Intelligent extraction method for decision-making elements of unstructured government affair document and related components

Abstract

The invention relates to an intelligent extraction method for decision elements of an unstructured government affair document and related components, which are applied to the technical field of government affair document processing and decision analysis, and the method comprises the following steps: obtaining a work document uploaded by a user and a user cue word; extracting document elements of the work document based on the user prompt words and an extraction model; analyzing the document elements based on the extraction model to obtain an analysis result; and generating an output result based on the analysis result and the document element. The method has the effect of improving the efficiency and reliability of extracting the decision-making elements of the unstructured government affair document.

Description

Technical Field

[0001] This application relates to the technical field of government document processing and decision analysis, and in particular to a method for intelligent extraction of decision elements from unstructured government documents and related components. Background Technology

[0002] In the field of government administration, with the continuous expansion of government affairs and the gradual deepening of informatization, the number of government documents has experienced explosive growth. These unstructured government documents contain a wealth of information of significant value to decision-making. Effective processing and utilization of these unstructured government documents can greatly improve the scientific nature and efficiency of government decision-making, promote the efficient conduct of government work, and drive government administration towards intelligence and refinement.

[0003] In the past, in order to extract valuable decision-making elements from unstructured government documents, manual reading and analysis were usually used. Staff members needed to read the documents word by word and extract key information and points. Some also used simple keyword search technology, which matched and found relevant content in the documents by setting some preset keywords. In addition, rule-based text processing methods were used to parse and filter documents according to pre-defined rules.

[0004] However, manual reading and analysis methods are extremely inefficient. Faced with massive amounts of government documents, staff need to invest a lot of time and energy, and are prone to human oversights and errors. Simple keyword search technology can only find surface-level matching content, making it difficult to understand the semantics and context of documents and accurately extract deeper decision-making elements. Rule-based text processing methods are not flexible enough to adapt to the diverse changes in document formats and content, and cannot effectively extract decision-making elements from unstructured government documents. Summary of the Invention

[0005] To improve the efficiency and reliability of decision element extraction from unstructured government documents, this application provides an intelligent extraction method and related components for decision elements from unstructured government documents.

[0006] Firstly, this application provides a method for intelligent extraction of decision-making elements from unstructured government documents, employing the following technical solution: A method for intelligent extraction of decision-making elements from unstructured government documents, comprising: Retrieve user-uploaded work documents and user prompts; Based on the user prompts and the extraction model, the document elements of the working document are extracted; The document elements are analyzed based on the extraction model to obtain the analysis results; Output results are generated based on the analysis results and the document elements.

[0007] By adopting the above technical solution, the extraction model can quickly extract document elements from the work documents uploaded by users and user prompts, and further analyze the document elements to obtain analysis results. Thus, users can quickly and accurately understand the content of the work documents based on the output results composed of document elements and analysis results. In other words, by using the extraction model to extract elements from work documents and analyze the results, the efficiency and reliability of extracting decision elements from unstructured government documents are improved.

[0008] Optionally, the step of extracting document elements of the working document based on the user prompt words and the extraction model includes: Determine the document type of the work document; Determine type hint words based on the document type; The document elements of the working document are extracted based on the user prompt words and the type prompt words.

[0009] By adopting the above technical solution, the extraction model not only considers user prompts when extracting document elements from working documents, but also determines type prompts based on the document type of the working document. It then combines user prompts and type prompts to extract document elements from the working document, enabling the extracted document elements to be extracted flexibly according to the document type and user needs, thereby improving the reliability of document element extraction.

[0010] Optionally, the step of analyzing the document elements based on the extraction model to obtain analysis results includes: If the document type is a weekly report, then domain knowledge is obtained based on the document elements; Identify potential problems based on the domain knowledge described above; Based on the potential problems and the document elements, guidance and recommendations were determined. Prioritize decisions based on urgency, strategic value, and the four-quadrant method; The analysis results are determined based on the potential problems, the guidance and recommendations, and the decision priorities.

[0011] By adopting the above technical solution, when the document type is weekly report, the extraction model determines potential problems, guidance suggestions, and evaluation decision priorities based on the domain knowledge corresponding to the document elements, so as to obtain analysis results. This enables the accurate extraction of decision elements for unstructured government documents of the weekly report type, providing strong support for government decision-making.

[0012] Optionally, the step of analyzing the document elements based on the extraction model to obtain analysis results includes: If the document type is a meeting type, then conflicting viewpoints are determined based on the document elements, and the conflicting viewpoints are the points of disagreement between different departments on the same topic; Based on the aforementioned issues and conflicting viewpoints, relevant historical decisions are obtained; Success rate analysis is performed on the associated historical decisions to generate a decision comparison; A risk assessment list is generated by conducting risk assessments on the related historical decisions based on fiscal, public opinion, and implementation dimensions. Decision recommendations are generated based on the aforementioned decision comparison and the aforementioned risk assessment list; The analysis results are determined based on the aforementioned conflict of viewpoints, the aforementioned risk assessment list, and the aforementioned decision recommendations.

[0013] By adopting the above technical solution, for meeting-type documents, related historical decisions are obtained through the meeting topics and the points of disagreement among different departments under those topics. Success rate analysis of related historical decisions generates decision comparisons. Risk assessment is conducted from the dimensions of finance, public opinion, and implementation to generate a risk assessment list, thereby generating decision recommendations and finally determining the analysis results. This helps to comprehensively analyze meeting-type documents and provide a more scientific and accurate basis for government decision-making.

[0014] Optionally, the step of analyzing the document elements based on the extraction model to obtain analysis results includes: If the document type is a survey type, then historical survey data is obtained based on the document elements; Based on the historical survey data, a list of essential questions, historical hot topics, and suggested questions for the survey will be generated. The analysis results are determined based on the list of mandatory questions, the historical hot topics, and the suggested questions in the survey.

[0015] By adopting the above technical solution, when the document type is a survey, the extraction model can obtain historical survey data based on document elements, and then generate a list of must-check questions, historical hot issues, and survey question suggestions. Finally, it generates analysis results and output results, which can extract decision elements more effectively for unstructured government documents of the survey type, and provide more effective support for survey work.

[0016] Optionally, the step of analyzing the document elements based on the extraction model to obtain analysis results includes: If the document type is an emergency report type, then the contingency plan clauses and the contact list of responsible persons are matched based on the document elements; Media response scripts are generated based on the document elements and public opinion sentiment analysis. The analysis results are determined based on the terms of the contingency plan, the contact list of the responsible persons, and the media response script.

[0017] By adopting the above technical solutions, for emergency report documents, the extraction model can automatically match contingency plan clauses and contact lists of responsible persons based on document elements, and generate media response scripts by combining document elements with public opinion sentiment analysis. This can quickly provide contingency plans, identify responsible persons, and generate appropriate media responses in handling government emergencies, thereby improving the efficiency and response capabilities of government affairs.

[0018] Optionally, the step of analyzing the document elements based on the extraction model to obtain analysis results includes: If the document type is an instruction document type, then the instruction type is determined based on the document elements; Obtain the approval criteria based on the approval type; The points of conflict are determined based on the aforementioned approval criteria and document elements; Based on the aforementioned contradictions and the aforementioned approval criteria, standardized approval suggestions are generated. The analysis results are determined based on the points of contradiction and the standardized approval recommendations.

[0019] By adopting the above technical solution, for documents of the approval document type, the approval type and approval standard can be determined according to the document elements, contradictions can be identified and standardized approval suggestions can be generated, and finally the analysis results can be obtained. This can realize intelligent extraction and intelligent decision-making of decision elements of unstructured government approval documents, and improve the efficiency and reliability of decision element extraction of unstructured government documents.

[0020] Secondly, this application provides an intelligent extraction system for decision-making elements from unstructured government documents, employing the following technical solution: A system for intelligent extraction of decision-making elements from unstructured government documents, comprising: The document acquisition module is used to acquire user-uploaded work documents and user prompts. The element extraction module is used to extract document elements of the working document based on the user prompt words and the extraction model; The element analysis module is used to analyze the document elements based on the extraction model and obtain analysis results; The results output module is used to generate output results based on the analysis results and the document elements.

[0021] By adopting the above technical solution, the extraction model can quickly extract document elements from the work documents uploaded by users and user prompts, and further analyze the document elements to obtain analysis results. Thus, users can quickly and accurately understand the content of the work documents based on the output results composed of document elements and analysis results. In other words, by using the extraction model to extract elements from work documents and analyze the results, the efficiency and reliability of extracting decision elements from unstructured government documents are improved.

[0022] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device includes a processor coupled to a memory; The memory stores a computer program capable of being loaded by a processor and executing the intelligent extraction method for decision elements of unstructured government documents as described in any of the first aspects.

[0023] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the intelligent extraction method for decision elements of unstructured government documents as described in any of the first aspects. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a method for intelligent extraction of decision elements from unstructured government documents, as provided in an embodiment of this application.

[0025] Figure 2 This is a structural block diagram of an intelligent extraction system for decision elements of unstructured government documents provided in an embodiment of this application.

[0026] Figure 3 This is a structural block diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0027] The present application will be further described in detail below with reference to the accompanying drawings.

[0028] This application provides a method for intelligent extraction of decision elements from unstructured government documents. This method can be executed by an electronic device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet computer, desktop computer, etc., but is not limited to these.

[0029] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0031] like Figure 1 As shown, a method for intelligent extraction of decision elements from unstructured government documents is described below (steps S101 to S104): Step S101: Obtain the user-uploaded work document and user prompts.

[0032] Users upload work documents and user prompts through web pages or mini-programs, and the system can retrieve these documents and prompts through the user-uploaded entry point. The user document is the document from which the user needs to extract elements, and the document format includes, but is not limited to, PDF, OFD, Word, WPS, etc.

[0033] Step S102: Extract document elements from the working document based on user prompts and extraction models.

[0034] The document elements include key facts and core content in the user document, and the extraction model is such as DeepSeek large model, GPT-4 / GPT-4o, etc., without specific limitations here.

[0035] Specifically, the document elements of the working document are extracted based on user prompts and extraction models, including: determining the document type of the working document; determining type prompts based on the document type; and extracting document elements of the working document based on user prompts and type prompts.

[0036] In this embodiment, different document types (including but not limited to weekly report type, meeting type, survey type, emergency report type, and instruction document type) adopt different methods and extract different types of elements when extracting document elements. For example, for the weekly report type, the elements include work points, decision focus, etc.; for the meeting type, the elements include the views of different departments, etc.; for the survey type, the elements include survey questions, etc.

[0037] Before extracting document elements using the extraction model, the document type of the working document must first be determined. The document type can be submitted synchronously by the user when uploading the user document, or it can be determined by the extraction model through preliminary analysis of the user document. The extraction model has different type prompt words preset for different document types. The extraction model extracts the document elements of the working document according to the user prompt words and type prompt words.

[0038] Step S103: Analyze the document elements based on the extraction model to obtain the analysis results.

[0039] The process of analyzing document elements to obtain analysis results is automatically completed by the extraction model. The following is the process of the extraction model in determining the analysis results for various document types.

[0040] Specifically, the document elements are analyzed based on the extraction model to obtain the analysis results, including: if the document type is a weekly report, then domain knowledge is obtained based on the document elements; potential problems are identified based on the domain knowledge; guidance and suggestions are determined based on the potential problems and document elements; decision priorities are assessed based on the urgency of the matter, strategic value, and the four-quadrant rule; and the analysis results are determined based on the potential problems, guidance and suggestions, and decision priorities.

[0041] In this embodiment, if the document type is a weekly report, the extraction model obtains built-in domain knowledge based on the extracted document elements; it analyzes the document elements using the domain knowledge to identify potential problems in the weekly report, and generates guidance suggestions (e.g., decision focus suggestions, work guidance suggestions, etc.) by analyzing the potential problems and document elements; the extraction model also uses the four-quadrant method to evaluate decision priority according to the two dimensions of urgency and strategic value; finally, the potential problems, guidance suggestions, and decision priorities are jointly determined as the analysis result.

[0042] Specifically, the document elements are analyzed based on the extraction model to obtain the analysis results, including: if the document type is a meeting, then conflicting viewpoints are identified based on the document elements, and conflicting viewpoints are the points of disagreement between different departments on the same topic; related historical decisions are obtained based on the topic and conflicting viewpoints; success rate analysis is performed on related historical decisions to generate a decision comparison; risk assessment is performed on related historical decisions based on financial, public opinion, and implementation dimensions to generate a risk assessment list; decision recommendations are generated based on the decision comparison and risk assessment list; and the analysis results are determined based on the conflicting viewpoints, the risk assessment list, and the decision recommendations.

[0043] In this embodiment, if the document type is a meeting type, the extraction model identifies conflicting viewpoints from the document elements and retrieves relevant historical decisions from the built-in knowledge base based on the topic and conflicting viewpoints. Relevant historical decisions are historical decisions corresponding to the current topic and the current conflicting viewpoints. Then, the extraction model performs a success rate analysis on the relevant historical decisions and generates a decision comparison of the success rates of all relevant historical decisions. It also conducts a risk assessment on the relevant historical decisions based on three dimensions: financial, public opinion, and implementation, and generates a risk assessment list. The decision model generates decision recommendations by comprehensively considering the decision comparison and the risk assessment list. The conflicting viewpoints, the risk assessment list, and the decision recommendations are collectively determined as the analysis results.

[0044] Specifically, the document elements are analyzed based on the extraction model to obtain the analysis results, including: if the document type is a survey, historical survey data is obtained based on the document elements; a list of mandatory questions, historical hot topics, and survey question suggestions are generated based on the historical survey data; and the analysis results are determined based on the list of mandatory questions, historical hot topics, and survey question suggestions.

[0045] In this embodiment, if the document type is a survey, the extraction model retrieves historical survey data from the built-in knowledge base based on the document elements, analyzes the historical survey data, and generates a list of mandatory questions, historical hot topics, and survey question suggestions; the list of mandatory questions, historical hot topics, and survey question suggestions are collectively determined as the analysis results.

[0046] Specifically, the document elements are analyzed based on the extraction model to obtain the analysis results, including: if the document type is an emergency report type, the contingency plan clauses and the contact list of responsible persons are matched based on the document elements; media response scripts are generated based on the document elements and public opinion sentiment analysis; and the analysis results are determined based on the contingency plan clauses, the contact list of responsible persons, and the media response scripts.

[0047] In this embodiment, if the document type is an emergency report, the extraction model automatically matches the contingency plan clauses and the contact list of responsible persons based on the document elements; and the extraction model also performs public opinion sentiment analysis based on the document elements to generate media response scripts, and the contingency plan clauses, the contact list of responsible persons, and the media response scripts are jointly determined as the analysis results.

[0048] Specifically, the document elements are analyzed based on the extraction model to obtain the analysis results, including: if the document type is an instruction document type, the instruction type is determined based on the document elements; the instruction standard is obtained based on the instruction type; the contradiction points are determined based on the instruction standard and document elements; standardized instruction suggestions are generated based on the contradiction points and instruction standard; and the analysis results are determined based on the contradiction points and standardized instruction suggestions.

[0049] In this embodiment, if the document type is an instruction document, the extraction model determines the instruction type based on the document elements. Instruction types include, for example, requests, reports, and supervision / implementation. Different instruction types correspond to different instruction standards. The extraction model retrieves the instruction standards from the built-in knowledge base based on the instruction type and analyzes the document elements according to the instruction standards to identify points of conflict, such as unclear responsible parties. Based on the points of conflict and the instruction standards, standardized instruction suggestions are generated, such as: "Please have XX take the lead in establishing a weekly scheduling mechanism." The points of conflict and the standardized instruction suggestions are combined to determine the analysis result.

[0050] The above content represents the analysis results automatically generated by the extraction model based on pre-set standards. Users can further ask questions using prompts based on these results. The extraction model can also statistically analyze user questions for each file type, identifying the areas of greatest interest for each file type (e.g., over 60% of users asked further questions about result 'a', and over 60% of users requested analysis result 'b' for file type A). This allows for the simultaneous generation of corresponding interest data during subsequent analysis of document types, improving user satisfaction.

[0051] Step S104: Generate output results based on the analysis results and document elements.

[0052] The document elements and analysis results are formatted into output and displayed to the user. This allows the user to quickly understand the core content and risks of the document and make better decisions based on the recommendations, greatly improving document processing efficiency.

[0053] Figure 2 This is a structural block diagram of a decision element intelligent extraction system 200 for unstructured government documents provided in an embodiment of this application.

[0054] like Figure 2 As shown, the intelligent extraction system for decision-making elements of unstructured government documents mainly includes: The document acquisition module 201 is used to acquire user-uploaded work documents and user prompts. The element extraction module 202 is used to extract document elements of the working document based on user prompts and the extraction model; The element analysis module 203 is used to analyze document elements based on the extraction model and obtain analysis results; The results output module 204 is used to generate output results based on the analysis results and document elements.

[0055] As an optional implementation of this embodiment, the element extraction module 202 is specifically used to extract document elements of the working document based on user prompts and extraction models, including: determining the document type of the working document; determining type prompts based on the document type; and extracting document elements of the working document based on user prompts and type prompts.

[0056] As an optional implementation of this embodiment, the element analysis module 203 is specifically used to analyze document elements based on the extraction model to obtain analysis results, including: if the document type is a weekly report type, then acquiring domain knowledge based on document elements; identifying potential problems based on domain knowledge; determining guidance suggestions based on potential problems and document elements; evaluating decision priority based on the urgency of the matter, strategic value, and the four-quadrant rule; and determining the analysis results based on potential problems, guidance suggestions, and decision priority.

[0057] As an optional implementation of this embodiment, the element analysis module 203 is specifically used to analyze document elements based on the extraction model to obtain analysis results, including: if the document type is a meeting type, then determining conflicting viewpoints based on document elements, where conflicting viewpoints are the points of disagreement between different departments on the same topic; obtaining related historical decisions based on the topic and conflicting viewpoints; performing success rate analysis on related historical decisions to generate a decision comparison; conducting risk assessment on related historical decisions based on financial, public opinion, and implementation dimensions to generate a risk assessment list; generating decision recommendations based on the decision comparison and risk assessment list; and determining the analysis results based on the conflicting viewpoints, the risk assessment list, and the decision recommendations.

[0058] As an optional implementation of this embodiment, the element analysis module 203 is specifically used to analyze document elements based on the extraction model to obtain analysis results, including: if the document type is a survey type, then obtaining historical survey data based on document elements; generating a list of mandatory questions, historical hot issues, and survey question suggestions based on the historical survey data; and determining the analysis results based on the list of mandatory questions, historical hot issues, and survey question suggestions.

[0059] As an optional implementation of this embodiment, the element analysis module 203 is specifically used to analyze document elements based on the extraction model to obtain analysis results, including: if the document type is an emergency report type, then matching the contingency plan clauses and the contact list of responsible persons based on the document elements; generating media response scripts based on document elements and public opinion sentiment analysis; and determining the analysis results based on the contingency plan clauses, the contact list of responsible persons, and the media response scripts.

[0060] As an optional implementation of this embodiment, the element analysis module 203 is specifically used to analyze document elements based on the extraction model to obtain analysis results, including: if the document type is an instruction document type, then determining the instruction type based on the document elements; obtaining the instruction standard based on the instruction type; determining the contradiction points based on the instruction standard and document elements; generating standardized instruction suggestions based on the contradiction points and instruction standards; and determining the analysis results based on the contradiction points and standardized instruction suggestions.

[0061] In one example, a module in any of the above systems may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0062] For example, when modules in a system can be implemented through a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together to form a system-on-a-chip (SOC).

[0063] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0064] Figure 3 This is a structural block diagram of an electronic device 300 provided in an embodiment of this application.

[0065] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0066] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps of the intelligent extraction method for decision elements of unstructured government documents described above. The memory 302 stores various types of data to support the operation of the electronic device 300. This data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of 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.

[0067] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used for wired or wireless communication between electronic device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0068] The electronic device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the intelligent extraction method for decision elements of unstructured government documents given in the above embodiments.

[0069] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0070] Electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers, and may also be servers.

[0071] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for intelligent extraction of decision elements from unstructured government documents.

[0072] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0073] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0074] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for intelligent extraction of decision-making elements from unstructured government documents, characterized in that, include: Retrieve user-uploaded work documents and user prompts; Based on the user prompts and the extraction model, the document elements of the working document are extracted; The document elements are analyzed based on the extraction model to obtain the analysis results; Output results are generated based on the analysis results and the document elements.

2. The method according to claim 1, characterized in that, The step of extracting document elements from the working document based on the user prompts and the extraction model includes: Determine the document type of the work document; Determine type hint words based on the document type; The document elements of the working document are extracted based on the user prompt words and the type prompt words.

3. The method according to claim 2, characterized in that, The analysis of the document elements based on the extraction model to obtain the analysis results includes: If the document type is a weekly report, then domain knowledge is obtained based on the document elements; Identify potential problems based on the knowledge of the aforementioned domain; Based on the potential problems and the document elements, guidance and recommendations were determined. Prioritize decisions based on urgency, strategic value, and the four-quadrant method; The analysis results are determined based on the potential problems, the guidance and recommendations, and the decision priorities.

4. The method according to claim 2, characterized in that, The analysis of the document elements based on the extraction model to obtain the analysis results includes: If the document type is a meeting type, then conflicting viewpoints are determined based on the document elements, and the conflicting viewpoints are the points of disagreement between different departments on the same topic; Based on the aforementioned issues and conflicting viewpoints, relevant historical decisions are obtained; Success rate analysis is performed on the associated historical decisions to generate a decision comparison; A risk assessment list is generated by conducting risk assessments on the related historical decisions based on fiscal, public opinion, and implementation dimensions. Decision recommendations are generated based on the aforementioned decision comparison and the aforementioned risk assessment list; The analysis results are determined based on the aforementioned conflict of viewpoints, the aforementioned risk assessment list, and the aforementioned decision recommendations.

5. The method according to claim 2, characterized in that, The analysis of the document elements based on the extraction model to obtain the analysis results includes: If the document type is a survey type, then historical survey data is obtained based on the document elements; Based on the historical survey data, a list of essential questions, historical hot topics, and suggested questions for the survey will be generated. The analysis results are determined based on the list of mandatory questions, the historical hot topics, and the suggested questions in the survey.

6. The method according to claim 2, characterized in that, The analysis of the document elements based on the extraction model to obtain the analysis results includes: If the document type is an emergency report type, then the contingency plan clauses and the contact list of responsible persons are matched based on the document elements; Media response scripts are generated based on the document elements and public opinion sentiment analysis. The analysis results are determined based on the terms of the contingency plan, the contact list of the responsible persons, and the media response script.

7. The method according to claim 2, characterized in that, The analysis of the document elements based on the extraction model to obtain the analysis results includes: If the document type is an instruction document type, then the instruction type is determined based on the document elements; Obtain the approval criteria based on the approval type; The points of conflict are determined based on the aforementioned approval criteria and document elements; Based on the aforementioned contradictions and the aforementioned approval criteria, standardized approval suggestions are generated. The analysis results are determined based on the points of contradiction and the standardized approval recommendations.

8. A system for intelligent extraction of decision-making elements from unstructured government documents, characterized in that, include: The document acquisition module is used to acquire user-uploaded work documents and user prompts. The element extraction module is used to extract document elements of the working document based on the user prompt words and the extraction model; The element analysis module is used to analyze the document elements based on the extraction model and obtain analysis results; The results output module is used to generate output results based on the analysis results and the document elements.

9. An electronic device, characterized in that, Includes a processor, which is coupled to a memory; The processor is configured to execute a computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It includes a computer program or instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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