An intelligent report generation and deep analysis method for government management personnel analysis

Abstract

The application belongs to the technical field of data processing, and specifically discloses an intelligent report generation and deep analysis method for government administration personnel analysis, which comprises the following steps: first, forming information data packets from the relevant data of target personnel; then, inputting the unstructured data in the data packets into a large language model for knowledge extraction and forming a personnel knowledge graph; then, generating corresponding language paragraphs according to the data in the personnel knowledge graph and filling them into a report template; based on the generated report content, question answering, correlation search and thinking chain analysis can also be performed. In view of the problem of low work efficiency of personnel analysis and research for government administration key personnel, the application realizes template-based automatic generation of reports by querying aggregated data and automatically extracting and integrating information by a large model, thereby solving the problem of low work efficiency of government personnel caused by the difficulty in processing unstructured data and the heavy burden of report writing in the personnel analysis and research of government administration key personnel under the prior art.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an intelligent report generation and in-depth analysis method for government governance personnel. Background Technology

[0002] Personnel analysis and assessment in the field of government governance is a core element in improving the accuracy of public services, optimizing the allocation of government resources, and maintaining social order. This work requires comprehensive analysis of multi-dimensional data, including basic identity information, public service processing history, social connections, and policy benefit records, to identify service needs, predict government processing trends, and support precise governance decisions. However, currently, government departments still rely primarily on manual operation for personnel analysis and assessment, which is limited by technical means and data management mechanisms, resulting in numerous problems, specifically in the following four aspects: I. Information Silos and Low Efficiency of Cross-System Queries: Personnel-related data is scattered across multiple business systems and platforms within the government intranet, such as the household registration system, government service processing system, livelihood security application system, and community service management system. Inconsistent data standards and lack of interoperability between these systems create isolated "information silos." When conducting analysis, staff must repeatedly search across multiple systems, which is not only time-consuming but also prone to missing crucial information.

[0003] Second, unstructured data processing is difficult and highly dependent on manual intervention: A large amount of core data involved in personnel analysis is typically in the form of unstructured text, including government processing reports, community visit records, and service feedback. Traditional technologies cannot automatically extract key information and also struggle to correlate and integrate unstructured and structured data. This usually relies on manual reading, sorting, and summarizing, which is inefficient and highly subjective, easily affecting the analysis results.

[0004] Third, the burden of report writing is heavy: The results of the analysis and judgment work need to be presented in the form of standardized reports, which include modules such as data summary, information sorting, and preliminary conclusions. Currently, the report writing work needs to be completed manually by staff, who not only need to manually integrate data collected from multiple channels, but also need to spend a lot of energy on text organization and format adjustment. Because the report writing work consumes a lot of staff's time, it restricts the improvement of the overall analysis and judgment efficiency.

[0005] Fourth, insufficient in-depth analysis capabilities: Traditional personnel management systems mainly focus on data storage and simple query functions, lacking intelligent interaction and in-depth reasoning capabilities. Therefore, it is difficult to conduct rapid question-and-answer verification, penetrating relationship mining, and experience-based automated judgment of key matters based on existing reports, making it difficult to carry out in-depth analysis work efficiently.

[0006] In conclusion, it is necessary to design an intelligent report generation and in-depth analysis method for government governance personnel to address the problem of low work efficiency caused by difficulties in processing unstructured data and heavy report writing burdens in the current analysis and analysis work of government governance personnel. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent report generation and in-depth analysis method for government administration personnel, in order to solve the problem of low work efficiency of government staff due to difficulties in processing unstructured data and heavy report writing burden in the analysis and judgment work of government administration personnel under the existing technology.

[0008] To achieve the above objectives, the basic solution provided by this invention is: an intelligent report generation and in-depth analysis method for government governance personnel, comprising the following steps: S1: Data Collection: First, query all structured and unstructured data related to the target personnel in the personnel holographic file system, and form a target personnel information data package; S2: Data Analysis and Integration: Then, the unstructured data in the personnel information data package of the target personnel is input into the large language model. The information extraction module in the large language model is used to identify key entities in the unstructured data, extract events, and mine relationships to form a personnel knowledge graph. S3: Generate Report: Then, staff select a report template according to the analysis needs. The personnel holographic file system then generates corresponding natural language paragraphs based on the text data in the personnel knowledge graph and automatically fills them into the corresponding positions in the report template, thus outputting a report in Word or PDF format. S4: In-depth analysis: When conducting thought chain analysis based on the generated report, the analysis experience of experts in the field is first abstracted into a reusable thought chain workflow through the personnel holographic file system. Then, the large language model reasons through the thought chain and prompt words, and finally outputs an analysis conclusion with reasoning basis and confidence score.

[0009] The beneficial effects of the present invention are as follows: (1) The present invention relies on the data governance achievements of the existing big data platform in the field of government governance to construct a holographic data view of personnel with the personnel ID number as the unique identifier, integrates their basic information, government affairs handling behavior, service trajectory, related personnel and other structured and unstructured data, and uses the big language model to deeply understand multi-source heterogeneous unstructured text, automatically identify, extract knowledge and associate key information entities and events, form a structured knowledge network and finally transform it into a standardized analysis report with standardized format, clear logic and fluent semantics, which greatly shortens the manual analysis time and greatly improves the efficiency, accuracy and depth of personnel analysis; (2) The big language model (LLM) is used to solve the problem of information extraction, association and summary of unstructured text in the field of government governance, form a structured personnel knowledge graph, and further embed the big model into the business process. Through the intelligent question answering of the report based on retrieval enhancement generation (RAG), the search of the relationship based on graph query and the automated judgment workflow based on "thinking chain", the system is upgraded from an "information display tool" to an "analysis and judgment assistant" with interactive and reasoning capabilities.

[0010] Option 2, which is the preferred option of the basic option, involves entering the target person's ID number into the front-end interface of the personnel holographic file system in S1 to query all structured and unstructured data related to the target person. The ID number is a unique and stable personnel identifier in the field of public security. Compared with name query, querying the target person's relevant data by ID number can more accurately retrieve relevant data, avoid query errors caused by the same name, and improve query efficiency.

[0011] Option 3, which is the preferred option of the basic option, includes structured data such as basic personnel information, government affairs processing records, social connection information, and information on the enjoyment of public services in S1, and unstructured data such as descriptions of government affairs processing, policy feedback, community visit records, and descriptions of public service behavior.

[0012] Option 4 is the preferred option of the basic option. In S2, after extracting information from unstructured data, it is also necessary to determine the service demand level of the text data in the unstructured data, including the determination of demand tendency and service suitability tendency. Preliminary determination of service demand level after extracting information from unstructured data can filter invalid information and reduce the workload of subsequent in-depth analysis.

[0013] Option 5, the preferred option among the basic options, involves the following steps in S2 to determine the service demand level: Step 1: First, perform word segmentation on the text data in the unstructured data of the target personnel information data package and output a word segmentation result table; Step 2: Then, mark the words that belong to the appeal words or mark the service-related words according to different scenarios in the text segmentation result table; Step 3: Finally, make a preliminary judgment on the degree of service needs of the target personnel based on various appeal words or service-related words in the text segmentation; By judging the appeal tendency and service suitability tendency of text data in unstructured data, invalid data in unstructured text data can be quickly filtered out, and the core data content can be extracted. In subsequent in-depth analysis, by integrating the features of the appeal tendency judgment results and service suitability tendency judgment results, the accuracy of the judgment can be improved, while reducing the misjudgment rate of single-dimensional judgment.

[0014] Option 6, which is the preferred option of the basic option, includes the following in S3: basic information of personnel, analysis of government affairs processing trajectory, network of related personnel and conclusions on service demand assessment.

[0015] Option 7, an optimal choice of the basic option, in S4, can also perform intelligent question answering and relational search based on the generated report content. When performing intelligent question answering on the report, the personnel holographic file system first locates relevant information fragments in the knowledge base through semantic retrieval, then understands and integrates them through a large language model, and finally outputs accurate and clearly based answers. When performing relational search, the personnel holographic file system uses graph database query technology based on the constructed personnel knowledge graph to present the results in the form of a visualized relational graph or structured list. Performing question answering and relational search based on the generated report content can significantly improve the utilization rate of the report, retrieval efficiency, and decision support capabilities. Attached Figure Description

[0016] Figure 1 This is a flowchart of an intelligent report generation and in-depth analysis method for government governance personnel, based on the present invention. Detailed Implementation

[0017] The present invention will be further described in detail below through specific embodiments: Example A method for intelligent report generation and in-depth analysis for government governance personnel, such as Figure 1 The steps shown are as follows: S1: Data Aggregation: First, the target person's ID number is entered into the front-end interface of the personnel holographic file system. Then, the personnel holographic file system queries all structured and unstructured data related to the target person from the personnel information database based on the entered ID number. The structured data includes basic personnel information, government affairs processing records, social association information, and information on access to public services. The unstructured data includes explanations of government affairs processing, policy feedback, community visit records, and descriptions of public service behavior. Then, the structured and unstructured data are aggregated to form the personnel information data package of the target person. S2: Information Analysis and Integration: The unstructured data from the personnel information data package formed in S1 is then input into a large language model. This model extracts names, locations, organization names, service items, and times from the unstructured data, while also recognizing specific behaviors such as "government application," "service processing," and "policy consultation." Relationships between entities are then established, such as "A and B are family members" or "C previously processed business at D's government service hall." Next, the text data in the unstructured data is used to determine the level of service demand, including judgment of demand tendency and service suitability tendency. Specifically, the text data in the unstructured personnel information data package is first segmented into words, and a segmentation result table is output. Then, words belonging to demand terms or service-related terms are marked in the segmentation result table, based on different scenarios. Finally, a preliminary judgment of the target personnel's service demand level is made based on the various demand terms or service-related terms in the segmented text. Finally, the processed unstructured data is merged with structured data to form a target personnel knowledge graph in the personnel holographic file system. S3: Report Generation: Staff can then design or select a built-in report template in the personnel holographic file system according to actual analysis needs. The content of the report template includes basic personnel information, government affairs processing trajectory analysis, related personnel network, and service demand assessment conclusions. Then, the personnel holographic file system generates corresponding natural language paragraphs based on the structured data in the personnel knowledge graph and the unstructured text data after analysis and integration, and automatically fills them into the corresponding positions of the report template, thus outputting a report in Word or PDF format. S4: In-depth Analysis: When conducting thought chain analysis based on the generated report, the analysis experience of experts in the field is first abstracted into a reusable "thought chain" workflow through the personnel holographic file system. Prompt words are set to guide the large language model to reason. For example, for the analysis of "matching the needs of people's livelihood services", the thought chain can be defined as: a. Extract the person's government affairs handling records in the past year; b. Identify the service types that are frequently handled and the unresolved matters; c. Check whether they meet the conditions for enjoying the new livelihood security policies; d. Conduct a comprehensive matching assessment based on the service needs characteristics of related personnel. Then, the large language model reasons according to the analysis type selected by the staff through the thought chain and prompt words, and finally outputs the analysis conclusion with reasoning basis and confidence score.

[0018] Based on the generated report content, the system can also perform intelligent question answering and relational search. For example, when a staff member asks a natural language question to the system, such as "Has this person repeatedly applied for similar government services in the past three months?", the personnel holographic file system first locates relevant information fragments in the knowledge base through semantic retrieval, and then understands and integrates them through a large language model to output an accurate and clearly based answer. When a staff member enters complex graph query conditions into the personnel holographic file system, such as "Find all family members of person A who have applied for elderly care services in the past six months", the personnel holographic file system will then use graph database query technology based on the constructed personnel knowledge graph to present the results in the form of a visualized relational graph or a structured list.

[0019] In summary, this invention reduces the time required for a single person to conduct a comprehensive analysis in the field of government governance from half a day to one hour. Simultaneously, it reduces human error through automated processing, improves the objectivity and traceability of analysis results, and achieves seamless integration and automation of the entire process from data querying, intelligent analysis, report generation to in-depth judgment. Furthermore, it utilizes a large language model to solve the challenges of extracting unstructured text information and automatically generating reports in the field of government governance. The large language model capabilities are deeply embedded into the core business processes of question answering, searching, and judgment, realizing the combination of cognitive intelligence and business rules. Through report question answering, intelligent search, and thought chain judgment functions, the system is endowed with dynamic interaction and deep reasoning capabilities, significantly improving the utilization rate of reports.

[0020] The above descriptions are merely embodiments of the present invention, and common knowledge regarding specific structures and characteristics is not elaborated upon here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the structure of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for intelligent report generation and in-depth analysis for government governance personnel, characterized in that, Includes the following steps: S1: Data Collection: First, query all structured and unstructured data related to the target personnel in the personnel holographic file system, and form a target personnel information data package; S2: Data Analysis and Integration: Then, the unstructured data in the personnel information data package of the target personnel is input into the large language model. The information extraction module in the large language model is used to identify key entities in the unstructured data, extract events, and mine relationships to form a personnel knowledge graph. S3: Generate Report: Then, staff select a report template according to the analysis needs. The personnel holographic file system then generates corresponding natural language paragraphs based on the text data in the personnel knowledge graph and automatically fills them into the corresponding positions in the report template, thus outputting a report in Word or PDF format. S4: In-depth analysis: When conducting thought chain analysis based on the generated report, the analysis experience of experts in the field is first abstracted into a reusable thought chain workflow through the personnel holographic file system. Then, the large language model reasons through the thought chain and prompt words, and finally outputs an analysis conclusion with reasoning basis and confidence score.

2. The intelligent report generation and in-depth analysis method for government governance personnel according to claim 1, characterized in that, In S1, by entering the target person's ID number into the front-end interface of the personnel holographic file system, all structured and unstructured data related to the target person can be queried.

3. The intelligent report generation and in-depth analysis method for government governance personnel as described in claim 1, characterized in that, In S1, structured data includes basic personnel information, government service records, social connection information, and information on access to public services, while unstructured data includes explanations of government service processing, policy feedback, community visit records, and descriptions of public service behaviors.

4. The intelligent report generation and in-depth analysis method for government governance personnel according to claim 1, characterized in that, In S2, after information extraction from unstructured data, it is also necessary to determine the service demand level of the text data in the unstructured data, including demand tendency judgment and service suitability tendency judgment.

5. The intelligent report generation and in-depth analysis method for government governance personnel according to claim 4, characterized in that, In S2, the steps for determining the risk level are as follows: Step 1: First, perform word segmentation on the text data in the unstructured data of the target personnel information data package and output a word segmentation result table; Step 2: Then, mark the words that belong to the appeal words or mark the service-related words according to different scenarios in the text segmentation result table; Step 3: Finally, make a preliminary judgment on the degree of service needs of the target personnel based on various appeal words or service-related words in the text segmentation.

6. The intelligent report generation and in-depth analysis method for government governance personnel according to claim 1, characterized in that, In S3, the report template includes basic information about personnel, analysis of government affairs processing trajectory, assessment conclusions on related personnel networks and service needs.

7. The intelligent report generation and in-depth analysis method for government governance personnel according to claim 1, characterized in that, In S4, based on the generated report content, intelligent question answering and relational search can also be performed. When performing intelligent question answering on the report, the personnel holographic file system first locates relevant information fragments in the knowledge base through semantic retrieval, then understands and integrates them through a large language model, and then outputs accurate and clearly based answers. When performing relational search, the personnel holographic file system uses graph database query technology based on the constructed personnel knowledge graph to present the results in the form of a visualized relational graph or a structured list.

Images