Historical data analysis and situation association method and system

Abstract

The invention discloses a historical data analysis and situation association method and system, and aims to solve the following limitations of complex management problems, insufficient cultural adaptability, insufficient decision support and high traditional culture learning cost of traditional management tools and theories in practical application. The method comprises the following steps: in response to input content described by a user in a natural language, performing semantic analysis on the input content, extracting key information, and retrieving at least one management smart case related to the input content from a knowledge base through a case matching algorithm to serve as alternative recommendation content; performing sentiment analysis by using a sentiment analysis large model based on the input content, and optimizing recommended content according to a sentiment analysis result; wherein the knowledge base is a database obtained by performing structured storage on pre-collected and sorted Chinese management philosophy literatures and historical cases; based on the optimized recommendation content, generating a solution adaptive to a user situation; and outputting the solution through a human-computer interaction interface.

Description

Technical Field

[0001] This application relates to a method and system for historical data analysis and contextual association. Background Technology

[0002] In modern enterprise management, managers face increasingly complex challenges, requiring them to make decisions quickly and effectively. While traditional management tools and theories offer a wealth of knowledge, they have the following limitations in practical application:

[0003] (1) Cultural Adaptability Issues: Most existing management tools and theories are based on Western management philosophies, which differ significantly from the actual operating environment of Chinese enterprises in some cases. For example, Western management theories emphasize individualism and competition, while Chinese enterprise management focuses more on collectivism and harmony. Therefore, existing management tools often require extensive adjustments and adaptations when applied to the management context of Chinese enterprises.

[0004] (2) High learning cost: Traditional management learning usually requires systematic learning and a lot of time investment. Managers need to spend a lot of time reading professional books, attending training courses, etc., to master relevant management knowledge and skills. This is a huge challenge for busy managers, especially those decision-makers who need to respond quickly to complex problems.

[0005] (3) Insufficient decision support: When faced with complex management problems, managers often lack timely and effective decision support. Although existing management tools provide a wealth of theoretical knowledge, in practical applications, managers need to spend a lot of time sifting through massive amounts of information to find useful content. In addition, existing tools often lack specificity and operability when providing solutions, making them difficult to apply directly to actual problems.

[0006] (4) Limited technical means: Existing management tools are relatively simple in terms of technical means and lack intelligent and personalized support. Most tools can only provide general solutions and cannot provide personalized suggestions based on the user's specific needs and situations. In addition, existing tools also have limitations in data processing and analysis, and cannot make full use of big data and artificial intelligence technologies to improve the accuracy and efficiency of decision-making. Summary of the Invention

[0007] This application provides a method and system for analyzing historical data and relating it to context, aiming to address the following limitations of traditional management tools and theories in practical applications: complex management problems, insufficient cultural adaptability, inadequate decision support, and high learning costs of traditional culture.

[0008] Firstly, it provides a method for historical data analysis and contextual correlation, including:

[0009] In response to user input described in natural language, semantic analysis is performed on the input to extract key information, and at least one relevant management wisdom case is retrieved from the knowledge base using a case matching algorithm as candidate recommended content; and sentiment analysis is performed based on the input using a sentiment analysis model, and the recommended content is optimized based on the sentiment analysis results; wherein, the knowledge base is a database obtained by structured storage of pre-collected and organized Chinese management philosophy literature and historical cases;

[0010] Based on the optimized recommended content, generate solutions that adapt to the user context;

[0011] The solution is output through a human-computer interaction interface.

[0012] Optionally, in the above scheme, the case matching algorithm is implemented based on an open-source or API-based Chinese NLP model.

[0013] Optionally, in the above scheme, the sentiment analysis based on the input content using a large sentiment analysis model includes: calling the sentiment analysis API when the user inputs content to obtain sentiment tendency information; and adjusting the case recommendation logic according to the sentiment analysis results.

[0014] Optionally, in the above solution, a simple backend service based on Python Flask or Node.js Express can be quickly built to handle frontend users' problem analysis requests and call the intelligent case matching algorithm for management.

[0015] Enable communication between the front-end and back-end to ensure that user questions are correctly transmitted to the management smart case matching algorithm and that the matching results are returned to the front-end user interface for display.

[0016] Optionally, in the above scheme, retrieving at least one relevant management wisdom case from the knowledge base using a case matching algorithm includes:

[0017] The system performs word segmentation, part-of-speech tagging, and syntactic analysis on the user's input to extract key information about the question.

[0018] Based on the extracted key information, retrieve management wisdom cases semantically related to the key information from the knowledge base;

[0019] The retrieved cases are sorted, and the most relevant case is selected as the recommendation result based on the relevance score.

[0020] Optionally, in the above scheme, the generation of a solution adapted to the user context includes:

[0021] Based on management strategies from historical cases, provide specific operational suggestions for addressing user issues;

[0022] It draws on classical Chinese philosophical thought to provide users with theoretical support and decision-making basis;

[0023] By adapting historical cases to modern enterprise management scenarios, they can be better suited to users' actual needs.

[0024] Optionally, in the above scheme, the sentiment analysis based on the input content using a large sentiment analysis model further includes: identifying the user's emotional tendency, including positive, negative, and neutral, and adjusting the tone and style of the recommended content according to the emotional tendency.

[0025] Optionally, the above solution may also include: collecting user feedback information; and optimizing the case matching algorithm and sentiment analysis model based on the feedback information.

[0026] Secondly, a historical data analysis and contextual association system is provided, including:

[0027] The data storage module is used to collect and organize Chinese management philosophy literature and historical cases, and store them in a structured database;

[0028] The case matching module uses Natural Language Processing (NLP) and machine learning technologies to achieve accurate matching between user questions and historical management smart cases.

[0029] The decision support module combines classical Chinese philosophy and historical examples, and provides users with solutions adapted to the management context of Chinese enterprises based on cases retrieved by the intelligent matching module.

[0030] The sentiment analysis module integrates major domestic sentiment analysis models to perform sentiment analysis on user-input questions, understand the user's emotional state, and optimize recommended content based on the sentiment analysis results.

[0031] The user interaction module features an intuitive and easy-to-use user interface, allowing users to describe problems in natural language and view the system's recommended solutions. It also collects user feedback to further optimize the intelligent matching and sentiment analysis modules.

[0032] Optionally, in the above scheme, the case matching module includes:

[0033] The semantic analysis unit is used to perform word segmentation, part-of-speech tagging, and syntactic analysis on the user-input question to extract key information from the question.

[0034] The case retrieval unit retrieves semantically relevant management wisdom cases from the knowledge base based on the extracted key information.

[0035] The sorting and selection unit sorts the retrieved cases and selects the most matching case as the recommendation result based on the relevance score.

[0036] Optionally, in the above scheme, the decision support module includes:

[0037] The action suggestion unit provides specific action suggestions for user problems based on management strategies from historical cases;

[0038] The theoretical support unit draws on classical Chinese philosophical thought to provide users with theoretical support and decision-making basis;

[0039] The scenario adjustment unit adapts historical cases to modern enterprise management scenarios, making them more suitable for users' actual needs.

[0040] Compared with the prior art, this application has at least the following beneficial effects:

[0041] Based on further analysis and research into existing technological problems, this application recognizes the following limitations of traditional management tools and theories in practical applications: complexity of management problems, insufficient cultural adaptability, inadequate decision support, and high learning costs associated with traditional culture. This method extracts key information about user problems through semantic analysis, optimizes recommended content through sentiment analysis, and retrieves highly relevant management wisdom cases from a knowledge base covering Chinese management philosophy and historical examples. It then generates solutions adapted to the user's context and presents them through an intuitive and user-friendly interface, collecting user feedback to optimize the algorithm and model. This approach not only reduces learning costs but also improves decision-making efficiency and quality, enabling managers to address complex problems more effectively.

[0042] This application not only addresses the shortcomings of traditional management tools in terms of cultural adaptability, learning costs, and decision support through this approach, but also provides more personalized and precise solutions via sentiment analysis and user feedback mechanisms. This enables managers to address complex management problems more efficiently, while reducing learning costs and improving decision-making efficiency and quality. In summary, by combining profound cultural heritage with cutting-edge technology, this application revitalizes traditional wisdom, helping Chinese business managers easily bridge the gap between theory and practice and navigate the ever-changing business environment with ease. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a historical data analysis and contextual association method provided in one embodiment of this application.

[0044] Figure 2 This is a block diagram of the module architecture of a historical data analysis and context association device provided in one embodiment of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0046] In the description of this application: unless otherwise stated, "a plurality of" means two or more. The terms "comprising," "including," "having," etc., used in this application also mean "not limited to" (certain units, components, materials, steps, etc.).

[0047] In one embodiment, a method for historical data analysis and contextual association is provided, including:

[0048] In response to user input described in natural language, semantic analysis is performed on the input to extract key information. At least one management wisdom case related to the input is retrieved from the knowledge base using a case matching algorithm as candidate recommended content. Sentiment analysis is also performed based on the input using a sentiment analysis model, and the recommended content is optimized based on the sentiment analysis results. The knowledge base is a database obtained by structuring and storing pre-collected and organized Chinese management philosophy literature and historical cases.

[0049] Based on the optimized recommended content, generate solutions that adapt to the user context;

[0050] Solutions are provided through a human-computer interaction interface.

[0051] In one embodiment, the method further includes: collecting user feedback information; and optimizing the case matching algorithm and sentiment analysis model based on the feedback information.

[0052] In this embodiment, the system first performs semantic analysis on the user input. Semantic analysis is a crucial step in Natural Language Processing (NLP), aiming to understand the true meaning of the user input. For example, if a user inputs "how to improve team execution," the system needs to identify key information such as "improve," "team," and "execution." The original form of the input is not limited to text; it can also be speech, for instance, converted from speech to text for subsequent semantic analysis.

[0053] By extracting key information, the system can more accurately retrieve highly relevant management wisdom cases from the knowledge base. For example, for the question above, the system might retrieve management wisdom cases related to "leadership and execution" and "team motivation and execution improvement."

[0054] Sentiment analysis is performed on user-input questions to identify the user's emotional state. Sentiment analysis helps the system understand the user's emotional inclination when asking a question—whether it's anxiety, confusion, or confidence. For example, if a user's tone is anxious when entering a question, such as "I'm about to be driven crazy by the team's inefficiency, what should I do?", the system can identify this anxiety through sentiment analysis.

[0055] The system optimizes recommended content based on sentiment analysis results. If a user is experiencing anxiety, the system may focus on providing actionable solutions that can quickly alleviate anxiety, such as "three small steps to improve team efficiency immediately," rather than overly theoretical content.

[0056] In this embodiment, the results of sentiment analysis include not only emotional state, but also attitude tendency, points of interest, intention and motivation.

[0057] Sentiment analysis models can identify basic emotions such as joy, anger, sorrow, and happiness contained in user questions. For example, if a user's question is, "This product is amazing, I love it!", sentiment analysis can determine that the user is in a joyful emotional state. However, if the user's question is, "This service is terrible, it's unbearable!", then it can be determined that the user is angry.

[0058] It also includes attitudes towards things, such as positive, negative, or neutral. For example, if a user asks, "Do you think this movie is worth watching?", in addition to analyzing possible emotions such as expectations, it can also determine that the user has an uncertain attitude towards the movie and needs to obtain further information to confirm their attitude. When a user says, "I feel that the interface of this software is very user-friendly," in addition to discovering that the user may have a satisfied mood, it also reflects a positive attitude towards the specific aspect of the software's interface.

[0059] Sentiment analysis can uncover the interests reflected behind user questions. For example, when a user asks, "What are some popular tech expos lately?" this may not only show the user's curiosity and positive emotions towards the technology field, but also indicate their interest in the specific event and their intention to follow the dynamics of the technology industry.

[0060] This analysis also helps determine users' intentions and motivations. For example, if a user asks, "Any recommendations for office software that can improve work efficiency?" this not only reflects the user's interest in office software but also suggests a desire to improve work efficiency. If a user says, "I'm looking for a suitable place for a family trip, any good suggestions?" this reflects the user's motivation to seek advice on relevant travel destinations for a family trip.

[0061] When optimizing recommended content, combining these sentiment analysis results (emotional state, attitude tendency, interests, intentions and motivations, etc.) can more accurately provide users with content that better matches their needs and expresses different emotional tendencies.

[0062] The knowledge base is a core component of this method. It pre-collects and organizes Chinese management philosophies and historical cases, storing them in a structured database. Chinese management philosophy is profound and extensive, encompassing the application of ancient Confucian, Taoist, and Legalist philosophies in the management field, as well as the management experiences and lessons learned from various dynasties throughout history. For example, the Confucian idea of ​​"governing the country with virtue" can be reflected in corporate management as "governing the enterprise with virtue," emphasizing the guiding role of the moral character of corporate leaders in the team.

[0063] The management wisdom cases in the knowledge base have been carefully selected and organized. Each case includes a specific management scenario, the measures taken, and the results achieved. When the system performs a search, it uses algorithms (such as keyword matching and semantic similarity calculation) to find highly relevant cases from the knowledge base based on the extracted key information. For example, when searching for key information related to "team motivation," it might find the historical case of "how Cao Cao motivated his soldiers through fair rewards and punishments," from which it can extract the wisdom for motivating teams, such as "clearly defining goals, fair rewards and punishments, and setting examples."

[0064] By combining retrieved management wisdom case studies with sentiment analysis results, solutions tailored to the user's context are generated. This process is comprehensive. The system generates specific solutions based on the key management wisdom points in the retrieved case studies and the user's emotional state. For example, for a user experiencing anxiety, a solution to improve team execution might be: "First, immediately hold a brief team meeting to clarify the week's top priorities and goals, ensuring each member understands their responsibilities; second, provide timely small rewards based on team members' contributions, such as small gifts, to boost team morale; finally, maintain a positive attitude and believe that the team can gradually improve its execution."

[0065] Present solutions using an intuitive and user-friendly interface. The design of the user interface is crucial; it needs to allow users to easily view and understand the solution. This can be achieved through a combination of text and graphics, with clear steps. For example, the solution's steps can be shown in flowchart form, with each step accompanied by concise text descriptions and relevant case study links, allowing users to click on the links to learn more about the case studies.

[0066] Collect user feedback. After using the system, users may have their own opinions on the effectiveness and ease of use of the solutions. The system can collect feedback through questionnaires, user comments, and other methods. For example, a feedback button can be set up in the user interface, allowing users to click and fill in their opinions on the solution, such as "This solution is very practical and solved my problem" or "This solution is too theoretical and difficult to implement."

[0067] The system optimizes its intelligent case matching algorithm and sentiment analysis model based on feedback. This is a continuous process of self-improvement. If a user reports that a solution is impractical, the system can analyze whether the matching algorithm failed to find the most suitable case or the sentiment analysis failed to accurately grasp the user's emotions, thus adjusting the corresponding algorithm and model. For example, if it finds that the recommended solutions are not realistic enough for users in certain emotional states, the sentiment analysis model can be optimized to improve its accuracy in identifying emotions, thereby optimizing the recommended content and further selecting (filtering) from the candidate cases. From optimizing the recommended content to providing solutions, the system mainly provides practical enterprise management strategies and / or communication scripts based on the historical cases recommended and the user's input (the specific scenario).

[0068] Through sentiment and semantic analysis, personalized solutions can be provided to meet the needs of different users in different emotional states. A knowledge base built upon Chinese management philosophy and historical cases provides users with profound cultural insights and practical management wisdom, helping them draw inspiration from historical experience. Algorithms and models are continuously optimized based on user feedback, enabling the system to better adapt to user needs and improve the quality and accuracy of solutions.

[0069] In one embodiment, the case matching algorithm is implemented based on a Chinese NLP model in the form of an open source or API.

[0070] Chinese NLP models in API form refer to models that provide services through network interfaces. Users can call these models and obtain their output results by sending HTTP requests or other methods. Examples include Tencent Cloud's Natural Language Processing API and Alibaba Cloud's NLP services. These APIs typically offer rich functionality, such as text analysis, machine translation, and sentiment analysis, and users do not need to train the models themselves; they only need to call the interface.

[0071] If using an open-source model, users can further train it according to their needs. For example, they can fine-tune the model using labeled datasets of management questions and solutions to better suit contextual understanding tasks in the management domain. During fine-tuning, hyperparameters (such as learning rate and batch size) can be adjusted to optimize performance. If using an API-based model, users can optimize the recommendation algorithm by adjusting call parameters or post-processing logic. For example, the results returned by the API can be filtered, sorted, or combined to generate recommendations that better match user needs. Furthermore, user feedback data can be incorporated to continuously optimize the recommendation algorithm.

[0072] In one embodiment, performing sentiment analysis based on input content using a large-scale sentiment analysis model includes: selecting a suitable sentiment analysis service, calling the sentiment analysis API when the user inputs content to obtain sentiment tendency information, and adjusting the case recommendation logic based on the sentiment analysis results.

[0073] In one embodiment, performing sentiment analysis based on the input content using a large sentiment analysis model further includes: identifying the user's emotional tendency, including positive, negative, and neutral, and adjusting the tone and style of the recommended content according to the emotional tendency.

[0074] When a user enters a management question, the system invokes the selected sentiment analysis service. The system first receives the management question described by the user in natural language. The user's input question is then sent as a parameter to the sentiment analysis API. The API returns the sentiment analysis results, typically including sentiment tendency (e.g., positive, negative, neutral) and a confidence score. For example, if a user enters, "I've recently encountered many difficulties managing my team and I feel very frustrated," the sentiment analysis API might return a "negative" sentiment tendency and a confidence score of 0.85.

[0075] After obtaining sentiment analysis results, the system adjusts its recommendation logic based on this information to generate solutions that better match the user's emotional state. If the user is in a positive mood, more challenging and forward-looking management wisdom cases are recommended to encourage the user to try new management methods. If the user is in a negative mood, more practical and easy-to-implement solutions are recommended to help the user quickly alleviate their emotions and solve problems. If the user is in a neutral mood, balanced and comprehensive management wisdom cases are recommended, providing a holistic perspective and solution.

[0076] In one embodiment, a simple backend service based on Python Flask or Node.js Express can be quickly built to handle frontend users' problem analysis requests and invoke the intelligent case matching algorithm for management.

[0077] Enable communication between the front-end and back-end to ensure that user questions are correctly transmitted to the management smart case matching algorithm and that the matching results are returned to the front-end user interface for display.

[0078] In this embodiment, the goal is to quickly build a simple backend service to handle frontend users' problem analysis requests and invoke the management smart case matching algorithm. The backend service will be implemented using either Python Flask or Node.js Express frameworks. Both frameworks are suitable for rapidly developing small projects and can efficiently handle HTTP requests and responses. The choice between Flask and Express depends on the developer's familiarity with the project and the project's requirements. Flask is known for its lightweight and flexibility, while Express excels in handling asynchronous requests.

[0079] The core functionality of the backend service includes receiving user questions from the frontend, processing them by calling the management's intelligent case matching algorithm, and returning the matching results to the frontend. To implement this functionality, the backend needs to define an API interface that receives user questions as input, retrieves relevant cases by calling the management's intelligent case matching algorithm, and then returns the results to the frontend in JSON format.

[0080] Front-end and back-end communication is a crucial aspect of this embodiment. The front-end user interface sends user questions to the back-end service via HTTP requests (such as GET or POST). Upon receiving the request, the back-end service parses the user question and invokes the management smart case matching algorithm for processing. The algorithm retrieves relevant management smart cases from the knowledge base based on the key information of the user question, sorts the retrieved cases, and selects the most matching case as the recommendation result. Finally, the back-end service returns the recommendation result to the front-end in JSON format, and the front-end user interface displays the result to the user.

[0081] To ensure smooth communication between the front-end and back-end, cross-origin resource sharing (CORS) issues need to be properly handled, especially when the front-end and back-end are running on different domains or ports. Furthermore, error handling logic needs to be added to both the front-end and back-end to provide user-friendly prompts when requests fail. Through testing and optimization, it's essential to ensure that the back-end service can respond quickly to front-end requests, thereby improving the user experience.

[0082] This embodiment establishes front-end and back-end communication by building a back-end service based on Python Flask or Node.js Express. This ensures that user issues are correctly transmitted to the management smart case matching algorithm, and the matching results are returned to the front-end user interface for display. This process not only improves the system's response speed but also enhances the user's interactive experience with the system.

[0083] In one embodiment, retrieving at least one relevant management wisdom case from a knowledge base using a case matching algorithm includes:

[0084] The system performs word segmentation, part-of-speech tagging, and syntactic analysis on the user's input to extract key information about the question.

[0085] Based on the extracted key information, retrieve management wisdom cases that are semantically related to the key information from the knowledge base;

[0086] The retrieved cases are sorted, and the most relevant case is selected as the recommendation result based on the relevance score.

[0087] In this embodiment, the management wisdom case matching algorithm effectively processes user input, extracts key information, and retrieves management wisdom cases semantically related to the question from the knowledge base. By sorting the retrieved cases and selecting the most matching case as the recommendation result, it provides users with accurate management wisdom suggestions. This method not only improves the accuracy of recommendations but also enhances the user experience.

[0088] In one embodiment, generating a solution adapted to the user context includes:

[0089] Based on management strategies from historical cases, provide specific operational suggestions for addressing user issues;

[0090] It draws on classical Chinese philosophical thought to provide users with theoretical support and decision-making basis;

[0091] By adapting historical cases to modern enterprise management scenarios, they can be better suited to users' actual needs.

[0092] In this embodiment, the goal is to generate a solution adapted to the user's context. This solution not only provides specific operational suggestions but also integrates classical Chinese philosophical thought with modern enterprise management practices, offering users comprehensive theoretical support and decision-making basis. The method aims to help users better solve practical problems by combining historical wisdom with modern management practices.

[0093] Based on management strategies from historical cases, provide users with specific and actionable suggestions. Retrieve management wisdom cases related to user problems from the knowledge base, extracting management strategies and specific measures. Transform the extracted management strategies into concrete operational steps to ensure users can directly apply them to real-world situations. Provide users with theoretical support and decision-making basis, enhancing the depth and credibility of the solution. Select relevant philosophical ideas: Based on user problems, select relevant classical Chinese philosophical ideas, such as Confucianism's "governing the country with virtue," Taoism's "governing by non-action," and Legalism's "clear distinction between rewards and punishments." Combine philosophical ideas with specific operational suggestions, explaining their application and significance in modern management.

[0094] Example: User question: "How to improve team execution?" Philosophical idea: "Confucianism emphasizes 'governing the country with virtue,' believing that leaders should influence team members with virtue and enhance team cohesion." Explanation: "In modern management, leaders should lead by example, set a good example, and influence team members with virtue to enhance team cohesion and execution."

[0095] Adapt historical case studies to better suit users' actual needs. Research the characteristics and requirements of modern enterprise management, and understand the challenges and opportunities in the current management environment. Adjust and optimize historical case studies according to the modern management context to better meet the actual needs of modern management.

[0096] In one embodiment, a historical data analysis and contextual association system is also provided, including:

[0097] The data storage module is used to collect and organize Chinese management philosophy literature and historical cases, and store them in a structured database;

[0098] The case matching module uses Natural Language Processing (NLP) and machine learning technologies to achieve accurate matching between user questions and historical management smart cases.

[0099] The decision support module combines classical Chinese philosophy and historical examples, and provides users with solutions adapted to the management context of Chinese enterprises based on cases retrieved by the intelligent matching module.

[0100] The sentiment analysis module integrates major domestic sentiment analysis models to perform sentiment analysis on user-input questions, understand the user's emotional state, and optimize recommended content based on the sentiment analysis results.

[0101] The user interaction module features an intuitive and easy-to-use user interface, allowing users to describe problems in natural language and view the system's recommended solutions. It also collects user feedback to further optimize the intelligent matching and sentiment analysis modules.

[0102] The specific implementation details of each module can be found in the above description of the limitations of a historical data analysis and contextual association method, and will not be repeated here.

[0103] In one embodiment, the case matching module includes:

[0104] The semantic analysis unit is used to perform word segmentation, part-of-speech tagging, and syntactic analysis on the user-input question to extract key information from the question.

[0105] The case retrieval unit retrieves semantically relevant management wisdom cases from the knowledge base based on the extracted key information.

[0106] The sorting and selection unit sorts the retrieved cases and selects the most matching case as the recommendation result based on the relevance score.

[0107] In one embodiment, the decision support module includes:

[0108] The action suggestion unit provides specific action suggestions for user problems based on management strategies from historical cases;

[0109] The theoretical support unit draws on classical Chinese philosophical thought to provide users with theoretical support and decision-making basis;

[0110] The scenario adjustment unit adapts historical cases to modern enterprise management scenarios, making them more suitable for users' actual needs.

[0111] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for analyzing historical data and relating it to context, characterized in that, include: In response to user input described in natural language, semantic analysis is performed on the input to extract key information, and at least one relevant management wisdom case is retrieved from the knowledge base using a case matching algorithm as candidate recommended content; and sentiment analysis is performed based on the input using a sentiment analysis model, and the recommended content is optimized based on the sentiment analysis results; wherein, the knowledge base is a database obtained by structured storage of pre-collected and organized Chinese management philosophy literature and historical cases; Based on the optimized recommended content, generate solutions that adapt to the user context; The solution is output through a human-computer interaction interface.

2. The historical data analysis and contextual association method according to claim 1, characterized in that, The case matching algorithm is implemented based on open-source or API-based Chinese NLP models.

3. The historical data analysis and contextual association method according to claim 1, characterized in that, The sentiment analysis based on the input content using a large-scale sentiment analysis model includes: calling the sentiment analysis API when the user inputs content to obtain sentiment tendency information; and adjusting the case recommendation logic according to the sentiment analysis results.

4. The historical data analysis and contextual association method according to claim 1, characterized in that, Quickly build a simple backend service based on Python Flask or Node.js Express to handle frontend user problem analysis requests and invoke management smart case matching algorithms; Enable communication between the front-end and back-end to ensure that user questions are correctly transmitted to the management smart case matching algorithm and that the matching results are returned to the front-end user interface for display.

5. The historical data analysis and contextual association method according to claim 1, characterized in that, The process of retrieving at least one relevant management wisdom case from the knowledge base using a case matching algorithm includes: The system performs word segmentation, part-of-speech tagging, and syntactic analysis on the user's input to extract key information about the question. Based on the extracted key information, retrieve management wisdom cases semantically related to the key information from the knowledge base; The retrieved cases are sorted, and the most relevant case is selected as the recommendation result based on the relevance score.

6. The historical data analysis and contextual association method according to claim 1, characterized in that, The solution for generating user-adaptive contexts includes: Based on management strategies from historical cases, provide specific operational suggestions for addressing user issues; It draws on classical Chinese philosophical thought to provide users with theoretical support and decision-making basis; By adapting historical cases to modern enterprise management scenarios, they can be better suited to users' actual needs.

7. The historical data analysis and contextual association method according to claim 1, characterized in that, The sentiment analysis based on the input content using a large sentiment analysis model also includes: identifying the user's emotional tendency, including positive, negative, and neutral, and adjusting the tone and style of the recommended content according to the emotional tendency.

8. The historical data analysis and contextual association method according to claim 1, characterized in that, Also includes: Collect user feedback information; The case matching algorithm and sentiment analysis model are optimized based on the feedback information.

9. A historical data analysis and contextual association system, characterized in that, include: The data storage module is used to collect and organize Chinese management philosophy literature and historical cases, and store them in a structured database; The case matching module uses Natural Language Processing (NLP) and machine learning technologies to achieve accurate matching between user questions and historical management smart cases. The decision support module combines classical Chinese philosophy and historical examples, and provides users with solutions adapted to the management context of Chinese enterprises based on cases retrieved by the intelligent matching module. The sentiment analysis module integrates major domestic sentiment analysis models to perform sentiment analysis on user-input questions, understand the user's emotional state, and optimize recommended content based on the sentiment analysis results. The user interaction module features an intuitive and easy-to-use user interface, allowing users to describe problems in natural language and view the system's recommended solutions. It also collects user feedback to further optimize the intelligent matching and sentiment analysis modules.

10. The historical data analysis and contextual association system according to claim 9, characterized in that, The case matching module includes: The semantic analysis unit is used to perform word segmentation, part-of-speech tagging, and syntactic analysis on the user-input question to extract key information from the question. The case retrieval unit retrieves semantically relevant management wisdom cases from the knowledge base based on the extracted key information. The sorting and selection unit sorts the retrieved cases and selects the most matching case as the recommendation result based on the relevance score.

11. The historical data analysis and contextual association system according to claim 9, characterized in that, The decision support module includes: The action suggestion unit provides specific action suggestions for user problems based on management strategies from historical cases; The theoretical support unit draws on classical Chinese philosophical thought to provide users with theoretical support and decision-making basis; The scenario adjustment unit adapts historical cases to modern enterprise management scenarios, making them more suitable for users' actual needs.

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