable attributes of text and vector components are fully preserved to meet users' needs for subsequent modification and reuse in multiple scenarios. To ensure system stability and output quality, a full-process consistency verification mechanism is used to promptly correct content and layout issues. An asynchronous and decoupled task scheduling mechanism is adopted to achieve streaming preview and breakpoint recovery. Multimodal content enhancement technology is combined to enrich the content presentation format, thereby comprehensively improving the quality of presentation output and the reliability of system operation. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the structure of a PPT generation system based on structured content understanding and intelligent layout design according to the present invention; Figure 2 This is a schematic diagram illustrating the steps of a PPT generation method based on structured content understanding and intelligent layout design according to the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0015] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific embodiments.

[0016] like Figure 1-2 As shown, this embodiment of the invention provides a PPT generation system based on structured content understanding and intelligent layout design, including: The structured content understanding and outline planning module is configured to respond to user input of topic descriptions and optional reference materials. It generates structured outline data containing chapter levels, page type identifiers, and key page text through semantic parsing, and persistently stores and visualizes the structured outline data as an editable intermediate result independent of the final presentation file. The intelligent layout decision module is configured to traverse each page in the structured outline data, extract the page type metadata of the page and calculate its content density characteristics, and determine the target layout that matches the page type and content density characteristics from the layout template library based on the preset layout matching rules. The page content generation module is configured to take the page key text in the structured outline data as input, call the trained content generation model, and follow a multi-dimensional consistency control strategy including terminology consistency, argument completeness, and length balance to generate the title, main text key points, and auxiliary explanatory text content for each page. The visual rendering engine is configured to acquire a preset theme system, which includes at least font hierarchy specifications, color palettes, and component style definitions. It integrates and renders the target layout, generated text content, and theme system to generate a visual page that corresponds one-to-one with each page in the structured outline data. The multi-format export module is configured to batch convert all visual pages into multiple delivery formats, including at least PPTX, PDF, and HTML, while preserving the editable properties of text layers and vector component layers in each delivery format during the export process.

[0017] In this embodiment, the structured content understanding and outline planning module is the core foundation for the intelligent content construction of the PPT generation system based on structured content understanding and intelligent layout design. Through in-depth analysis of user input and scientific arrangement of the presentation outline, it provides standardized and structured data support for subsequent PPT layout design and content generation. The structured content understanding and outline planning module includes: Topic semantic parsing unit: Input reception and preliminary processing: Real-time reception of PPT creation theme description information input by users, formatting and filtering invalid information in the input text, removing redundant symbols and meaningless words, ensuring the validity and neatness of the input information, and providing a high-quality text foundation for subsequent semantic analysis; Deep semantic parsing algorithm: Natural language processing technology is used to perform deep semantic parsing on the normalized topic description text, extract core semantic information from the text and generate structured constraint tags; the structured constraint tags include key dimensions such as target audience type, expression tone tendency, core target statement information density level, page number tendency range, etc. Each dimension of information is obtained through steps such as syntactic analysis, semantic matching and intent recognition of the text, comprehensively reflecting the user's creative needs and presentation positioning; Constraint Tag Optimization and Confirmation: The initial structured constraint tags are validated for rationality. Based on the pre-set demonstration scenario feature library, the information in each dimension of the tags is adjusted for adaptability to ensure that the tag information matches the actual demonstration requirements. The optimized constraint tags will serve as the core basis for subsequent outline arrangement, directly guiding the logic of chapter hierarchy order and page number allocation. Reference Material Analysis Unit: Unstructured text parsing: When users provide reference materials, unstructured text parsing is performed on reference materials of different formats in a unified manner, breaking down the information barriers between different file formats, and converting the content in the reference materials into plain text format that can be used for information extraction, while preserving the original logical relationship of the text. Key information extraction: Extract information units, including factual data, definitions, statements, and process steps, from the parsed plain text. Through techniques such as keyword extraction, entity recognition, and relationship extraction, accurately locate the core information in the reference materials, eliminate redundant content that is irrelevant to the topic, and ensure the relevance and coreness of the extracted information. Information processing and source tracing: Extracted information units are compressed and rewritten for presentation purposes. Based on the information presentation granularity requirements of the PPT page, the information units are transformed into key texts that conform to the page display logic. At the same time, source metadata is associated with each key text. The metadata includes information such as the name, location, and page number of the reference materials, enabling the source tracing of the generated key texts and providing support for the traceability of subsequent content use. Intelligent outline arrangement unit: Presentation logic template matching: Based on the generated structured constraint tags, the appropriate presentation logic template is matched from the preset presentation logic template library; the presentation logic template library contains logic templates corresponding to different presentation scenarios and presentation goals, covering a variety of types such as reporting, presentation, and popular science, and each template has different preset chapter hierarchy structure and page allocation rules; Intelligent chapter and page allocation: Based on the matched presentation logic template and structured constraint tags, the chapter hierarchy is automatically arranged to determine the logical progression of each chapter. At the same time, combined with the information density level and page number tendency range, the number of pages in each chapter is reasonably allocated to ensure that the logic between chapters is clear and the number of pages and information density are appropriate. Structured outline generation: Integrate the arranged chapter-level page type identifiers and the processed page key text to generate standardized structured outline data; standardize the format of the structured outline data to give it a unified data structure that can be directly read and called by subsequent system modules; Outline Data Management Unit: Persistent storage: The generated structured outline data is stored in the system's database, using an independent storage path and data format. This makes the data an editable intermediate result independent of the final presentation file, ensuring the stability and independence of data storage and supporting multiple editing and retrieval of the outline data in the future. Visual presentation: Transform structured outline data into a visual outline display format. Through hierarchical lists, graphical structural diagrams, and other methods, the system front end displays the outline's chapter hierarchy, page types, and key text information to users, allowing them to intuitively understand the content framework of the PPT. Editing Interaction Support: Provides editing interaction functions for the visually presented outline data, allowing users to add, delete, modify, and query chapter levels, re-identify page types, and modify and supplement key text on pages. The system will synchronize user editing operations in real time and update the structured outline data to meet users' personalized creative needs.

[0018] In this embodiment, the intelligent layout decision module is the core module of the PPT generation system based on structured content understanding and intelligent layout design to achieve accurate layout matching and visual rhythm control. It matches the appropriate layout for each page through in-depth analysis of structured outline data and quantitative calculation of content features. At the same time, it optimizes the layout selection logic by combining presentation rhythm control strategy, so that the layout of the PPT not only fits the content features but also conforms to the visual expression rules of the presentation. The intelligent layout decision module is primarily responsible for traversing each page in the structured outline data, accurately extracting page type metadata, and scientifically calculating content density characteristics. Based on preset layout matching rules, it selects target layouts from the layout template library that are highly compatible with the page type and content density characteristics. Simultaneously, the module can acquire preset presentation rhythm control strategies, intelligently identify key and transition pages in the structured outline data, and match layouts that meet visual expression needs for different page types. This achieves personalized and scientific layout selection, providing a standardized layout basis for subsequent visual rendering, improving the fit between PPT layout and content, and enhancing the visual effect of the presentation. The intelligent layout decision module includes: Page metadata extraction unit: Outline data traversal: According to the chapter hierarchy and page sorting of the structured outline data, traverse all data information of each page in turn, establish an independent reading and processing channel for page data, and ensure that the information of each page can be parsed and processed separately, without any omission or confusion of information. Page type metadata extraction: Extract page type metadata from the structured data of each page. This metadata contains core information such as the page's function, positioning, and display type, covering common page types such as title page, table of contents page, content page, data page, and summary page. It accurately identifies the core attributes of each page, providing a basis for the initial layout selection. Metadata validation and standardization: The extracted page type metadata is validated for reasonableness, and the matching degree between the metadata and the key text of the page is checked. If the metadata identifier is found to be inconsistent with the actual content of the page, the metadata will be automatically corrected according to the page content characteristics. At the same time, the type metadata of all pages is standardized to unify the data format and identifier rules, ensuring that the metadata can be directly read and recognized by the layout matching module. Content density feature calculation unit: Basic information statistics: Accurately count the number of key items, total number of characters, and number of data objects to be visualized on each page. These data objects include table data, chart data, numerical data, and other content that needs to be presented in a visual format. Meaningless spaces and symbols are removed during the statistical process to ensure the accuracy of the statistical data. Content density feature quantification: The number of key points, total number of characters, and number of data objects to be visualized are used as the core dimensions to generate a content density feature value. This feature value comprehensively reflects the information carrying capacity and visual display complexity of the page. The higher the value, the more information the page needs to carry and the higher the visual display complexity. Feature value normalization: The calculated content density feature values ​​are normalized and mapped to a fixed numerical range. This eliminates the feature value deviation caused by large differences in statistical dimension values ​​between different pages, making the content density feature values ​​of different pages comparable and providing a unified quantitative basis for accurate layout matching. Layout template matching unit: Retrieve layout template library: Retrieve the preset layout template library according to system instructions. The template library contains at least one candidate layout for multiple page types. Each layout contains core design information such as layout structure, placeholder position, component arrangement, and different layout capacity versions are designed for different content density characteristics. Initial screening of candidate layouts: First, select the corresponding set of candidate layouts from the layout template library based on the page type metadata, and remove layouts that do not match the page type to ensure that all layouts in the candidate layout set meet the functional positioning and display type requirements of the page, thus narrowing down the scope of layout matching; Precise determination of target layout: In the set of candidate layouts after screening, the matching degree between each candidate layout and the layout capacity of the page is calculated based on the content density feature value of the page. The layout with the highest matching degree is selected as the target layout to ensure that the layout space of the target layout can adapt to the information carrying capacity of the page and avoid information crowding or excessive white space in the layout. Demonstration rhythm and layout optimization unit: Demonstration rhythm strategy acquisition: The demonstration rhythm control strategy is obtained from the system's preset configuration information. This strategy includes the identification criteria for key pages and transition pages, as well as the corresponding layout selection rules, clarifying the visual expression requirements of various pages under different demonstration rhythms. Intelligent Page Type Recognition: Based on the recognition criteria in the presentation rhythm control strategy, all pages in the structured outline data are intelligently identified to distinguish between key pages and transition pages. Key pages include core viewpoint pages, important data pages, conclusion pages, etc., while transition pages include chapter transition pages, content connection pages, etc. Layout optimization and adjustment: Layout optimization and adjustment are carried out on the identified key pages and transition pages. For key pages, layouts with high visual contrast are preferred to enhance the visual impact and highlight the core information. For transition pages, simple layouts are preferred to weaken the visual expression and achieve a natural transition of the presentation rhythm, so that the overall layout conforms to the rhythm of visual browsing. Furthermore, the page metadata extraction technology, based on structured data parsing theory and relying on preset metadata extraction rules, accurately locates and reads the page type identifier field in the structured outline data. Through a dual mechanism of field matching and content verification, it ensures that the extracted metadata highly matches the actual page content. Simultaneously, by standardizing the processing to unify the metadata format and identifier rules, the metadata becomes a standardized index for layout matching, enabling rapid retrieval and filtering of the layout template library and improving the initial efficiency of layout matching. The content density feature quantification technology, based on multi-dimensional data statistics and feature value calculation theory, uses the number of key items, the total number of characters, and the number of data objects to be visualized as three core quantification dimensions. It calculates the content density feature value through a weighted summation method, using the following formula: (in, For content density feature value, The weighting factor is the number of key items. The number of key points. The weighting coefficient is the total number of characters. Total number of characters The weighting factor is the number of data objects to be visualized. (The number of data objects to be visualized); each weight coefficient is preset according to the information display characteristics of different page types, which can accurately reflect the influence of each dimension on content density. Then, the feature values ​​are mapped to a fixed range through the normalization algorithm to realize the quantitative comparison of content density characteristics of different pages. The layout capacity matching technology, based on spatial adaptation theory and similarity calculation algorithms, presets a layout capacity value for each layout in the template library. This value reflects the total amount of information the layout can carry, sharing the same quantification dimension as the content density feature value. It calculates the similarity between the page's content density feature value and the candidate layout capacity value using the following formula: (in, To ensure layout capacity matching, (This refers to the layout capacity value); the closer the matching value is to 1, the higher the compatibility between the layout capacity and the page content density. Based on this, the target layout can be accurately selected to ensure that the layout and the page information carrying capacity are highly compatible. The presentation pacing and layout adaptation technology is based on visual psychology and presentation design theory. It combines the presentation's expressive logic with the audience's visual browsing habits to formulate a presentation pacing control strategy. Natural language processing technology is used to analyze the semantics of key text on the page, and key pages and transition pages are identified by combining chapter hierarchy relationships. Then, based on the strength of visual expression, high-contrast layouts are matched to key pages, using strong contrast in color layout components to highlight core information. Simple layouts are matched to transition pages, achieving a natural transition in presentation pacing through concise layouts and soft visual elements, ensuring a high degree of unity between the visual rhythm of the layout and the rhythm of the presentation content.

[0019] In this embodiment, the page content generation module is the core carrier of human-computer interaction for the IoT-based SF6 gas state intelligent diagnosis and early warning system. It achieves efficient presentation of diagnostic results, equipment status and early warning information through structured analysis and multi-dimensional visualization rendering of the system's underlying data. The page content generation module is primarily responsible for receiving diagnostic results data from the cloud platform's intelligent analysis module, system status data from the self-inspection and fault tolerance module, and archived data from the historical database. Following preset page templates and rendering rules, it completes the structured arrangement of data, generates visual charts, and assembles dynamic pages. The module supports multi-terminal adaptive display, automatically adjusting the page layout based on the screen size and resolution of the accessing device. It also enables real-time content updates and historical data review, providing administrators with a comprehensive, intuitive, and efficient information viewing interface to support rapid operational decision-making. The page content generation module includes: Data parsing and adaptation unit: Multi-source data reception and verification: Real-time reception of SF6 gas status diagnostic data and fault warning data pushed by the cloud platform's intelligent analysis module, system operation status data uploaded by the self-test and fault tolerance module, and trend analysis data retrieved from the historical database; Data verification algorithms are used to verify the integrity and format compliance of the received data, eliminating invalid and abnormal data to ensure that the input data meets the basic requirements for page rendering; Data standardization and transformation: Convert raw data from different sources and in different formats into a unified page rendering data model; unify the units of numerical data, encode and map state data, and normalize the format of time data to form a structured page rendering dataset, achieving seamless integration of multi-source data at the page layer. Data tiered processing: Based on the importance and real-time requirements of the data, the data is divided into three levels: core real-time data, regular status data, and historical analysis data. Core real-time data is given priority in allocating rendering resources, regular status data is updated periodically, and historical analysis data is loaded on demand, ensuring the efficiency and smoothness of displaying the core information on the page. Visualization rendering engine unit: Chart generation algorithms: For different types of data, corresponding visualization algorithms are used to generate charts. The core algorithms include line chart generation algorithm, bar chart generation algorithm and pie chart generation algorithm. Line chart generation algorithms are used for displaying time series data; the formula is: (in, For the first The chart's pixel y-axis for each data point For the first The timestamp value of each data point These are the pixel mapping coefficients along the time axis. (pixel coordinate offset); The bar chart generation algorithm is used for comparative display of categorical data. The formula is: (in, No. The pixel height of the bar chart for each category, The conversion factor between data values ​​and pixels. For the first (Actual data values ​​for each category); The pie chart generation algorithm is used to display percentage data; the formula is: (in, For the first The sector angle corresponding to each data item For the first The value of each data item. (Total number of data items); Early warning information visualization rendering: Different rendering rules are adopted for different levels of fault warnings; through a combination of color coding mechanism, dynamic flashing effect and icon identification, the warning level is presented intuitively; high-level warnings are rendered in red with dynamic flashing, medium-level warnings are rendered in yellow, and low-level warnings are rendered in blue, while matching the corresponding warning icons to ensure that the warning information can be quickly identified by managers. Dynamic assembly of page elements: Based on standardized page rendering datasets and generated visualization charts, the core page elements such as navigation bar, data overview area, detailed analysis area, early warning area, and historical query area are dynamically assembled according to the preset page template structure; the structured presentation of page content is achieved through DOM node generation and style mounting. Multi-terminal adaptive layout unit: Terminal type identification: By parsing the terminal identification information of the access request, the type of accessing device is identified, including desktop, tablet and mobile devices; core parameters such as screen resolution, screen size and pixel density of the device are extracted to provide a basis for adaptive layout; Layout adaptation algorithm: A responsive layout algorithm is used to achieve multi-terminal page adaptation. The core formula is as follows: (in, This represents the relative width of the page element. To access the device's screen width, (Standard desktop screen width); According to this algorithm, the width of page elements is proportionally adapted to the screen size of different terminals, while automatically adjusting the size of charts, fonts and element spacing to ensure consistent display effect and user experience on different terminals. Functional module adaptation and cropping: In response to the smaller screen size of mobile devices, the page functional modules are adaptively cropped; core real-time data display, early warning prompts and quick query functions are retained first, while secondary functional modules are hidden in the collapsible menu and displayed by clicking, so as to achieve the convenience and efficiency of mobile operation; Content refresh and interaction control unit: Multi-mode content refresh: Supports three modes: real-time refresh, timed refresh, and manual refresh; core real-time data uses the real-time refresh mode, implementing push-based data updates via the WebSocket protocol; regular status data uses the timed refresh mode, as shown in the formula. (in, For the next refresh time, This is the initial refresh time. To refresh the count, (This is the preset refresh interval); historical analysis data and custom query data use a manual refresh mode, triggered by administrators. Interactive event handling: Receives page interaction operation instructions from administrators, including chart zooming, data filtering, time range selection, and viewing alert details; parses the interaction instructions, triggers corresponding data retrieval, chart redrawing, and page content update operations, and realizes real-time response of human-computer interaction; Page state saving: The page operation state of administrators is saved in real time, including filter conditions, time range, chart perspective, etc. When administrators revisit the page or refresh the page, the saved operation state is automatically loaded, eliminating the need for repeated settings and improving the user experience.

[0020] In this embodiment, the visual rendering engine is the core engine of the PPT generation system based on structured content understanding and intelligent layout design to achieve visual presentation. It transforms standardized structured data into a visual PPT page that conforms to visual design specifications by integrating the layout text content and theme system. The visual rendering engine is primarily responsible for acquiring the preset theme system, deeply integrating and rendering the text content generated by the target layout page content generation module (determined by the intelligent layout decision module) with the theme system to generate visual pages that correspond one-to-one with each page in the structured outline data. It also supports rapid switching between theme systems, completing batch replacement and re-rendering of all page styles without regenerating page content, ensuring the consistency and flexibility of the PPT's visual style, providing a standardized visual page foundation for multi-format export, and improving the visual effects and design specifications of generated PPTs. The visual rendering engine includes: Thematic System Analysis Unit: Theme Acquisition and Verification: The system acquires the preset or user-selected theme system in real time. This system includes core design information such as font hierarchy specifications, color matching, color palettes, and component style definitions. The system performs a completeness verification of the acquired theme system, checking whether the hierarchical definitions of font hierarchy, color matching, color value specifications, and parameter settings of component styles are complete. Invalid style definitions are removed to ensure that the theme system meets the rendering requirements. Structured parsing of style information: Transforming unstructured design information in the theme system into structured rendering parameters; parsing font hierarchy specifications into parameters such as font, font size, font weight, and line spacing corresponding to different text types such as headings, first-level body text, and second-level body text; parsing color palettes into corresponding RGB color values ​​such as main color, auxiliary color, accent color, and background color; and parsing component style definitions into parameters such as border style, fill effect, and shadow of components such as text boxes, shapes, and charts, forming a standardized rendering style dataset; Style parameter caching: The parsed structured rendering style dataset is cached locally to establish a fast retrieval channel for style parameters; when rendering multiple pages or switching themes, style parameters are read directly from the cache to avoid repeated parsing and improve rendering efficiency. At the same time, the cache will synchronize the theme system update information in real time to ensure the accuracy of style parameters. Layout and content integration unit: Layout data reading: Read the target layout data determined by the intelligent layout decision module. This data includes core information such as the layout structure, placeholder positions, component arrangement areas, and dimensions of the page. According to the visual hierarchy of the page, the layout data is analyzed in layers to determine the position and size parameters of different areas such as the title area, body text area, chart area, and material area, forming a coordinate data model of the layout. Text content adaptation mapping: Receives title, body text, key points, and supplementary explanatory text content generated by the page content generation module. Based on the coordinate data model of the layout, it maps different types of text content to the corresponding page areas. According to the font style parameters provided by the theme system parsing unit, it matches the corresponding font attributes for different types of text content. At the same time, it automatically adjusts the text for line breaks and font size adaptively based on the area size to ensure that the text content is displayed completely and beautifully in the corresponding area. Multi-element layered integration: The layout data text content and corresponding style parameters are layered and integrated; according to the visual hierarchy of background layer, basic component layer, text layer, material layer, and chart layer, each element is loaded to the corresponding page coordinate position in turn, establishing the hierarchical relationship between each element, ensuring that the upper layer elements do not obscure the core information of the lower layer, and at the same time ensuring that the position and style of each element meet the requirements of the theme system and layout, forming preliminary visualized page data; Theme switching rendering unit: Switching command reception and parsing: Receives user's theme system switching commands in real time, parses the target theme system identifier contained in the command; retrieves the corresponding target theme system from the system theme library based on the identifier, and the theme system parsing unit completes structured parsing and parameter caching to prepare for batch style replacement; Batch replacement of full-page styles: Without regenerating page content, read the element hierarchy data of all current visualization pages; batch replace the style parameters of all elements in each page, including font attributes, color schemes, component styles, etc., with the style parameters corresponding to the target theme system; during the replacement process, the position layout and content of the elements remain unchanged, only the visual styles are updated, ensuring the integrity of the page content and the consistency of the styles; The new theme version is re-rendered: After completing the batch replacement of style parameters, all visualization pages are re-rendered; according to the layered integration rules of the layout content integration unit, each element after the style update is reloaded to the corresponding coordinate position to generate the visualization page data of the new theme version; at the same time, the re-rendered page is visually verified to ensure that the style replacement is complete and error-free, and to ensure that the visual effect of the new theme version page meets the design specifications. Rendering result output unit: Visual page data standardization: The rendered visual page data is transformed into a unified standardized format; the resolution, size, proportion, and element format of the page are standardized to ensure that the basic parameters of all pages are consistent. At the same time, the page data is stored as a vector data format that can be directly read by the multi-format export module, while retaining the editable attributes of the elements. Single-page rendering result preview: The standardized single-page visualization data is pushed to the system front end in real time and displayed to the user in the form of a preview. The user can intuitively view the visual effect of the page, including the layout, text style and color scheme. At the same time, the user can make local style adjustments to the single page. The adjustment command will be fed back to the rendering engine in real time to complete the re-rendering and preview update of the corresponding page. Full-page data integration: After all pages have been rendered and confirmed by the user preview, all standardized visual page data is integrated according to the chapter hierarchy and page sorting of the structured outline data to form a complete presentation visualization data set; a unified index is established for this data set to facilitate batch retrieval of data from each page by the multi-format export module, enabling fast export.

[0021] In this embodiment, the multi-format export module is the core carrier for extending the data value of the IoT-based SF6 gas state intelligent diagnosis and early warning system. It meets the data use, archiving and interaction needs of different application scenarios by standardizing and integrating the system's full life cycle data and converting it into multiple formats. The multi-format export module is primarily responsible for receiving structured and unstructured data output from the system status monitoring and recording unit and the cloud platform intelligent analysis module. It performs core operations such as data classification, format mapping, template rendering, and file generation. The module supports both static batch export and dynamic real-time export modes. Based on user-configured export commands, it can generate various file formats that conform to industry standards and general office needs. Simultaneously, it tracks the progress of export tasks, verifies results, and retains logs, ensuring the integrity of exported data, the standardization of formats, and the traceability of the process. The multi-format export module includes: Data adaptation and preprocessing unit: Multi-source data access: Real-time reception of structured data within the system, including sensor monitoring data, fault diagnosis results, and system operating parameters; and unstructured data, including status report text, event tracing records, and algorithm analysis logs; unified access and caching of different types and dimensions of data through standardized data interfaces; Data cleaning and normalization: The incoming data is cleaned by filling in missing values, removing outliers, and deduplicating duplicate data to eliminate data redundancy and errors; normalization formulas are used to process numerical data to ensure data consistency across modules. Data classification and mapping: According to the export requirements, the cleaned data is divided into four categories: monitoring dataset, diagnostic result set, operation report set, and fault traceability set. Through preset format mapping rules, the correspondence between the original data fields and the target export format elements is established, laying the foundation for subsequent format conversion. Format conversion engine unit: Standardized format for generating sub-units: A structured tagging algorithm is used to generate industry standard formats. Taking the generation of IEC 61970 standard E format files commonly used in the power industry as an example, the core mapping formula is: ,in, The structured tag matrix for E format files. The number of tag levels in the data mapping. for Hierarchical tag type matrix, for Hierarchical tag attribute matrix, This is a normalized standard dataset. Using this formula, the normalized data is matrix-reorganized according to the hierarchical tags and attribute requirements of the E format to generate a standardized file that conforms to industry standards and is suitable for the docking and data archiving of power equipment operation and maintenance systems. General Office Format Generation Sub-unit: To address the need for exporting tables, a two-dimensional data rendering formula is used to generate CSV and Excel formats. The formula is as follows: ,in, To export the first table Line number The content of the column cells, The values ​​corresponding to the cells in the original dataset. For the first Column format rendering coefficients, For the first The column header identifier value; this formula maps raw data to table cells and renders the format, supporting data filtering, formula embedding, and cell style configuration; for document export requirements, a template-driven rendering algorithm is used to fill the categorized data into a preset Word template, enabling the automated generation of documents such as inspection reports and maintenance summaries; Visual format generation sub-unit: To meet the needs of data visualization, statistical reports and chart files are generated in PDF format. The core chart rendering formula is as follows: ,in, For the first Vector graphics data that resemble visual charts. For visual rendering operators, For the dataset after statistical analysis, For the first The drawing parameter matrix for charts. This is the layout matrix for the report; through this formula, various charts such as line charts, bar charts, and pie charts can be vector-drawn and generated into PDF format visual reports according to the preset layout, ensuring the clarity and printability of the charts; Export Task Management Snap-in: Export command parsing: Receives export commands sent by users through the system client or API. The commands include core parameters such as the range of data to be exported, the target format type, the file storage path, and the export priority. The module performs syntax validation and permission verification on the commands, and generates a standardized export task form after passing the verification. Task scheduling and execution: A task scheduling algorithm is used to prioritize and allocate resources for multiple concurrent exported tasks. The scheduling formula is as follows: ,in, For the first The scheduling priority value of each export task. This is the priority weight coefficient. Set priorities for tasks. This is a weighting factor based on task size. For the size of the task data, This is the weighting coefficient for task urgency. The system determines the urgency of the task; based on... Export tasks are executed from high to low values ​​to allocate CPU, memory, and storage resources reasonably and avoid task congestion. Result verification and feedback: After the file is generated, the integrity, format standardization and data consistency of the file are verified by the format verification algorithm. If the verification is successful, the file is stored in the specified path and the user is fed back with the export success information and file access link; if the verification fails, an error report is generated, the reason for the failure is recorded and the task retry mechanism is triggered. Exporting logs and archive units: Task Log Recording: Records the entire process information of each export task in real time, including task number, command parameters, execution time, data size, generated file format, execution result, error message, etc., forming a standardized export task log; Exported file archiving: The generated exported files are classified and archived according to file format, export time, and data type, and a file index library is established to enable fast retrieval and retrieval of exported files; it also supports automatic file backup to prevent file loss. Log and file query: Provides a multi-condition query interface, allowing users to query and export task logs and corresponding exported files by conditions such as task number, time range, data type, and file format, meeting the needs of data traceability and compliance auditing.

[0022] In this embodiment, the system also includes a multimodal content enhancement module. The multimodal content enhancement module is a core component of the IoT-based SF6 gas state intelligent diagnosis and early warning system to achieve efficient information expression. It ensures that the system diagnosis results can be presented in a more intuitive and accurate way by fusing and processing multi-source heterogeneous data and generating and outputting multi-form content. The multimodal content enhancement module is primarily responsible for multi-dimensional fusion processing of diagnostic conclusions, raw monitoring data, and historical trend data output by the cloud platform's intelligent analysis module. Leveraging core capabilities such as data transformation, feature extraction, and modality generation, the module converts single numerical data into multiple modalities including text, images, and audio, while simultaneously enabling the linkage and complementarity of different modalities. Through this module's processing, the system can output structured diagnostic reports, visualized trend charts, and voice prompts, providing managers with multi-layered and easily understandable information support, comprehensively improving the efficiency and accuracy of system information interaction. The multimodal content enhancement module includes: Multi-source data feature extraction unit: Multi-source data normalization processing: Simultaneously receive fault diagnosis results from the cloud platform's intelligent analysis module, raw monitoring data uploaded by the sensor array module, and historical trend data stored in the database; perform normalization operations on data of different dimensions and formats using formulas. (in, These are the normalized data values. The data represents the actual measured values. This is the historical minimum value for this type of data. Numerical standardization was performed on the historical maximum value of this type of data to eliminate the impact of dimensional differences on feature extraction and ensure the fusion compatibility of various data types; a multi-dimensional feature extraction algorithm was used to extract time-domain features, frequency-domain features, and semantic features from the normalized data; and a formula was employed. Calculate the time-domain mean characteristic (where, The characteristic value of the mean in the time domain, The number of samples in a single data collection session. For the first (Number of normalized data samples); using the Fast Fourier Transform formula. Extract frequency domain features (where, For frequency domain eigenvalues, The total number of sampling points. For the first Each time-domain sampled value, For frequency components, (where the imaginary unit is used); for the text tags of the diagnostic conclusions, semantic feature vectors are extracted through a word embedding model, ultimately forming a multi-dimensional fusion feature set; Feature selection and optimization mechanism: To eliminate redundant features and improve the efficiency of subsequent mode generation, a formula is used. Calculate feature importance (where, For the first The importance coefficient of each feature For the first Features and diagnostic result tags covariance, For the first The variance of each feature (variance of diagnostic result labels); filter out Features exceeding a set threshold form the optimal feature set, providing core data support for subsequent modality generation; Multimodal content generation unit: Text content generation: Based on the optimal feature set and the diagnostic conclusions of the cloud platform, text content is constructed through a pre-trained natural language generation model. The model takes core features such as fault type, fault severity, data deviation value, and historical comparison results as input, and automatically generates structured diagnostic reports, fault warning prompts, and operation and maintenance suggestions according to preset professional text templates. The generation process strictly follows the professional terminology standards in the field of power equipment monitoring to ensure the accuracy, professionalism, and logic of the text content. Image content generation: Based on numerical feature data, various types of visualization images are automatically generated; for real-time monitoring data, real-time numerical dashboard images are generated; for historical trend data, formulas are used. Fitting trend curve (where, To predict data values, For time dimension variables, The coefficient of the quadratic term, The coefficient of the linear term, (For constant terms), generate a data trend line chart; for fault distribution, generate a fault location diagram and a data heat map, transforming abstract numerical data into intuitive image information and realizing the visualization of data characteristics; Voice content generation: The generated text warning information and core diagnostic conclusions are converted into voice signals; a formula is used. Generate basic speech waveforms (where, The amplitude of the voice signal. For waveform amplitude, For speech frequency, For time variables, (For phase), combined with a speech synthesis model, the waveform is encoded and optimized to match the professional broadcast tone, generating clear and standardized voice broadcast content to achieve auditory delivery of warning information; multimodal content linkage and output unit: Multimodal content association mapping: Establish association mapping relationships between different modal content to achieve precise linkage of text, image and voice content; establish index association between key data nodes in the text diagnostic report and corresponding positions in the visualization image; keep the voice broadcast warning information synchronized with the text prompts and image warning labels to ensure that the content of the three modalities is logically consistent and informationally complementary; Multi-channel content output control: Automatically selects the optimal content output combination and output channel according to different application scenarios; for the back-end management platform, outputs complete text diagnostic reports and all types of visualization images; for mobile terminals, outputs simplified text prompts and core trend images; for on-site monitoring terminals, triggers voice broadcast content and simplified image warning labels, achieving adaptable output for multiple scenarios. Modal content update and synchronization: When the cloud platform's intelligent analysis module outputs new diagnostic results or the sensor uploads new monitoring data, the module automatically starts the update process; following the process of data feature extraction, modal content generation, and association mapping, it completes the real-time update of all modal content and synchronizes it to all connected output terminals to ensure that the information from all channels is always up-to-date and consistent.

[0023] In this embodiment, the system also includes a system governance and scheduling module. The system governance and scheduling module is the core hub for the efficient operation of the IoT-based SF6 gas state intelligent diagnosis and early warning system. It achieves the collaborative work and maximizes the efficiency of each functional module through the overall planning of system resources and the dynamic control of business processes. The system governance and scheduling module is primarily responsible for the allocation and management of system resources, the scheduling and execution of business tasks, the collaborative control between multiple modules, and the dynamic optimization of operating strategies. This module receives instructions from upper-layer applications and status feedback from lower-layer modules, and formulates the optimal scheduling scheme based on preset rules and real-time operating data. This ensures that the system can complete monitoring, diagnosis, and early warning tasks with maximum efficiency under different operating conditions, while minimizing resource consumption and optimizing response speed. The system governance and scheduling module includes: Resource pool management unit: Resource awareness and modeling: Real-time collection of core resource data such as computing resource load rate, storage resource remaining capacity, communication bandwidth utilization rate, and online status of edge nodes within the system; construction of system resource models based on the collected data to accurately represent the real-time availability and performance status of resources in a digital manner, providing basic data support for resource scheduling; Resource allocation and scheduling: using formulas (in, The target resource node to be allocated; The set of available resource nodes in the system; For resource nodes The current utility value; Tasks to be performed Resource allocation is performed based on the resource demand matching degree; through this formula, the resource node with the highest utility and best fit is matched for each task to be executed, so as to achieve fine-grained allocation of computing, storage and communication resources. Elastic resource scaling: Based on changes in the workload of the system, elastic resource adjustments are performed; when the workload surges and the resource load exceeds the threshold, the scaling mechanism is automatically activated, and backup resource nodes are called in to join the operation; when the workload decreases and resources become idle, the scaling mechanism is triggered to release redundant resources, thereby achieving dynamic supply and demand balance of system resources. Task scheduling and execution unit: Task Reception and Parsing: Receives various task requests from the self-checking and fault tolerance module, the cloud platform intelligent analysis module, and user terminals, including data collection tasks, diagnostic analysis tasks, early warning push tasks, and firmware upgrade tasks; parses the task requests and extracts key attributes such as task type, priority, execution time limit, and resource requirements; Task priority ranking: using formula (in, For the task Overall priority; This is a weighting coefficient for task urgency. This is a metric for the urgency of the task. This refers to the weighting coefficient for task importance. As a metric of task importance; This represents the task coupling degree weighting coefficient. Priority is calculated for the coupling metric of tasks with other tasks; based on the calculation results, all tasks to be executed are sorted to determine the execution order of the tasks; Task distribution and execution monitoring: Based on the sorting results and resource allocation scheme, tasks are distributed to the corresponding execution nodes; the execution progress, execution status and execution results of tasks are monitored in real time, and key data during task execution are recorded; if a task execution times out or fails, the task rescheduling process is immediately initiated to ensure that the task is eventually completed; Cooperative control unit: Cross-module communication coordination: Establish communication rules and data interaction protocols between various functional modules of the system, unify data transmission formats and communication interface standards; be responsible for the establishment, maintenance and release of communication links between modules, coordinate the data interaction timing between self-test and fault-tolerant modules, sensor array modules and anti-interference communication modules, and avoid data conflicts and communication congestion; Business process collaborative scheduling: Based on the overall business logic of the system, modular collaborative workflows are formulated; for example, when completing a complete SF6 gas status diagnosis, the collaborative control unit first schedules the sensor array module to complete data acquisition, then schedules the anti-interference communication module to transmit data, then schedules the cloud platform intelligent analysis module to execute the diagnostic algorithm, and finally schedules the early warning module to push the results, realizing the automated collaborative operation of the entire process; Edge and cloud collaborative optimization: using formula (in, To optimize task execution time; This represents the time taken for the task to be executed locally on the edge node. The time taken for the task to be executed in the cloud; The system makes task offloading decisions based on the time it takes to transmit data to the cloud. According to this formula, it determines whether the task should be executed locally on the edge node or offloaded to the cloud, so as to achieve the complementary advantages of edge computing and cloud computing and improve the overall system response efficiency. Strategy library and optimization unit: Strategy Repository Construction and Management: Construct a system operation strategy repository, including resource scheduling strategies, task priority strategies, collaborative work strategies, and fault emergency strategies; the strategy repository adopts a modular storage method, supports adding, modifying, deleting, and querying strategies, and ensures the flexibility and scalability of strategies; Dynamic optimization of operating strategies: Based on historical system operating data and real-time status feedback, formulas are used to optimize the operating strategy. (in, Adjustment amount for strategy parameters; Optimize the learning rate for the strategy; Optimize the learning rate for the strategy; This is a function for evaluating system performance. Optimize the strategy parameters (to be optimized); continuously adjust the strategy parameters to improve the system performance evaluation function. The value is maximized to achieve self-optimization and self-adaptation of the scheduling strategy; Strategy execution and feedback: Based on the current operating conditions of the system, the optimal operating strategy is retrieved from the strategy library and distributed to each execution unit; system performance data after strategy execution is collected as input for strategy optimization, forming a closed-loop management of "strategy execution - performance feedback - strategy optimization".

[0024] In this embodiment, the system also includes a version management and re-export module, configured as follows: Record the theme system configuration snapshot, structured outline data version identifier, and export parameters for each export operation; Receive user re-export instructions, and based on the same structured outline data version, call different historical theme system configuration snapshots or newly selected theme systems to batch regenerate and export multi-version delivery packages.

[0025] A PPT generation method based on structured content understanding and intelligent layout design is disclosed. The method is executed by a PPT generation system based on structured content understanding and intelligent layout design. The execution architecture of this method consists of five core modules: a structured text preprocessing module, a content depth analysis module, a layout knowledge base module, an intelligent layout matching and layout module, and a PPT rendering and output module. Each module works sequentially and collaboratively according to the data flow order, while also supporting reverse feedback optimization, forming a closed-loop operation system of "analysis-matching-rendering-optimization". Specifically, the structured text preprocessing module cleans and standardizes the format of the original text; the content depth analysis module extracts the text logic and core information; the layout knowledge base module provides domain-specific layout rules and material support; the intelligent layout matching and layout module completes page splitting and layout planning; and the PPT rendering and output module generates and exports the final document. The method includes the following steps: Structured text preprocessing: The core objective of this step is to transform the raw, non-standardized text into structured data that meets the parsing requirements, laying the foundation for subsequent content understanding. Text format cleaning: Automatically identifies and removes redundant symbols, whitespace characters, repeated paragraphs, and formatting marks from the original text, and standardizes the font, font size, and line spacing of the text; for texts containing technical terms such as patent descriptions and technical documents, it preserves the integrity and standardization of the terminology and avoids information loss during the cleaning process; Text structure annotation: Using natural language processing technology, identify hierarchical markers in the text, including chapter titles, subheadings, body paragraphs, list items, chart descriptions, etc.; according to the hierarchical relationship of "general-specific-detail", add structured tags to the text at different levels, construct an initial text hierarchy tree, and clarify the logical belonging of each text segment; Multi-source content integration: If the original content includes external charts, data tables, formula explanations and other supplementary materials, the system will automatically integrate the multi-source content, establish a mapping relationship between the charts and the corresponding text paragraphs, and mark the reference position and explanatory information of the charts to ensure the integrity of the content. In-depth analysis of structured content: This step is the core of achieving intelligent generation. Through semantic understanding and logical mining, the core information, logical structure and key points of the text are extracted. Core information extraction: Based on a pre-trained domain-specific language model, semantic analysis is performed on the annotated structured text to extract core information such as the core theme, key arguments, technical points, and data conclusions of each chapter; for patent texts, the focus is on extracting core module information such as invention name, technical field, background technology, invention content, specific implementation method, and beneficial effects; for technical reports, the focus is on extracting content such as research objectives, technical solutions, experimental data, conclusions, and prospects. Logical Relationship Mining: Through dependency parsing and discourse structure analysis, logical connections within the text are mined, including causal relationships, progressive relationships, parallel relationships, and contrastive relationships. Based on the mining results, the initial text hierarchy tree is optimized, logical nodes and related edges are added, and a visual content logic graph is formed. For example, in the description of patent implementation methods, the causal logic of "technical problem - solution - beneficial effect" is automatically identified, and the progressive logic of "overall architecture - sub-module - key technology" is identified in the description of technical solutions. Content Priority Determination: Based on the semantic importance and logical position of the text, priority is assigned to each text segment; chapter titles, core arguments, and core technical solutions are given the highest priority, body explanations, data supplements, and detailed descriptions are given medium priority, and background information and secondary arguments are given low priority, providing a basis for subsequent page content breakdown; Chart requirement identification: Analyze the data descriptions, comparisons, and structural explanations in the text to automatically identify potential chart generation requirements; for example, for paragraphs containing multiple sets of comparative data, determine that a bar chart or line chart needs to be generated; for system architecture descriptions, determine that a hierarchical structure diagram or flowchart needs to be generated; for step descriptions of patented technical solutions, determine that a method flowchart needs to be generated. Layout knowledge base retrieval and adaptation: This step, based on the parsed content features, retrieves matching layout rules and visual materials from the layout knowledge base to provide support for layout design. The layout knowledge base consists of three core parts: a domain-specific layout library, a logic-adaptive layout library, and a visual style library. The domain-specific layout library provides pre-set standardized templates for different fields such as patent presentations, academic exchanges, and technical summaries, including pages for core patent solutions, technical architecture, experimental data, and conclusions. The logic-adaptive layout library offers corresponding layout solutions for different logical relationships such as parallel, progressive, causal, and comparative relationships. The visual style library includes visual elements such as color schemes, font combinations, icon materials, and chart styles, categorized into styles such as professional simplicity, technological feel, and business style. Domain and scenario matching: Based on the content attributes of the text, the application domain and scenario of the generated PPT are automatically identified; for example, if the text contains the core content of a patent application document, the exclusive format for the patent reporting domain is matched; if the text is a technical project completion report, the exclusive format for a scientific research project report is matched; at the same time, the basic tone of the visual style is determined in combination with the user's preset scenario requirements. Layout rule extraction: Based on the content logic graph and content priority, extract the corresponding layout design rules from the layout knowledge base; including page content carrying rules, clarifying the page allocation method for content of different priorities; logical expression rules, clarifying the layout form corresponding to different logical relationships; visual presentation rules, clarifying the selection criteria for fonts, color schemes, and chart styles; Intelligent page splitting and layout: This step combines the content analysis results with the layout rules to complete the page splitting, content arrangement, and layout planning of the PPT, which is a key step in realizing intelligent design. Page content splitting: Based on content priority and page capacity thresholds in the layout rules, the structured text is divided into several PPT pages; the highest priority core theme is used as a separate cover page or chapter title page; the mid-to-high priority core arguments and technical solutions are split into different pages according to logical relevance to ensure that the content on each page is focused; low priority detailed explanations are attached to the core content pages as needed, or integrated into appendix pages; during the splitting process, the placement of charts is considered simultaneously, and charts and their corresponding explanatory texts are assigned to the same page to ensure the continuity of the content; Intelligent layout matching: Based on the content characteristics and logical relationships of each page, the optimal layout is matched from the logically adapted layout library; for example, the cover page matches a centered symmetrical layout to highlight the invention name and core theme; the technical point page with parallel relationships matches a multi-column layout; the method and step page with progressive relationships matches a vertical flow layout; the technical effect page with causal relationships matches a "solution-effect" left-right split layout; for pages containing charts, a chart-first layout is matched to reserve sufficient space for chart display. Refined Element Layout: After completing the basic layout matching, the layout of text, charts, icons, and other elements on the page is refined; according to the principle of visual flow, the placement and arrangement order of elements are determined, with core content placed in the visual center and auxiliary content placed in secondary positions; the font size, line spacing, and paragraph spacing of the text are automatically adjusted to ensure neat text layout; the size and position of charts are automatically planned to achieve a harmonious combination of charts and text; domain-specific icons are added as needed to enhance the visual expression of the content, while avoiding excessive element stacking and ensuring the simplicity and readability of the page; PPT rendering and output optimization: This step completes the final rendering of the PPT document and provides optimization and export functions to ensure that the generated result meets actual usage requirements; Content and layout rendering: Based on the results of the intelligent layout, the PPT rendering engine is invoked to complete operations such as text entry, chart generation, and addition of visual elements; color schemes and font effects that conform to the visual style library are automatically generated, a unified visual identity is added to the chapter title page, and corresponding separator elements are added to pages with different logic to achieve the unity and coherence of the overall layout; for the generated charts, data is automatically filled in and chart styles are set to ensure the accuracy and aesthetics of the charts; Consistency check: Perform a global consistency check on the generated PPT document, including the uniformity of font style, color scheme, layout structure, page numbering, and chart style; if inconsistencies are found, they will be automatically corrected, such as unifying the title font size of all pages, adjusting the color saturation of different pages, and standardizing the legend style of charts, etc.; at the same time, the integrity of the content is checked to ensure that no core information is missing and no text fragments are repeated. Human-computer interaction optimization: Provides a visual interactive editing interface, allowing users to make partial adjustments to the generated PPT; users can modify page content, adjust layout, change visual style, and edit chart data, and the modification instructions are fed back to the system in real time; based on the user's modification operations, the system learns the user's layout preferences and updates the personalized rules in the layout knowledge base to provide more suitable services for subsequent generation; Multi-format export: Supports exporting generated PPT documents in multiple formats, including standard PPT format, PDF format, image format, etc.; For scenarios such as patent presentations and academic exchanges, it provides a print-optimized export mode that automatically adjusts page size and layout to adapt to printing needs; For online presentation scenarios, it provides a lightweight export mode that compresses file size to ensure smooth transmission and presentation.

[0026] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A PPT generation system based on structured content understanding and intelligent layout design, characterized in that: include: The structured content understanding and outline planning module is configured to respond to user input of topic descriptions and optional reference materials. It generates structured outline data containing chapter levels, page type identifiers, and key page text through semantic parsing, and persistently stores and visualizes the structured outline data as an editable intermediate result independent of the final presentation file. The intelligent layout decision module is configured to traverse each page in the structured outline data, extract the page type metadata of the page and calculate its content density characteristics, and determine the target layout that matches the page type and content density characteristics from the layout template library based on the preset layout matching rules. The page content generation module is configured to take the page key text in the structured outline data as input, call the trained content generation model, and follow a multi-dimensional consistency control strategy including terminology consistency, argument completeness, and length balance to generate the title, main text key points, and auxiliary explanatory text content for each page. The visual rendering engine is configured to acquire a preset theme system, which includes at least font hierarchy specifications, color palettes, and component style definitions. It integrates and renders the target layout, generated text content, and theme system to generate a visual page that corresponds one-to-one with each page in the structured outline data. The multi-format export module is configured to batch convert all visual pages into multiple delivery formats, including at least PPTX, PDF, and HTML, while preserving the editable properties of text layers and vector component layers in each delivery format during the export process.

2. The PPT generation system based on structured content understanding and intelligent layout design according to claim 1, characterized in that: The structured content understanding and outline planning module is further configured as follows: The system performs deep semantic analysis on the user-input topic description, generating structured constraint tags that include target audience type, tone of voice, core objective statement, information density level, and page count range. Based on these structured constraint tags and a preset presentation logic template, it automatically arranges the chapter hierarchy and allocates the number of pages in each chapter. When the user provides reference materials, it performs unstructured text parsing and key information extraction on the reference materials, obtaining information units including factual data, definitions, and process steps. The extracted information units are then compressed and rewritten for presentation purposes, generating key text that meets page granularity requirements. Each key text is associated with source metadata to support content traceability.

3. The PPT generation system based on structured content understanding and intelligent layout design according to claim 1, characterized in that: The multi-dimensional consistency control strategy in the page content generation module includes: Terminology consistency control: Build a contextual glossary to ensure that the same concept uses the same name in the same presentation document; Argument completeness verification: Based on a pre-defined conclusion and reason structure model, check whether the key points on the page contain a complete loop of arguments and evidence; Length balance adjustment: The number of key points and paragraph lengths on each page are normalized to keep the difference in information density between pages within a threshold range; Readability enhancement processing: Grouping and indentation are applied to multi-level information, and keywords are visually highlighted.

4. The PPT generation system based on structured content understanding and intelligent layout design according to claim 1, characterized in that: In the intelligent layout decision-making module: The content density feature is generated by statistically analyzing the number of key items, the total number of characters, and the number of data objects to be visualized on the current page. The layout template library pre-sets at least one candidate layout for multiple page types; The layout matching rule is configured as follows: firstly, the candidate layout set is filtered according to the page type identifier, and then the layout with the highest layout capacity matching degree is selected as the target layout according to the content density characteristics in the set. The intelligent layout decision module is also configured to acquire a preset presentation rhythm control strategy, identify key pages and transition pages in the structured outline data, prioritize layouts with high visual contrast for key pages, and prioritize simple layouts for transition pages.

5. The PPT generation system based on structured content understanding and intelligent layout design according to claim 1, characterized in that: The visual rendering engine is further configured as follows: In response to the user's command to switch the theme system, without re-generating the page content, the fonts, colors, and component styles of all current visualization pages are batch replaced with the style definitions corresponding to the target theme system, and the presentation of the new theme version is re-rendered and generated.

6. The PPT generation system based on structured content understanding and intelligent layout design according to claim 1, characterized in that: It also includes a multimodal content enhancement module, configured as follows: Based on the semantic vector of the current page, the system retrieves matching image or icon resources from the preset material library or third-party material interface. After performing intelligent cropping, subject detection and composition adaptation on the retrieved material resources, the system embeds them into the specified placeholders of the visual page and records the material source identifier and license type information in the page metadata for generating a material source list during export. Additionally, it identifies implicit structured data relationships from key text on the page or receives tabular data directly input by the user, maps the identified or received data to a predefined basic chart type or structure diagram type data structure, calls the chart rendering engine to generate vector chart graphics, and inserts the chart graphics as editable objects into the target layout area of ​​the visualization page.

7. The PPT generation system based on structured content understanding and intelligent layout design according to claim 1, characterized in that: It also includes a system governance and scheduling module, configured as follows: After the structured outline data is generated, an outline structure consistency check is performed, including chapter completeness, logical progression, and page number control compliance. After the visualization page is generated, a visual consistency check is performed, including cross-page theme element reuse consistency, white space ratio, and component alignment status. When any check fails, the corresponding content adjustment strategy or layout adjustment strategy is triggered and adjustment suggestions are pushed to the front-end interaction layer. Furthermore, the page content generation task, visual rendering task, and multi-format export task are separated into mutually decoupled asynchronous execution units. A status tracking and progress reporting mechanism is established for each asynchronous execution unit, and the generated visual page previews are pushed page by page in a streaming manner. Failure retry and degradation strategies are set for model calls, rendering compositing, and file export, and state snapshots are recorded in case of exceptions to support breakpoint recovery.

8. The PPT generation system based on structured content understanding and intelligent layout design according to claim 1, characterized in that: It also includes a version management and re-export module, configured as follows: Record the theme system configuration snapshot, structured outline data version identifier, and export parameters for each export operation; Receive user re-export instructions, and based on the same structured outline data version, call different historical theme system configuration snapshots or newly selected theme systems to batch regenerate and export multi-version delivery packages.

9. A PPT generation method based on structured content understanding and intelligent layout design, characterized in that: The method is performed by the PPT generation system based on structured content understanding and intelligent layout design as described in any one of claims 1-8, and the method includes the following steps: In response to the user's input of a topic description and optional reference materials, the system generates structured outline data containing chapter levels, page type identifiers, and key page text through semantic parsing. The structured outline data is then persistently stored and visualized as an editable intermediate result independent of the final presentation file. Iterate through each page in the structured outline data, extract the page type metadata of the page and calculate its content density characteristics, and determine the target layout that matches the page type and content density characteristics from the layout template library based on the preset layout matching rules. Using the page key text in the structured outline data as input, the trained content generation model is invoked, and a multi-dimensional consistency control strategy including terminology consistency, argument completeness, and length balance is followed to generate the title, main text key points, and auxiliary explanatory text content for each page. Obtain the preset theme system, which includes at least font hierarchy specifications, color palettes, and component style definitions. Then, integrate and render the target layout, generated text content, and theme system to generate a visual page that corresponds one-to-one with each page in the structured outline data. Batch convert all visual pages into multiple delivery formats, including at least PPTX, PDF, and HTML, while preserving the editable properties of text layers and vector component layers in each delivery format during the export process.

10. The PPT generation method based on structured content understanding and intelligent layout design according to claim 9, characterized in that: Also includes: In response to the user's command to switch the theme system, without re-generating the page content, the fonts, colors and component styles of all current visualization pages are replaced in batches with the style definitions corresponding to the target theme system, and the presentation of the new theme version is re-rendered and generated. Additionally, based on the semantic vector of the current page, the system retrieves matching image or icon resources, performs intelligent cropping, subject detection, and composition adaptation on the retrieved resources, embeds them into specified placeholders on the visual page, and records the source identifier and license type information of the materials in the page metadata.

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