Automatic official document generation method based on dynamic rules

Abstract

The invention discloses an automatic official document generation method based on a dynamic rule, and relates to the technical field of official document generation, and the method comprises the following steps: receiving official document writing demand data input by a target user, carrying out semantic analysis on the official document writing demand data, extracting structured parameter data based on a semantic analysis result, and generating an official document according to the structured parameter data. Performing official document type identification on the structured parameter data to confirm an official document type identification code; a dynamic rule engine is used for matching the corresponding format verification rule base and the content logic knowledge graph, and an official document writing framework is generated; receiving business data corresponding to the target user, extracting a key information triple from the business data, inputting the key information triple into a preset intelligent mapping model for semantic level association analysis, and generating a preliminary official document manuscript in combination with an official document writing framework; and verifying the preliminary official document manuscript, and adjusting and optimizing the preliminary official document manuscript according to a verification result to generate a final official document manuscript. The method has the effect of improving the document generation efficiency.

Description

Technical Field

[0001] This application relates to the field of official document generation technology, and in particular to an automated official document generation method based on dynamic rules. Background Technology

[0002] In administrative units, official documents are typically used to convey government orders and policies and to handle official business. Official documents (official documents) are written materials created and used by statutory organs and organizations in their official activities, following a specific format and processing procedures.

[0003] In related technologies, existing document generation technologies mainly rely on template filling or simple keyword replacement modes. Users need to fill in the work results, problems and other content item by item according to the template, and the format verification needs to be completed manually. This leads to problems such as poor format standardization and insufficient content coherence, resulting in reduced document generation efficiency and areas for improvement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a method for automatically generating official documents based on dynamic rules.

[0005] Firstly, this application provides a method for automatically generating official documents based on dynamic rules, comprising the following steps:

[0006] Step S1: Receive the document writing requirement data input by the target user, perform semantic analysis on the document writing requirement data, extract structured parameter data based on the results of semantic analysis, identify the document type on the structured parameter data, and confirm the document type identification code.

[0007] Step S2: Based on the structured parameter data and document type identifier, call the dynamic rule engine and match the corresponding format validation rule library and content logic knowledge graph to generate a document writing framework;

[0008] Step S3: Receive business data corresponding to the target user, extract key information triples from the business data, input the key information triples into the preset intelligent mapping model for semantic-level association analysis, and combine them with the official document writing framework to generate a preliminary official document draft.

[0009] Step S4: Verify the preliminary official document draft, and adjust and optimize the preliminary official document draft according to the verification results to generate the final official document draft.

[0010] Preferably, step S1 includes the following steps:

[0011] The system receives document writing requirement data input by the target user through a natural language processing interface. The document writing requirement data includes voice requirement data and text requirement data. Voiceprint feature recognition and text translation are performed on the voice requirement data. Based on the text translation results and combined with the text requirement data, an original requirement text sequence is generated.

[0012] The original demand text sequence is subjected to noise filtering, and the original demand text sequence is classified according to intent based on the result of noise filtering to generate structured parameter data.

[0013] The structured parameter data is subjected to feature analysis to identify the target feature vector. The target feature vector is then input into a preset classifier network to generate a document type probability distribution matrix.

[0014] Obtain the maximum value index in the document type probability distribution matrix, and output the document type identifier code by combining it with the preset type code lookup table.

[0015] The preferred process for generating an official document writing framework specifically includes:

[0016] Based on the document type identification code, the corresponding format validation rule set is extracted from the format validation rule base, and the corresponding node is matched in the content logic knowledge graph to generate a content arrangement scheme.

[0017] The dynamic rule engine is activated to convert the format validation rule set and content arrangement scheme into a rule instruction stream. The rule instruction stream is prioritized according to the structured parameter data to generate a document writing framework.

[0018] Preferably, step S3 includes the following steps:

[0019] Connect to the business system, extract business data from the business system, construct a data cleaning channel, perform entity alignment and attribute fusion processing on the business data, and generate a business information graph;

[0020] The business information graph is semantically mapped to the official document writing framework to extract key information triples. The key information triples are then input into a preset intelligent mapping model for association reasoning analysis. The preset intelligent mapping model is set as a graph neural network architecture.

[0021] Based on the results of the association reasoning analysis, confidence text fragments are generated, and the content is arranged according to the official document writing framework to generate a preliminary official document draft.

[0022] Preferably, the preset intelligent mapping model includes an entity linking unit, a conflict detection unit, and a credibility evaluation unit;

[0023] The entity linking unit is used to identify the corresponding named entity data in the business data and establish a link between the named entity data and a preset standard knowledge base.

[0024] The conflict detection unit is used to detect the compatibility value between the result of the correlation reasoning analysis and the official document writing framework based on logical rules, and to issue a conflict warning based on the compatibility value.

[0025] The credibility assessment unit is used to calculate the confidence score corresponding to each text segment.

[0026] Preferably, the preliminary document draft is verified, and adjustments and optimizations are made based on the verification results to generate the final document draft, specifically including:

[0027] The preliminary official document draft is subjected to format compliance check and logical integrity verification to confirm the compliance coefficient and integrity coefficient corresponding to the preliminary official document draft, and then a comprehensive verification coefficient is generated based on the compliance coefficient and integrity coefficient corresponding to the preliminary official document draft.

[0028] The comprehensive test coefficient is compared with the preset comprehensive test threshold.

[0029] If the comprehensive inspection coefficient is higher than the preset comprehensive inspection threshold, then there is no need to adjust or optimize the preliminary official document draft.

[0030] If the overall inspection coefficient is not higher than the preset overall inspection threshold, the preliminary official document draft needs to be adjusted and optimized.

[0031] Preferably, it also includes a processing module, which includes a version management unit, a knowledge update unit, and a security audit unit;

[0032] The version management unit is used to store the official document drafts generated each time and their revision records;

[0033] The knowledge update unit is used to dynamically optimize the dynamic rule engine based on the feedback data corresponding to the target user.

[0034] The security audit unit is used to perform security audits on the document generation process and embeds digital watermarks.

[0035] Secondly, this application provides an automated document generation system based on dynamic rules, comprising:

[0036] The data acquisition module is used to receive document writing requirement data input by the target user, perform semantic analysis on the document writing requirement data, extract structured parameter data based on the results of semantic analysis, identify document type on the structured parameter data, and confirm the document type identification code.

[0037] The framework generation module is used to call the dynamic rule engine based on structured parameter data and document type identifier code, and match the corresponding format validation rule library and content logic knowledge graph to generate a document writing framework.

[0038] The analysis module is used to receive business data corresponding to the target user, extract key information triples from the business data, input the key information triples into a preset intelligent mapping model for semantic-level association analysis, and combine them with the official document writing framework to generate a preliminary official document draft.

[0039] The optimization module is used to verify the preliminary official document draft and adjust and optimize the preliminary official document draft according to the verification results to generate the final official document draft.

[0040] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform any of the above-described methods for automatically generating official documents based on dynamic rules.

[0041] In summary, this application includes the following beneficial technical effects:

[0042] This application provides a method for automated document generation based on dynamic rules. By performing semantic analysis and document type identification on document writing requirement data, structured parameter data and document type identifier codes are identified. A dynamic rule engine is then invoked to match the corresponding format verification rule library and content logic knowledge graph to generate a document writing framework. This effectively reduces the inefficiency caused by relying on template filling or simple keyword replacement patterns. Furthermore, key information triples are input into a preset intelligent mapping model for semantic-level association analysis. Combined with the document writing framework, a preliminary document draft is generated. The preliminary document draft is then verified, and adjustments and optimizations are made based on the verification results to generate the final document draft. This effectively reduces problems such as poor format standardization and insufficient content coherence, thereby significantly improving the efficiency of document generation. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of a method for automatically generating official documents based on dynamic rules, according to an embodiment of this application.

[0045] Figure 2This is a schematic diagram of a system for the automated generation of official documents based on dynamic rules, according to an embodiment of this application. Detailed Implementation

[0046] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0047] Example 1

[0048] This application discloses a method for automatically generating official documents based on dynamic rules.

[0049] Reference Figure 1 A method for automatically generating official documents based on dynamic rules includes the following steps:

[0050] Step S1: Receive the document writing requirement data input by the target user, perform semantic analysis on the document writing requirement data, extract structured parameter data based on the results of semantic analysis, identify the document type on the structured parameter data, and confirm the document type identification code.

[0051] Step S2: Based on the structured parameter data and document type identifier, call the dynamic rule engine and match the corresponding format validation rule library and content logic knowledge graph to generate a document writing framework;

[0052] Step S3: Receive business data corresponding to the target user, extract key information triples from the business data, input the key information triples into the preset intelligent mapping model for semantic-level association analysis, and combine them with the official document writing framework to generate a preliminary official document draft.

[0053] Step S4: Verify the preliminary official document draft, and adjust and optimize the preliminary official document draft according to the verification results to generate the final official document draft.

[0054] It should be noted that step S1 includes the following steps:

[0055] The system receives document writing requirement data input by the target user through a natural language processing interface. The document writing requirement data includes voice requirement data and text requirement data. Voiceprint feature recognition and text translation are performed on the voice requirement data. Based on the text translation results and combined with the text requirement data, an original requirement text sequence is generated.

[0056] The original demand text sequence is subjected to noise filtering, and the original demand text sequence is classified according to intent based on the result of noise filtering to generate structured parameter data.

[0057] The structured parameter data is subjected to feature analysis to identify the target feature vector. The target feature vector is then input into a preset classifier network to generate a document type probability distribution matrix. The preset classifier network can be obtained by training with historical data.

[0058] Obtain the maximum value index in the document type probability distribution matrix, and output the document type identification code by combining it with the preset type code lookup table.

[0059] Specifically, the system receives document writing requirement data input by the target user through a natural language processing interface. This requirement data may be in voice format (e.g., the target user dictates a notice about a project delay) or text format (e.g., inputs a draft: department quarterly work summary). For voice requirements, voiceprint feature recognition is first performed to confirm the user's identity. Then, voice is converted to text using speech-to-text technology and integrated with the text requirement data to form an original requirement text sequence. Noise filtering is applied to the original requirement text sequence to remove irrelevant information (for example, filtering out colloquialisms, interjections, or garbled data). Based on the filtering results, intent classification is performed, and structured parameters are extracted. For example, if the original requirement text sequence is: "Request to purchase 10 office computers, budget 50,000 yuan," then the identified structured parameters are: Item type: Purchase request, Quantity: 10, and Budget: 50,000 yuan.

[0060] The structured parameters are subjected to feature analysis, and key information is transformed into target feature vectors (such as using numerical values ​​or codes to represent core elements like procurement). These vectors are then input into a pre-defined classifier network to generate a document type probability distribution matrix. For example, the probability of a procurement application is 85%, and the probability of a funding request is 10%. The index corresponding to the maximum value in the document type probability distribution matrix is ​​selected, and the document type identifier code is output by referring to the type coding table, for example: "003-Procurement Application".

[0061] By adopting the above technical solution, the flexibility of demand reception is improved through input processing, and the accuracy of data is ensured through noise filtering. Structured parameter extraction and feature analysis enable the accurate transformation of demands. The classifier network improves the intelligence and accuracy of type recognition based on historical data. Finally, the document template is quickly matched through the identification code, which greatly improves the efficiency and standardization of document writing.

[0062] It should be noted that the process of generating an official document writing framework specifically includes:

[0063] Based on the document type identification code, the corresponding format validation rule set is extracted from the format validation rule base, and the corresponding node is matched in the content logic knowledge graph to generate a content arrangement scheme.

[0064] The dynamic rule engine is activated to convert the format validation rule set and content arrangement scheme into a rule instruction stream. The rule instruction stream is prioritized according to the structured parameter data to generate a document writing framework.

[0065] Specifically, based on the established document type identifier code "003-Purchase Request", the corresponding rule set is extracted from the format validation rule library. For example, a purchase request must include mandatory fields such as the applying department, a list of purchased items, budget details, and the reason for the application, as well as the format of the title "Purchase Request Regarding XX", and the format of the body paragraphs must be left-aligned. At the same time, the purchase request node is matched in the content logic knowledge graph, and the standard logical chain of this type of document (such as "Reason for Application → Item Details → Budget Basis → Approval Process") is retrieved to generate a content arrangement scheme.

[0066] The dynamic rule engine is activated to convert the format validation rules and content arrangement scheme into an executable rule instruction stream. For example, 1. Generate a title bar with a font size 3 bold; 2. Insert the application department field, associate it with the department name in the structured parameters, and then sort the instruction stream according to the importance of the structured parameter data (such as the instruction corresponding to core parameters such as the purchase item list and budget amount has higher priority than auxiliary information such as contact number), and finally generate a complete document writing framework.

[0067] It should be noted that step S3 includes the following steps:

[0068] Connect to the business system, extract business data from the business system, construct a data cleaning channel, perform entity alignment and attribute fusion processing on the business data, and generate a business information graph;

[0069] The business information graph is semantically mapped to the official document writing framework to extract key information triples. The key information triples are then input into a preset intelligent mapping model for association reasoning analysis. The preset intelligent mapping model is set as a graph neural network architecture and can be obtained by fitting historical data.

[0070] Based on the results of the association reasoning analysis, confidence text fragments are generated, and the content is arranged according to the official document writing framework to generate a preliminary official document draft.

[0071] Specifically, the system connects to business systems, including but not limited to financial systems, human resources systems, and project management platforms, to extract business data related to document requirements. Then, a data cleaning channel is built to perform entity alignment and attribute fusion on the extracted business data, ultimately forming a structured business information graph. The business information graph uses nodes to represent entities such as projects or acceptance indicators, and edges to represent relationships such as inclusion and completion rate.

[0072] The business information graph is semantically mapped to the official document writing framework. Key information triples are extracted from the graph, such as [Project A]-[Completed]-[95% progress]. These are then input into an intelligent mapping model based on a graph neural network architecture. The intelligent mapping model performs inference analysis by learning the relationships between entities, generating text fragments with confidence. Finally, the text fragments are arranged according to the paragraph order and module requirements of the official document framework to generate a preliminary official document draft.

[0073] By adopting the above technical solutions, the direct extraction of business data avoids errors caused by manual input, and entity alignment and attribute fusion ensure the consistency and accuracy of the data; the business information graph can intuitively present the logical relationships between data, providing a reliable basis for content generation; the associative reasoning of graph neural networks can uncover potential information and enhance the depth of official document content; and confidence labeling helps users identify information that needs to be verified.

[0074] Furthermore, the preset intelligent mapping model includes an entity linking unit, a conflict detection unit, and a credibility evaluation unit;

[0075] The entity linking unit is used to identify the corresponding named entity data in the business data and establish a link between the named entity data and a preset standard knowledge base.

[0076] The conflict detection unit is used to detect the compatibility value between the result of the correlation reasoning analysis and the official document writing framework based on logical rules, and to issue a conflict warning based on the compatibility value.

[0077] The credibility assessment unit is used to calculate the confidence score corresponding to each text segment.

[0078] It should be noted that the preliminary official document draft is verified, and adjustments and optimizations are made based on the verification results to generate the final official document draft. Specifically, this includes:

[0079] The preliminary official document draft is subjected to format compliance check and logical integrity verification to confirm the compliance coefficient and integrity coefficient corresponding to the preliminary official document draft, and then a comprehensive verification coefficient is generated based on the compliance coefficient and integrity coefficient corresponding to the preliminary official document draft.

[0080] The comprehensive test coefficient is compared with the preset comprehensive test threshold, which can be obtained by fitting historical data.

[0081] If the comprehensive inspection coefficient is higher than the preset comprehensive inspection threshold, then there is no need to adjust or optimize the preliminary official document draft.

[0082] If the overall inspection coefficient is not higher than the preset overall inspection threshold, the preliminary official document draft needs to be adjusted and optimized.

[0083] Specifically, the generated preliminary official document drafts undergo a format compliance check. Based on the format rules corresponding to the document type, in this application embodiment, the compliance coefficient is output from dimensions such as font size, line spacing, page margin, and the position of signature and seal. At the same time, logical integrity verification is performed, checking whether the core elements are complete in accordance with the content arrangement scheme, confirming the integrity coefficient. Subsequently, the comprehensive inspection coefficient is calculated according to the preset weight and compared with the preset comprehensive inspection threshold.

[0084] If the comprehensive inspection coefficient is higher than the preset comprehensive inspection threshold, the document will pass directly; if it does not reach the preset comprehensive inspection threshold, adjustment and optimization will be initiated, automatically supplementing missing fields or prompting the user to complete them. This not only saves manual verification time but also ensures that the content of the official document is rigorous and compliant.

[0085] Furthermore, it also includes a processing module, which comprises a version management unit, a knowledge update unit, and a security audit unit;

[0086] The version management unit is used to store the official document drafts generated each time and their revision records;

[0087] The knowledge update unit is used to dynamically optimize the dynamic rule engine based on the feedback data corresponding to the target user.

[0088] The security audit unit is used to perform security audits on the document generation process and embeds digital watermarks.

[0089] Example 2

[0090] This application also discloses an automated document generation system based on dynamic rules.

[0091] Reference Figure 2 A dynamic rule-based automated document generation system includes:

[0092] The data acquisition module is used to receive document writing requirement data input by the target user, perform semantic analysis on the document writing requirement data, extract structured parameter data based on the results of semantic analysis, identify document type on the structured parameter data, and confirm the document type identification code.

[0093] The framework generation module is used to call the dynamic rule engine based on structured parameter data and document type identifier code, and match the corresponding format validation rule library and content logic knowledge graph to generate a document writing framework.

[0094] The analysis module is used to receive business data corresponding to the target user, extract key information triples from the business data, input the key information triples into a preset intelligent mapping model for semantic-level association analysis, and combine them with the official document writing framework to generate a preliminary official document draft.

[0095] The optimization module is used to verify the preliminary official document draft and adjust and optimize the preliminary official document draft according to the verification results to generate the final official document draft.

[0096] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0097] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0098] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A method for automatically generating official documents based on dynamic rules, characterized in that, Includes the following steps: Step S1: Receive the document writing requirement data input by the target user, perform semantic analysis on the document writing requirement data, extract structured parameter data based on the results of semantic analysis, identify the document type on the structured parameter data, and confirm the document type identification code. Step S2: Based on the structured parameter data and document type identifier, call the dynamic rule engine and match the corresponding format validation rule library and content logic knowledge graph to generate a document writing framework; Step S3: Receive business data corresponding to the target user, extract key information triples from the business data, input the key information triples into the preset intelligent mapping model for semantic-level association analysis, and combine them with the official document writing framework to generate a preliminary official document draft. Step S4: Verify the preliminary official document draft, and adjust and optimize the preliminary official document draft according to the verification results to generate the final official document draft.

2. The method for automatically generating official documents based on dynamic rules according to claim 1, characterized in that, Step S1 includes the following steps: The system receives document writing requirement data input by the target user through a natural language processing interface. The document writing requirement data includes voice requirement data and text requirement data. Voiceprint feature recognition and text translation are performed on the voice requirement data. Based on the text translation results and combined with the text requirement data, an original requirement text sequence is generated. The original demand text sequence is subjected to noise filtering, and the original demand text sequence is classified according to intent based on the result of noise filtering to generate structured parameter data. The structured parameter data is subjected to feature analysis to identify the target feature vector. The target feature vector is then input into a preset classifier network to generate a document type probability distribution matrix. Obtain the maximum value index in the document type probability distribution matrix, and output the document type identifier code by combining it with the preset type code lookup table.

3. The method for automatically generating official documents based on dynamic rules according to claim 1, characterized in that, The process of generating an official document writing framework specifically includes: Based on the document type identification code, the corresponding format validation rule set is extracted from the format validation rule base, and the corresponding node is matched in the content logic knowledge graph to generate a content arrangement scheme. The dynamic rule engine is activated to convert the format validation rule set and content arrangement scheme into a rule instruction stream. The rule instruction stream is prioritized according to the structured parameter data to generate a document writing framework.

4. The method for automatically generating official documents based on dynamic rules according to claim 1, characterized in that, Step S3 includes the following steps: Connect to the business system, extract business data from the business system, construct a data cleaning channel, perform entity alignment and attribute fusion processing on the business data, and generate a business information graph; The business information graph is semantically mapped to the official document writing framework to extract key information triples. The key information triples are then input into a preset intelligent mapping model for association reasoning analysis. The preset intelligent mapping model is set as a graph neural network architecture. Based on the results of the association reasoning analysis, confidence text fragments are generated, and the content is arranged according to the official document writing framework to generate a preliminary official document draft.

5. The method for automatically generating official documents based on dynamic rules according to claim 4, characterized in that, The preset intelligent mapping model includes an entity linking unit, a conflict detection unit, and a credibility evaluation unit. The entity linking unit is used to identify the corresponding named entity data in the business data and establish a link between the named entity data and a preset standard knowledge base. The conflict detection unit is used to detect the compatibility value between the result of the correlation reasoning analysis and the official document writing framework based on logical rules, and to issue a conflict warning based on the compatibility value. The credibility assessment unit is used to calculate the confidence score corresponding to each text segment.

6. The method for automatically generating official documents based on dynamic rules according to claim 1, characterized in that, The preliminary document draft is verified, and adjustments and optimizations are made based on the verification results to generate the final document draft. This process specifically includes: The preliminary official document draft is subjected to format compliance check and logical integrity verification to confirm the compliance coefficient and integrity coefficient corresponding to the preliminary official document draft, and then a comprehensive verification coefficient is generated based on the compliance coefficient and integrity coefficient corresponding to the preliminary official document draft. The comprehensive test coefficient is compared with the preset comprehensive test threshold. If the comprehensive inspection coefficient is higher than the preset comprehensive inspection threshold, then there is no need to adjust or optimize the preliminary official document draft. If the overall inspection coefficient is not higher than the preset overall inspection threshold, the preliminary official document draft needs to be adjusted and optimized.

7. The method for automatically generating official documents based on dynamic rules according to claim 1, characterized in that, It also includes a processing module, which comprises a version management unit, a knowledge update unit, and a security audit unit; The version management unit is used to store the official document drafts generated each time and their revision records; The knowledge update unit is used to dynamically optimize the dynamic rule engine based on the feedback data corresponding to the target user. The security audit unit is used to perform security audits on the document generation process and embeds digital watermarks.

8. A dynamic rule-based automated document generation system, applied to the dynamic rule-based automated document generation method described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to receive document writing requirement data input by the target user, perform semantic analysis on the document writing requirement data, extract structured parameter data based on the results of semantic analysis, identify document type on the structured parameter data, and confirm the document type identification code. The framework generation module is used to call the dynamic rule engine based on structured parameter data and document type identifier code, and match the corresponding format validation rule library and content logic knowledge graph to generate a document writing framework. The analysis module is used to receive business data corresponding to the target user, extract key information triples from the business data, input the key information triples into a preset intelligent mapping model for semantic-level association analysis, and combine them with the official document writing framework to generate a preliminary official document draft. The optimization module is used to verify the preliminary official document draft and adjust and optimize the preliminary official document draft according to the verification results to generate the final official document draft.

9. A computer-readable storage medium, characterized in that: The system stores instructions that, when executed on a computer, cause the computer to perform a method for automatically generating official documents based on dynamic rules as described in any one of claims 1 to 7.

Images