Document generation system and method based on hierarchical control

Abstract

This application provides a document generation system and method based on hierarchical control, relating to the field of artificial intelligence technology. The system includes a hierarchical framework construction module, a content scope control module, and a clause generation module. The hierarchical framework construction module matches document templates according to the target document category and performs hierarchical transformation processing on the undetermined content blocks in the templates using a classification model to generate a hierarchical document framework. The content scope control module evaluates the content importance of the framework based on an attention gating mechanism and determines the content boundaries to generate a content outline based on the evaluation results. The clause generation module constrains and generates clauses based on the content outline and a pre-set structured knowledge base, ultimately outputting the target document. This application, through a hierarchical control strategy and an intelligent evaluation mechanism, achieves a high degree of automation in document generation while ensuring the professionalism and accuracy of the document in multiple dimensions such as structure, logic, and content, significantly improving the efficiency and quality of document preparation.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a document generation system and method based on hierarchical control. Background Technology

[0002] Standard documents, especially technical standards for relay protection devices, are crucial for ensuring the safe and stable operation of power systems. With the accelerating pace of technological iteration and the increasing complexity of industry regulations, higher demands are being placed on the professionalism, timeliness, and consistency of standard document preparation.

[0003] In related technologies, template-based autofill schemes and simple rule-based document generation schemes are commonly used. Template-based autofill schemes typically require a predefined complete document structure, manually or semi-automatically filling content fragments into fixed positions in the template; for example, filling parameters of a specific device into the corresponding section of a standard template. Simple rule-based document generation schemes, on the other hand, trigger the generation of corresponding text paragraphs based on a series of logical conditions (e.g., "If device type = overcurrent protection, then generate action time test items"). However, these types of document generation schemes still suffer from low document production efficiency and quality. Summary of the Invention

[0004] This application provides a document generation system and method based on hierarchical control, which aims to improve the problem of low efficiency and quality in document preparation in related technologies.

[0005] In a first aspect, this application provides a document generation system based on hierarchical control, comprising: a hierarchical framework construction module, a content scope control module, and a clause generation module, wherein:

[0006] The hierarchical framework building module is used to match document templates according to the target document category and perform hierarchical transformation on the undetermined content blocks in the document template to generate a hierarchical document framework; wherein, the hierarchical transformation process includes classifying and transforming the undetermined content blocks based on a preset classification model;

[0007] The content scope control module, connected to the hierarchical framework construction module, is used to evaluate the importance of content in the hierarchical document framework based on the attention gating mechanism, and determine the content boundaries to generate a content outline based on the evaluation results. The attention gating mechanism is configured to dynamically distinguish the importance of content elements by combining the rule compliance and contextual relevance of the content elements.

[0008] The clause generation module, connected to the content scope control module, is used to constrain and generate clauses based on the content outline and a preset structured knowledge base, in order to form a document corresponding to the target document category.

[0009] Secondly, this application provides a document generation method based on hierarchical control, applicable to any of the hierarchical control-based document generation systems in the first aspect. The document generation system includes a hierarchical framework construction module, a content scope control module, and a clause generation module; the document generation method includes:

[0010] The hierarchical framework construction module matches document templates according to the target document category and performs hierarchical transformation on the undetermined content blocks in the document template to generate a hierarchical document framework; wherein, the hierarchical transformation process includes classifying and transforming the undetermined content blocks based on a preset classification model;

[0011] The content scope control module evaluates the importance of content in a hierarchical document framework based on an attention gating mechanism, and determines the content boundaries to generate a content outline based on the evaluation results. The attention gating mechanism is configured to dynamically distinguish the importance of content elements by combining the rule compliance and contextual relevance of the content elements.

[0012] The clause generation module uses the content outline and a pre-set structured knowledge base to constrain and generate clauses, thus forming a document corresponding to the target document category.

[0013] Thirdly, this application provides a computer-readable storage medium storing computer-executable instructions that, when executed, are used to implement the method in the second aspect.

[0014] Fourthly, this application provides a computer program product, including a computer program that, when executed, implements the method in the second aspect.

[0015] The document generation system and method based on hierarchical control provided in this application automatically classifies and transforms undetermined content blocks in document templates using a hierarchical framework construction module based on a preset classification model, replacing the inefficient manual identification and filling of each item in traditional solutions. Simultaneously, the content scope control module automatically derives content boundaries and generates an outline using an attention gating mechanism, while the clause generation module automatically generates constrained technical clauses based on a structured knowledge base. The entire process achieves a fully automated pipeline from framework construction and content planning to clause generation, significantly improving document compilation efficiency. Furthermore, the hierarchical framework constructed by the hierarchical transformation process fundamentally ensures the rationality and integrity of the document structure; the attention gating mechanism dynamically distinguishes content importance by comprehensively considering rule compliance and contextual relevance, ensuring that the generated outline is logically rigorous and highlights key points, effectively improving the quality problems of uncontrolled content scope and weak relevance in traditional solutions; and the clause generation module, combined with a structured knowledge base, ensures that the final clauses are not only semantically accurate but also maintain consistency and compliance in technical logic, effectively overcoming the quality defects of fragmented and inconsistent content generated by traditional solutions. In summary, through a structured, layered control system and intelligent algorithmic mechanisms, the automation and efficiency of document generation are significantly improved, while the professionalism and accuracy of documents in multiple dimensions such as structure, logic, and content are systematically guaranteed, thereby significantly improving the efficiency and quality of document preparation. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] Figure 1 A schematic diagram of the structure of a hierarchical control-based document generation system provided for an exemplary embodiment of this application;

[0018] Figure 2 A schematic diagram of a layered framework building module provided for an exemplary embodiment of this application;

[0019] Figure 3 A schematic diagram of the structure of a content scope control module provided for an exemplary embodiment of this application;

[0020] Figure 4 A schematic diagram of the structure of an attention weight evaluation unit provided in an exemplary embodiment of this application;

[0021] Figure 5 A schematic diagram of the structure of a clause generation module provided for an exemplary embodiment of this application;

[0022] Figure 6 A schematic diagram of the structure of a clause generation execution unit provided for an exemplary embodiment of this application;

[0023] Figure 7 A schematic diagram of the structure of a knowledge base management unit provided for an exemplary embodiment of this application;

[0024] Figure 8 A schematic diagram of the structure of a quality verification module provided for an exemplary embodiment of this application;

[0025] Figure 9 Another schematic diagram of the structure of a hierarchical control-based document generation system provided for an exemplary embodiment of this application;

[0026] Figure 10 A schematic diagram of the structure of an intelligent learning module provided for an exemplary embodiment of this application;

[0027] Figure 11 Another structural schematic diagram of the content scope control module provided for an exemplary embodiment of this application;

[0028] Figure 12 A flowchart illustrating a hierarchical control-based document generation method provided for an exemplary embodiment of this application.

[0029] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0031] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0032] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0033] Traditional standard document development methods rely heavily on human experience, which leads to problems such as low efficiency, poor consistency, and difficulty in knowledge transfer. In particular, the construction of standard frameworks and the generation of content usually require a large number of experts to invest a lot of time in repeated modifications and discussions, resulting in long standard document development cycles and problems such as content duplication, omissions, or inconsistencies.

[0034] In related technologies, existing standard document compilation schemes typically only handle simple text generation tasks and are ill-equipped to meet complex standard compilation needs, generally resulting in low document compilation efficiency and quality. Specifically, this manifests in two ways: first, a lack of precise control over the content scope, leading to generated content that is either too broad or too narrow; second, weak correlation between generated content, making it difficult to form a systematic and structured standard framework. Furthermore, existing technologies often employ single-dimensional template filling or rule matching approaches, which struggle to handle the entire process from framework construction to content generation, lacking systematic control and optimization of the entire standard document compilation process. Therefore, how to construct a technical solution capable of intelligent document structure generation, precise control of content scope, high-quality automatic filling of clauses, and support for systematic regulation and continuous optimization throughout the entire process has become a key technical challenge for improving the efficiency and quality of standard document compilation.

[0035] To address the aforementioned issues, this application provides a document generation scheme based on hierarchical control. By employing a three-tiered generation strategy, it replaces the traditional single-dimensional template filling or rule matching approach, systematically deconstructing the document generation process into three logically rigorous and progressively advancing control layers: The first layer is the framework construction layer, which introduces a preset classification model to intelligently determine and transform the categories of undetermined content blocks in the document template, achieving automated construction from a static template to a dynamic, hierarchical document framework, laying a structured foundation for subsequent processing; the second layer is the content control layer, which introduces an attention gating mechanism on top of the constructed hierarchical framework. By dynamically calculating and integrating the rule compliance and contextual relevance of content elements, it achieves intelligent importance assessment and scope boundary determination for each part of the content within the framework, thereby generating a logically coherent and focused content outline; the third layer is the clause generation layer, which, based on the content outline and combined with a preset structured knowledge base, imposes relevant constraints on the clause generation process, thereby driving the automatic generation of complete, compliant, and high-quality standard documents. By deeply integrating this hierarchical and progressive control strategy with artificial intelligence technologies (such as classification models and attention mechanisms), expert experience and domain knowledge are structured and algorithmized, thereby effectively improving the shortcomings of traditional solutions in terms of document preparation efficiency, content quality, and logical consistency.

[0036] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0037] Figure 1 This is a schematic diagram of the structure of a hierarchical control-based document generation system provided as an exemplary embodiment of this application. Figure 1 As shown, the hierarchical control-based document generation system 10 includes: a hierarchical framework construction module 11, a content scope control module 12, and a clause generation module 13, wherein:

[0038] The hierarchical framework construction module 11 is used to match document templates according to the target document category and perform hierarchical transformation processing on the undetermined content blocks in the document template to generate a hierarchical document framework; wherein, the hierarchical transformation processing includes classifying and transforming the undetermined content blocks based on a preset classification model;

[0039] The content scope control module 12, connected to the hierarchical framework construction module 11, is used to evaluate the importance of content in the hierarchical document framework based on the attention gating mechanism, and determine the content boundaries based on the evaluation results to generate a content outline; the attention gating mechanism is configured to dynamically distinguish the importance of content elements by comprehensively considering the rule compliance and contextual relevance of the content elements.

[0040] The clause generation module 13 is connected to the content scope control module 12. It is used to constrain and generate clauses based on the content outline and a preset structured knowledge base to form a document corresponding to the target document category.

[0041] For example, suppose the target document category is "Technical Specification for Relay Protection Devices". Accordingly, after the system starts, the hierarchical framework construction module 11 performs the framework generation task. Based on the category "Technical Specification for Relay Protection Devices", this module matches the corresponding document template from the system's preset template library. This template usually contains fixed parts (such as cover, table of contents, preface and other general chapter formats) and undetermined parts (such as chapter frameworks that need to be filled with specific technical content, such as "Technical Parameters", "Performance Requirements" and "Test Methods"). Subsequently, the hierarchical framework construction module 11 performs intelligent hierarchical transformation processing on the pending content blocks in the document template. For example, for a table content block to be processed under the "rated parameters" section, the module calls a pre-trained classification model (such as a deep learning-based text classifier) ​​to analyze the text features of the content block and automatically determine the technical content category to which it should belong, such as "electrical parameters". Based on the determination result, the hierarchical framework construction module 11 transforms the original content block from an abstract description in the template into a standardized content unit with a clear structure that can be filled with specific technical data later. By traversing and completing the above category determination and structural transformation of all pending content blocks, the hierarchical framework construction module 11 finally automatically assembles and generates a hierarchical document framework with clear chapter levels, complete logical structure, and each technical content unit has been initially positioned and standardized, laying a precise structural foundation for subsequent content planning and detailed generation.

[0042] Accordingly, after the hierarchical document framework is generated, it is sent to the content scope control module 12. The core task of this module is to determine the detailed scope and depth of each technical content in the document and generate a content outline to guide the writing of specific clauses. To achieve precise control, the content scope control module 12 adopts an attention gating mechanism. This mechanism analyzes and evaluates each potential content element (such as the functional description of "overcurrent protection") in the aforementioned hierarchical document framework. During the evaluation, the attention gating mechanism considers two factors: first, the rule compliance of the element, that is, judging whether the element is a core point that must be included in this type of technical specification based on a pre-set rule base; second, its contextual relevance, that is, analyzing the logical and technical relevance between the element and other elements in the framework (such as "short-circuit withstand capability"). By dynamically weighing these two factors, the mechanism assigns a dynamic importance score to each content element, thereby intelligently distinguishing between key content and secondary descriptions. Based on this score distribution, the content scope control module 12 can automatically identify the content boundaries, thereby outputting a detailed content outline with a clear structure and distinct focus.

[0043] Furthermore, the clause generation module 13 drafts specific technical clauses based on the content outline. This module is tightly integrated with a pre-set structured knowledge base (e.g., a knowledge graph of relay protection technology standards stored in the form of "triples"). For example, when a clause about "operation time" needs to be generated, the clause generation module 13 will, according to the outline instructions, query the knowledge base for verified compliance parameters and expressions associated with keywords such as "overcurrent protection" and "operation time" (e.g., "should operate within 0.5 seconds"). Based on these knowledge constraints, the clause generation module 13 selects an appropriate generation strategy (e.g., calling templates, performing reasoning), automatically forms accurate and standardized specific technical clause texts, integrates all clauses, and finally outputs a complete document that meets the requirements of the "Technical Specification for Relay Protection Devices". Through the collaboration of the above three modules, the system 10 realizes intelligent generation of the entire process from document category input to complete standardized document output.

[0044] The document generation system based on hierarchical control provided in this application adopts a hierarchical framework construction module to automatically determine and convert the categories of undetermined content blocks in the document template based on a preset classification model, replacing the inefficient operation of manual item-by-item identification and filling in the traditional solution. Simultaneously, the content scope control module uses an attention gating mechanism to automatically deduce content boundaries and generate an outline, while the clause generation module automatically generates constrained technical clauses based on a structured knowledge base. The entire process achieves a fully automated pipeline from framework construction and content planning to clause generation, significantly improving document compilation efficiency. Furthermore, the hierarchical framework constructed by the hierarchical conversion process fundamentally ensures the rationality and integrity of the document structure; the attention gating mechanism dynamically distinguishes the importance of content by comprehensively considering rule compliance and contextual relevance, ensuring that the generated outline is logically rigorous and highlights key points, effectively improving the quality problems of uncontrolled content scope and weak relevance in traditional solutions; and the clause generation module, combined with a structured knowledge base, ensures that the final clauses are not only semantically accurate but also consistent and compliant in technical logic, effectively overcoming the quality defects of fragmented and inconsistent content generated by traditional solutions. In summary, through a structured, layered control system and intelligent algorithmic mechanisms, the automation and efficiency of document generation are significantly improved, while the professionalism and accuracy of documents in multiple dimensions such as structure, logic, and content are systematically guaranteed, thereby significantly improving the efficiency and quality of document preparation.

[0045] In some embodiments, the hierarchical framework construction module includes: a template matching unit, used to match a corresponding document template from a preset template library according to the target document category; a content conversion unit, connected to the template matching unit, used to traverse the undetermined parts in the document template, and perform category determination and conversion for the undetermined content blocks of the undetermined parts based on a preset classification model; a parameter mapping unit, connected to the content conversion unit, used to perform placeholder replacement operations to generate a parameter mapping list based on the converted undetermined content blocks; and a framework synthesis unit, connected to the parameter mapping unit, used to generate a hierarchical document framework based on the parameter mapping list.

[0046] For example, Figure 2 A schematic diagram of a layered framework building module provided for an exemplary embodiment of this application. For example... Figure 2 As shown, the layered framework construction module 11 includes: a standard template library establishment unit 111, a template matching unit 112, a content conversion unit 113, a parameter mapping unit 114, and a framework synthesis unit 115.

[0047] The preset template library is pre-established and maintained by the standard template library creation unit 111. This unit stores a correspondence between standard categories as category labels and document templates used to define the fixed and undefined parts of relay protection devices. For example, the preset template library uses a key-value pair structure, where the key is the standard category (e.g., relay protection device technical specifications, relay protection device test procedures, etc.) and the value is the corresponding document template. The document templates are further divided into fixed parts (e.g., standard formats, general chapters, etc.) and undefined parts (e.g., specific technical parameters, test requirements, etc.).

[0048] The template matching unit 112 is connected to the standard template library establishment unit 111 and is used to match the corresponding document template from the preset template library according to the target document category (such as "Technical Specification for Relay Protection Device").

[0049] The content conversion unit 113 is connected to the template matching unit 112 and is used to traverse the pending content blocks in the pending section of the selected document template, establishing a document template linked list for each pending content block. Pending content blocks typically include technical terms, parameter requirements, or test items that need to be determined based on specific circumstances. In practical applications, pending content blocks may contain various types, such as text blocks, table blocks, and formula blocks, requiring the system to adopt different processing strategies for different types of content blocks. Accordingly, the classification model is sequentially called on the pending content blocks in the document template linked list to determine whether the pending content blocks have been successfully converted and to update the document template linked list. The classification model maps the pending content blocks to predefined content categories, such as technical parameters, test requirements, and applicable scope, based on their characteristics. For example, the classification model uses deep learning-based text classification technology, constructing a mapping relationship between content blocks and category labels through learning from a large number of standard documents.

[0050] In some embodiments, the classification model's judgment process can be represented by the following formula:

[0051]

[0052] in, Content block to be determined Category The probability of; For content blocks With category Similarity score; Total number of categories; is the base of the natural logarithm, approximately equal to 2.71828; when When the value exceeds a preset threshold, such as 0.75, the conversion is considered successful. It should be noted that this preset threshold (such as 0.75) was determined based on a large amount of experimental data, aiming to balance classification accuracy and content conversion coverage.

[0053] The parameter mapping unit 114 is connected to the content conversion unit 113 and is used to traverse the updated document template linked list. For each pending content block, it determines whether a placeholder exists in the document template. When a placeholder is present in the document template, it replaces the placeholder with a parameter and stores it in the parameter mapping list. The placeholder is typically marked with a specific format, such as {{parameter name}}. The system identifies these placeholders using techniques such as regular expressions and determines the replacement parameter based on the content type and context information. In some embodiments, the parameter mapping process considers the dependencies between parameters to ensure consistency. For example, when processing rated voltage, rated current and rated power are considered. This dependency can be represented and managed using a dependency graph.

[0054] The frame synthesis unit 115 is connected to the parameter mapping unit 114 and is used to fill in parameters and integrate the structure of fixed parts of the document template based on the generated parameter mapping list, ultimately generating a hierarchical document frame with a complete structure and parameterized content. In practical applications, to ensure the quality of the generated frame, this process may include automatic verification of the completeness of placeholder replacements and the rationality of the content structure.

[0055] In some embodiments, the content scope control module includes: an attention weight evaluation unit, used to evaluate the importance of content elements in a hierarchical document framework through an attention gating mechanism, and output the weight distribution of each content element; the importance evaluation is achieved by dynamically calculating and weighting the basic weight, context weight, and historical feedback weight of the content elements; a content boundary determination unit, connected to the attention weight evaluation unit, used to identify potential content boundaries based on the weight distribution by applying a preset threshold algorithm, and determine the target content boundary among the potential content boundaries; and a content outline generation unit, connected to the content boundary determination unit, used to construct a hierarchical representation of the content outline based on the target content boundary and document template structure information.

[0056] For example, Figure 3 A schematic diagram of the structure of a content scope control module provided for an exemplary embodiment of this application. For example... Figure 3 As shown, the content scope control module 12 includes: an attention weight evaluation unit 121, a content boundary determination unit 122, and a content outline generation unit 123. The attention weight evaluation unit 121 is used to evaluate the importance of each content element in the input hierarchical document framework through an attention gating mechanism, and outputs the weight distribution of each content element.

[0057] In some embodiments, the attention weights satisfy the following formula

[0058]

[0059] in, For content elements The final weight; The base weights (calculated directly from the rule base); Context weights (contributed by the interconnected network); Historical feedback weights (based on historical optimization records); , And γ are weighting coefficients, and In practical applications, these coefficients can be dynamically adjusted according to the content type. For example, for technical parameter content, a certain setting can be used. For descriptive text, settings can be configured. 0.5, .

[0060] The content boundary determination unit 122 is connected to the attention weight evaluation unit 121. Based on the weight distribution map calculated by the attention weight evaluation unit 121, it identifies locations with significant weight gradient changes as potential boundaries and applies an adaptive threshold algorithm to determine the final boundary. The weight gradient satisfies the following formula:

[0061]

[0062] in, For adjacent content elements With content elements The weight gradient between them; For content elements The weights; For content elements The weights; Indicates taking The absolute value of the gradient; when the gradient value exceeds the adaptive threshold, the location is identified as a potential boundary. The adaptive threshold is dynamically adjusted according to the content density, satisfying the following formula:

[0063]

[0064] in, For the threshold, The gradient mean, The standard deviation of the gradient; This is an adjustment factor, typically ranging from 2.0 to 3.0; in areas with high content density, A larger value, such as 3.0, is typically chosen to reduce the number of borders; in areas with low content density, Usually, a smaller value is taken, for example. To increase the number of boundaries.

[0065] The content outline generation unit 123 is connected to the content boundary determination unit 122. Based on the content boundaries determined by the content boundary determination unit 122 and the standard template structure information, it constructs a hierarchical structure of the content through a recursive partitioning algorithm, thereby generating a clear and logically complete content outline. During the recursive partitioning process, the system comprehensively considers the logical relationships and importance of the content, subdividing and organizing the content layer by layer to ensure that the final outline not only meets the specifications but also reflects a reasonable hierarchy.

[0066] In this embodiment, an attention weight evaluation unit performs intelligent dynamic weighted evaluation of document content elements, achieving precise quantification and ranking of content importance. Then, a content boundary determination unit, based on the evaluation results and applying an adaptive threshold algorithm, objectively and automatically identifies and delineates the content scope boundaries, effectively replacing the traditional model that relies on subjective human judgment. Furthermore, a content outline generation unit automatically constructs a hierarchical and logically rigorous content outline based on clear boundary and structural information. This module transforms the traditionally difficult-to-manage "content scope control" problem into a calculable and optimizable technical process, significantly improving the accuracy, consistency, and automation level of document content planning. This provides a reliable foundation for the subsequent generation of high-quality clauses and is a core element ensuring the logical integrity and highlighting of key points in the generated document.

[0067] In some embodiments, the attention weight evaluation unit includes: a feature extraction subunit for extracting multiple feature dimensions of content elements in a hierarchical document framework; a weight determination subunit connected to the feature extraction subunit for determining the basic weight, context weight, and historical feedback weight of the content elements based on the extracted feature dimensions; and a dynamic weighted fusion subunit connected to the weight determination subunit for dynamically adjusting the weighting coefficients to dynamically weight and combine the basic weight, context weight, and historical feedback weight to generate the final importance weight of each content element, thereby obtaining the weight distribution.

[0068] For example, Figure 4 A schematic diagram of the structure of an attention weight evaluation unit provided for an exemplary embodiment of this application. For example... Figure 4 As shown, the attention weight evaluation unit 121 includes: a feature extraction subunit 1211, a weight determination subunit 1212, and a dynamic weighted fusion subunit 1213. The feature extraction subunit 1211 maps each content element in the hierarchical document framework to a feature space and extracts multiple feature dimensions. These feature dimensions include, but are not limited to: semantic relevance (measuring the semantic closeness between the content element and the core topic), rule compliance (reflecting the degree of matching between the content element and predefined technical specifications), historical importance (based on the frequency of occurrence and adjustment records of the element in historical documents or feedback), and domain relevance (assessing the criticality of the content element within this technical field).

[0069] The weight determination subunit 1212 is connected to the feature extraction subunit 1211 and is used to calculate three core weights for each content element based on the extracted feature dimensions: basic weight, context weight, and historical feedback weight. When calculating the basic weight, features such as rule compliance are primarily used to reflect the standardization and necessity of the content. When calculating the context weight, this subunit establishes a network of relationships between content elements, analyzes the logical and technical dependencies between elements, and calculates the influence weight of adjacent elements accordingly. When calculating the historical feedback weight, features such as historical importance are primarily used. Furthermore, the weight determination subunit 1212 is also responsible for integrating and calibrating the initially calculated weights at different scales, including micro (e.g., specific parameters), meso (e.g., functional modules), and macro (e.g., chapter themes), to ensure that the final weights have reasonable consistency at different granularity levels.

[0070] The dynamic weighted fusion subunit 1213 is connected to the weight determination subunit 1212, and is used to receive the calculated basic weights, context weights, and historical feedback weights, and to dynamically adjust the weighting coefficients (such as...). , The three weights (γ) are combined and weighted to generate a unified scalar weight value for each content element that represents its final importance; the final importance weight of all content elements constitutes the weight distribution on which the system bases its subsequent content boundary division.

[0071] This embodiment of the application achieves accurate and automated assessment of document content importance through hierarchical processing involving feature extraction, multi-weight calculation, and dynamic weighted fusion. Specifically, the unit first extracts multi-dimensional features to provide a comprehensive and structured basis for weight calculation; then, it independently calculates three types of weights: foundational, contextual, and historical feedback, quantifying the content's standardization, relevance, and experiential value, respectively; finally, it intelligently fuses the weights by dynamically adjusting the weighting coefficients to generate a final weight that comprehensively reflects the content's importance. This hierarchical processing transforms subjective and vague judgments of content importance into an objective and computable parameterized process, significantly improving the accuracy, consistency, and adaptability of the assessment, and providing a scientific and reliable data foundation for core content scope control and outline generation.

[0072] In some embodiments, the clause generation module includes: a knowledge constraint application unit, used to obtain corresponding knowledge from a structured knowledge base according to the content outline, as the constraint basis for clause generation; wherein, the structured knowledge base stores domain knowledge in the form of triples; a generation strategy decision unit, connected to the knowledge constraint application unit, used to determine the clause generation strategy according to the constraint basis and the content outline, the generation strategy including knowledge reasoning, template adaptation and manual editing mode; a clause generation execution unit, connected to the generation strategy decision unit, used to execute clause content generation based on the generation strategy; and a document synthesis unit, connected to the clause generation execution unit, used to perform quality verification and integration of the generated clause content to form a document corresponding to the target document category.

[0073] For example, Figure 5 A schematic diagram of the structure of a clause generation module provided for an exemplary embodiment of this application. Figure 5 As shown, the clause generation module 13 includes: a knowledge constraint application unit 131, a generation strategy decision unit 132, a clause generation execution unit 133, and a document synthesis unit 134. The knowledge constraint application unit 131 retrieves and obtains corresponding domain knowledge from a preset structured knowledge base based on the content outline, serving as the semantic and logical constraint basis for subsequent clause generation. This structured knowledge base stores knowledge in triplet form. The generation strategy decision unit 132 is connected to the knowledge constraint application unit 131. This unit integrates the semantic information of the content outline, the constraint basis obtained from the knowledge base, and other factors (such as content complexity, the knowledge base's coverage of the current topic, and the historical generation effects of similar tasks) to determine the most suitable clause generation strategy. Available generation strategies include: a knowledge reasoning mode (generating through logical deduction based on the knowledge base), a template adaptation mode (calling and filling preset clause templates), and a manual editing mode (providing an auxiliary interface to guide or prompt manual writing).

[0074] In some embodiments, the system employs a decision model (such as a decision tree model) for strategy selection. The decision-making process can be formally represented as: selecting the strategy that maximizes the evaluation function value from a set of strategies {knowledge reasoning, template adaptation, manual editing}. The evaluation function can be calculated based on weighted scores across multiple dimensions, such as content complexity, knowledge coverage, and historical performance, to achieve adaptive optimal strategy selection. For example, the decision-making process can be represented as:

[0075]

[0076] in, The chosen strategy; Three optional strategies are provided (i.e., knowledge reasoning, template adaptation, and manual editing). For strategy In terms of content complexity Knowledge coverage and historical effects The scoring function below; This represents taking the parameter value that maximizes the function value. In practical applications, the scoring function is usually implemented using a weighted summation:

[0077]

[0078] in, , and These are the weighting coefficients, and , and respectively strategy The scoring function is based on three dimensions: content complexity, knowledge coverage, and historical impact.

[0079] The clause generation execution unit 133 is connected to the generation strategy decision unit 132 and is used to specifically execute the determined generation strategy to automatically generate specific clause content corresponding to the content outline. For example, if the strategy is a knowledge reasoning mode, the unit performs logical deduction based on the knowledge base to generate text; if it is a template adaptation mode, the corresponding template is called from the template library and parameters are filled in.

[0080] The document synthesis unit 134 is connected to the clause generation and execution unit 133, and is used to perform quality verification (such as format and terminology standardization checks) and logical integration on all generated clause content, and finally form a complete and standardized document that meets the requirements of the target document category.

[0081] This application embodiment achieves high-quality and efficient generation of clause content through a closed-loop process of knowledge constraint application, intelligent strategy decision-making, efficient execution, and automated synthesis. Specifically, the knowledge constraint application unit ensures that the generation process is always guided by structured knowledge, guaranteeing technical accuracy and compliance; the generation strategy decision-making unit intelligently selects the optimal generation mode (reasoning, template, or manual assistance) by dynamically evaluating factors such as content complexity and knowledge coverage, achieving adaptive optimization of the generation method; the clause generation execution unit executes precisely according to the selected strategy, ensuring generation efficiency and flexibility; finally, the document synthesis unit automatically verifies and integrates the generated content, outputting a standardized document with a complete structure and logical coherence. This design upgrades the traditional, single, and rigid generation method to an intelligent, multi-modal, and controllable modern process, significantly improving the intelligence level, processing efficiency, and overall quality of document generation.

[0082] In some embodiments, the clause generation execution unit includes: a content segmentation unit, used to divide the clause generation task based on the generation strategy and content outline into multiple subtasks with logical dependencies; a parallel generation control unit, connected to the content segmentation unit, used to control the execution of each subtask to generate clause content fragments based on the logical dependencies between the subtasks; correspondingly, the document synthesis unit includes a content integration unit, used to perform quality verification and logical consistency checks on each clause content fragment, and synthesize each clause content fragment into a document corresponding to the target document category based on the check results.

[0083] For example, Figure 6 A schematic diagram of the structure of a clause generation execution unit provided for an exemplary embodiment of this application. For example... Figure 6 As shown, the clause generation execution unit 133 includes: a content fragmentation unit 1331 and a parallel generation control unit 1332.

[0084] The content segmentation unit 1331 is connected to the generation strategy decision unit 132, and is used to divide the content generation task into multiple segments, determine segment boundaries, analyze the logical relationships between segments, and set segment priorities. The content segmentation unit 1331 further includes a segment granularity determination subunit, a segment boundary identification subunit, a segment dependency analysis subunit, and a segment priority setting subunit. The segment granularity determination subunit dynamically determines the segment granularity based on content complexity and generation requirements. This granularity can be at the chapter, paragraph, or clause level, and the system dynamically adjusts the granularity according to content complexity. For example, for highly complex technical clauses, fine-grained segmentation at the paragraph or clause level may be used; while for less complex general instructions, coarse-grained segmentation at the chapter level may be used. The segment boundary identification subunit identifies segment boundaries based on content structure and semantic integrity. The segment dependency analysis subunit determines the logical relationships and dependencies between segments. The segment priority setting subunit sets the segment processing priority based on content importance and dependencies. In practical applications, the system constructs a partition dependency graph to clearly record and manage the above analysis results, and determines the processing order of partitions based on the graph.

[0085] The parallel generation control unit 1332 is connected to the content sharding unit 1331 and is used to construct a task graph based on sharding dependencies, allocate computing resources, monitor execution status, and handle exceptions. The task graph is a directed acyclic graph (DAG), where nodes represent sharded tasks and edges represent dependencies between tasks. Accordingly, the system schedules tasks based on this task graph to maximize parallelism while satisfying dependencies.

[0086] Correspondingly, the document synthesis unit 134 includes a content integration unit connected to the parallel generation control unit 1332. This content integration unit verifies the quality of the fragmentation results, checks the consistency between fragments, generates transitional content between fragments, and performs global optimization on the integrated content. During the content integration process, the system executes a series of coherent quality control and optimization steps to ensure the professionalism and integrity of the final document. Specifically, this includes: 1) Performing segment quality verification: Independently reviewing each clause content segment generated by parallel tasks to ensure that its technical accuracy, format standardization, and content completeness meet preset standards; 2) Implementing inter-segment consistency checks: Systematically comparing the consistency of different segments in key terminology, cross-reference relationships, data logic, and document format style to eliminate potential contradictions; 3) Executing intelligent transition content generation: When semantic gaps or logical jumps are detected between adjacent segments, the system will determine the necessity of the transition based on a predefined rule base and automatically call the matching text template to generate connecting statements (e.g., "Based on the aforementioned performance indicators, the corresponding verification methods are as follows:..."), thereby ensuring the smoothness and coherence of the document; 4) Performing global optimization and synthesis: Based on the integration of all segments, the overall document undergoes structural adjustments, redundant content removal, and consistency enhancement processing to ultimately output a complete document with a rigorous structure, smooth logic, and unified format.

[0087] This application's embodiments achieve efficient, reliable, and high-quality document content generation through a collaborative mechanism of task fragmentation, parallel execution, and intelligent integration. Specifically, the content fragmentation unit decouples complex generation tasks into manageable logical subtasks, laying the foundation for parallelization; the parallel generation control unit intelligently schedules tasks based on subtask dependencies, maximizing the utilization of computing resources and significantly improving generation speed; the content integration unit performs automated quality verification, consistency checks, and intelligent synthesis on the parallel-generated fragments, ensuring that the final document meets high standards in accuracy, logical coherence, and format compliance. This design transforms the traditional serial and inefficient document generation process into a highly automated, scalable, and intelligent pipeline, fundamentally improving the efficiency, reliability, and overall quality of document preparation.

[0088] In some embodiments, the clause generation module further includes a knowledge base management unit for managing and maintaining a structured knowledge base. The knowledge base management unit includes: a knowledge structure definition subunit for defining and maintaining the subject type, relation type, and object type of triple data in the structured knowledge base; a knowledge base organization structure subunit connected to the knowledge structure definition subunit for hierarchical and modular organization of knowledge in the structured knowledge base based on domain ontology; a knowledge reasoning subunit connected to the knowledge base organization structure subunit for performing knowledge reasoning operations on the structured knowledge base during clause generation; knowledge reasoning operations include basic reasoning, transitive reasoning, and combinatorial reasoning; and a consistency maintenance subunit connected to the knowledge reasoning subunit for maintaining the consistency of knowledge in the structured knowledge base when its content is updated.

[0089] For example, Figure 7 A schematic diagram of the structure of a knowledge base management unit provided for an exemplary embodiment of this application. For example... Figure 7 As shown, the knowledge base management unit 135 includes: a knowledge structure definition subunit 1351, a knowledge base organization structure subunit 1352, a knowledge reasoning subunit 1353, and a consistency maintenance subunit 1354. This knowledge base management unit 135 is used to manage and maintain the structured knowledge base upon which the clause generation module relies. This knowledge base can be, for example, a "relay protection device technical standard knowledge base," whose core feature is representing domain knowledge as triplets with a fixed structure, in the form of (Subject, Relation, Object).

[0090] The knowledge structure definition subunit 1351 is responsible for defining and maintaining the triplet data model that forms the cornerstone of the knowledge base. Specifically, it designs the triplet structure, clearly defining: 1) Subject types, such as technical concepts (e.g., "overcurrent protection"), equipment components, functional modules, test items, etc.; 2) Relation types, such as "definition," "inclusion," "requirement," "test," "compatibility," "restriction," etc.; 3) Object types, such as performance parameters, technical indicators, standards and specifications, test methods, etc. For example, a typical triplet implementation is: (overcurrent protection, requirement, action time not exceeding 50ms).

[0091] The knowledge base organization structure subunit 1352 is connected to the knowledge structure definition subunit 1351, and is responsible for the systematic organization and management of massive triples in the knowledge base based on the domain ontology. This subunit constructs a multi-layered knowledge architecture, including a basic knowledge layer (core concepts and definitions), a normative knowledge layer (technical standards and requirements), and an application knowledge layer (specific cases and methods). Simultaneously, knowledge is divided into different knowledge modules, such as "basic definitions," "technical requirements," "testing methods," and "scope of application," to achieve modular storage and efficient retrieval of knowledge.

[0092] The knowledge reasoning subunit 1353 is connected to the knowledge base organization structure subunit 1352. During the clause generation process, it performs advanced knowledge reasoning operations on the structured knowledge base to uncover implicit knowledge and ensure logical consistency. Its reasoning capabilities include: 1) Basic reasoning: directly deriving new facts from existing triples; 2) Transitive reasoning: using the transitivity of relations to perform chain-like deductions (e.g., if A contains B, and B contains C, then A contains C); 3) Combinatorial reasoning: integrating multiple triples and rules to perform complex logical deductions. Additionally, this subunit is responsible for handling knowledge conflicts that may arise during reasoning or knowledge updating.

[0093] The consistency maintenance subunit 1354, connected to the knowledge reasoning subunit 1353, is a key component ensuring the long-term healthy operation of the knowledge base. It is primarily used to verify the consistency between new and existing knowledge when the knowledge base content is updated (e.g., adding or modifying triples), preventing the introduction of contradictory information. Furthermore, this subunit is also responsible for implementing version control and source traceability of knowledge, ensuring the manageability and auditability of knowledge evolution.

[0094] This application's embodiments, through a unified definition of a triplet structure and a hierarchical organization based on domain ontology, achieve structured and semantic storage of knowledge, providing a precise and efficient foundation for knowledge retrieval and association in clause generation. By supporting reasoning mechanisms that encompass basic, transitive, and combinatorial reasoning, it can proactively uncover and apply implicit logical relationships, significantly enhancing the technical depth and logical consistency of generated clauses. Furthermore, this unit ensures the accuracy and consistency of knowledge during continuous updates through a consistency maintenance mechanism. Through the combined effect of these technical means, a high-quality, maintainable knowledge core with deep reasoning capabilities is constructed, thereby guaranteeing the technical professionalism, logical rigor, and industry compliance of the generated documents from the source.

[0095] Based on the above embodiments, in some embodiments, the document generation system based on hierarchical control further includes: a quality verification module, connected to the clause generation module, used to perform format verification on the document output by the clause generation module; and, based on a preset rule base, to perform content standardization verification on the document.

[0096] For example, Figure 1 The hierarchical control-based document generation system 10 also includes a quality verification module 14. Accordingly, Figure 8 A schematic diagram of a quality verification module provided for an exemplary embodiment of this application. For example... Figure 8 As shown, the quality verification module 14 includes a format verification unit 141, a compliance verification unit 142, and a compliance rule base unit 143.

[0097] The format verification unit 141 is used to automatically verify the format of the document output by the clause generation module 13. Based on a predefined set of format rules, this unit comprehensively reviews the formal elements of the document through content analysis technology. Specific verification items include, but are not limited to: 1) Document structure: checking whether the chapter hierarchy and paragraph structure are reasonable and complete; 2) Numbering system: verifying whether the chapter numbers, figure numbers, etc., conform to the established sequence and format (e.g., first-level headings are “1.”, second-level headings are “(1)”); 3) Terminology consistency: ensuring that the same technical concept or entity is expressed consistently throughout the document; 4) Punctuation and basic format: checking whether the use of punctuation marks, fonts, spacing, etc., conform to the specifications. This unit automatically identifies and marks content that does not meet the format requirements through rule matching, ensuring that the output document conforms to professional standards in form.

[0098] The compliance rule base unit 143 stores the rule knowledge required for content compliance verification. This rule base is stored in a structured format, organized in two ways: 1) centered on technical clauses, specifying the constraints that various clauses must meet; 2) centered on key node information (such as technical parameters and performance indicators), specifying in which clauses these should be reflected and their specific requirements. Preferably, rules can be stored in a triple-like format, for example, (overcurrent protection, action time, ≤50ms) represents the action time requirement for the "overcurrent protection" function, or (rated voltage, technical parameter clause, mandatory) represents that the "rated voltage" must be explicitly specified in the technical parameter clause. This structure supports efficient bidirectional queries.

[0099] The compliance verification unit 142 is connected to both the format verification unit 141 and the compliance rule base unit 143. It performs content compliance verification on the document based on pre-defined rules in the compliance rule base unit 143. The verification process typically includes: 1) Key content extraction: automatically extracting key content elements such as technical parameters, performance indicators, and testing methods from the document; 2) Rule query and matching: retrieving relevant compliance rules and standard requirements from the compliance rule base based on the extracted content; 3) Compliance judgment: comparing the extracted content with the rule requirements to determine whether it meets technical specifications and industry standards; 4) Result generation: generating a compliance report, clearly identifying non-compliant content items, and providing modification suggestions. The format verification unit 141 and the compliance verification unit 142 work together to automatically check the quality of the generated document from both formal and content dimensions, ensuring that the final output document conforms to both format specifications and pre-defined standards and compliance requirements in terms of technical content.

[0100] This application embodiment provides crucial quality assurance for the document generation system through a dual automated verification mechanism of format validation and content compliance validation. This module not only automatically checks the document's structure, numbering, terminology, and other formatting elements to ensure its standardized and consistent form, but also performs in-depth compliance validation on the document's technical parameters, performance requirements, and other core content based on a preset structured rule base, ensuring strict adherence to industry standards and technical specifications. This dual verification mechanism automates and systematizes the traditional quality control process that relies on manual review, significantly improving the standardization, accuracy, and reliability of document output. This effectively guarantees the immediate usability and professional authority of the generated documents, making it an indispensable key link in achieving a closed loop of end-to-end high-quality document generation.

[0101] Considering the limited ability of related technologies to accumulate and reuse expert experience, the system struggles to continuously learn and optimize. Therefore, in some embodiments, the document generation system based on hierarchical control further includes: an intelligent learning module, connected to the content scope control module, the clause generation module, and the quality verification module, respectively. The intelligent learning module includes a rule base update unit, a parameter mapping list learning unit, and a knowledge base update unit. The rule base update unit is used to collect manual adjustment information on the content outline or generated document when the conversion processing of the hierarchical framework construction module fails or the clause content generated by the clause generation module is inaccurate, analyze the impact of the adjustments, and update the preset rule base used by the quality verification module based on the impact. The parameter mapping list learning unit, connected to the rule base update unit, learns and updates the parameter mapping list in the hierarchical framework construction module based on the manual adjustment information. The knowledge base update unit, connected to the parameter mapping list learning unit, updates the structured knowledge base combined with the clause generation module in the form of triples based on the manual adjustment information or the verification results of the quality verification module.

[0102] For example, Figure 9 Another schematic diagram of a hierarchical control-based document generation system provided for an exemplary embodiment of this application. (See diagram below.) Figure 9 As shown, the hierarchical control-based document generation system 10 also includes an intelligent learning module 15. The intelligent learning module 15 is connected to the content scope control module 12, the clause generation module 13, and the quality verification module 14, respectively, to achieve adaptive optimization and continuous learning of the system. Accordingly, Figure 10 A schematic diagram of the structure of an intelligent learning module provided for an exemplary embodiment of this application. For example... Figure 10 As shown, the intelligent learning module 15 includes a rule base update unit 151, a parameter mapping list learning unit 152, and a knowledge base update unit 153.

[0103] The rule base update unit 151 is one of the core units of the intelligent learning module 15. When content problems occur during system operation, such as the conversion processing of the layered framework construction module 11 failing, or the clause content generated by the clause generation module 13 being marked as inaccurate, this unit collects information on manual adjustments to the corresponding content outline or the final generated document. Subsequently, the rule base update unit 151 analyzes the specific content of the manual adjustments and assesses their guiding impact on the generation of relevant technical clauses (for example, a five-level quantification method can be used, from "critical impact" to "no significant impact"). Furthermore, based on this analysis result, the rule base update unit 151 dynamically updates the preset rule base on which the quality verification module 14 relies, including adding new rules, adjusting the weight of existing rules, or modifying rule content, thereby improving the accuracy and adaptability of subsequent document generation and verification.

[0104] The parameter mapping list learning unit 152 is connected to the rule base update unit 151. This unit is responsible for learning and optimizing the parameter mapping strategy in the document framework generation process based on the same manually adjusted information. Specifically, it records the mapping relationship between manually corrected placeholders and specific parameter values, and uses mechanisms such as reinforcement learning to increase the weight of frequently occurring correct mapping relationships. The weight update satisfies the following formula:

[0105]

[0106] in, Placeholder for the updated version to parameters Mapping weights; These are the original mapping weights; The learning rate (usually set to 0.1 to...) (between). In this way, the weight of frequently occurring mapping relationships gradually approaches 1, while the weight of less frequently occurring mapping relationships is relatively low. Through continuous learning, the system can continuously optimize the parameter mapping list in the layered framework building module 11, thereby making the framework generation more accurate.

[0107] The knowledge base update unit 153 is connected to the parameter mapping list learning unit 152 and is responsible for maintaining and enhancing the core knowledge assets (i.e., the structured knowledge base) upon which the clause generation module 13 relies. This unit triggers knowledge updates based on two main inputs: the aforementioned manual adjustment information and the automated verification results from the quality verification module 14 (when verification reveals non-compliance). During updates, the knowledge base update unit 153 integrates new domain knowledge or revised knowledge into the knowledge base in a standardized triplet format (Subject, Relation, Object). To ensure the long-term quality and consistency of the knowledge base, the update strategy can combine incremental updates (for minor changes) with periodic refactoring (for major adjustments to the knowledge system), maintaining the overall logical consistency and structural integrity of the knowledge base while ensuring system responsiveness.

[0108] Through the collaborative work of the three units mentioned above, the intelligent learning module forms a complete closed loop from "user feedback" to "core configuration optimization," enabling the entire document generation system to continuously learn from operational experience and improve itself.

[0109] Correspondingly, Figure 11 Another structural diagram of the content scope control module provided for an exemplary embodiment of this application. (See diagram below.) Figure 11 As shown, the content scope control module 12 also includes a feedback collection and analysis unit 124 and a rule optimization and update unit 125.

[0110] The feedback collection and analysis unit 124 is used to collect feedback information provided by human experts, and to perform semantic analysis and structuring processing on the feedback information to output a structured feedback feature vector. The types of feedback information include structural adjustments, content supplements, scope corrections, priority adjustments, etc., and the feedback sources include standard drafting experts, domain technical experts, and end users. The feedback information is first subjected to semantic analysis to extract key information, and then converted into a structured feature vector for subsequent rule optimization.

[0111] Preferably, the feedback feature vector includes the following dimensions: target element (the content element to which the feedback is directed), modification type (such as addition, deletion, adjustment, etc.), modification content (specific modification suggestions), and modification reason (the reason and basis for the modification). In addition, each piece of feedback is assigned a priority based on its source and content, with priorities ranging from high to low, for example, critical (5), important (4), general (3), suggestion (2), and reference (1).

[0112] The rule optimization and update unit 125 is connected to the feedback collection and analysis unit 124. It is used to calculate the correlation matrix between feedback and each rule based on the feedback feature vector and the rule set in the rule base, generate rule adjustment suggestions, and optimize the rule base based on a confidence-weighted rule update algorithm. The rule optimization process can be represented as follows:

[0113]

[0114] in, For the updated rules; The original rules apply; The adjustment amount for the rule is determined based on the correlation between the feedback feature vector and the rule. The learning rate (usually set between 0.1 and 0.3) is dynamically adjusted based on the credibility of feedback and the effectiveness of historical adjustments.

[0115] In some embodiments, the system employs an incremental learning approach, adjusting rules immediately upon receiving feedback rather than batch processing. This allows for rapid response to new requirements and changes, significantly improving the system's adaptability. Simultaneously, the system maintains a rule dependency graph reflecting the relationships between rules. By applying cycle detection algorithms from graph theory, it can automatically identify and warn of potential logical conflicts that may be caused by rule updates. Furthermore, based on feedback priority, rule confidence, and conflict type, a priority weighting mechanism and a dynamic rule rewriting strategy are used to intelligently resolve detected rule conflicts, ensuring the logical consistency of the rule base and system stability during continuous optimization.

[0116] The above embodiments illustrate the implementation of a document generation system based on hierarchical control. The following specific embodiments will introduce its application in standard document compilation.

[0117] Figure 12 This is a flowchart illustrating a hierarchical control-based document generation method provided as an exemplary embodiment of this application. The hierarchical control-based document generation method provided in this embodiment is applied to the hierarchical control-based document generation system described above. This hierarchical control-based document generation system includes a hierarchical framework construction module, a content scope control module, and a clause generation module. For example... Figure 12 As shown, this hierarchical control-based document generation method includes:

[0118] S1201, The hierarchical framework construction module matches a document template according to the target document category and performs hierarchical transformation on the undetermined content blocks in the document template to generate a hierarchical document framework; wherein, the hierarchical transformation process includes classifying and transforming the undetermined content blocks based on a preset classification model.

[0119] Specifically, the system matches a corresponding template from a pre-defined document template library based on the user-specified target document category (e.g., "Technical Specifications for Relay Protection Devices"). This template contains fixed sections (e.g., standard formats, general chapters) and undefined sections. It iterates through undefined content blocks (e.g., specific technical parameters, test requirements) within the undefined sections. For each undefined content block, it performs category determination and conversion based on a pre-trained classification model (e.g., a deep learning-based text classifier). This classification model maps the undefined content block to a predefined content category (e.g., technical parameters, test requirements, scope of application). If the conversion is successful (probability exceeding a preset threshold, such as 0.75), the internal structure is updated. Subsequently, the system identifies and replaces placeholders (e.g., {{parameter names}}) in the document template, generating a parameter mapping list. Finally, the mapping relationships are applied to the fixed sections of the template, forming a hierarchical document framework with a clear hierarchical structure.

[0120] S1202 The content scope control module evaluates the importance of content in a hierarchical document framework based on an attention gating mechanism, and determines the content boundaries to generate a content outline based on the evaluation results. The attention gating mechanism is configured to dynamically distinguish the importance of content elements by combining the rule compliance and contextual relevance of the content elements.

[0121] Accordingly, the content scope control module includes: an attention weight evaluation unit, a content boundary determination unit, and a content outline generation unit. Upon receiving the hierarchical document framework generated in step S1201, the attention weight evaluation unit employs an attention gating mechanism to evaluate the importance of each content element (such as clauses and parameter items) within the framework. This mechanism dynamically calculates and weights each element's base weight (directly derived from the rule base), context weight (calculated based on its association network with adjacent elements), and historical feedback weight (based on past manual adjustment records) to obtain the final importance weight distribution.

[0122] In some embodiments, the attention weights satisfy the following formula

[0123]

[0124] in, For content elements The final weight; The base weights (calculated directly from the rule base); Context weights (contributed by the interconnected network); Historical feedback weights (based on historical optimization records); , And γ are weighting coefficients, and .

[0125] The content boundary determination unit is connected to the attention weight evaluation unit. Based on the weight distribution map calculated by the attention weight evaluation unit, it identifies locations with significant changes in weight gradients as potential content boundaries and applies an adaptive threshold algorithm to determine the final content boundaries. Subsequently, the content outline generation unit constructs a logically clear and hierarchical content outline based on these determined content boundaries and the inherent structural information of the template through a recursive partitioning algorithm.

[0126] S1203 The clause generation module, based on the content outline and combined with a preset structured knowledge base, constrains and generates clauses to form a document corresponding to the target document category.

[0127] For example, the clause generation module includes: a knowledge constraint application unit, a generation strategy decision unit, a clause generation execution unit, and a document synthesis unit. Accordingly, upon receiving the content outline generated in step S1202, the knowledge constraint application unit retrieves relevant domain knowledge from a pre-set structured knowledge base as the basis for constraints, guided by the outline. This knowledge base stores information in the form of (subject, relation, object) triples, for example (overcurrent protection, action time, ≤50ms). The generation strategy decision unit then decides on the generation strategy for each clause based on factors such as content complexity and knowledge coverage, such as knowledge reasoning mode, template adaptation mode, or manual editing mode. The clause generation execution unit generates content according to the selected strategy: for complex content, the content segmentation unit may divide the task into sub-tasks with logical dependencies, which are then coordinated and generated by the parallel generation control unit; for standard content, template adaptation can be used directly. Finally, the document synthesis unit performs quality verification and logical consistency checks on all generated clause content fragments and integrates them into a coherent and complete final document.

[0128] Furthermore, to ensure the quality of the generated documents and achieve continuous system optimization, this method may include subsequent processing steps after completing the core generation steps described above:

[0129] 1) Quality verification steps: The quality verification module, which is connected to the clause generation module, performs automated format verification (such as document structure, terminology consistency, numbering format) and rule-based content standardization verification (compliance verification) on the generated document to ensure that the document conforms to the established standards and specifications.

[0130] 2) Intelligent Learning Steps: An intelligent learning module, connected to the content scope control module, clause generation module, and quality verification module respectively, collects human feedback (such as adjustments to the content outline or generated documents) and automatic verification results during system operation. By analyzing this information, the module dynamically updates the rule base, optimizes the parameter mapping list, and expands and corrects the structured knowledge base through incremental learning (e.g., adding new technical standards in the form of triples). This enables the system to learn from experience, continuously improving the accuracy and adaptability of subsequent document generation, thus forming a complete closed-loop workflow of "generation-verification-learning".

[0131] For example, taking the standard compilation of relay protection devices for a power company as an example, after adopting the hierarchical control-based document generation system of this application, the efficiency of standard document compilation has been significantly improved. The traditional compilation method requires 3 to 4 months of manual work, while the application of this system reduces it to 3 to 4 weeks, improving the overall compilation efficiency by about 300%. At the same time, the quality of the standard content generated by the system has been significantly improved. Through automated verification and structured knowledge constraints, the content error rate has been reduced by about 80%. In addition, the direct human and time costs in the compilation process have been reduced by about 60%. Through the synergistic effect of hierarchical control generation and intelligent verification, this system not only improves efficiency but also fundamentally ensures the professionalism and consistency of the standard documents.

[0132] Furthermore, this system also enables the structured accumulation and systematic reuse of technical standard knowledge. During implementation, the system automatically builds and continuously improves a domain knowledge base, forming a complete standard knowledge system for relay protection devices. For example, in typical application scenarios, this knowledge base has accumulated over 10,000 knowledge triplets organized in the form of (subject, relation, object), comprehensively covering core dimensions such as technical parameter definitions, performance index requirements, test method specifications, and safety boundary conditions. This structured knowledge system not only provides precise constraints for current standard development but can also be directly applied to business processes such as compliance checks in equipment R&D, test case generation for testing and verification, and optimization of operation and maintenance procedures, forming a cross-process knowledge loop and bringing comprehensive intelligent value enhancement to enterprises. It is evident that the organic combination of a hierarchical control generation framework and a structured knowledge system not only effectively improves the pain points of low efficiency, poor consistency, and difficulty in knowledge transfer in traditional standard development but also constructs a sustainable and evolving enterprise-level standard knowledge infrastructure. While improving the efficiency and quality of single-stage standard development, it provides reliable technical support and knowledge assurance for the long-term development of enterprise standardization work through continuous learning and knowledge accumulation mechanisms.

[0133] In summary, this application has at least the following advantages: through architectural layered control and intelligent algorithm mechanisms, it significantly improves the automation and efficiency of document generation while systematically ensuring the professionalism and accuracy of documents in multiple dimensions such as structure, logic, and content, thereby significantly improving document preparation efficiency and quality.

[0134] This application also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed, they are used to implement the method steps as described in the above method embodiments. The specific implementation methods and technical effects are similar and will not be repeated here.

[0135] The aforementioned computer-readable storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0136] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in a private network access system.

[0137] This application also provides a computer program product, including a computer program, which, when executed, implements the method steps as described in the above method embodiments. The specific implementation and technical effects are similar and will not be repeated here.

[0138] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0139] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A document generation system based on hierarchical control, characterized in that, include: The module consists of a layered framework construction module, a content scope control module, and a clause generation module, among which: The hierarchical framework construction module is used to match a document template according to the target document category and perform hierarchical transformation processing on the undetermined content blocks in the document template to generate a hierarchical document framework; wherein, the hierarchical transformation processing includes classifying and transforming the undetermined content blocks based on a preset classification model; The content scope control module is connected to the hierarchical framework construction module and is used to evaluate the importance of the content in the hierarchical document framework based on the attention gating mechanism, and determine the content boundaries based on the evaluation results to generate a content outline; the attention gating mechanism is configured to dynamically distinguish the importance of content elements by comprehensively considering the rule compliance and contextual relevance of the content elements. The clause generation module is connected to the content scope control module and is used to constrain and generate clauses based on the content outline and a preset structured knowledge base, so as to form a document corresponding to the target document category.

2. The document generation system based on hierarchical control according to claim 1, characterized in that, The layered framework construction module includes: The template matching unit is used to match the corresponding document template from a preset template library according to the target document category; The content conversion unit, connected to the template matching unit, is used to traverse the undetermined parts in the document template and, for the undetermined content blocks of the undetermined parts, perform category determination and conversion based on the preset classification model. The parameter mapping unit, connected to the content conversion unit, is used to perform a placeholder replacement operation based on the converted undetermined content block to generate a parameter mapping list; The framework synthesis unit, connected to the parameter mapping unit, is used to generate the hierarchical document framework based on the parameter mapping list.

3. The document generation system based on hierarchical control according to claim 2, characterized in that, The content scope control module includes: The attention weight evaluation unit is used to evaluate the importance of content elements in the hierarchical document framework through the attention gating mechanism and output the weight distribution of each content element; the importance evaluation is achieved by dynamically calculating and weighting the basic weight, context weight and historical feedback weight of the content elements. The content boundary determination unit, connected to the attention weight evaluation unit, is used to identify potential content boundaries based on the weight distribution by applying a preset threshold algorithm, and to determine the target content boundary within the potential content boundaries. The content outline generation unit, connected to the content boundary determination unit, is used to construct a hierarchical representation of the content outline based on the target content boundary and document template structure information.

4. The document generation system based on hierarchical control according to claim 2, characterized in that, The terms generation module includes: The knowledge constraint application unit is used to retrieve corresponding knowledge from the structured knowledge base according to the content outline, so as to serve as the constraint basis for clause generation; wherein, the structured knowledge base stores domain knowledge in the form of triples; A generation strategy decision unit, connected to the knowledge constraint application unit, is used to determine the generation strategy of the clauses based on the constraint basis and the content outline. The generation strategy includes knowledge reasoning, template adaptation and manual editing mode. A clause generation and execution unit, connected to the generation strategy decision unit, is used to generate clause content based on the generation strategy. The document synthesis unit, connected to the clause generation and execution unit, is used to perform quality verification and integration on the generated clause content to form a document corresponding to the target document category.

5. The document generation system based on hierarchical control according to claim 4, characterized in that, The clause generation and execution unit includes: The content segmentation unit is used to divide the clause generation task based on the generation strategy and content outline into multiple sub-tasks with logical dependencies. A parallel generation control unit, connected to the content fragmentation unit, is used to control the execution of each subtask to generate clause content fragments based on the logical dependencies between the subtasks. Correspondingly, the document synthesis unit includes a content integration unit, which is used to perform quality verification and logical consistency checks on each of the clause content fragments, and synthesize each of the clause content fragments into a document corresponding to the target document category based on the check results.

6. The document generation system based on hierarchical control according to claim 4, characterized in that, The clause generation module further includes a knowledge base management unit for managing and maintaining the structured knowledge base; wherein, the knowledge base management unit includes: The knowledge structure definition subunit is used to define and maintain the subject type, relation type, and object type of the triple data in the structured knowledge base; The knowledge base organization structure sub-unit, connected to the knowledge structure definition sub-unit, is used to hierarchically and modularly organize the knowledge in the structured knowledge base based on the domain ontology; The knowledge reasoning subunit, connected to the knowledge base organization structure subunit, is used to perform knowledge reasoning operations on the structured knowledge base during the clause generation process; the knowledge reasoning operations include basic reasoning, transitive reasoning, and combinatorial reasoning. The consistency maintenance subunit, connected to the knowledge reasoning subunit, is used to maintain the consistency of knowledge in the structured knowledge base when the content of the structured knowledge base is updated.

7. The document generation system based on hierarchical control according to claim 3, characterized in that, The attention weight evaluation unit includes: The feature extraction subunit is used to extract multiple feature dimensions of content elements in the hierarchical document framework; The weight determination subunit, connected to the feature extraction subunit, is used to determine the basic weight, context weight, and historical feedback weight of the content element based on the extracted feature dimensions. The dynamic weighted fusion subunit, connected to the weight determination subunit, is used to dynamically adjust the weighting coefficients to dynamically weight and combine the basic weights, the context weights, and the historical feedback weights to generate the final importance weights of each content element, thereby obtaining the weight distribution.

8. The document generation system based on hierarchical control according to any one of claims 2 to 4, characterized in that, The hierarchical control-based document generation system also includes: A quality verification module, connected to the clause generation module, is used to perform format verification on the document output by the clause generation module; and to perform content standardization verification on the document based on a preset rule base.

9. The document generation system based on hierarchical control according to claim 8, characterized in that, The hierarchical control-based document generation system also includes: The intelligent learning module is connected to the content scope control module, the clause generation module, and the quality verification module, respectively. The intelligent learning module includes a rule base update unit, a parameter mapping list learning unit, and a knowledge base update unit; wherein: The rule base update unit is used to collect manual adjustment information for the content outline or generated document when the conversion processing of the layered framework construction module fails or the clause content generated by the clause generation module is inaccurate, analyze the degree of impact of the adjusted content, and update the preset rule base on which the quality verification module is based according to the degree of impact. The parameter mapping list learning unit is connected to the rule base update unit and is used to learn and update the parameter mapping list in the layered framework construction module based on the manual adjustment information. The knowledge base update unit, connected to the parameter mapping list learning unit, is used to update the structured knowledge base combined with the clause generation module in the form of triples based on the manual adjustment information or the verification result of the quality verification module.

10. A document generation method based on hierarchical control, characterized in that, The document generation system based on hierarchical control, as described in any one of claims 1 to 9, comprises a hierarchical framework construction module, a content scope control module, and a clause generation module; The document generation method includes: The hierarchical framework construction module matches a document template according to the target document category and performs hierarchical transformation processing on the undetermined content blocks in the document template to generate a hierarchical document framework; wherein, the hierarchical transformation processing includes classifying and transforming the undetermined content blocks based on a preset classification model; The content scope control module evaluates the importance of the content in the hierarchical document framework based on an attention gating mechanism, and determines the content boundaries to generate a content outline based on the evaluation results. The attention gating mechanism is configured to dynamically distinguish the importance of content elements by combining the rule compliance and contextual relevance of the content elements. The clause generation module, based on the content outline and in conjunction with a preset structured knowledge base, constrains and generates clauses to form a document corresponding to the target document category.

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