Knowledge management system based on artificial intelligence big data

Abstract

The invention discloses a knowledge management system based on artificial intelligence big data, and relates to the technical field of knowledge management. Comprising a data integration module, an intelligent screening and writing auxiliary module, an intelligent tool module, a knowledge graph dynamic optimization module and a user participation and feedback module. According to the invention, by integrating functional modules of policy research, intelligent assistance, intelligence analysis, knowledge graph and the like, comprehensive management and deep mining of knowledge are realized, so that the working efficiency and result quality of researchers are greatly improved. Efficient management and utilization of enterprise knowledge are realized, the quality and output efficiency of research results are improved, and the decision-making demand of a management layer is better met.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a knowledge management system based on artificial intelligence big data. Background Art

[0002] In the current context of digital transformation, knowledge management within enterprises faces numerous challenges. For example, during the research process, employees must sift through vast amounts of documents and write various notifications, research reports, and professional documents. This process consumes considerable energy and is prone to omissions, time-consuming, and labor-intensive. Furthermore, unprocessed and unmined knowledge, intelligence, and project data can lead to incomplete, unauthoritative, and inaccurate research data, hindering the production of high-quality research results and failing to effectively meet management's decision-making needs.

[0003] Traditional knowledge management systems have the following shortcomings:

[0004] 1. Inefficiency: The screening and writing process is time-consuming and labor-intensive, making it difficult to quickly generate high-quality research results.

[0005] 2. Data silos: Knowledge, intelligence, and project data are stored in a decentralized manner, lacking unified management and in-depth mining.

[0006] 3. Lack of intelligence: The system has single functions and cannot provide intelligent auxiliary tools to improve work efficiency.

[0007] 4. Poor user experience: Lack of personalized recommendations and convenient mobile support leads to low user willingness.

[0008] 5. Static association: After the knowledge graph is built, there is a lack of dynamic optimization, and the association path cannot be updated in real time based on user behavior.

[0009] 6. Insufficient participation: Users can only passively receive knowledge and lack active selection and feedback mechanisms.

[0010] In summary, the present invention designs a knowledge management system based on artificial intelligence big data. Summary of the Invention

[0011] To address the shortcomings of existing technologies, the present invention aims to provide a knowledge management system based on artificial intelligence and big data. By integrating functional modules such as policy research, intelligent assistance, intelligence analysis, and knowledge graphs, this system enables comprehensive management and in-depth mining of knowledge, significantly improving researchers' work efficiency and the quality of their research results. This system enables efficient management and utilization of corporate knowledge, improves the quality and efficiency of research results, and better meets management's decision-making needs.

[0012] In order to achieve the above-mentioned object, the present invention is implemented through the following technical solutions: a knowledge management system based on artificial intelligence big data, including a data integration module, an intelligent screening and writing assistance module, an intelligent tool module, a knowledge graph dynamic optimization module and a user participation and feedback module;

[0013] The data integration module is used to collect and integrate the knowledge, intelligence and project data stored in a dispersed manner within the enterprise, eliminating data silos;

[0014] The intelligent screening and writing assistance module uses natural language processing technology and machine learning algorithms to intelligently screen massive documents, providing users with writing assistance functions and improving work efficiency;

[0015] The intelligent tool module integrates multiple intelligent tools such as intelligent search, intelligent question and answer, and intelligent recommendation to provide users with convenient and efficient knowledge services;

[0016] The knowledge graph dynamic optimization module constructs the enterprise knowledge graph and updates the associated paths and node information of the knowledge graph in real time according to user behavior;

[0017] The user participation and feedback module provides users with a mechanism for active selection and feedback, promoting continuous improvement and optimization of the system.

[0018] Preferably, the data integration module is connected to various existing data sources of the enterprise through a data interface, automatically captures relevant data, and cleans, converts and loads it, and stores it in a unified data warehouse.

[0019] Preferably, the intelligent screening and writing assistance module quickly locates relevant documents, extracts abstracts and key information, generates document frameworks, recommends relevant cases and data, and performs grammar and logic checks based on keywords, topics and requirements input by the user.

[0020] Preferably, the intelligent search function in the intelligent tool module supports natural language search, the intelligent question-and-answer function automatically answers user questions, and the intelligent recommendation function makes personalized recommendations based on the user's historical behavior and preferences.

[0021] The working steps of the data integration module are as follows:

[0022] 1. Data connection: The data integration module connects to various existing data sources of the enterprise (such as financial system, human resources system, business database, etc.) through data interfaces to prepare for data capture.

[0023] 2. Data capture: Automatically capture relevant knowledge, intelligence and project data from various data sources, which may exist in different formats and structures.

[0024] 3. Data processing: Clean the captured data to remove duplicate, erroneous, and incomplete data; perform conversion operations to unify data in different formats into a standard format; finally, load the processed data into a unified data warehouse to eliminate data silos and provide a unified and accurate data foundation for subsequent analysis and application.

[0025] The working steps of the intelligent screening and writing assistance module are as follows:

[0026] 1. Demand input: Users input keywords, topics, and specific requirements into the intelligent screening and writing assistance module to clarify their document screening and writing goals.

[0027] 2. Document screening: The module uses natural language processing technology and machine learning algorithms to intelligently screen massive documents in the data warehouse and quickly locate documents related to user needs.

[0028] 3. Information extraction: Extract summaries and key information from the filtered documents to provide users with the core content of the documents, saving users reading time.

[0029] 4. Framework generation: Generate a document framework based on user needs and extracted information to provide clear structural guidance for users' writing.

[0030] 5. Case and data recommendations: Recommend relevant cases and data to enrich the user's document content, making it more convincing and practical.

[0031] 6. Content check: Perform grammar and logic checks on documents written by users to help users improve document quality.

[0032] The working steps of the knowledge graph dynamic optimization module are as follows:

[0033] 1. Graph construction: The knowledge graph dynamic optimization module builds an enterprise knowledge graph based on the data in the data warehouse, displays and associates various types of knowledge and information in a graphical manner, and presents the relationship between knowledge.

[0034] 2. Behavior monitoring: Continuously monitor user behavior in the system, including searching, browsing, using smart tools, and other operations.

[0035] 3. Real-time update: Based on user behavior data, the associated paths and node information of the knowledge graph are updated in real time, so that the knowledge graph can reflect the dynamic changes of corporate knowledge and maintain its accuracy and timeliness.

[0036] The workflow of the user participation and feedback module is as follows:

[0037] 1. Active selection: Users can actively select knowledge areas, topics, and functional modules based on their needs and interests in the system to customize their user experience.

[0038] 2. Feedback submission: Users can evaluate and provide feedback on the knowledge, services and functions provided by the system, and put forward suggestions and opinions for improvement.

[0039] 3. System optimization: The system collects user feedback information and continuously improves and optimizes each module to better meet user needs and improve system performance and user satisfaction.

[0040] The present invention has the following beneficial effects:

[0041] 1. Improve efficiency: Through intelligent screening and writing assistance functions, employees can reduce the time and energy spent on screening and writing from massive documents, quickly generate high-quality research results, and improve work efficiency.

[0042] 2. Eliminate data silos: The data integration module unifies the management and deep mining of scattered data within the enterprise, realizes data sharing and collaboration, and provides more comprehensive and accurate information support for enterprise decision-making.

[0043] 3. Enhanced intelligence: Integrate a variety of intelligent tools, such as intelligent search, intelligent question and answer, and intelligent recommendation, to provide users with more convenient and efficient knowledge services and improve user experience.

[0044] 4. Optimize the knowledge graph: The knowledge graph dynamic optimization module can update the knowledge graph in real time based on user behavior, making knowledge associations more accurate and dynamic, and providing users with more valuable knowledge discovery and recommendations.

[0045] 5. Improve user participation: The user participation and feedback module provides users with opportunities to actively choose and provide feedback, enhances user participation and satisfaction, and promotes the continuous improvement and development of enterprise knowledge management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments;

[0047] Figure 1 This is a system architecture diagram of the present invention;

[0048] Figure 2 This is a workflow diagram of the data integration module of the present invention;

[0049] Figure 3 This is a workflow diagram of the intelligent screening and writing assistance module of the present invention;

[0050] Figure 4 This is a workflow diagram of the knowledge graph dynamic optimization module of the present invention;

[0051] Figure 5 This is a workflow diagram of the user participation and feedback module of the present invention. DETAILED DESCRIPTION

[0052] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0053] Reference Figure 1-5 ,This specific implementation adopts the following technical solutions: a knowledge management system based on artificial intelligence big data, including a data integration module, an intelligent screening and writing assistance module, an intelligent tool module, a knowledge graph dynamic optimization module and a user participation and feedback module;

[0054] The data integration module is used to centrally collect and integrate knowledge, intelligence, and project data stored dispersed across the enterprise. It connects to various existing data sources (such as document management systems, databases, and business systems) through data interfaces, automatically capturing relevant data, cleaning, converting, and loading it into a unified data warehouse, eliminating data silos.

[0055] The intelligent screening and writing assistance module utilizes natural language processing technology and machine learning algorithms to intelligently filter the massive amount of documents in the data warehouse. Based on user-entered keywords, topics, and requirements, it quickly locates relevant documents, extracts summaries, and identifies key information. Furthermore, it provides writing assistance features such as automatic document framework generation, recommendation of relevant cases and data, and grammar and logic checking, helping users quickly produce high-quality research reports and professional documents, improving work efficiency.

[0056] The intelligent tool module integrates a variety of intelligent tools, such as intelligent search, intelligent Q&A, and intelligent recommendations. The intelligent search function supports natural language search, understands the user's semantic intent, and provides more accurate search results. The intelligent Q&A function automatically answers user questions by analyzing knowledge graphs and data warehouses. The intelligent recommendation function recommends personalized knowledge and information based on the user's historical behavior and preferences, enhancing the user experience.

[0057] The knowledge graph dynamic optimization module constructs an enterprise knowledge graph, linking and integrating various types of knowledge and information within the enterprise. Simultaneously, it leverages artificial intelligence technology to dynamically optimize the knowledge graph, updating its associated paths and node information in real time based on user behavior (such as searching, browsing, and annotation). This allows the knowledge graph to reflect the latest state and relationships of enterprise knowledge, providing users with more accurate knowledge associations and recommendations.

[0058] The user engagement and feedback module provides users with a mechanism for active selection and feedback. Users can customize their knowledge subscriptions based on their needs, selecting topics and content of interest. Furthermore, users can evaluate and provide feedback on the knowledge and services provided by the system. The system continuously improves and optimizes based on user feedback, increasing user engagement and satisfaction.

[0059] The working steps of the data integration module are as follows:

[0060] 1. Data connection: The data integration module connects to various existing data sources of the enterprise (such as financial system, human resources system, business database, etc.) through data interfaces to prepare for data capture.

[0061] 2. Data capture: Automatically capture relevant knowledge, intelligence and project data from various data sources, which may exist in different formats and structures.

[0062] 3. Data processing: Clean the captured data to remove duplicate, erroneous, and incomplete data; perform conversion operations to unify data in different formats into a standard format; finally, load the processed data into a unified data warehouse to eliminate data silos and provide a unified and accurate data foundation for subsequent analysis and application.

[0063] The knowledge management system of the present invention can be deployed on an internal server of an enterprise or in the cloud using cloud computing. During deployment, it is necessary to ensure that the system seamlessly connects with the enterprise's existing business systems to achieve real-time synchronization and sharing of data.

[0064] The data integration module collects and integrates knowledge, intelligence, and project data that are dispersed across the enterprise. During the data collection process, data needs to be cleaned and converted to remove duplicate, erroneous, and incomplete data to ensure accuracy and consistency.

[0065] The knowledge mining and analysis module conducts in-depth mining and analysis of the integrated data. Natural language processing technology is used to perform semantic analysis and keyword extraction on text data, and machine learning algorithms are used to classify, cluster, and predict data, providing valuable decision support for enterprises.

[0066] When writing notices, research reports, and professional documents, employees can use the intelligent writing assistance module. Based on user-entered topics and keywords, this module automatically generates a document outline and content framework, and provides relevant reference materials and case studies. It also performs grammar checking and vocabulary recommendations on user-entered content, improving document quality and writing efficiency.

[0067] The system provides users with personalized knowledge recommendation services based on their historical behavior data and personal preferences. Users can independently select knowledge areas and topics of interest based on their needs and interests, and provide feedback and adjustments to the recommended content to continuously optimize the recommendation effect.

[0068] Build an enterprise knowledge graph, graphically displaying and linking various types of knowledge and information. The knowledge graph supports intelligent search and reasoning, helping users quickly find the knowledge and information they need. Furthermore, the knowledge graph's association paths are continuously updated based on user behavior and new data, ensuring the graph's real-time and accuracy.

[0069] Users are encouraged to actively participate in the knowledge management process and a proactive selection and feedback mechanism is provided. Users can evaluate and provide feedback on the knowledge and services provided by the system. The system continuously improves and optimizes its own functions and services based on user feedback.

[0070] Example 1: Take the example of a user writing a "New Energy Vehicle Market Trend Report":

[0071] 1. The data integration module captures the latest policies and sales data from the enterprise ERP and industry database and stores them in the data warehouse.

[0072] 2. The intelligent screening and writing assistance module extracts related documents from the data warehouse based on the user input topic, generates a report outline, and recommends keywords such as "battery technology" and "policy subsidies".

[0073] 3. The intelligent tool module returns competitive product analysis cases through intelligent search and answers the question "2025 market size forecast" through intelligent Q&A.

[0074] 4. The knowledge graph dynamic optimization module monitors users' frequent visits to the "solid-state battery" node and automatically increases its association weight with "new energy vehicles".

[0075] 5. The user participation and feedback module records users’ ratings of recommended content and subsequently reduces the push of similar low-scoring content.

[0076] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A knowledge management system based on artificial intelligence big data, characterized in that: It includes data integration module, intelligent screening and writing assistance module, intelligent tool module, knowledge graph dynamic optimization module and user participation and feedback module; The data integration module is used to collect and integrate the knowledge, intelligence and project data stored in a dispersed manner within the enterprise, eliminating data silos; The intelligent screening and writing assistance module uses natural language processing technology and machine learning algorithms to intelligently screen massive documents, providing users with writing assistance functions and improving work efficiency; The intelligent tool module integrates intelligent search, intelligent question and answer, and intelligent recommendation tools to provide users with convenient and efficient knowledge services; The knowledge graph dynamic optimization module constructs the enterprise knowledge graph and updates the associated paths and node information of the knowledge graph in real time according to user behavior; The user participation and feedback module provides users with a mechanism for active selection and feedback, promoting continuous improvement and optimization of the system.

2. The knowledge management system based on artificial intelligence big data according to claim 1, characterized in that: The data integration module connects to various existing data sources of the enterprise through data interfaces, automatically captures relevant data, and cleans, converts and loads it, and stores it in a unified data warehouse.

3. The knowledge management system based on artificial intelligence big data according to claim 1, characterized in that: The intelligent screening and writing assistance module quickly locates relevant documents based on the keywords, topics and requirements input by the user, extracts summaries and key information, generates a document framework, recommends relevant cases and data, and performs grammar and logic checks.

4. The knowledge management system based on artificial intelligence big data according to claim 1, characterized in that: The intelligent search function in the intelligent tool module supports natural language search, the intelligent question and answer function automatically answers users' questions, and the intelligent recommendation function makes personalized recommendations based on users' historical behavior and preferences.

5. The knowledge management system based on artificial intelligence big data according to claim 1, characterized in that: The working steps of the data integration module are as follows: (1) Data connection: The data integration module connects with various existing data sources of the enterprise through data interfaces to prepare for data capture; (2) Data capture: Automatically capture relevant knowledge, intelligence, and project data from various data sources; (3) Data processing: Clean the captured data to remove duplicate, erroneous and incomplete data; Perform conversion operations to unify data in different formats into a standard format; finally, load the processed data into a unified data warehouse to eliminate data silos and provide a unified and accurate data foundation for subsequent analysis and application.

6. The knowledge management system based on artificial intelligence big data according to claim 1, characterized in that: The working steps of the intelligent screening and writing assistance module are as follows: (1) Demand input: Users input keywords, topics, and specific requirements into the intelligent screening and writing assistance module to clarify their own document screening and writing goals; (2) Document screening: The module uses natural language processing technology and machine learning algorithms to intelligently screen the massive amount of documents in the data warehouse and quickly locate documents related to user needs; (3) Information extraction: extracting summaries and key information from the selected documents to provide users with the core content of the documents, saving users’ reading time; (4) Framework generation: Generate a document framework based on user needs and extracted information to provide clear structural guidance for users’ writing; (5) Case and data recommendation: Recommend relevant cases and data to enrich the user's document content, making it more persuasive and practical; (6) Content checking: Perform grammar and logic checks on user-written documents to help users improve document quality.

7. The knowledge management system based on artificial intelligence big data according to claim 1, characterized in that: The working steps of the knowledge graph dynamic optimization module are as follows: (1) Graph construction: The knowledge graph dynamic optimization module constructs an enterprise knowledge graph based on the data in the data warehouse, displays and associates various types of knowledge and information in a graphical way, and presents the relationship between knowledge; (2) Behavior monitoring: Continuously monitor user behavior in the system, including operations such as searching, browsing, and using smart tools; (3) Real-time update: Based on user behavior data, the associated paths and node information of the knowledge graph are updated in real time, so that the knowledge graph can reflect the dynamic changes of enterprise knowledge and maintain its accuracy and timeliness.

8. The knowledge management system based on artificial intelligence big data according to claim 1, characterized in that: The workflow of the user participation and feedback module is as follows: (1) Active selection: Users can actively select knowledge areas, topics, and functional modules based on their needs and interests in the system to customize their user experience; (2) Feedback submission: Users can evaluate and provide feedback on the knowledge, services, and functions provided by the system, and put forward suggestions and opinions for improvement; (3) System optimization: The system collects user feedback information and continuously improves and optimizes each module to better meet user needs and improve system performance and user satisfaction.

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