A method and system for intelligent generation of a formatted document

Abstract

The application discloses a kind of formatted document intelligent generation method and system, comprising: obtaining multi-source formatted document sample, structured analysis is carried out by depth learning technology, constructs hierarchical document structure knowledge base;Based on the knowledge base, in combination with the document demand of user input, through neural manifold representation learning and contrast self-supervised learning, output optimal document template;Template is carried out structure analysis, in combination with user content, through diffusion model generation document content and using best loss value estimation carries out quality diagnosis and optimization;Through the valueless strategy optimization algorithm of reward partition carries out format self-adaptive adjustment and style consistency check;The finished product of document is converted and exported and quality evaluation in multiple formats.This application solves the technical problems that the existing system lacks flexibility, lacks intelligent format adjustment, and is difficult to maintain document semantic coherence and style consistency.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and document processing technology, and in particular to a method and system for intelligent generation of formatted documents. Background Technology

[0002] Standardized documents are an indispensable part of modern organizational work, including various official documents, contracts, reports, and other standardized documents. These documents typically have strict format requirements, fixed structural layouts, and specific expression standards, placing high demands on their professionalism and standardization.

[0003] Currently, the generation of formatted documents mainly relies on two methods: one is to manually fill in the content based on preset templates using word processing software such as Microsoft Word. This method is simple to operate but inefficient. The other is to use document generation systems for specific fields, such as automatic legal document generation systems, which generate standardized documents by filling in forms. However, these systems usually lack flexibility and have poor cross-domain adaptability.

[0004] More advanced technical solutions employ rule-based template matching and content filling techniques, combined with natural language processing technology to analyze and reorganize user input, generating documents that meet specific formatting requirements. This technology uses a predefined template library and rule set to map the core content provided by the user to a suitable document structure and automatically adjusts the layout.

[0005] However, existing technologies still have significant shortcomings: First, template matching mechanisms rely too heavily on manually preset rules, making it difficult to cope with diverse document needs; second, the format adjustment of generated documents lacks intelligence, and layout chaos easily occurs when the content does not completely match the template; in addition, existing systems have difficulty maintaining the semantic coherence and style consistency of documents, especially when dealing with long documents with complex structures. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for intelligent generation of formatted documents, so as to solve the technical problems in the prior art, such as template matching mechanism relying too much on manual preset rules, lack of intelligent format adjustment, and difficulty in maintaining the semantic coherence and style consistency of documents.

[0007] To achieve the above objectives, the present invention provides a method for intelligently generating formatted documents, comprising the following steps:

[0008] Obtain multi-source formatted document samples, perform structured parsing on the multi-source formatted document samples using deep learning technology, identify visual layout features, semantic structure representation and style feature representation, and construct a hierarchical document structure knowledge base;

[0009] Based on the hierarchical document structure knowledge base, combined with the document requirements input by the user, document feature vectors are extracted through natural language processing technology, and neural manifold representation learning is used to map the document feature vectors to a low-dimensional representation space to obtain a low-dimensional neural manifold representation. Contrastive self-supervised learning is used to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base and to perform multi-dimensional matching degree scoring, outputting the optimal document template.

[0010] The optimal document template is structurally analyzed to identify content slots and their constraints. Combined with user content data, document content is generated through a diffusion model, and the best loss value estimation is used for quality diagnosis and iterative optimization to obtain optimized document content.

[0011] Based on the optimized document content and the optimal document template, the text flow, pagination, and chart positions of the optimized document content are adaptively adjusted using a reward partitioning no-value strategy optimization algorithm, and style consistency checks and corrections are performed to generate a formatted document product.

[0012] The document output conforming to the specified format is converted and exported to multiple formats. The quality is evaluated from multiple dimensions, including format conformity, content completeness, language expression and layout aesthetics, and outputs multi-format document files and quality evaluation reports.

[0013] Preferably, the step of performing structured parsing of the multi-source formatted document samples using deep learning technology to identify visual layout features, semantic structure representations, and style feature representations, and constructing a hierarchical document structure knowledge base, includes:

[0014] The multi-source formatted document sample is preprocessed by converting the file format, removing watermarks and repairing damaged parts. The visual layout features are analyzed by computer vision technology, and a preliminary structural representation containing basic components such as title, body text, tables and charts is output.

[0015] Based on the preliminary structural representation, natural language processing technology is used to perform semantic analysis on the document content, identify chapter hierarchy relationships, logical organization structure and functional content units, and output semantic structural representation;

[0016] From the visual layout features and the semantic structure representation, style feature representations including font, font size, line spacing and indentation are extracted to form a standardized style description language, thus obtaining the hierarchical document structure knowledge base.

[0017] Preferably, the step involves mapping the document feature vectors to a low-dimensional representation space using neural manifold representation learning to obtain a low-dimensional neural manifold representation. Contrastive self-supervised learning is then used to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base, and a multi-dimensional matching score is performed, including:

[0018] The system receives the document requirement description and content elements input by the user, and uses natural language processing technology to perform word segmentation, part-of-speech tagging, named entity recognition and semantic analysis to extract document type features, content features and style features to form a document feature vector.

[0019] Based on the document feature vector, the high-dimensional feature vector is mapped to the low-dimensional latent space by a variational autoencoder. Self-supervised learning training is performed using feature reconstruction task, document type prediction task and structural consistency task to obtain a low-dimensional neural manifold representation.

[0020] For the low-dimensional neural manifold representation, a contrastive learning framework is used to construct positive and negative sample pairs. The similarity between user needs and each template in the hierarchical document structure knowledge base is calculated using the InfoNCE loss function, and a similarity ranking list is generated.

[0021] Preferably, the step involves mapping the document feature vectors to a low-dimensional representation space using neural manifold representation learning to obtain a low-dimensional neural manifold representation. Contrastive self-supervised learning is then used to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base, and a multi-dimensional matching score is performed, including:

[0022] Based on the candidate templates in the similarity ranking list, scores are given from the dimensions of structural adaptability, content completeness, style compatibility, domain relevance, and usage frequency, and a single matching score is calculated for each dimension.

[0023] For each dimension's individual matching score, a machine learning model is used for weighted fusion, and combined with the user's historical preferences, a collaborative filtering algorithm is used for personalized adjustment, outputting the optimal document template and matching score report.

[0024] Preferably, the step of generating document content through a diffusion model and performing quality diagnosis and iterative optimization using optimal loss value estimation to obtain optimized document content includes:

[0025] The optimal document template is structured and parsed to identify explicit and implicit slots and analyze the content type, length limit, format requirements, semantic constraints and dependencies of each slot to generate a content generation task list.

[0026] Based on the content generation task list and combined with user content data, determine the content filling strategies of direct filling, adaptive adjustment, automatic generation, and hybrid filling, and formulate a content generation plan;

[0027] According to the content generation plan, a diffusion model based on a pre-trained language model is used, combined with stylistic adaptation, structure awareness, domain knowledge injection, and context consistency control mechanisms, to obtain the optimized document content.

[0028] Preferably, the step of generating document content through a diffusion model and performing quality diagnosis and iterative optimization using optimal loss value estimation to obtain optimized document content includes:

[0029] For the document content generated by the diffusion model, a multi-dimensional loss function is calculated, including semantic loss, style loss, structural loss, coherence loss, and professional loss, to obtain the actual loss value of each dimension;

[0030] Based on the comparison between the actual loss values ​​of each dimension and the preset ideal values ​​and acceptable thresholds, the sliding window technique is used to evaluate the quality of the overall content and local paragraphs, and generate a quality diagnostic report that includes an overall quality score, dimension subdivision scores, problem area identification and improvement suggestions.

[0031] Based on the severity of the quality diagnostic report, a graded optimization strategy of mild, moderate and severe optimization is adopted, and iterative optimization is performed through a targeted control generation method to output the iterative document content;

[0032] The iterated document content is checked for consistency in terms of terminology, data, tense, person, and format, and the consistency check results are obtained.

[0033] Based on the consistency check results, coreference resolution and dependency analysis techniques are used to identify potential problems, automatically correct simple errors and mark complex problems, and obtain the optimized document content.

[0034] Preferably, based on the optimized document content and the optimal document template, the text flow, pagination, and chart positions of the optimized document content are adaptively adjusted using a reward partitioning-based no-value strategy optimization algorithm, and style consistency checks and corrections are performed to generate a formatted document product, including:

[0035] The matching degree analysis is performed on the optimized document content and the template structure of the optimal document template. The overall structure matching degree, chapter-level matching degree, paragraph-level matching degree and element-level matching degree are calculated. Content overflow, insufficient content, structural mismatch, special element conflict and pagination problem are identified, and a priority list of typesetting tasks sorted by severity is generated.

[0036] Based on the aforementioned typesetting task priority list, a reward partition is designed for the basic usable interval, the standard qualified interval, the professional and aesthetically pleasing interval, and the perfect presentation interval. For each interval, text adjustment, space reallocation, content rearrangement, special element processing, and pagination optimization strategies are formulated to construct a typesetting decision tree.

[0037] Based on the layout decision tree, a state space is constructed that includes document structure status, content distribution, space utilization and aesthetic score, as well as an action space that includes text flow adjustment, space allocation, special element positioning and pagination control, and a valueless strategy optimization framework is established.

[0038] Under the aforementioned valueless strategy optimization framework, a strategy network is used to evaluate the advantage function of the adjustment action, and layout optimization is performed according to the progressive adjustment strategies of large scale, medium scale and small scale to achieve adaptive adjustment of text flow, pagination and chart position, resulting in an adaptively adjusted layout.

[0039] The adaptively adjusted layout is processed to handle boundary cases including text overflow, isolated lines, table spanning pages, and maintaining the relationship between text and images, resulting in a document product with the specified format.

[0040] Preferably, based on the optimized document content and the optimal document template, the text flow, pagination, and chart positions of the optimized document content are adaptively adjusted using a reward partitioning-based no-value strategy optimization algorithm, and style consistency checks and corrections are performed to generate a formatted document product, including:

[0041] Establish a style consistency standard library that includes font styles, spatial specifications, punctuation specifications, number representation specifications, chart specifications, and page specifications. Use a two-stage strategy of rule checking and statistical checking to scan the adaptively adjusted layout for style consistency, identify problems that violate style specifications and abnormal style usage patterns, and obtain style consistency issues.

[0042] For the aforementioned style consistency issues, a classification process is performed based on severity and type, including automatic correction, suggested correction, and manual confirmation, to generate a correction plan;

[0043] Based on the aforementioned correction scheme, a style propagation algorithm is used to automatically correct and update the document style by referencing the mainstream styles of elements at the same level or parent elements, thus obtaining the updated document style.

[0044] For the updated document style, quantify the overall style consistency level, generate an issue distribution map, a correction list and style usage statistics, and output a style consistency report;

[0045] Based on the style consistency report, confirm the effect of style correction and perform final style unification to ensure that the finished document conforms to the standardization requirements.

[0046] Preferably, the step of converting and exporting the document in the specified format to multiple formats includes:

[0047] For the finished document with the specified format, the document content and format are converted into a standardized intermediate representation, thereby separating the format information from the content and generating a standardized document representation;

[0048] Based on the standardized document representation, the rendering engine parameters are optimized for different output formats, taking into account device characteristics, media differences, reading environment and document purpose, to establish a multi-resolution rendering engine;

[0049] The multi-resolution rendering engine converts documents into specified formats according to user needs, preserving the original structure, style, and interactive features, and outputs multi-format document files.

[0050] An automatic evaluation mechanism is established for the aforementioned multi-format documents, taking into account dimensions such as format standardization, content completeness, language expression, and layout aesthetics, to generate a quality evaluation report;

[0051] Based on the quality assessment report and user feedback, the problems and areas for improvement in the document generation process are analyzed, and the hierarchical document structure knowledge base and algorithm parameters are updated to achieve continuous improvement and closed-loop optimization of the system.

[0052] This invention also provides a formatted document intelligent generation system, comprising:

[0053] The document structure parsing module is used to acquire multi-source formatted document samples, perform structured parsing on the multi-source formatted document samples using deep learning technology, identify visual layout features, semantic structure representation and style feature representation, and construct a hierarchical document structure knowledge base;

[0054] The template matching module is used to extract document feature vectors based on the hierarchical document structure knowledge base and combined with the document requirements input by the user, through natural language processing technology, and to map the document feature vectors to a low-dimensional representation space using neural manifold representation learning to obtain a low-dimensional neural manifold representation. It then uses contrastive self-supervised learning to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base and performs multi-dimensional matching degree scoring to output the optimal document template.

[0055] The content generation module is used to perform structural analysis on the optimal document template, identify content slots and their constraints, combine user content data, generate document content through a diffusion model, and perform quality diagnosis and iterative optimization using the best loss value estimation to obtain optimized document content.

[0056] The layout adjustment module is used to adaptively adjust the text flow, pagination, and chart positions of the optimized document content based on the optimized document content and the optimal document template, using a reward partitioning no-value strategy optimization algorithm, and to perform style consistency checks and corrections to generate a formatted document product.

[0057] The format export module is used to convert and export documents that meet the specified formatting standards to multiple formats, and to conduct quality assessments from multiple dimensions, including formatting compliance, content completeness, language expression, and layout aesthetics, outputting multi-format document files and quality assessment reports.

[0058] The beneficial effects of this invention are:

[0059] 1. By applying the best loss value estimation technique to diagnose the diffusion model, the content quality is objectively diagnosed and iteratively optimized during the document content generation process, which significantly improves the accuracy and coherence of the generated content;

[0060] 2. Based on contrastive self-supervised learning of neural manifolds, the document feature vectors are mapped to the neural manifold space. The essential features of document structure and content are captured by contrastive learning methods, achieving high-precision template matching and effectively solving the problem that traditional rule-based matching methods are difficult to handle complex document structures.

[0061] 3. The reward-partitioning no-value strategy optimization is adopted to decompose the document layout task into multiple reward intervals. The no-value strategy optimization algorithm realizes the adaptive adjustment of the layout, which solves the layout problem caused by the mismatch between content and template without affecting the overall aesthetics of the document.

[0062] 4. Construct a hierarchical document structure knowledge representation, integrating a multi-level document structure knowledge base that combines visual layout, semantic structure, and style rules, providing a unified knowledge foundation for template matching and content generation, and enabling universal processing capabilities for cross-domain and multi-type documents;

[0063] 5. Establish a closed-loop optimization mechanism for document quality, from user needs analysis to final quality assessment. Through multi-dimensional evaluation and user feedback, continuously optimize the system to ensure that the quality of generated documents is constantly improved. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1A flowchart of the intelligent generation method for formatted documents provided in an embodiment of the present invention;

[0066] Figure 2 A flowchart illustrating the document content intelligent generation method based on a diffusion model provided in this embodiment of the invention;

[0067] Figure 3 This is a structural block diagram of the formatted document intelligent generation system provided in an embodiment of the present invention. Detailed Implementation

[0068] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present invention are shown in the accompanying drawings.

[0069] Figure 1 A flowchart illustrating the intelligent generation method for formatted documents provided in an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0070] Step 101: Obtain multi-source formatted document samples, perform structured parsing on the multi-source formatted document samples using deep learning technology, identify visual layout features, semantic structure representation and style feature representation, and construct a hierarchical document structure knowledge base.

[0071] In this step, we first collect various formatted document samples from multiple sources, including standardized documents of different types and fields such as government documents, corporate reports, academic papers, and legal documents. These document samples come from diverse sources, including both publicly available standard document templates and document examples from actual applications, ensuring the richness and representativeness of the data. The collected document samples undergo preprocessing, including file format standardization, watermark removal, and repair of damaged parts, forming a standardized document corpus that provides the foundation for subsequent in-depth analysis.

[0072] It should be noted that in the process of collecting document samples, this application has obtained written permission or authorization from the individual user or their organization if personal information or related data is used.

[0073] Deep learning techniques are employed to perform structured analysis on preprocessed document samples. First, computer vision techniques are used to analyze the visual layout features of the documents, including page structure, spatial distribution, and element positional relationships. Models such as Convolutional Neural Networks (CNNs) and Region Proposal Networks (RPNs) are used to identify basic components in the documents, such as titles, body text, tables, charts, headers, and footers, and to analyze their spatial layout and visual hierarchy. For example, features such as titles typically being located at the top of the page with larger fonts, tables having a regular grid structure, and charts usually having explanatory text around them can be identified.

[0074] Next, semantic analysis of the document content is performed using natural language processing techniques to identify its semantic structure. Pre-trained language models (such as BERT and RoBERTa) and sequence labeling models (such as BiLSTM-CRF) are used to analyze the text content, identifying chapter hierarchy, logical organization, and functional content units. By analyzing the semantic content and contextual relationships of headings, the hierarchical relationships between different headings are determined; by analyzing conjunctions and topic changes between paragraphs, the logical organization of the document is identified; and through specific language patterns and positional features, functional content units such as abstracts and conclusions are identified.

[0075] Simultaneously, the document's style features are extracted, including style attributes such as font, font size, line spacing, indentation, alignment, and color, and the correspondence between these style attributes and document structural elements is analyzed. Through image processing and pattern recognition techniques, style rules for different structural elements are extracted, such as using specific fonts and sizes for first-level headings, and specific line spacing and alignment for body text. These style rules are formalized into a standardized style description language for easy subsequent application.

[0076] Finally, visual layout features, semantic structure representations, and style feature representations are integrated to construct a multi-level, cross-domain document structure knowledge base. This knowledge base adopts a graph or tree structure representation, containing structural patterns, content organization methods, and style application rules for different types of documents, and establishing the relationships between various elements. The knowledge base supports efficient retrieval and similarity calculation, providing a unified knowledge foundation for subsequent template matching and content generation.

[0077] Step 102: Based on the hierarchical document structure knowledge base and combined with the document requirements input by the user, extract document feature vectors through natural language processing technology, and use neural manifold representation learning to map the document feature vectors to a low-dimensional representation space to obtain a low-dimensional neural manifold representation. Use contrastive self-supervised learning to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base and perform multi-dimensional matching degree scoring to output the optimal document template.

[0078] This step first receives and analyzes the user's document requirements, which typically include information such as document type, usage scenario, content elements, and formatting preferences. Natural language processing (NLP) technology is used to conduct in-depth analysis of the user requirements, including word segmentation, part-of-speech tagging, named entity recognition, and semantic understanding. For example, when a user inputs "I need to generate a project acceptance report, including project overview, completion status, and test results, in a formal style," the system can accurately identify the document type as "project acceptance report," the content elements as "project overview," "completion status," and "test results," and the style requirement as "formal."

[0079] Pre-trained language models (such as BERT and GPT) are used to extract document features from user needs, forming a high-dimensional feature vector. These features are mainly divided into three categories: document type features (representing the basic category and purpose of the document), content features (representing the main content elements and logical structure that the document needs to include), and style features (representing the user's preferences for document format and layout). Feature fusion technology is used to integrate these different types of features into a unified document feature vector, comprehensively expressing the user's document needs.

[0080] Next, a neural manifold representation learning technique is employed to map high-dimensional document feature vectors to a low-dimensional representation space. Deep generative models, such as variational autoencoders (VAEs) or diffusion models, are used to learn the manifold structure of the feature space, compressing high-dimensional features into a low-dimensional latent space (typically 32-128 dimensions). To improve the quality and semantic richness of the representation, several self-supervised learning tasks are designed for joint training, such as a feature reconstruction task (randomly masking some features and predicting the masked features), a document type prediction task (predicting the document type based on partial features), and a structural consistency task (determining whether two document features come from the same document type). Through training on these self-supervised tasks, a more robust and semantically rich neural manifold representation is learned.

[0081] A contrastive self-supervised learning framework is employed to calculate the similarity between user needs and document templates in the knowledge base. The core idea of ​​contrastive learning is to group semantically similar samples closer together in the representation space, while dissimilar samples are kept apart. Positive sample pairs (feature pairs of documents of the same type and similar structure) and negative sample pairs (feature pairs of documents of different types and significantly different structures) are constructed, and the feature extractor is optimized using contrastive loss functions such as InfoNCE. In similarity calculation, cosine similarity, Euclidean distance, and a specially trained semantic matching network are combined to comprehensively evaluate the matching degree between user needs and each template, generating a similarity ranking list.

[0082] Finally, the candidate templates in the similarity ranking list are scored for matching across multiple dimensions. These dimensions include structural adaptability (whether the template structure meets the requirements), content completeness (whether the template contains the required content elements), style compatibility (whether the template style matches preferences), domain relevance (whether the template is suitable for a specific domain), and usage frequency (the template's historical usage). Machine learning models (such as random forests, gradient boosting trees, or neural networks) are used to weight and fuse the scores across these dimensions. Furthermore, user historical preferences are incorporated into the personalized adjustments made through collaborative filtering algorithms. The final output is the document template with the highest matching degree, along with a detailed scoring report.

[0083] Step 103: Perform structural analysis on the optimal document template, identify content slots and their constraints, combine user content data, generate document content through a diffusion model, and use the best loss value estimation for quality diagnosis and iterative optimization to obtain optimized document content.

[0084] In this step, the selected optimal document template is first subjected to in-depth structural analysis, transforming the template into a hierarchical tree structure representation. This representation clearly shows the document's organizational structure, including the nesting relationships and hierarchical structure of elements such as chapters, paragraphs, lists, and tables. A depth-first search algorithm is then used to recursively identify and classify each structural element, starting from the root node of the document, to construct a complete document structure tree.

[0085] The system identifies slots in the template that require content filling. These slots are reserved in the document template and need to be filled with actual content based on specific circumstances. A combination of rule-based and machine learning methods is used to identify these slots: explicit slots (such as "{Project Name}", "[Person in Charge]", etc.) are identified using regular expressions; implicit slots (slots without explicit markings but requiring content filling) are identified using sequence labeling models (such as BiLSTM-CRF or BERT-CRF). For each identified slot, its attributes and constraints are further analyzed, including content type (text, number, date, etc.), length limits, formatting requirements, semantic constraints, and dependencies with other slots. A slot dependency graph is also constructed to represent the logical relationships and filling order between different slots.

[0086] Next, a content generation plan is developed based on user-provided content data. Multimodal content parsing technology is used to process various forms of user-provided content materials (such as text descriptions, structured data, reference documents, etc.) and match them with identified slots. Based on the matching results and slot characteristics, a filling strategy for each slot is determined: direct filling (directly filling using user-provided content), adaptive adjustment (formatting or expanding user content), automatic generation (automatically generating content based on context and other content), or hybrid filling (combining user content and automatically generated content). Content generation dependencies are also considered, and a directed acyclic graph is constructed to ensure content is generated in the correct order.

[0087] A diffusion model is employed as the core technology for content generation. Diffusion models are a type of generative model that generates new samples by progressively adding noise to the data and then learning the reverse process (denoising). A large, pre-trained language model is used as a foundation, upon which a diffusion layer and conditional control mechanisms are added to achieve high-quality document content generation. The diffusion model has been specifically optimized, including stylistic adaptation (ensuring the generated content conforms to the language style of a specific document type), structure awareness (ensuring the content meets document structure requirements), domain knowledge injection (integrating professional terminology and concepts), and contextual consistency (maintaining coherence between different parts of the document). During the generation process, fine-grained conditional controls are provided for each content slot, including content description, contextual information, user-relevant content, style guidance, and structural constraints.

[0088] After content generation, the optimal loss estimation method is used to evaluate content quality. A multi-dimensional loss function is designed, including semantic loss, style loss, structural loss, coherence loss, and professionalism loss, to assess the gap between the generated content and the ideal content. Using a sliding window technique, not only is the overall content evaluated, but the quality of individual paragraphs is also analyzed to identify weak areas. Based on the evaluation results, a detailed quality diagnostic report is generated, including an overall quality score, detailed scores for each dimension, identification of problem areas, and improvement suggestions.

[0089] Based on the quality diagnostic report, the generated content undergoes iterative optimization. Optimization strategies are categorized by severity: mild optimization (minor style adjustments, polishing, etc.), moderate optimization (clear logic, unreasonable structure, etc.), and severe optimization (content errors, lack of professionalism, etc.). A targeted, controlled generation method is employed, clearly specifying key information to be retained, providing concrete directions for modification, referencing high-quality examples, and avoiding known errors. An iterative optimization strategy is used, with quality assessments conducted after each optimization until the content quality meets preset standards. Finally, a full-text consistency check is performed to ensure consistency in terminology, data, tense, person, and formatting, resulting in high-quality, coherent, and consistent optimized document content.

[0090] Step 104: Based on the optimized document content and the optimal document template, the text flow, pagination, and chart positions of the optimized document content are adaptively adjusted using a reward partitioning no-value strategy optimization algorithm, and style consistency checks and corrections are performed to generate a formatted document product.

[0091] In this step, a precise matching analysis is first performed on the optimized document content and template structure. A template structure representation (a tree structure with spatial constraints and style rules) and a content filling representation (a logically organized and transformed content tree) are constructed and compared using a structure matching algorithm. Overall structure matching, chapter-level matching, paragraph-level matching, and element-level matching are calculated to comprehensively assess the content's fit with the template. During the analysis, special attention is paid to potential formatting issues, such as content overflow (content exceeding reserved space), insufficient content (resulting in excessive blank space), structural mismatch (inconsistent hierarchical structure), conflicts with special elements (unsuitable chart size or position), and pagination issues (e.g., titles at the end of pages). Heuristic rules and machine learning models are used to assess the severity of these issues, generating a priority list of formatting tasks ranked by severity.

[0092] Next, the concept of reward partitioning is introduced, dividing the layout optimization target space into multiple intervals, each corresponding to a different level of layout optimization. Reward intervals are designed as follows: Basic Usable Interval (ensuring basic readability), Standardized and Qualified Interval (conforming to basic layout standards), Professional and Aesthetically Pleasing Interval (professional and aesthetically pleasing layout), and Perfect Presentation Interval (reaching the level of professional publications). For each layout task, its reward interval is determined, and corresponding optimization strategies are designed, including text adjustment strategies (adjusting character spacing, line spacing, and font size), space redistribution strategies (optimizing margins and spacing), content rearrangement strategies (paragraph reorganization and content adjustment), special element handling strategies (table resetting and image scaling), and pagination optimization strategies (setting pagination points and handling isolated lines). A decision tree algorithm is used to construct a layout decision tree to guide the selection of the most suitable strategy combination.

[0093] A valueless strategy optimization framework is employed to address layout adjustment issues. Valueless strategy optimization is a special type of reinforcement learning method that directly optimizes policies through policy gradient methods, making it particularly suitable for handling high-dimensional, continuous decision spaces in typesetting. A state space (current document structure, content distribution, etc.) and an action space (text flow adjustment, space allocation, etc.) for layout adjustments are constructed. A policy network is used to evaluate the dominance function of adjustment actions, selecting actions with high dominance values ​​for execution. To ensure the stability of the adjustment process, a progressive adjustment strategy is adopted: first, large-scale adjustments (overall margins, main columns), then medium-scale adjustments (paragraph spacing, chart positions), and finally fine-tuning (character and line spacing). Special attention is paid to boundary conditions in layout adjustments, such as handling text overflow, isolated lines, table crossovers, and maintaining text-image relationships, ensuring high-quality typesetting results under complex and ever-changing content conditions.

[0094] After completing the layout adjustments, a style consistency check and correction are performed. A style consistency standard library is established, defining various style specifications that should be followed in the document, including font style specifications, spacing specifications, punctuation specifications, number representation specifications, chart specifications, and page specifications. A two-stage strategy is employed for style consistency scanning: rule checking (using predefined rule sets to check for violations) and statistical checking (analyzing style usage patterns to identify anomalies or inconsistencies). For identified style inconsistencies, they are categorized according to severity and type: automatic correction (for clearly defined violations with only one possible correction), suggested correction (for issues with multiple possible corrections), or manual confirmation (for major modifications that may affect content comprehension). A style propagation algorithm is used for automatic correction, propagating correct styles from elements at the same level or parent elements to elements requiring correction based on the document's hierarchical structure. Finally, a style consistency report is generated, quantifying the overall style consistency level of the document and displaying information such as problem distribution, a correction list, and style usage statistics, ensuring that the document maintains a unified and professional appearance after layout adjustments, meeting the high standards of standardized documents.

[0095] Step 105: Convert and export the document with the specified format to multiple formats, and conduct a quality assessment from multiple dimensions, including format conformity, content completeness, language expression and layout aesthetics, and output multi-format document files and a quality assessment report.

[0096] In this step, the formatted document is first converted into a standardized intermediate representation. This intermediate representation uses a structured data format (such as XML or JSON) that clearly distinguishes between content elements and formatting attributes, achieving separation of formatting information from content. For example, a paragraph might be represented as a structured object containing content text and style attributes (font, font size, line spacing, alignment, etc.). This separation allows for flexible adjustment of formatting attributes according to the requirements of different output formats while keeping the content unchanged. The standardized representation also includes the document's overall structural information, logical relationships between elements, and cross-references, ensuring the document's integrity and consistency are maintained during the format conversion process.

[0097] Next, a multi-resolution rendering engine is built to accurately represent the actual effect of documents on different media (such as screens and printed paper). The rendering engine employs vector graphics technology to ensure clear text and graphics display at any scaling level. Rendering engine parameters are optimized for different output formats (such as PDF, Word, and HTML), taking into account factors such as device characteristics (resolution, color space, etc.), media differences (screen display vs. print effect), reading environment (brightness, distance, etc.), and document purpose (formal publication, collaborative editing, etc.). For example, for PDF output, font embedding, image compression, and print compatibility are optimized; for HTML output, responsive layout, CSS styles, and browser compatibility are optimized. Through these optimizations, consistent visual effects and professional quality are ensured across different formats.

[0098] Based on the user's specific needs, documents are converted to the specified output format. The conversion process not only preserves the document's content and basic style but also retains as much of the original structure, advanced typesetting features, and interactive functionality as possible. Specialized conversion strategies are employed for different output formats: PDF generates high-quality printable documents, preserving precise typesetting and pagination; Word generates editable documents, retaining style definitions and document structure; HTML generates web-based documents, supporting responsive layouts and interactive features; furthermore, conversion to specific domain-specific professional formats (such as legal document formats, medical record formats, etc.) is also supported. During the conversion process, special attention is paid to the compatibility mapping of format characteristics, ensuring that the original functionality and appearance are preserved as much as possible when converting between different formats, such as converting a Word table of contents to PDF bookmarks and converting HTML hyperlinks to Word cross-references.

[0099] A comprehensive quality assessment is conducted on the generated multi-format documents. The assessment employs a multi-dimensional indicator system, measuring document quality from key aspects such as format compliance (whether it conforms to relevant format standards and specifications), content completeness (whether the content is complete and accurate), language expression (grammatical correctness, clarity, and professionalism), and layout aesthetics (balanced layout, efficient use of space, and overall visual appeal). Automated assessment tools and algorithms are used to quantify and score these dimensions, generating a detailed quality assessment report. The report includes an overall score, detailed analysis of each dimension, a list of issues, and improvement suggestions, providing users with a comprehensive understanding of document quality and directions for improvement.

[0100] Finally, a continuous improvement mechanism is established to continuously optimize document generation by collecting and analyzing quality assessment results and user feedback. User evaluations and suggestions on generated documents are collected through questionnaires, ratings, and feedback; common problems and areas for improvement in the document generation process are identified by combining quality assessment reports and user feedback; based on the analysis results, the hierarchical document structure knowledge base is updated and expanded, adding new templates, style rules, and content patterns; the algorithm parameters of each module are adjusted and optimized to improve template matching accuracy, content generation quality, and layout effects; and the machine learning model is regularly retrained using newly collected data to improve model performance and adaptability. Through this closed-loop optimization mechanism, continuous learning and progress can be made to adapt to constantly changing document needs and quality standards, continuously improving the quality of generated documents and user satisfaction.

[0101] The following is a detailed explanation of each step.

[0102] Step 101 specifically includes:

[0103] Step 201: Perform file format conversion, watermark removal and damaged part repair preprocessing on the multi-source formatted document sample, analyze the visual layout features through computer vision technology, and output a preliminary structural representation containing basic components such as title, body text, table and chart.

[0104] In this step, we first collect formatted document samples from different fields and types, including various official documents, contracts, reports, and other standardized documents. These document samples undergo preprocessing, including file format conversion (such as converting PDF to an editable format), watermark removal, and repair of damaged parts, to form a standardized document corpus.

[0105] Then, computer vision technology is used to analyze the visual layout features of the document. Specifically, image processing and document analysis algorithms are used to identify basic components such as titles, body text, tables, and charts, and to analyze their spatial relationships, size ratios, and arrangement. For example, it can identify features such as titles typically being located at the top of the page with larger fonts, tables having a regular grid structure, and charts usually having explanatory text around them. Through these analyses, a preliminary structural representation of the document is output, including the type, position, and hierarchical relationships of each basic component.

[0106] Step 202: Based on the preliminary structural representation, natural language processing technology is used to perform semantic analysis on the document content, identify chapter hierarchy relationships, logical organization structure and functional content units, and output semantic structural representation.

[0107] In this step, based on the preliminary structural representation obtained in step 201, natural language processing techniques are further employed to conduct in-depth semantic analysis of the document content. Using techniques such as text classification, sequence labeling, and semantic parsing, the document's chapter hierarchy (e.g., first-level headings, second-level headings), logical organization structure (e.g., introduction-body-conclusion structure), and functional content units (e.g., abstract, keywords, references) are identified.

[0108] For example, by analyzing the semantic content and context of headings, the hierarchical relationships between different headings can be determined; by analyzing conjunctions and thematic changes between paragraphs, the logical organizational structure of the document can be identified; and by using specific language patterns and positional features, functional content units such as abstracts and conclusions can be identified. Through these analyses, a semantic structural representation of the document is output, describing the organization and semantic connections of the document's content.

[0109] Step 203: Extract style feature representations including font, font size, line spacing and indentation from the visual layout features and the semantic structure representation to form a standardized style description language, and obtain the hierarchical document structure knowledge base.

[0110] In this step, the visual layout features and semantic structure representations obtained in steps 201 and 202 are further analyzed to extract style features from the document. Style attributes such as font, font size, line spacing, indentation, alignment, and color used in the document are analyzed, and the correspondence between these style attributes and document structural elements (such as headings, body text, tables, etc.) is studied.

[0111] For example, you might find that in certain types of reports, first-level headings are typically displayed in 16-point bold, centered font; body text uses 12-point Song font with 1.5 line spacing; and table headings use bold Kai font, size 5. Extracting these style rules creates a standardized style description language that describes the style attributes that should be used for different structural elements and their variation patterns.

[0112] Finally, visual layout features, semantic structure representations, and style knowledge representations are integrated to construct a multi-level, cross-domain document structure knowledge base. This knowledge base contains structural patterns, content organization methods, and style application rules for different types of documents, providing basic data support for subsequent template matching and content generation.

[0113] Step 102 specifically includes:

[0114] Step 301: Receive the user's input of document requirements description and content elements, and use natural language processing technology to perform word segmentation, part-of-speech tagging, named entity recognition and semantic analysis to extract document type features, content features and style features to form a document feature vector.

[0115] This step begins by receiving the user's document requirements and content elements input through the interface. User input is typically expressed in natural language and includes descriptions of the required document type, content scope, style requirements, etc. For example, a user might input: "I need a project completion report, including four parts: project background, implementation process, results summary, and lessons learned. The style should be formal and professional, and it needs to include data charts." The task is to extract useful structured information from this unstructured description as the basis for subsequent template matching.

[0116] A series of natural language processing (NLP) techniques are employed to analyze user input. First, word segmentation is performed, dividing continuous text into meaningful word units. Then, part-of-speech tagging is performed to identify the grammatical role of each word (e.g., noun, verb, adjective, etc.). Next, named entity recognition is performed to identify specific categories of entities in the text (e.g., document type, technical terms, time expressions, etc.). Finally, semantic analysis is performed to understand the overall meaning of the sentence and the logical relationships between its parts. These processes utilize advanced NLP models, such as BERT, SpaCy, or custom domain-specific models, to ensure accurate understanding of domain-specific terminology and expressions in professional documents.

[0117] Based on language analysis results, three key features are extracted: document type features, content features, and style features. Document type features represent the basic category and purpose of the document, such as "project report," "contract agreement," and "meeting minutes." Keyword matching and classification models are used to identify the document types mentioned in user requirements and map them to a predefined document type system. Content features represent the main content elements and logical structure that the document needs to include, such as chapter composition, necessary content modules, and data requirements. Thematic modeling and content analysis are used to identify the content elements mentioned by users and understand their organizational relationships. Style features represent users' preferences for document format, layout, and visual presentation, such as "formal," "concise," and "illustrated." Sentiment analysis and style recognition algorithms are used to capture these preferences.

[0118] User requirements are transformed into high-dimensional feature vectors using feature extraction models (typically pre-trained language models such as BERT and RoBERTa). This process involves inputting text into the model, obtaining its hidden layer representations, and then using feature selection or dimensionality reduction to obtain a more compact representation. To enhance the expressive power of the features, user historical preferences, domain knowledge, and contextual information are also incorporated. Ultimately, a comprehensive document feature vector is formed, typically a fixed-dimensional vector (e.g., 768-dimensional), comprehensively expressing the user's document requirements and providing a foundation for subsequent template matching.

[0119] Step 302: Based on the document feature vector, the high-dimensional feature vector is mapped to the low-dimensional latent space through a variational autoencoder. Self-supervised learning training is performed using feature reconstruction task, document type prediction task and structural consistency task to obtain a low-dimensional neural manifold representation.

[0120] In this step, the high-dimensional document feature vector obtained in step 301 needs to be converted into a more compact and semantically rich low-dimensional representation in order to more effectively perform subsequent similarity calculations and template matching. This process employs a neural manifold learning method to map high-dimensional data to a well-structured low-dimensional manifold space.

[0121] First, we introduce a Variational Autoencoder (VAE) as the core tool for neural manifold learning. A VAE is a generative model consisting of an encoder and a decoder. The encoder maps a high-dimensional feature vector x to the distribution parameters (typically the mean μ and variance σ) of a low-dimensional latent space, and then samples the latent variable z from this distribution. The decoder then attempts to reconstruct the original feature vector x from the latent variable z. The VAE is trained using a combined loss function that minimizes the reconstruction error and the KL divergence (which makes the latent space distribution approximate a standard normal distribution). This design allows the VAE to not only learn a compact representation of the data but also capture the data's generation process and latent structure.

[0122] To improve the quality and semantic richness of neural manifold representations, a variety of self-supervised learning tasks were designed for joint training:

[0123] Feature reconstruction task: This is a fundamental task of VAEs. It involves randomly masking a portion of the feature vectors and then training the model to predict these masked features. This encourages the model to learn the intrinsic relationships between features, forming more robust representations. For example, if a user's request mentions "project report" and "executive summary," even if "executive summary" is masked, the model can infer from "project report" that the content element "executive summary" might be needed.

[0124] Document type prediction task: Predict the type category of a document based on partial features. This task requires the model to understand the essential characteristics of different document types and enhance the sensitivity of the representation to document type information. For example, the model needs to learn to identify which feature combinations are more likely to correspond to "technical report" rather than "business proposal".

[0125] Structural consistency task: Determine whether two document features originate from documents of the same type or similar structure. This comparative task helps the model learn a similarity metric for document structures, which is particularly important for subsequent template matching. For example, the model needs to understand the structural similarities between "project progress report" and "project summary report," even though their specific content and purpose differ.

[0126] These self-supervised tasks do not require additional labeled data; instead, they leverage the structural characteristics of the data itself to design prediction targets, enabling the model to learn useful representations from large amounts of unlabeled document data. A multi-task learning framework is employed, weighting and combining the loss functions of these tasks to jointly optimize the neural manifold representation.

[0127] This method maps the original high-dimensional feature vectors (which may have hundreds or thousands of dimensions) to a low-dimensional latent space (typically 32-128 dimensions). This low-dimensional space not only reduces computational complexity, but more importantly, it forms a structured neural manifold where representations of similar documents are close to each other, while representations of different documents are far apart. This representation can effectively capture the essential features of documents, including type, structure, content organization, and stylistic characteristics, providing ideal feature representations for subsequent comparative learning and template matching.

[0128] The variational autoencoder (VAE) used in this invention has the following architecture: The encoder consists of a 4-layer fully connected network with hidden layer dimensions of 512, 256, 128, and 64, respectively. Each layer is followed by a LeakyReLU activation function and Dropout (0.2). Finally, the output layer generates the mean μ and log-variance logσ of the latent space. 2The latent space dimension is set to 64 dimensions. The decoder also consists of a 4-layer fully connected network with a structure symmetrical to the encoder, used to reconstruct the latent variables into the original feature vectors.

[0129] The VAE is trained using a combination of reconstruction loss (using mean squared error, MSE) and KL divergence loss, with the KL divergence term adjusted with a weight of 0.01 to balance reconstruction quality and the regularity of the latent space. For the self-supervised learning task, the feature reconstruction task randomly masks 30% of the feature dimensions; the document type prediction task uses cross-entropy loss; and the structural consistency task uses contrastive loss, with the temperature parameter for positive sample pairs set to 0.1. The losses for the three tasks are combined in a ratio of 0.4:0.3:0.3.

[0130] Step 303: For the low-dimensional neural manifold representation, construct positive and negative sample pairs using a contrastive learning framework, calculate the similarity between user needs and each template in the hierarchical document structure knowledge base using the InfoNCE loss function, and generate a similarity ranking list.

[0131] The goal of this step is to calculate the similarity between user needs and document templates in the knowledge base based on the low-dimensional neural manifold representation obtained in step 302, and to find the most matching template candidate. A contrastive learning method is employed, a powerful self-supervised learning technique particularly suitable for similarity calculation and matching tasks.

[0132] First, a training framework for contrastive learning is constructed. The core idea of ​​contrastive learning is to group semantically similar samples close together in the representation space, while dissimilar samples are kept apart. A contrastive learning architecture similar to SimCLR or MoCo is adopted, but specifically optimized for document matching tasks. During the training phase, positive and negative sample pairs need to be constructed: positive sample pairs refer to document feature pairs of the same type, similar structure, or function, such as two project reports with similar structures; negative sample pairs refer to document feature pairs of different types with significant structural differences, such as a project report and a contract agreement.

[0133] The contrastive learning model is trained using the InfoNCE (Noise-Contrastive Estimation) loss function. This loss function aims to maximize the similarity between positive sample pairs while minimizing the similarity between negative sample pairs. Specifically, for an anchor sample (such as a user request), the InfoNCE loss calculates the log-likelihood ratio of its similarity to a positive sample relative to its similarity to multiple negative samples. This prompts the model to learn a highly discriminative similarity metric, effectively distinguishing between relevant and irrelevant document templates.

[0134] In practical similarity calculation, the neural manifold representation of the user's request is compared with the neural manifold representations of all document templates in the knowledge base. Multiple similarity calculation methods are employed, combining their advantages:

[0135] Cosine similarity: Calculates the cosine of the angle between two vectors, ranging from -1 to 1. A larger value indicates greater similarity. Cosine similarity is not sensitive to the length of the vectors and mainly focuses on the consistency of direction, making it suitable for capturing thematic similarity of document features.

[0136] Euclidean distance: Calculates the straight-line distance between two vectors in Euclidean space. The smaller the value, the more similar they are. Euclidean distance takes into account the absolute differences between vectors and is suitable for measuring the overall degree of difference in feature space.

[0137] Semantic similarity: The semantic similarity between two document features is calculated using a specially trained semantic matching network. This method can capture more complex semantic relationships, especially for matching document structure and function.

[0138] These similarity metrics are combined using a weighted fusion approach to obtain the final similarity score. The weights can be determined through performance tuning on the validation set or adaptively adjusted using meta-learning methods. To improve computational efficiency, especially when the knowledge base is large, a lightweight similarity calculation method (such as cosine similarity) may be used for preliminary screening to obtain Top-N candidates, and then a more complex semantic similarity model is used to perform precise calculations on these candidates.

[0139] Finally, based on the calculated similarity scores, all templates in the knowledge base are sorted to generate a similarity ranking list. This list includes each template's ID, similarity score, key matching aspects (such as structural similarity and content coverage), and preliminary matching rationale, providing foundational data for the next step of multi-dimensional scoring. This contrastive learning-based similarity calculation method effectively identifies candidate document templates that best match user needs, laying the groundwork for subsequent refined scoring and selection.

[0140] Step 304: Based on the candidate templates in the similarity ranking list, score them from the dimensions of structural adaptability, content completeness, style compatibility, domain relevance and usage frequency, and calculate the individual matching score for each dimension.

[0141] In this step, the candidate templates in the similarity ranking list obtained in step 303 are scored in multiple dimensions to comprehensively evaluate the degree of matching between each template and user needs. This multi-dimensional scoring goes beyond simple overall similarity calculation, and can analyze the applicability of templates from different perspectives, providing a more accurate basis for template selection.

[0142] The scoring system is designed with five key dimensions, each focusing on a different aspect of document matching:

[0143] Structural Adaptability: This dimension assesses whether the template's structural framework matches the document structure required by the user, including chapter division, logical organization, and hierarchical relationships. Structural similarity is calculated by comparing the template's tree structure with the expected tree structure extracted from the user's requirements. Specific methods include tree edit distance calculation (measuring the minimum number of operations required to transform one tree into another), structural overlap analysis (calculating the proportion of common structural elements), and hierarchical matching evaluation (checking the correspondence between chapter levels). For example, if a user requires a research report with a "Background-Methods-Results-Discussion" structure, each candidate template will be evaluated for including these chapters, and their organizational order will be checked for rationality.

[0144] Content Completeness: This dimension assesses whether the template contains all the content elements the user needs and whether their organization is reasonable. A list of key content elements is extracted from the user's needs, and then each template's coverage of these elements is checked. The scoring considers content element coverage (the proportion of necessary elements included in the template), element placement appropriateness (whether the content elements are appropriately positioned within the document), and content expansion space (whether the template provides sufficient space for each content element). For example, if the user needs to include a "risk assessment" section in a project report, the template will be checked for the existence of a corresponding chapter, and whether the chapter's placement and length are appropriate.

[0145] Style Compatibility: This dimension assesses whether the template's visual style (such as font, typography, formatting, etc.) aligns with user preferences. It involves analyzing style-related statements in user requirements, extracting user style preferences, and then matching them with the template's style characteristics. Scoring considers font style matching (formal / informal, traditional / modern, etc.), typography consistency (simple / complex, conservative / innovative, etc.), and the use of visual elements (charts, colors, decorative elements, etc.). For example, if a user expresses a preference for a "simple and professional" style, the assessment will evaluate whether the template uses clear typography, adequate white space, and professional font selection.

[0146] Domain Relevance: This dimension assesses whether the template matches the domain of the user's document and whether the terminology and expressions are appropriate. It identifies domain indicators in the user's needs (such as industry terminology, professional background, etc.), determines the target domain of the document, and then assesses the template's domain applicability. The scoring considers terminology matching (whether the professional terminology used in the template conforms to domain conventions), expression suitability (whether the language style conforms to domain conventions), and structural conformity (whether the organizational structure conforms to domain standards). For example, for legal documents, the assessment evaluates whether the template contains appropriate legal terminology, citation formats, and structural arrangement.

[0147] Usage Frequency: This dimension considers the template's historical usage and user reviews, reflecting its universality and practicality. Historical application data is analyzed, including usage frequency, user ratings, and success stories. Ratings consider overall usage frequency (total number of times the template was selected), similar scenario usage rate (frequency of use under similar needs), and user satisfaction (post-use ratings). This dimension helps identify templates that have proven effective in practice, especially when other dimensions have similar ratings.

[0148] Specific scoring algorithms and standards are designed for each dimension, typically using a standardized score range of 0-1 or 0-10 to facilitate subsequent weighted fusion. The scoring process may combine rules and machine learning methods: for well-defined dimensions such as structural adaptability, rule-based scoring standards are used; for more subjective dimensions such as style compatibility, trained machine learning models may be used for evaluation. The scoring results include not only numerical scores but also the reasons for the scores and key considerations, improving the interpretability of the scoring process.

[0149] Step 305: For the individual matching scores of each dimension, a machine learning model is used to perform weighted fusion, and the scores are personalized by using a collaborative filtering algorithm combined with the user's historical preferences, and the optimal document template and matching score report are output.

[0150] In this step, the multi-dimensional scoring results obtained in step 304 are combined to derive the final matching score, and the optimal document template is selected. This process is not a simple score averaging, but rather takes into account the relative importance of different dimensions, user personalized preferences, and the specific needs of the application scenario.

[0151] First, a machine learning model is used to weight and fuse the scores across multiple dimensions. Commonly used models include random forests, gradient boosting trees, or multi-layer neural networks. These models can learn complex non-linear relationships and are suitable for handling the fusion of multi-dimensional scores. The model is trained using historical matching data to learn the importance weights of each dimension in different scenarios. For example, in formal document generation scenarios, structural adaptability and style compatibility may have higher weights; while in technical report generation scenarios, content completeness and domain relevance may be more important. The model's training data comes from historical successful matching cases, including user requirement descriptions, selected templates, scores for each dimension, and final satisfaction ratings.

[0152] It also considers the interaction effects between different dimensions. Combinations of certain dimensions may produce synergistic or offsetting effects; for example, the combination of high structural adaptability and high content integrity may be more valuable than considering these two dimensions individually. Machine learning models can automatically learn these complex interactions, providing more accurate comprehensive scores.

[0153] Next, the ratings are personalized using a collaborative filtering algorithm, taking into account the user's historical preferences. Collaborative filtering is a commonly used technique in recommendation systems, making recommendations based on the behavioral patterns of similar users. It analyzes the current user's historical choices and preferences to identify user groups with similar preferences, and then adjusts the current rating by referencing these similar users' evaluations of different templates. For example, if a user has repeatedly chosen a concise report template, the matching score for similar style templates will be appropriately increased.

[0154] Content-based recommendation methods may also be employed, analyzing the characteristics of templates previously selected by users, identifying template attributes that users prefer (such as specific structural patterns, visual styles, or content organization methods), and then improving the ratings of templates with these attributes. This approach is particularly suitable for new users or situations with sparse data.

[0155] Finally, by combining the weighted scores from all dimensions and the results of personalized adjustments, a final matching score is calculated for each candidate template, and they are re-ranked from highest to lowest score. The template with the highest matching score is selected as the optimal document template, and a detailed matching score report is generated. This report not only includes the overall matching score but also details the scoring for each dimension, the main advantages of the matching, potential shortcomings, and the reasons for selection. The report uses intuitive visualizations (such as radar charts and bar charts) to present the scoring results and provides textual explanations to help users understand the basis for template selection.

[0156] If the matching scores of multiple templates are very close (e.g., the difference is less than a preset threshold), these templates may be provided as alternatives, with their respective advantages and features explained, allowing the user to make a final choice. This approach utilizes the system's intelligent matching capabilities while preserving the user's decision-making autonomy.

[0157] By comprehensively considering multi-dimensional scoring, learning from historical data, and personalized adjustments, this approach can provide users with document templates that best suit their needs, greatly improving the quality and efficiency of document generation.

[0158] Figure 2 A flowchart illustrating the intelligent document content generation method based on a diffusion model provided in this embodiment of the invention. Figure 2 As shown, step 103 specifically includes:

[0159] Step 401: Perform structured parsing on the optimal document template, identify explicit and implicit slots, and analyze the content type, length limit, format requirements, semantic constraints and dependencies of each slot to generate a content generation task list.

[0160] In this step, the optimal document template obtained in step 102 is first subjected to in-depth structured parsing, converting it into a hierarchical tree structure representation that can be processed by a computer. This representation clearly shows the document's organizational structure, including the nesting relationships and hierarchical structure between elements such as chapters, paragraphs, lists, and tables. A depth-first search algorithm is used to recursively identify and classify each structural element, starting from the root node of the document, to construct a complete document structure tree. For example, a standard report may be parsed into a tree structure containing main chapters such as "Abstract," "Introduction," "Methods," "Results," and "Discussion," with each chapter potentially containing multiple sub-chapter sections, paragraphs, or other elements.

[0161] Next, the slots that need to be filled with actual content are identified in the parsed template. These slots are reserved in the document template and are the locations where actual content needs to be filled in according to the specific situation. Two methods are used to identify the slots: for explicit slots, that is, content locations clearly marked in the template (such as "{Project Name}", "[Person in Charge]", etc.), regular expressions and pattern matching techniques are used for identification; for implicit slots, that is, locations that are not clearly marked but need to be filled with content, sequence labeling models (such as BiLSTM-CRF or BERT-based sequence labeling models) are used for identification. These models have been trained on a large number of document samples and can accurately identify the locations that need to be filled with content, even if these locations are not clearly marked.

[0162] For each identified slot, its attributes and constraints are further analyzed to generate content that meets the requirements. The analyzed attributes include: content type (e.g., text, number, date, enumerated value, chart, etc.) to determine the type of content that should be filled in the slot; length limits, specifying the minimum and maximum number of characters or words to avoid content that is too long or too short; formatting requirements, such as date format (YYYY-MM-DD), currency format, specific number representation methods, etc.; semantic constraints, such as semantic rules that content must conform to, such as "project names must be noun phrases" and "the conclusion section should contain evaluative language"; and dependencies, i.e., logical connections with other slots, such as "the end date must be later than the start date" and "the summary section should be based on the main findings mentioned above."

[0163] It also analyzes the relationships and dependencies between slots and constructs a slot dependency graph. This graph represents the logical relationships and filling order between different slots, ensuring that the correct dependency chain is followed when generating content. For example, the content of the "Project Summary" slot should be based on the content of the "Project Goals" and "Project Results" slots. Therefore, in the dependency graph, the "Project Summary" node will have edges pointing to the "Project Goals" and "Project Results" nodes, indicating that the content of these two slots should be generated before the content of "Project Summary" is generated.

[0164] Finally, the identified slots, their attributes, constraints, and dependencies are organized into a content generation task list. This list is ordered according to logical dependencies and importance, ensuring that the content generation process follows a logical sequence. Each task includes a slot ID and location information (the exact location in the document), content type and constraints (the various attributes analyzed above), reference context information (surrounding fixed content and related slots), generation priority (determined based on dependencies and importance), and expected output format (the format specifications the content should conform to). This task list provides clear guidance and constraints for subsequent content planning and generation.

[0165] Step 402: Based on the content, generate a task list, combine user content data, determine content filling strategies such as direct filling, adaptive adjustment, automatic generation, and hybrid filling, and formulate a content generation plan.

[0166] In this step, it is necessary to analyze the content data provided by the user and match it with the content generation task list generated in step 401 to determine the optimal filling strategy for each slot. The content provided by the user may exist in various forms, including text descriptions, structured data (such as tables, database records), reference documents (such as previous reports, related materials), etc. First, these diverse contents need to be processed and understood uniformly.

[0167] Multimodal content parsing technology is employed to process various types of user-provided content. For text content, natural language processing techniques are used for word segmentation, syntactic analysis, semantic understanding, and topic extraction to identify key information and logical structures within the text. For structured data, field names, data types, value ranges, and relational patterns are extracted to understand the data's organization and meaning. For reference documents, document analysis techniques are used to identify document structure and extract relevant paragraphs and key information points. Through these processes, different forms of user content are transformed into a unified internal representation, facilitating matching with slots.

[0168] Next, the user-provided content is matched with slots in the content generation task list to determine which slots can be directly filled with user-provided content. The matching process is based on content-slot relevance calculations, considering multiple aspects: semantic relevance, using a semantic similarity model (such as a BERT-based semantic matching model) to calculate the relevance between the content and the slot description; structural fit, assessing whether the content's structure (e.g., paragraph organization, logical relationships) meets the slot's structural requirements; and constraint satisfaction, checking whether the content meets the slot's constraints (e.g., length, format, semantic requirements). A matching score is calculated for each content-slot pair, and a successful match is determined based on a preset threshold.

[0169] Based on the matching results and slot characteristics, the most suitable content filling strategy is determined for each slot. These strategies are divided into four categories: direct filling, suitable for user-provided content that perfectly matches the slot and can be used directly without modification; adaptive adjustment, suitable for user content that basically matches the slot but requires formatting adjustments, rewriting, or expansion, such as adjusting paragraph length, standardizing expression, or supplementing necessary information; automatic generation, suitable for slots where the user has not provided relevant content or the provided content is completely mismatched, requiring automatic generation based on context and other relevant content; and hybrid filling, which combines user-provided partial content with automatically generated content to fill the slot, suitable for situations where the user has provided some relevant information but it is incomplete.

[0170] It also considers content generation dependencies, constructing a directed acyclic graph (DAG) for content generation. This graph represents the order and dependencies of content generation in different slots, ensuring that all dependent slot content is ready before a particular slot's content is generated. For example, the "Execution Summary" should only be generated after all major content has been generated, as it needs to summarize the key points of the entire document; while "Project Background" may need to be generated before "Project Implementation," because the latter may reference the former's content.

[0171] Finally, a detailed content generation plan was developed, including the following elements: the filling strategy and priority for each slot, specifying the filling strategy and order for each slot; the generation order and dependencies, based on the content generation process determined by the Directed Acyclic Graph (DAG); the input parameters and constraints for each automated generation task, including reference content, contextual information, and style requirements; generation quality control standards, defining the quality requirements and evaluation criteria for content generation; and the estimated computational resource requirements, forecasting the time and computational resources needed to complete all generation tasks. This plan provides a clear roadmap and quality assurance for the subsequent content generation process.

[0172] Step 403: According to the content generation plan, a diffusion model based on a pre-trained language model is used, combined with stylistic adaptation, structure awareness, domain knowledge injection and context consistency control mechanisms, to obtain the optimized document content.

[0173] In this step, high-quality document content is generated using a diffusion model based on the content generation plan established in step 402. The diffusion model is a powerful generative model whose core idea is to generate new samples by progressively adding noise to the data and then learning the reverse process (denoising). Compared to traditional generative models, the diffusion model has significant advantages in terms of generation quality, diversity, and stability, and is particularly suitable for generating structured, formatted text content.

[0174] The diffusion model architecture consists of three main parts: First, the base language model, which uses pre-trained large-scale language models (such as T5, GPT series, BERT, etc.) as the foundation. These models are pre-trained on massive amounts of text data and have powerful language understanding and generation capabilities. Second, the diffusion layer adds a diffusion process on top of the language model, including two processes: forward diffusion (gradually adding noise to the text representation) and backward diffusion (gradually removing noise and restoring or generating text). Finally, the conditional control mechanism introduces multiple conditional signals to guide the generation process and ensure that the generated content meets specific requirements.

[0175] To address the characteristics of document content generation, four key optimizations were implemented in the diffusion model: style adaptation optimization, which uses style transfer technology and style condition control to ensure that the generated content conforms to the language style of specific document types, such as the rigor and objectivity of formal documents, the conciseness and professionalism of business reports, or the rigorous logic of academic papers; structure awareness optimization, which introduces structure control signals and hierarchical constraints to ensure that the generated content meets the structural requirements of documents, such as chapter organization, paragraph division, and logical flow; domain knowledge injection, which integrates domain knowledge graphs and professional terminology databases to ensure that the generated content contains correct professional terminology, conceptual relationships, and domain common sense, thereby improving the professionalism and accuracy of the content; and contextual consistency control, which designs long-range dependency modeling mechanisms and global consistency checks to maintain the coherence and consistency between different parts of the document and avoid inconsistencies or redundancy.

[0176] During the actual generation process, each slot requiring content generation is processed sequentially according to the order determined in the content generation plan. For each slot, fine-grained conditional controls are provided: content description, i.e., the basic description and requirements of the slot, guiding the theme and scope of the generated content; contextual information, including existing content surrounding the slot and content from related slots, providing background and reference for generation; relevant user-provided content, if there is user input related to the slot, it is used as an important reference and constraint for generation; style guidelines, specifying desired language style parameters, such as formality, professional depth, and expression; and structural constraints, specifying the organizational structure requirements of the content, such as paragraph division, key point arrangement, and argumentation methods.

[0177] A multi-step iterative generation strategy is employed, especially for longer paragraphs. First, a rough outline or synopsis is generated to identify key points and logical structure. Then, based on this outline, each part is gradually refined and expanded to generate the complete content. Finally, polishing and optimization are performed, adjusting the expression, refining details, and ensuring overall coherence. This coarse-to-fine generation method helps maintain the logic and structure of long texts, avoiding deviations or omissions during the generation process.

[0178] Different processing methods are used for slots with different filling strategies: For direct-fill slots, the user-provided content is used directly, with only necessary formatting adjustments; for adaptive slots, the user content is used as a basis, and the content is rewritten, expanded, or compressed through a diffusion model to better meet the slot requirements; for automatically generated slots, the diffusion model is relied upon entirely to generate entirely new content based on context and condition control; for hybrid-fill slots, the user content is used as part of the generation conditions and constraints, combined with the automatically generated content to form a complete fill.

[0179] This content generation method, based on a diffusion model, can produce high-quality document content that conforms to stylistic features while maintaining consistency with user-provided content and contextual coherence. The generated content not only fills in the blanks in the template but also preserves the professionalism, formality, and logic expected of a document, laying a solid foundation for subsequent quality assessment and optimization.

[0180] The specific architecture of the diffusion model used in this invention is as follows: The basic language model adopts a pre-trained T5-large model (containing a 24-layer Transformer encoder and a 24-layer Transformer decoder, with a hidden layer dimension of 1024 and a total of approximately 770 million parameters). The diffusion layer is designed as a 10-step diffusion process, employing a cosine noise schedule to smooth the noise addition process. The conditional control mechanism is implemented through a cross-attention mechanism, allowing the conditional signal to guide the generation direction at each step of the diffusion process.

[0181] The training data for the diffusion model comes from three parts: (1) a publicly available dataset of formatted documents, containing approximately 500,000 documents of various types, including official documents and reports; (2) internally collected industry-specific documents, categorized by field, totaling approximately 300,000 documents; and (3) synthetic document samples, generated through rules and existing models, totaling approximately 200,000 documents. After cleaning, deduplication, and format standardization, the training data ultimately yielded approximately 900,000 high-quality training samples.

[0182] The training process used the following hyperparameter settings: batch size of 32, Adam optimizer, initial learning rate of 3e-5 with linear decay, and a total of 20 training epochs. The loss function was a weighted combination, including noise prediction loss (weight 0.8), content relevance loss (weight 0.1), and style consistency loss (weight 0.1). Training was performed on eight V100 GPUs, with a total training time of approximately seven days.

[0183] Step 404: For the document content generated by the diffusion model, calculate the multi-dimensional loss function of semantic loss, style loss, structural loss, coherence loss and professionalism loss to obtain the actual loss value of each dimension.

[0184] In this step, a comprehensive quality assessment is performed on the document content generated in step 403. A multi-dimensional loss function is calculated to quantify the content quality, providing precise guidance for subsequent optimization. Unlike traditional generative model evaluation methods (such as BLEU and ROUGE, which primarily focus on similarity to the reference text), this method employs the optimal loss estimation method, focusing on analyzing the gap between the generated content and the ideal content across multiple dimensions.

[0185] Five key dimensions of loss functions were designed to comprehensively evaluate content quality: First, semantic loss assesses the semantic accuracy and relevance of the content, i.e., whether the generated content accurately expresses the expected meaning and conforms to the slot requirements and context. Semantic similarity models (such as BERT-based semantic matching models) are used to calculate the semantic distance between the generated content and the expected content description, or information theory metrics such as cross-entropy are used to measure the relevance of the content to the topic. Ideally, the semantic loss should be close to zero, indicating that the content fully meets the semantic requirements.

[0186] Secondly, there's style loss, which assesses whether the content conforms to the target writing style, such as formality, professionalism, or objectivity. Style classifiers or style matching models are used to analyze the linguistic features of the generated content (such as vocabulary selection, sentence structure, and rhetorical devices) and calculate the degree of deviation from the target style. For example, for formal documents, the system checks for the use of informal expressions, colloquial vocabulary, or subjective emotional words, all of which increase style loss. Ideally, the style match should be above 0.9, indicating a high degree of stylistic compliance.

[0187] Thirdly, there is structural loss, which assesses the rationality of the content's organizational structure, including paragraph division, logical hierarchy, and argumentation structure. Using a structural analysis model, the structural elements of the generated content (such as introduction, arguments, evidence, and conclusion) and their organization are identified, and the difference from the expected structure is calculated. Structural loss considers structural integrity (whether all necessary structural elements are included), structural balance (whether the length of each part is reasonable), and structural logic (whether the arrangement of elements is logical). Ideally, the structural loss should be less than 0.3, indicating that the content structure basically meets the requirements.

[0188] Fourth is coherence loss, which assesses the logical coherence and consistency of the content, i.e., whether there are natural and smooth transitions and tight logical connections between different parts of the text. Using a coherence assessment model, factors such as the use of conjunctions between sentences and paragraphs, thematic coherence, and referential consistency are analyzed to calculate the degree of coherence. Coherence loss also considers global consistency, detecting whether there are logical contradictions, factual conflicts, or conceptual confusion in the text. Ideally, the coherence loss should be less than 0.4, indicating that the content has a good logical flow and coherence.

[0189] Fifth is professionalism loss, which assesses the accuracy and depth of the content within the professional field, including the use of professional terminology, explanation of concepts, and application of theories. Using a domain knowledge base and a professionalism assessment model, it checks whether the generated content uses professional terminology accurately, explains professional concepts clearly, and applies professional knowledge appropriately. Professionalism loss also considers the information density and knowledge depth of the content to assess whether the expected level of professionalism has been achieved. Ideally, professionalism loss should be less than 0.5, indicating that the content possesses sufficient professionalism and accuracy.

[0190] For each dimension of loss, corresponding ideal values ​​and acceptable thresholds are set. The ideal value represents the best performance in that dimension, usually close to zero or a specific value; the acceptable threshold is the maximum loss value that is acceptable in practical applications, exceeding this threshold means that optimization is needed in that dimension. For example, the ideal value for semantic loss is close to 0, and the acceptable threshold is 0.5; the ideal value for style loss is 0.1 (indicating almost perfect conformity to the target style), and the acceptable threshold is 0.3; and so on.

[0191] The generated content is evaluated using these loss functions, calculating the actual loss values ​​for each dimension and comparing them with ideal values ​​and thresholds. The evaluation process employs a sliding window technique, assessing not only the overall content quality but also the quality of individual paragraphs and sentences. The text is divided into multiple overlapping windows (which may be sentences, paragraphs, or fixed-length text segments), and the loss value is calculated for each window. These results are then aggregated to obtain the overall evaluation. This method can identify weak areas in the content—those with particularly high loss values—providing a basis for subsequent targeted optimization.

[0192] Step 405: Based on the comparison between the actual loss values ​​of each dimension and the preset ideal values ​​and acceptable thresholds, the sliding window technique is used to conduct a quality assessment of the overall content and local paragraphs, and a quality diagnostic report is generated that includes an overall quality score, dimension subdivision scores, problem area identification, and improvement suggestions.

[0193] In this step, based on the loss values ​​for each dimension calculated in step 404, a comprehensive and detailed quality diagnostic report is generated, providing precise guidance for subsequent content optimization. The quality diagnostic report not only quantifies content quality but also identifies specific problems and provides targeted improvement suggestions, serving as a crucial basis for iterative content optimization.

[0194] First, calculate the overall quality score, which is a comprehensive evaluation of the overall quality of the document content. The overall score is obtained by weighted fusion of the loss values ​​of each dimension, with the weights reflecting the relative importance of different dimensions in a specific document type. For example, for a scientific report, professionalism and structure may have higher weights; while for promotional materials, style and coherence may be more important. The overall score is calculated using the following formula: Overall Score = 100 - Σ(Wi × Li), where Wi is the weight of dimension i, and Li is the standardized loss value of dimension i. The overall score calculated in this way is usually between 0 and 100, with higher scores indicating better quality. The overall score is also mapped to quality levels (such as excellent, good, average, need improvement, etc.) to provide an intuitive quality judgment.

[0195] Next, detailed scores are generated for each dimension, providing a comprehensive overview of the evaluation results for each quality dimension. For each dimension (semantic, style, structure, coherence, professionalism), the following information is provided: a dimension score, a standardized score calculated based on the dimension's loss value; relative performance, comparing its strength to other dimensions; a problem overview, outlining the main types of problems present in that dimension; and room for improvement, indicating the gap from the ideal value and the potential for enhancement. These detailed scores help users understand the content's performance in different aspects and identify key areas for improvement.

[0196] It also uses a sliding window technique to identify problem areas, i.e., specific locations in the content with low quality. The text is divided into multiple overlapping windows (which could be sentences, paragraphs, or fixed-length text segments). A loss value is calculated for each window, and windows with loss values ​​significantly higher than the average are identified as problem areas. For each problem area, the following information is provided: location identifier (the exact location of the problem area in the document); problem type (the main problem dimensions present in the area, such as semantic ambiguity, inconsistent style, structural disorder, etc.); severity (a severity rating of the problem, such as mild, moderate, severe, etc.); and detailed description (a detailed description and specific manifestations of the problem). This precise problem identification allows subsequent optimization to be targeted, focusing on addressing the most prominent issues.

[0197] Based on problem analysis, specific improvement suggestions are provided for each problem area. These suggestions are not general but practical guidance tailored to specific problems, including: rewriting direction, suggesting how to adjust or rewrite the problem content; reference expressions, providing compliant expressions or sentence structures; key elements, pointing out the key information points that should be retained or emphasized during rewriting; and things to avoid, reminding you of common mistakes to avoid during the revision process. Reference examples, i.e., high-quality similar content snippets, may also be provided as references and inspiration for rewriting.

[0198] Visualization techniques are also used to intuitively display diagnostic results, helping to better understand the content quality status. Commonly used visualization methods include: heatmaps, which use color to show the quality distribution of different parts of the document, with red indicating areas with serious problems and green indicating areas with good quality; radar charts, which display the scores of each quality dimension from multiple perspectives, forming an intuitive quality profile; problem distribution charts, which show the distribution and concentration of different types of problems in the document; and improvement priority charts, which show the optimization priority of each problem area based on the severity of the problem and the difficulty of improvement.

[0199] Finally, summary recommendations are generated to provide overall guidance for content optimization. These recommendations include: key strengths (the content's most prominent advantages and aspects that should be maintained); critical weaknesses (the areas most in need of improvement and priority issues to address); optimization strategies (the recommended overall optimization approach and methods); and expected results (the anticipated quality level after implementing the recommended optimizations). These summary recommendations help users understand the overall direction and expected goals of optimization, providing strategic guidance for subsequent iterative optimization.

[0200] This comprehensive, detailed, and visualized quality diagnostic report not only assesses the quality of generated content but also identifies specific problems and provides targeted improvement suggestions, offering clear guidance for further content optimization. This optimization approach based on detailed diagnosis significantly improves the quality and usability of the final content.

[0201] Step 406: Based on the severity of the quality diagnostic report, adopt a graded strategy of mild optimization, moderate optimization and severe optimization, and perform iterative optimization through a targeted control generation method to output the iterated document content.

[0202] In this step, the document content is iteratively optimized in a targeted manner based on the quality diagnostic report generated in step 405. Unlike the initial generation, the optimization process more precisely targets the identified problems, employing a hierarchical strategy and targeted control methods to ensure significant and efficient optimization results.

[0203] First, based on the severity of the issues in the quality diagnostic report, the content requiring optimization is divided into three levels, and corresponding optimization strategies are developed for each level: Light optimization, suitable for content with minor issues, such as minor style adjustments, polishing of expression, and supplementation of details. This type of optimization mainly focuses on improving the expression method and does not involve substantial changes to the content. For example, adjusting sentence structure to better conform to the writing style, replacing unprofessional vocabulary, optimizing punctuation usage, or adjusting paragraph length for better balance. Light optimization can usually be accomplished through simple text processing or shallow generation models, requiring less computational resources and carrying less optimization risk.

[0204] Moderate optimization is suitable for content sections with obvious problems but whose basic framework can be retained, such as unclear logic, unreasonable structure, or insufficient professionalism. This type of optimization requires partial restructuring or significant enhancement of the content, but not a complete rewrite. For example, rearranging paragraph order to make the argument clearer, adding necessary professional explanations and supporting evidence, adjusting the focus of the content to better align with the document's purpose, or enhancing the coherence and transitions between paragraphs. Moderate optimization typically requires the use of more complex generative models and the provision of more control parameters and contextual information.

[0205] Heavy optimization is suitable for sections with serious problems requiring complete rewriting, such as content errors, severe lack of professionalism, or inconsistencies with the overall document's purpose. This type of optimization essentially regenerates the content, but unlike initial generation, heavy optimization more explicitly specifies the generation direction and requirements, avoiding previous issues. For example, it might completely rewrite technical descriptions that don't meet professional standards, reconstruct logically flawed arguments, or replace paragraphs that severely clash with the document's overall style. Heavy optimization requires the most powerful generation model and provides comprehensive control signals and constraints.

[0206] For each section requiring optimization, a targeted generation method is employed. Unlike the initial generation in step 403, the optimization process more precisely controls the generation direction, ensuring that the newly generated content addresses the identified problems. The following control signals guide the optimization process: retention constraints explicitly specify the key information and expressions that must be retained, ensuring that important content is not lost during optimization. For example, when optimizing a project description, information such as the project name, key objectives, and core technologies must be retained, even if the expression may change.

[0207] The revision guidelines provide specific directions and requirements for improvement, clearly indicating the aspects that need to be improved and the desired effects. For example, "enhance the professionalism of paragraphs by adding more technical details," "simplify expression and reduce redundant content," or "adjust the tone to make it more objective and neutral." Reference examples provide high-quality reference expressions or content excerpts as learning objects and target templates for optimization. These examples may be drawn from excellent sections of similar documents or generated demonstration content that meets specific requirements. Error avoidance clearly identifies errors and problems that need to be avoided to prevent the introduction of new problems or the repetition of existing ones during the optimization process. For example, avoid using specific unprofessional terminology, avoid overly complex sentence structures, or avoid subjective evaluative language.

[0208] An iterative optimization strategy is employed, with quality assessments performed after each optimization until the content quality reaches a preset standard or the maximum number of iterations is reached. Specifically, the problem areas identified in the diagnostic report are first optimized to generate a new content version. Then, a quality assessment is applied again to the new version, calculating the loss values ​​for each dimension. If the loss values ​​for some dimensions still exceed acceptable thresholds, a next round of optimization is performed, focusing on the remaining problematic aspects. This process continues until the loss values ​​for all dimensions are reduced to an acceptable range, or the preset maximum number of iterations (usually 3-5 times) is reached.

[0209] To improve optimization efficiency, parallel optimization is employed for different parts, processing multiple content paragraphs requiring improvement simultaneously. This parallel processing is particularly suitable for independent content sections, such as different chapters or relatively independent paragraphs. For content sections with dependencies, optimization is performed in the order of dependency, ensuring that the optimization results of preceding content correctly influence the optimization process of subsequent content.

[0210] This iterative optimization method, employing a tiered strategy and targeted control, efficiently addresses various issues within the content, significantly improving content quality while maintaining its core information and overall structure. The optimized content retains the original key information and document characteristics, while also showing significant improvements in expression quality, professionalism, and coherence.

[0211] Step 407: Perform terminology consistency, data consistency, tense consistency, person consistency, and format consistency checks on the iterated document content to obtain the consistency check results.

[0212] In this step, a comprehensive consistency check is performed on the optimized document content from step 406 to ensure that the entire document remains consistent across all aspects, avoiding overall inconsistencies caused by localized optimizations. Consistency is a crucial characteristic of high-quality documents, directly impacting the reader's comprehension and professional impression.

[0213] First, a terminology consistency check is performed to ensure that the same concept is expressed using consistent terminology throughout the document. A terminology extraction algorithm is used to identify technical terms and key concepts in the document, and then all occurrences of each term are checked to ensure consistent expression. For example, if the term "artificial intelligence" is used in the beginning of the document, it should not suddenly change to "AI" or "intelligent" in later sections; if a specific product name or technical standard is used, its spelling and formatting should remain consistent throughout the document. The consistency of abbreviations is also checked to ensure that a complete explanation is given upon its first appearance, and that consistent abbreviations are used thereafter.

[0214] Next, a data consistency check is performed to ensure that data references and numerical representations throughout the document are consistent, avoiding contradictions or confusion. All numerical data, statistics, and quantitative statements in the document are extracted and checked for inconsistencies. For example, if one section mentions "project completion rate is 85%" while another section states "project completion rate is 82%", this is data inconsistency; if the report mentions "a total of 5 sub-items" in the beginning but lists 6 sub-items later, this is also data inconsistency. The consistency of data representation formats is also checked, such as the number of decimal places, the use of thousands separators, and the percentage representation method, ensuring that the entire document uses a consistent data representation standard.

[0215] Furthermore, tense consistency checks are performed to ensure that the document maintains consistency in tense usage and avoids unnecessary tense shifts. Natural language processing techniques are used to identify verb tenses in the text, analyze tense usage patterns, and detect inconsistent tense transitions. For example, past tense should be used consistently in reports describing completed projects; present tense should be used consistently in documents describing methods or procedures; and future tense should be used consistently in documents describing forecasts or plans for future events. Special attention is paid to tense consistency within paragraphs and between related paragraphs to ensure that tense changes are meaningful and not arbitrary or confusing.

[0216] Person consistency checks are another crucial aspect, ensuring consistency in the use of pronouns and perspectives throughout the document, especially in formal writing. Identify personal pronouns (such as "we," "the company," "this study," etc.) and narrative perspectives in the text, checking for inconsistencies. For example, formal reports should generally use either the third person ("the company," "the team") or the first-person plural ("we") consistently, avoiding mixing or sudden shifts to the second person ("you," "your company"). Also check for consistency in the use of passive and active voice to ensure stylistic coherence.

[0217] Finally, perform a format consistency check to ensure that the document follows unified specifications in terms of punctuation, abbreviations, number formats, list styles, etc. Check the use of punctuation marks (such as the mixing of Chinese and English punctuation, types of quotation marks, ellipsis formats, etc.), abbreviated forms (such as the use of "etc." and "等", "for example" and "如"), number representations (such as the rules for using Arabic numerals and Chinese numerals), list formats (such as numbering methods, indentation levels, punctuation usage, etc.), and other format elements. These seemingly minor format details can significantly affect the professionalism and consistency of the document when accumulated.

[0218] Use natural language processing techniques such as coreference resolution (identifying different expressions that refer to the same entity in the text), dependency analysis (analyzing the grammatical relationships between sentence components), and semantic role labeling (identifying the agents and patients in a sentence) to identify potential consistency problems. Also use statistical analysis methods to identify the dominant patterns in the document (such as the main tenses, persons, etc.) and anomalies that deviate from these patterns.

[0219] Organize the inspection results into a consistency inspection report, which details various consistency problems found, including problem types, locations, severity levels, and recommended correction methods. This report provides a detailed basis for the next steps of automatic correction and manual confirmation, ensuring that consistency problems can be resolved.

[0220] Step 408: Based on the consistency inspection results, use coreference resolution and dependency analysis techniques to identify potential problems, automatically correct simple errors, and mark complex problems to obtain the optimized document content.

[0221] In this step, based on the consistency inspection results in Step 407, perform final corrections and improvements on the document content to ensure that the content is not only of high quality locally but also consistent and coherent as a whole. Use intelligent methods to handle consistency problems, which can not only automatically correct simple errors but also mark complex problems that require manual attention.

[0222] First, use coreference resolution technology to identify the referential relationships in the text, that is, determine which different expressions actually refer to the same entity or concept. Coreference resolution is a key technology in natural language processing that helps to understand the semantic coherence in the text. For example, it can identify that "artificial intelligence technology", "AI", "this technology", and "this innovative method" may all refer to the same concept. By establishing the referential chains between these expressions, it is possible to more accurately identify inconsistent term usage and propose reasonable unification schemes.

[0223] Dependency analysis is also used to gain a deeper understanding of sentence structure and the relationships between its components. Dependency analysis represents a sentence as a dependency tree, where each word depends on another word (except the root node). This analysis helps to understand the sentence's grammatical structure, verb tenses, subject-verb relationships, etc., thus more accurately identifying issues such as tense inconsistency and person misuse. For example, by analyzing the dependency structure of the two phrases "the company carried out" and "we implemented," inconsistency in person use (switching from the third-person "the company" to the first-person "we") can be identified.

[0224] For identified consistency issues, different handling strategies are adopted based on the complexity of the problem and the certainty of the correction: automatic correction is suitable for simple problems with clearly defined correction solutions. For example, if 90% of the document uses "artificial intelligence" but only a few places use "AI," "AI" will be automatically replaced with "artificial intelligence"; if the document mainly uses the past tense to describe project activities, the few inconsistent present tense verbs will be automatically adjusted to the past tense form; if the document mainly uses Chinese numerals to represent chapter numbers, the format of all chapter numbers will be automatically standardized. This type of automatic correction usually has a high degree of certainty and low risk.

[0225] Suggested corrections are suitable for issues with multiple possible corrections or where context needs to be considered. They do not directly modify the content but instead provide suggested corrections and mark relevant locations. For example, if a document contains two different values ​​for "project completion rate," and it's impossible to automatically determine which is correct, both locations will be marked, and the data will be suggested for verification. Similarly, if a document uses a roughly even distribution of pronouns (e.g., half using "we" and half using "the company"), a consistent expression will be suggested, but the change will not be automatic. These suggested corrections require manual confirmation or selection of a final solution.

[0226] Manual review is used for major revisions that may affect the understanding of the content or require professional judgment. These issues will be clearly marked, and detailed descriptions and possible solutions will be provided, but specific revision suggestions will not be given. For example, if there are potential factual inconsistencies in the document (such as conflicting descriptions of the same technical principle in different chapters), these locations will be marked and a prompt will indicate that professional review is required; if the use of certain technical terms in the document may be inaccurate or inconsistent, this will also be marked and await professional confirmation. These types of issues typically involve substantive understanding of the content and require judgment from personnel with relevant domain knowledge.

[0227] Automatic correction is achieved using a style propagation algorithm. This algorithm, based on the document's structural hierarchy and dominant patterns, propagates correct or mainstream expressions to locations requiring correction. For example, if most chapter titles in a document use a specific format and expression, this pattern will be applied to inconsistent chapter titles; if a standard expression for a term is established in the early part of the document, it will ensure that the same concept is expressed consistently in subsequent occurrences. The style propagation algorithm considers the document's hierarchical structure, ensuring that corrections conform to the document's overall style and organizational logic.

[0228] Finally, the optimized document content is generated, along with a revision report detailing all automatically corrected content, suggested correction locations, and issues requiring manual confirmation. The revision report includes: revision statistics, outlining the number and handling status of different types of consistency issues; an automatic correction list, detailing all automatically performed modifications, including before-and-after comparisons; issues requiring confirmation, listing all issues requiring manual confirmation or decision-making, including issue descriptions, locations, and possible solutions; and a consistency score, quantifying the document's consistency level before and after revision, showcasing the optimization effect.

[0229] This intelligent consistency processing method ensures that the generated document content is not only of high quality in its parts, but also consistent and coherent overall, meeting the high standards required for professional documents. The final optimized document content retains the original core information and structural features, while achieving a comprehensive improvement in expression quality, professionalism, consistency, and coherence, laying a solid content foundation for subsequent typesetting adjustments.

[0230] Step 104 specifically includes:

[0231] Step 501: Perform a matching degree analysis on the optimized document content and the template structure of the optimal document template, calculate the overall structure matching degree, chapter-level matching degree, paragraph-level matching degree and element-level matching degree, identify content overflow, insufficient content, structural mismatch, special element conflict and pagination problem, and generate a priority list of typesetting tasks sorted by severity.

[0232] The goal of this step is to conduct an in-depth analysis of the matching between the completed document content and the template structure, identify potential layout conflicts, and lay the foundation for subsequent layout optimization. The specific implementation is as follows:

[0233] First, a precise comparative analysis of the document content and template structure is conducted. This process requires constructing two key representations:

[0234] Template structure representation: The original template structure is represented as a tree structure with spatial constraints and style rules;

[0235] Content filling means: organizing the generated content into a content tree according to its logic.

[0236] The two representations are compared using a structure matching algorithm to calculate the degree of matching between the content and the template at different levels:

[0237] Overall structure matching: Evaluate whether the overall document structure and organization conform to the template expectations;

[0238] Chapter-level matching: Evaluate whether the length and organization of each chapter's content match the template design;

[0239] Paragraph-level matching: Evaluate whether the number and length of paragraphs conform to the template plan;

[0240] Element-level matching: Evaluate whether the size and position of special elements such as tables, charts, and lists are suitable for the space reserved in the template.

[0241] In the matching analysis, special attention is paid to the following situations that may lead to layout problems:

[0242] Content overflow: The amount of content exceeds the space reserved in the template;

[0243] Insufficient content: The amount of content is significantly less than the template expected, resulting in a large number of blank spaces;

[0244] Structure mismatch: The hierarchical structure of the content is inconsistent with the template;

[0245] Special element conflict: The size or position of special elements such as charts and tables is not suitable for the template layout;

[0246] Pagination issues: Content distribution can lead to poor pagination, such as titles being at the bottom of the page or tables spanning multiple pages.

[0247] The severity of these problems is assessed using a combination of heuristic rules and machine learning models. For example, for content overflow problems, the degree of overflow (e.g., exceeding 10%, 50%, etc.) is calculated and the difficulty of adjustment is evaluated; for structural mismatch problems, the depth and scope of the mismatch are analyzed.

[0248] Based on the analysis results, a priority list of typesetting tasks is generated. This list is sorted according to the severity and scope of the problem, and each task includes:

[0249] Problem type and location;

[0250] Severity rating;

[0251] Suggested handling methods;

[0252] The expected difficulty of the adjustment;

[0253] Possible side effects.

[0254] Step 502: Based on the aforementioned typesetting task priority list, design reward partitions for the basic usable interval, the standard qualified interval, the professional and aesthetically pleasing interval, and the perfect presentation interval. For each interval, formulate strategies for text adjustment, space reallocation, content rearrangement, special element processing, and pagination optimization, and construct a typesetting decision tree.

[0255] The goal of this step is to break down the typesetting task into multiple reward ranges and design targeted typesetting optimization strategies for each range. The specific implementation is as follows:

[0256] First, we introduce the concept of reward partitioning. Reward partitioning refers to dividing the layout optimization target space into multiple intervals, each interval corresponding to a different level of layout optimization, and setting a corresponding reward value. This method originates from the field of reinforcement learning, and by decomposing the complex layout problem into multiple staged objectives, the optimization process becomes more controllable and efficient.

[0257] The following reward ranges have been designed:

[0258] Basic usable range: Ensures the document is basically readable, without serious formatting errors, and has a lower reward value;

[0259] Standardized acceptable range: Meets basic typesetting standards and template requirements, with a moderate reward value;

[0260] Professional and aesthetically pleasing section: Professional and aesthetically pleasing layout, reasonable space utilization, higher reward value;

[0261] Perfect Presentation Section: The layout is perfect, reaching the level of a professional publication, and the reward value is the highest.

[0262] For each typesetting task identified in step 501, analyze which reward range it belongs to and determine the corresponding optimization target. For example, for a severe content overflow problem, first ensure that it reaches the "basic usable range" before considering optimization for higher ranges.

[0263] Next, we will design a set of layout strategies for each reward range. These strategies include:

[0264] Text adjustment strategies: such as adjusting character spacing, changing line spacing, and fine-tuning font size;

[0265] Space redistribution strategies: such as margin adjustment, paragraph spacing optimization, etc.;

[0266] Content rearrangement strategies: such as paragraph reorganization, content simplification or expansion, etc.;

[0267] Special element handling strategies: such as table reset, image scaling and repositioning, etc.

[0268] Pagination optimization strategies include: such as setting forced pagination points and handling isolated lines.

[0269] A decision tree algorithm is used to construct a typesetting decision tree, which guides the selection of the most suitable strategy combination based on document characteristics and question type. The construction of the decision tree is based on machine learning results from expert knowledge and historical typesetting cases, and can handle various common and complex typesetting scenarios.

[0270] Each path in a decision tree represents a combination of layout strategies. The expected reward and execution cost are calculated for each path, helping to find a balance between effectiveness and resource consumption. For example, some high-reward strategies may require more computational resources or human intervention.

[0271] Step 503: Based on the layout decision tree, construct a state space that includes document structure status, content distribution, space utilization and aesthetic score, and an action space that includes text flow adjustment, space allocation, special element positioning and pagination control, and establish a valueless strategy optimization framework.

[0272] The goal of this step is to apply a valueless strategy optimization algorithm to adaptively adjust text flow, pagination, and chart placement while maintaining the overall aesthetic appeal of the document. The specific implementation is as follows:

[0273] First, a valueless policy optimization framework is introduced to address the layout adjustment problem. Valueless policy optimization is a special type of reinforcement learning method that does not directly learn the value function but instead optimizes the policy directly through the policy gradient method. It is particularly suitable for handling high-dimensional, continuous decision spaces, such as various fine adjustments in document layout.

[0274] Constructing the state space and action space for layout adjustments:

[0275] State space: includes the current document's structural state, content distribution, space utilization, and aesthetic score;

[0276] Action space: includes text flow adjustment (such as paragraph reflow, line break adjustment), space allocation (such as margin and spacing changes), special element positioning (such as chart repositioning), pagination control (such as pagination point setting), and other operations.

[0277] Step 504: Under the aforementioned valueless strategy optimization framework, the advantage function of the adjustment action is evaluated using a strategy network. The layout optimization is performed according to the progressive adjustment strategies of large scale, medium scale, and small scale to achieve adaptive adjustment of text flow, pagination, and chart position, resulting in an adaptively adjusted layout.

[0278] The layout strategy generated in step 502 is adaptively adjusted using a valueless strategy optimization algorithm, which mainly includes the following processes:

[0279] Initialize the layout state and load the current document layout;

[0280] Based on the layout strategy, a series of candidate adjustment actions are generated;

[0281] Use a policy network to evaluate the advantage function of each action.

[0282] Select the action with the highest advantage value to execute and update the document format status;

[0283] Repeat steps 2-4 until the layout quality reaches the target threshold or the number of iterations reaches the upper limit.

[0284] The policy network used is a deep neural network, which is trained using the following features:

[0285] Global features: such as page utilization, overall balance, style consistency, etc.

[0286] Local features: such as text block density, line spacing uniformity, element spacing, etc.;

[0287] Interactive features: such as the relative position and alignment of elements;

[0288] Professional rules and characteristics: such as typesetting industry standards, design principles, etc.

[0289] To ensure the stability of the adjustment process, a gradual adjustment strategy is adopted: first, large-scale adjustments are made (such as overall margins and main columns), then medium-scale adjustments are made (such as paragraph spacing and chart positions), and finally, fine-tuning is done (such as fine-tuning character spacing and line spacing). The impact of each scale adjustment on the overall effect is evaluated to avoid local optimization leading to global deterioration.

[0290] The policy network used in this invention consists of a shared feature extractor and multiple task-specific policy heads. The feature extractor is a 6-layer Transformer structure with a hidden layer dimension of 512, 8 attention heads, and a feedforward network dimension of 2048. The policy heads are 2-layer fully connected networks with a hidden layer dimension of 256 and an output layer dimension equal to the dimension of their respective action spaces.

[0291] The advantage function was calculated using the General Advantage Estimation (GAE) method, with a discount factor γ set to 0.99 and a GAE parameter λ set to 0.95. The policy gradient was updated using the Proximal Policy Optimization (PPO) algorithm, with a pruning parameter ε set to 0.2. The initial learning rate was 5e-5, and cosine annealing was used for scheduling. The value function loss weight was 0.5, and the entropy regularization coefficient was 0.01.

[0292] The training process took place in a simulated document formatting environment, containing approximately 10,000 document samples of different types and content. Training consisted of two phases: a pre-training phase with 20,000 updates using simulated data, and a fine-tuning phase with 5,000 updates using actual document samples. The policy network stopped training after reaching a stable average reward or completing a preset number of updates.

[0293] Step 505: For the adaptively adjusted layout, handle boundary cases including text overflow, isolated lines, table spanning pages, and image-text relationship maintenance to obtain the document product with the specified format.

[0294] Pay special attention to the boundaries of layout adjustments, such as:

[0295] Text overflow handling: When the content exceeds the expected space, non-critical space will be intelligently compressed or font parameters will be adjusted;

[0296] Handling isolated lines: Avoid having the first line of a paragraph appear alone at the end of the page or the last line appear alone at the beginning of the page;

[0297] Table page spanning: Ensure that the table header is maintained or that appropriate continuation prompts are provided when the table spans multiple pages;

[0298] Maintaining the relationship between text and graphics: Keep charts and their referenced text at a reasonable distance and maintain a proper connection.

[0299] Through valueless strategy optimization and adaptive layout adjustment, high-quality typesetting effects can be achieved under complex and ever-changing document content conditions, so that the document can present a professional and beautiful visual effect while maintaining the integrity of the content.

[0300] Step 506: Establish a style consistency standard library that includes font styles, space specifications, punctuation specifications, number representation specifications, chart specifications, and page specifications. Use a two-stage strategy of rule checking and statistical checking to scan the adaptively adjusted layout for style consistency, identify problems that violate style specifications and abnormal style usage patterns, and obtain style consistency issues.

[0301] The goal of this step is to check the consistency of the style in the adjusted document, identify and automatically correct style deviations, and ensure that the document format is standardized and consistent. The specific implementation is as follows:

[0302] First, establish a style consistency standard library to define various style specifications that should be followed in documents. These specifications include:

[0303] Font style guidelines: specify the font, font size, weight, etc. to be used for different levels of headings, body text, charts, etc.

[0304] Spatial specifications: These define spatial parameters such as paragraph spacing, line spacing, indentation, and margins.

[0305] Punctuation Standards: These standards specify the rules and format requirements for the use of punctuation marks.

[0306] Numerical representation standards: These standards define the methods (such as Arabic numerals and Chinese numerals) and formats (such as thousands separators) for representing numbers.

[0307] Chart specifications: Define the size, labeling method, numbering format, etc. of charts;

[0308] Page specifications: Define the format of page elements such as headers, footers, page numbers, and watermarks.

[0309] A two-stage inspection strategy is used for style consistency checks:

[0310] Rule checking phase: The document is scanned using a predefined set of rules to check for any violations of style specifications;

[0311] Statistical inspection phase: Statistical analysis is conducted on the use of styles in the documents to identify abnormal or inconsistent style usage patterns.

[0312] The rule check uses pattern matching and attribute validation to examine whether the style attributes of each document element conform to the specifications that should be followed according to its role in the document structure. For example, it checks whether all first-level headings use a consistent font and font size, and whether all tables use a uniform border style.

[0313] Statistical checks utilize clustering and anomaly detection algorithms to group style usage patterns in the document, identifying styles that appear infrequently or differ significantly from the mainstream patterns as potential inconsistencies. For example, if 90% of paragraphs in a document use 1.5 line spacing, but a few paragraphs use single line spacing, these paragraphs will be marked as style inconsistencies.

[0314] Step 507: For the style consistency issues, classify them into automatic correction, suggested correction, and manual confirmation according to their severity and type, and generate a correction plan.

[0315] For any discovered style inconsistencies, they will be categorized and handled according to their severity and type:

[0316] Automatic correction: For issues that clearly violate specifications and have only one corrective solution, the correction is automatically applied;

[0317] Suggested Fixes: For issues with multiple possible fixes, provide suggested fixes and mark them;

[0318] Manual confirmation: For major changes that may affect the understanding of the content, mark them and wait for confirmation.

[0319] Step 508: Based on the correction scheme, the style propagation algorithm is used to automatically correct and update the document style by referring to the mainstream styles of elements at the same level or parent elements, and the updated document style is obtained.

[0320] Automatic correction is achieved using a style propagation algorithm. This algorithm, based on the document's hierarchy, propagates correct styles from elements at the same level or their parent elements to the elements that need correction. For example, if an inconsistent style is found in a second-level heading, it will be corrected by referencing the mainstream styles of other second-level headings.

[0321] Step 509: For the updated document style, quantify the overall style consistency level, generate a problem distribution map, a correction list and style usage statistics, and output a style consistency report.

[0322] Finally, a style consistency report is generated, detailing the inspection process, identified issues, and their resolution, including:

[0323] Style consistency score: quantifies the overall style consistency level of the document;

[0324] Issue distribution chart: Shows the distribution of style issues throughout the document;

[0325] Correction List: Lists all corrected and pending style issues;

[0326] Style Usage Statistics: Shows the usage of various styles in the document.

[0327] Step 510: Based on the style consistency report, confirm the style correction effect and perform final style unification to ensure that the finished document with the specified format meets the standardization requirements.

[0328] Through style consistency checks and automatic corrections, we ensure that documents maintain a uniform and professional appearance after layout adjustments, meeting the high standards required for standardized documents.

[0329] Step 105 specifically includes:

[0330] Step 601: For the finished document with the specified format, convert the document content and format into a standardized intermediate representation to separate the format information from the content and generate a standardized document representation.

[0331] In this step, the completed document content and format are converted into a standardized intermediate representation format. This intermediate representation uses a structured data format (such as XML or JSON), clearly distinguishing between content elements and format attributes, thus separating format information from content.

[0332] This separation allows for flexible adjustment of format attributes based on the requirements of different output formats while maintaining the content. The standardized representation also includes the document's overall structural information, logical relationships between elements, and cross-references, ensuring the document's integrity and consistency are maintained during format conversion.

[0333] Step 602: Based on the standardized document representation, optimize the rendering engine parameters for different output formats, taking into account device characteristics, media differences, reading environment and document purpose, and establish a multi-resolution rendering engine.

[0334] In this step, a multi-resolution rendering engine is built to accurately represent the actual appearance of documents on different media (such as screens and printed paper). The rendering engine uses vector graphics technology to ensure clear text and graphics display at any scaling level. The rendering process considers the following factors:

[0335] Device characteristics: The resolution, color space, and display characteristics of different devices;

[0336] Media differences: the difference between screen display and print quality;

[0337] Reading environment: The impact of different reading environments (such as brightness and distance) on readability;

[0338] Document Purpose: Optimize rendering parameters based on the document's primary usage scenario.

[0339] For different output formats (such as PDF, Word, HTML, etc.), we optimize the corresponding rendering engine parameters. For example, for PDF output, we optimize font embedding, image compression, and print compatibility; for HTML output, we optimize responsive layout, CSS styles, and browser compatibility. Through these optimizations, we ensure that documents maintain a consistent visual effect and professional quality across different formats.

[0340] Step 603: Using the multi-resolution rendering engine, convert the document into a specified format according to the user's needs, retain the original structure, style and interactive features, and output multi-format document files.

[0341] In this step, the document is converted to the specified output format according to the user's specific needs. The conversion process not only preserves the document's content and basic style, but also retains as much of the original structure, advanced typesetting features, and interactive functions as possible.

[0342] For different output formats, specific conversion strategies are used:

[0343] PDF format: Generates high-quality printable documents, retains accurate layout and pagination, and supports digital signatures and security settings;

[0344] Word format: Generates editable documents, retains style definitions and document structure, and supports subsequent modifications and collaboration;

[0345] HTML format: Generates web-based documents, supports responsive layout and interactive features, and optimizes the online reading experience;

[0346] Other professional formats: Based on the needs of specific fields, it supports conversion to industry-specific formats (such as legal document formats, medical record formats, etc.).

[0347] During the conversion process, special attention is paid to the compatibility mapping of format characteristics to ensure that the original functionality and appearance are preserved as much as possible when converting between different formats. For example, converting a table of contents in Word to bookmarks in PDF, and converting hyperlinks in HTML to cross-references in Word.

[0348] Step 604: For the multi-format documents, establish an automatic evaluation mechanism from the dimensions of format standardization, content completeness, language expression and layout aesthetics, and generate a quality evaluation report.

[0349] In this step, a comprehensive quality assessment is conducted on the generated multi-format documents. The assessment employs a multi-dimensional indicator system, measuring document quality from the following key aspects:

[0350] Format compliance: Assess whether the document conforms to relevant format standards and specifications, including consistency of layout, correctness of format, and completeness of structure;

[0351] Content completeness: Assess whether the document content is complete and accurate, including information coverage, data accuracy, and logical integrity;

[0352] Language expression: Assess the language quality of the document, including grammatical correctness, clarity of expression, and professionalism;

[0353] Aesthetic Layout: Evaluate the visual appeal of the document, including layout balance, space utilization, and overall aesthetics.

[0354] These dimensions are quantitatively scored using automated assessment tools and algorithms, generating a detailed quality assessment report. The report includes an overall score, detailed analysis of each dimension, a list of issues, and improvement suggestions, providing users with a comprehensive understanding of document quality and directions for improvement.

[0355] Step 605: Based on the quality assessment report and user feedback, analyze the problems and improvement points in the document generation process, update the hierarchical document structure knowledge base and optimize the algorithm parameters to achieve continuous improvement and closed-loop optimization.

[0356] In this step, a continuous improvement mechanism is established to continuously optimize document generation by collecting and analyzing quality assessment results and user feedback. Specifically, this includes:

[0357] User feedback collection: Collect user evaluations and suggestions on the generated documents through questionnaires, ratings, and feedback.

[0358] Problem Analysis: Combining quality assessment reports and user feedback, identify common problems and areas for improvement in the document generation process;

[0359] Knowledge base update: Based on the analysis results, update and expand the hierarchical document structure knowledge base, and add new templates, style rules and content patterns;

[0360] Algorithm optimization: Adjust and optimize the algorithm parameters of each module to improve template matching accuracy, content generation quality, and layout effect;

[0361] Model retraining: Regularly retraining a machine learning model using newly collected data to improve model performance and adaptability.

[0362] Through this closed-loop optimization mechanism, it can continuously learn and improve, adapt to ever-changing document needs and quality standards, and continuously improve the quality of generated documents and user satisfaction.

[0363] Figure 3 This is a structural block diagram of the formatted document intelligent generation system provided in an embodiment of the present invention. Figure 3 As shown, the system includes:

[0364] The document structure parsing module 701 is used to acquire multi-source formatted document samples, perform structured parsing on the multi-source formatted document samples using deep learning technology, identify visual layout features, semantic structure representation and style feature representation, and construct a hierarchical document structure knowledge base.

[0365] The template matching module 702 is used to extract document feature vectors based on the hierarchical document structure knowledge base and combined with the document requirements input by the user, through natural language processing technology, and to map the document feature vectors to a low-dimensional representation space using neural manifold representation learning to obtain a low-dimensional neural manifold representation. It then uses contrastive self-supervised learning to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base and performs multi-dimensional matching degree scoring to output the optimal document template.

[0366] The content generation module 703 is used to perform structural analysis on the optimal document template, identify content slots and their constraints, combine user content data, generate document content through a diffusion model, and perform quality diagnosis and iterative optimization using the best loss value estimation to obtain optimized document content.

[0367] The layout adjustment module 704 is used to adaptively adjust the text flow, pagination, and chart positions of the optimized document content based on the optimized document content and the optimal document template, using a reward partitioning no-value strategy optimization algorithm, and to perform style consistency checks and corrections to generate a formatted document product.

[0368] The format export module 705 is used to convert and export the document product with the specified format into multiple formats, and to conduct quality assessment from multiple dimensions including format standardization, content completeness, language expression and layout aesthetics, and output multi-format document files and quality assessment reports.

[0369] The formatted document intelligent generation system provided in this invention constructs a hierarchical document structure knowledge base through a document structure parsing module, achieves high-precision template matching based on neural manifolds and contrastive self-supervised learning through a template matching module, generates high-quality content based on diffusion models and optimal loss value estimation through a content generation module, achieves adaptive layout adjustment based on a reward partitioning-based no-value strategy optimization through a layout adjustment module, and exports documents in multiple formats and performs quality assessment through a format export module. This solves the technical problems in the prior art, such as template matching mechanisms relying too much on manually preset rules, lack of intelligent format adjustment, and difficulty in maintaining semantic coherence and style consistency of documents.

[0370] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligently generating formatted documents, characterized in that, Includes the following steps: Obtain multi-source formatted document samples, perform structured parsing on the multi-source formatted document samples using deep learning technology, identify visual layout features, semantic structure representation and style feature representation, and construct a hierarchical document structure knowledge base; Based on the hierarchical document structure knowledge base, combined with the document requirements input by the user, document feature vectors are extracted through natural language processing technology, and neural manifold representation learning is used to map the document feature vectors to a low-dimensional representation space to obtain a low-dimensional neural manifold representation. Contrastive self-supervised learning is used to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base and to perform multi-dimensional matching degree scoring, outputting the optimal document template. The optimal document template is structurally parsed to identify content slots and their constraints. Combined with user content data, document content is generated using a diffusion model, and quality diagnosis and iterative optimization are performed using optimal loss value estimation to obtain optimized document content. The user content data refers to the content data provided by the user to fill the content slots, including: structurally parsing the optimal document template to identify explicit and implicit slots and analyzing the content type, length limits, format requirements, semantic constraints, and dependencies of each slot to generate a content generation task list; based on the content generation task list and combined with user content data, content filling strategies such as direct filling, adaptive adjustment, automatic generation, and hybrid filling are determined. A content generation plan is formulated, wherein direct filling refers to directly using user content, adaptive adjustment refers to formatting or expanding user content, automatic generation refers to automatically generating content based on context, and hybrid filling refers to combining user content and automatically generated content. According to the content generation plan, a diffusion model based on a pre-trained language model is used, combined with stylistic adaptation, structure awareness, domain knowledge injection, and contextual consistency control mechanisms, to obtain the optimized document content, including: ensuring the generated content conforms to the target document's language style through style condition control, ensuring the generated content conforms to the document's structural requirements through structural control signals, integrating a domain knowledge graph, and maintaining document coherence through global consistency checks. The process of generating document content through a diffusion model and performing quality diagnosis and iterative optimization using optimal loss value estimation to obtain optimized document content further includes: calculating a multi-dimensional loss function for the document content generated by the diffusion model, including semantic loss, style loss, structural loss, coherence loss, and professionalism loss, to obtain the actual loss value for each dimension; comparing the actual loss value of each dimension with preset ideal values ​​and acceptable thresholds, using a sliding window technique to perform quality assessment on the overall content and local paragraphs, generating a quality diagnosis report that includes an overall quality score, dimension subdivision scores, problem area identification, and improvement suggestions; according to the severity of the quality diagnosis report, using a graded strategy of mild optimization, moderate optimization, and severe optimization, iteratively optimizing through a targeted control generation method, and outputting the iterated document content; performing terminology consistency, data consistency, tense consistency, person consistency, and format consistency checks on the iterated document content to obtain consistency check results; and using coreference resolution and dependency analysis techniques to identify potential problems, automatically correcting simple errors and marking complex problems to obtain the optimized document content. Based on the optimized document content and the optimal document template, the text flow, pagination, and chart positions of the optimized document content are adaptively adjusted using a reward partitioning no-value strategy optimization algorithm, and style consistency checks and corrections are performed to generate a formatted document product. The reward partitioning no-value strategy optimization algorithm includes: designing reward partitions including a basic usable range, a standard qualified range, a professional and aesthetically pleasing range, and a perfect presentation range; optimizing the strategy by evaluating the advantage function of the adjustment action through a strategy network; and adaptively adjusting the text flow, pagination, and chart positions of the optimized document content. The document output conforming to the specified format is converted and exported to multiple formats. The quality is evaluated from multiple dimensions, including format conformity, content completeness, language expression and layout aesthetics, and the output is a multi-format document file and a quality evaluation report.

2. The method according to claim 1, characterized in that, The process involves using deep learning technology to perform structured analysis on the multi-source formatted document samples, identifying visual layout features, semantic structure representations, and style feature representations, and constructing a hierarchical document structure knowledge base, including: The multi-source formatted document sample is preprocessed by converting the file format, removing watermarks and repairing damaged parts. The visual layout features are analyzed by computer vision technology, and a preliminary structural representation containing basic components such as title, body text, tables and charts is output. Based on the preliminary structural representation, natural language processing technology is used to perform semantic analysis on the document content, identify chapter hierarchy relationships, logical organization structure and functional content units, and output semantic structural representation; From the visual layout features and the semantic structure representation, style feature representations including font, font size, line spacing and indentation are extracted to form a standardized style description language, thus obtaining the hierarchical document structure knowledge base.

3. The method according to claim 1, characterized in that, The process involves mapping the document feature vectors to a low-dimensional representation space using neural manifold representation learning to obtain a low-dimensional neural manifold representation. Contrastive self-supervised learning is then used to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base, and a multi-dimensional matching score is performed, including: The system receives the document requirement description and content elements input by the user, and uses natural language processing technology to perform word segmentation, part-of-speech tagging, named entity recognition and semantic analysis to extract document type features, content features and style features to form a document feature vector. Based on the document feature vector, the high-dimensional feature vector is mapped to the low-dimensional latent space by a variational autoencoder. Self-supervised learning training is performed using feature reconstruction task, document type prediction task and structural consistency task to obtain a low-dimensional neural manifold representation. For the low-dimensional neural manifold representation, a contrastive learning framework is used to construct positive and negative sample pairs. The similarity between user needs and each template in the hierarchical document structure knowledge base is calculated using the InfoNCE loss function, and a similarity ranking list is generated.

4. The method according to claim 3, characterized in that, The process involves mapping the document feature vectors to a low-dimensional representation space using neural manifold representation learning to obtain a low-dimensional neural manifold representation. Contrastive self-supervised learning is then used to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base, and a multi-dimensional matching score is performed, including: Based on the candidate templates in the similarity ranking list, scores are given from the dimensions of structural adaptability, content completeness, style compatibility, domain relevance, and usage frequency, and a single matching score is calculated for each dimension. For each dimension's individual matching score, a machine learning model is used for weighted fusion, and combined with the user's historical preferences, a collaborative filtering algorithm is used for personalized adjustment, outputting the optimal document template and matching score report.

5. The method according to claim 1, characterized in that, Based on the optimized document content and the optimal document template, the text flow, pagination, and chart positions of the optimized document content are adaptively adjusted using a reward-partitioning no-value strategy optimization algorithm. Style consistency checks and corrections are also performed to generate a formatted document, including: The matching degree analysis is performed on the optimized document content and the template structure of the optimal document template. The overall structure matching degree, chapter-level matching degree, paragraph-level matching degree and element-level matching degree are calculated. Content overflow, insufficient content, structural mismatch, special element conflict and pagination problem are identified, and a priority list of typesetting tasks sorted by severity is generated. Based on the aforementioned typesetting task priority list, a reward partition is designed for the basic usable interval, the standard qualified interval, the professional and aesthetically pleasing interval, and the perfect presentation interval. For each interval, text adjustment, space reallocation, content rearrangement, special element processing, and pagination optimization strategies are formulated to construct a typesetting decision tree. Based on the layout decision tree, a state space is constructed that includes document structure status, content distribution, space utilization and aesthetic score, as well as an action space that includes text flow adjustment, space allocation, special element positioning and pagination control, and a valueless strategy optimization framework is established. Under the aforementioned valueless strategy optimization framework, a strategy network is used to evaluate the advantage function of the adjustment action, and layout optimization is performed according to the progressive adjustment strategies of large scale, medium scale and small scale to achieve adaptive adjustment of text flow, pagination and chart position, resulting in an adaptively adjusted layout. The adaptively adjusted layout is processed to handle boundary cases including text overflow, isolated lines, table spanning pages, and maintaining the relationship between text and images, resulting in a document product with the specified format.

6. The method according to claim 5, characterized in that, Based on the optimized document content and the optimal document template, the text flow, pagination, and chart positions of the optimized document content are adaptively adjusted using a reward-partitioning no-value strategy optimization algorithm. Style consistency checks and corrections are also performed to generate a formatted document, including: Establish a style consistency standard library that includes font styles, spatial specifications, punctuation specifications, number representation specifications, chart specifications, and page specifications. Use a two-stage strategy of rule checking and statistical checking to scan the adaptively adjusted layout for style consistency, identify problems that violate style specifications and abnormal style usage patterns, and obtain style consistency issues. For the aforementioned style consistency issues, a classification process is performed based on severity and type, including automatic correction, suggested correction, and manual confirmation, to generate a correction plan; Based on the aforementioned correction scheme, a style propagation algorithm is used to automatically correct and update the document style by referencing the mainstream styles of elements at the same level or parent elements, thereby obtaining the updated document style. The style propagation algorithm includes: based on the document's hierarchical structure, propagating the mainstream styles of elements at the same level or parent elements to the elements that need correction for automatic correction. For the updated document style, quantify the overall style consistency level, generate an issue distribution map, a correction list and style usage statistics, and output a style consistency report; Based on the style consistency report, confirm the effect of style correction and perform final style unification to ensure that the finished document conforms to the standardization requirements.

7. The method according to claim 1, characterized in that, The process of converting and exporting the document to multiple formats according to the specified format includes: For the finished document with the specified format, the document content and format are converted into a standardized intermediate representation, thereby separating the format information from the content and generating a standardized document representation; Based on the standardized document representation, the rendering engine parameters are optimized for different output formats, taking into account device characteristics, media differences, reading environment and document purpose, to establish a multi-resolution rendering engine; The multi-resolution rendering engine converts documents into specified formats according to user needs, preserving the original structure, style, and interactive features, and outputs multi-format document files. An automatic evaluation mechanism is established for the aforementioned multi-format documents, taking into account dimensions such as format standardization, content completeness, language expression, and layout aesthetics, to generate a quality evaluation report; Based on the quality assessment report and user feedback, the problems and areas for improvement in the document generation process are analyzed, and the hierarchical document structure knowledge base and algorithm parameters are updated to achieve continuous improvement and closed-loop optimization of the system.

8. A formatted document intelligent generation system, characterized in that, include: The document structure parsing module is used to acquire multi-source formatted document samples, perform structured parsing on the multi-source formatted document samples using deep learning technology, identify visual layout features, semantic structure representation and style feature representation, and construct a hierarchical document structure knowledge base; The template matching module is used to extract document feature vectors based on the hierarchical document structure knowledge base and combined with the document requirements input by the user, through natural language processing technology, and to map the document feature vectors to a low-dimensional representation space using neural manifold representation learning to obtain a low-dimensional neural manifold representation. It then uses contrastive self-supervised learning to calculate the similarity between the low-dimensional neural manifold representation and each template in the hierarchical document structure knowledge base and performs multi-dimensional matching degree scoring to output the optimal document template. The content generation module is used to perform structural analysis on the optimal document template, identify content slots and their constraints, and, in conjunction with user content data, generate document content through a diffusion model. It then uses optimal loss value estimation for quality diagnosis and iterative optimization to obtain optimized document content. The user content data refers to content data provided by the user to fill the content slots, including: performing structural analysis on the optimal document template, identifying explicit and implicit slots, and analyzing the content type, length limits, format requirements, semantic constraints, and dependencies of each slot to generate a content generation task list; and, based on the content generation task list and user content data, determining whether to directly fill, adaptively adjust, automatically generate, or hybrid fill the content slots. The strategy involves formulating a content generation plan, where direct filling refers to directly using user content, adaptive adjustment refers to formatting or expanding user content, automatic generation refers to automatically generating content based on context, and hybrid filling refers to combining user content and automatically generated content. Based on the content generation plan, a diffusion model based on a pre-trained language model is used, combined with stylistic adaptation, structure awareness, domain knowledge injection, and contextual consistency control mechanisms, to obtain the optimized document content. This includes: ensuring the generated content conforms to the target document's language style through style condition control, ensuring the generated content conforms to document structure requirements through structure control signals, integrating domain knowledge graphs, and maintaining document coherence through global consistency checks. The process of generating document content through a diffusion model and performing quality diagnosis and iterative optimization using optimal loss value estimation to obtain optimized document content further includes: calculating a multi-dimensional loss function for the document content generated by the diffusion model, including semantic loss, style loss, structural loss, coherence loss, and professionalism loss, to obtain the actual loss value for each dimension; comparing the actual loss value of each dimension with preset ideal values ​​and acceptable thresholds, using a sliding window technique to perform quality assessment on the overall content and local paragraphs, generating a quality diagnosis report that includes an overall quality score, dimension subdivision scores, problem area identification, and improvement suggestions; according to the severity of the quality diagnosis report, using a graded strategy of mild optimization, moderate optimization, and severe optimization, iteratively optimizing through a targeted control generation method, and outputting the iterated document content; performing terminology consistency, data consistency, tense consistency, person consistency, and format consistency checks on the iterated document content to obtain consistency check results; and using coreference resolution and dependency analysis techniques to identify potential problems, automatically correcting simple errors and marking complex problems to obtain the optimized document content. The layout adjustment module is used to adaptively adjust the text flow, pagination, and chart positions of the optimized document content based on the optimized document content and the optimal document template, using a reward partitioning no-value strategy optimization algorithm. It also performs style consistency checks and corrections to generate a formatted document. The reward partitioning no-value strategy optimization algorithm includes: designing reward partitions comprising a basic usable range, a standardized acceptable range, a professional and aesthetically pleasing range, and a perfect presentation range; optimizing the strategy by evaluating the advantage function of the adjustment action through a strategy network; and adaptively adjusting the text flow, pagination, and chart positions of the optimized document content. The format export module is used to convert and export documents that meet the specified formatting standards to multiple formats, and to conduct quality assessments from multiple dimensions, including formatting compliance, content completeness, language expression, and layout aesthetics, outputting multi-format document files and quality assessment reports.

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