Data fuzzy recommendation system and method for database

Abstract

The invention relates to the technical field of databases, and discloses a data fuzzy recommendation system and method for a database, and the system comprises a database construction module which is configured to store terahertz data through the combination of different databases; the login verification module is configured to obtain login personnel information, perform security verification according to the login personnel information and judge whether access is authorized or not according to a verification result; the fuzzy recommendation module is configured to obtain the information of the authorized access personnel, obtain historical browsing information of the personnel information in the historical browsing database, and perform fuzzy recommendation on the authorized access personnel according to the historical browsing information; and the data adjustment module is configured to obtain the operation information of the authorized visitors and adjust the recommendation preferences of the authorized visitors according to the operation information. Terahertz data are stored by combining different databases, so that centralized management and efficient retrieval of the data are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of databases, and in particular to a data fuzzy recommendation system and method for a database. Background Art

[0002] With the rapid development of information technology, the demand for data storage and management is growing. In specific areas, such as terahertz (THz) technology, the efficiency of data storage and retrieval directly impacts the progress of research and applications. Terahertz technology, operating in the frequency range between microwaves and infrared in the electromagnetic spectrum, has broad application prospects, including but not limited to imaging, communications, and security detection. However, the complexity and diversity of THz data pose challenges to data management.

[0003] Traditional database systems often have limitations when processing such complex data. For example, they may not be able to effectively integrate data from different sources and formats, resulting in data silos and affecting data availability and integrity. Furthermore, as data volumes increase, traditional database systems also face challenges in data retrieval efficiency.

[0004] Therefore, it is necessary to provide a database data fuzzy recommendation system and method to solve the problem that traditional database systems have limitations in integrating and retrieving complex, multi-source data, resulting in low retrieval efficiency. Summary of the Invention

[0005] In view of this, the present invention proposes a database data fuzzy recommendation system and method, aiming to solve the problem that traditional database systems have limitations in integrating and retrieving complex, multi-source data, resulting in low retrieval efficiency.

[0006] In one aspect, the present invention proposes a data fuzzy recommendation system for a database, comprising:

[0007] A database construction module is configured to store terahertz data by combining different databases;

[0008] A login verification module is configured to obtain login personnel information, perform security verification based on the login personnel information, and determine whether to authorize access based on the verification result;

[0009] A fuzzy recommendation module is configured to obtain information of personnel authorized to access and obtain historical browsing information of the personnel information in a historical browsing database, and make fuzzy recommendations to the authorized personnel based on the historical browsing information;

[0010] The data adjustment module is configured to obtain operation information of the authorized access personnel and adjust the recommendation preferences of the authorized access personnel according to the operation information.

[0011] Furthermore, the different databases include:

[0012] A relational database for storing structured data in terahertz data; wherein the structured data includes terahertz spectrum data and material property data;

[0013] A non-relational database is used to store semi-structured data or unstructured data; wherein the semi-structured data includes emails, XML files and JSON files; the unstructured data includes text files, images and videos.

[0014] Furthermore, the login verification module is configured to obtain login personnel information, and perform security verification based on the login personnel information, including:

[0015] The login personnel information includes identity ID, user name and password;

[0016] The security verification includes verifying whether the identity ID matches the password and verifying whether the identity ID has the authority to access the database.

[0017] Furthermore, the login verification module is configured to determine whether to authorize access based on the verification result, including:

[0018] If the identity ID and the password match, and the identity ID has permission to access the database, access is authorized;

[0019] If the identity ID and the password do not match, or the identity ID does not have permission to access the database, access is denied and a log record of access failure is generated.

[0020] Furthermore, when the fuzzy recommendation module is configured to obtain the information of the person authorized to access and obtain the historical browsing information of the person in the historical browsing database, it includes:

[0021] According to the identity ID in the authorized access personnel information, determine whether the authorized access personnel has historical browsing records;

[0022] If there is a historical browsing record, extract the historical browsing record, obtain recommended preferences based on the historical browsing record, and make fuzzy recommendations to authorized access personnel based on the recommended preferences;

[0023] If there is no historical browsing record, fuzzy recommendations are made to authorized access personnel based on the historical browsing database.

[0024] Furthermore, if there is a historical browsing record, the historical browsing record is extracted, and a recommended preference is obtained based on the historical browsing record. When a fuzzy recommendation is made to the authorized access person based on the recommended preference, the method includes:

[0025] Obtaining the browsing data formats of the authorized access personnel in the historical browsing records, counting the number of views of each browsing data format, and arranging the browsing data formats in descending order of the number of views; wherein the browsing data formats include text format, image format, audio format, and video format;

[0026] The result of arranging the browsing data format in descending order from high to low is the result of arranging the recommended preferences in descending order from high to low;

[0027] The search terms of the authorized access personnel are obtained, the search results corresponding to the search terms are filtered, and the search results are arranged in descending order according to the recommendation preferences for recommendation.

[0028] Furthermore, if there is no historical browsing record, making a fuzzy recommendation for authorized access personnel based on the historical browsing database includes:

[0029] Obtaining a search term of an authorized access person, filtering search results corresponding to the search term, and obtaining a total number of views of the search results in different viewing data formats;

[0030] Arrange the browsing data formats in descending order according to the total number of browsing times;

[0031] The search results are arranged in descending order according to the total number of views and recommended from first to last.

[0032] Furthermore, the data adjustment module is configured to obtain operation information of the authorized access personnel and adjust the recommendation preferences of the authorized access personnel according to the operation information, including:

[0033] The operation information includes the browsing time of the data format search results and whether the browsing data format search results are blocked;

[0034] Adjust the recommended preferences of authorized visitors based on the browsing time and blocking behavior.

[0035] Furthermore, when adjusting the recommended preferences of authorized access personnel according to the browsing time and blocking behavior, it also includes:

[0036] If a blocking action is set, the browsing data format corresponding to the blocking action is lowered by one level in the recommended preference ranking;

[0037] Otherwise, a browsing time threshold is set. If the browsing time corresponding to the browsing data format is greater than or equal to the browsing time threshold, the browsing data format is moved up one level in the recommended preference ranking.

[0038] Compared with the prior art, the beneficial effect of the present invention is that the present invention stores terahertz data by combining different databases, thereby realizing centralized management and efficient retrieval of data. First, through the database construction module, data from different sources and formats are integrated together, providing a solid foundation for subsequent data processing and recommendation. This integration not only improves the availability of data, but also enhances the integrity and consistency of data. The login verification module ensures the security of the system. By obtaining the login personnel information and performing security verification, the system can effectively control the access rights to sensitive data. The access control mechanism based on identity authentication not only protects the data from being accessed by unauthorized users, but also ensures that only verified users can use the services provided by the system, thereby ensuring data security while maintaining the privacy rights of users. The fuzzy recommendation module can intelligently recommend relevant data to users by analyzing the user's historical browsing information. This recommendation mechanism based on user behavior not only improves the user's work efficiency, but also enhances the user experience. Users no longer need to spend a lot of time looking for the required information. The system will automatically provide recommendations for relevant data based on the user's interests and historical behavior, thereby saving the user's time and improving work efficiency. By monitoring user operations in real time, the data adjustment module allows the system to dynamically adjust recommendation preferences, ensuring that recommendations always align with the user's latest needs and evolving interests. This dynamic adjustment mechanism makes the recommendation system more flexible and intelligent, adapting to evolving user behavior and providing consistently high-quality recommendation services. In summary, this invention, through the integration of multi-source data, enhanced security verification, intelligent recommendation, and dynamic adjustment of recommendation preferences, not only improves the efficiency and security of data management, but also significantly enhances user experience and work efficiency.

[0039] On the other hand, the present application also provides a method for fuzzy recommendation of data in a database, comprising:

[0040] Using a combination of different databases to store terahertz data;

[0041] Obtaining login personnel information, performing security verification based on the login personnel information, and determining whether to authorize access based on the verification result;

[0042] Obtaining information of personnel authorized to access, and obtaining historical browsing information of the personnel information in a historical browsing database, and making fuzzy recommendations for authorized access personnel based on the historical browsing information;

[0043] The operation information of the authorized access personnel is obtained, and the recommendation preferences of the authorized access personnel are adjusted according to the operation information.

[0044] It is understandable that the database data fuzzy recommendation system and method provided in this application have the same beneficial effects and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0046] Figure 1 A functional block diagram of a database data fuzzy recommendation system provided by an embodiment of the present invention;

[0047] Figure 2 This is a flowchart of a method for fuzzy recommendation of database data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0049] In some embodiments of this application, see Figure 1 As shown, this embodiment provides a data fuzzy recommendation system for a database, including:

[0050] A database construction module is configured to store terahertz data by combining different databases;

[0051] A login verification module is configured to obtain login personnel information, perform security verification based on the login personnel information, and determine whether to authorize access based on the verification result;

[0052] A fuzzy recommendation module is configured to obtain information of personnel authorized to access and obtain historical browsing information of the personnel information in a historical browsing database, and make fuzzy recommendations to the authorized personnel based on the historical browsing information;

[0053] The data adjustment module is configured to obtain operation information of the authorized access personnel and adjust the recommendation preferences of the authorized access personnel according to the operation information.

[0054] It is understandable that the present invention stores terahertz data by combining different databases, thereby achieving centralized management and efficient retrieval of data. First, through the database construction module, data from different sources and formats are integrated together, providing a solid foundation for subsequent data processing and recommendation. This integration not only improves the availability of data, but also enhances the integrity and consistency of data. The login verification module ensures the security of the system. By obtaining the login personnel information and performing security verification, the system can effectively control the access rights to sensitive data. The access control mechanism based on identity authentication not only protects the data from being accessed by unauthorized users, but also ensures that only verified users can use the services provided by the system, thereby ensuring data security while maintaining the privacy rights of users. The fuzzy recommendation module can intelligently recommend relevant data to users by analyzing the user's historical browsing information. This recommendation mechanism based on user behavior not only improves the user's work efficiency, but also enhances the user experience. Users no longer need to spend a lot of time looking for the information they need. The system will automatically provide recommendations for relevant data based on the user's interests and historical behavior, thereby saving the user's time and improving work efficiency. By monitoring user operations in real time, the data adjustment module allows the system to dynamically adjust recommendation preferences, ensuring that recommendations always align with the user's latest needs and evolving interests. This dynamic adjustment mechanism makes the recommendation system more flexible and intelligent, adapting to evolving user behavior and providing consistently high-quality recommendation services. In summary, this invention, through the integration of multi-source data, enhanced security verification, intelligent recommendation, and dynamic adjustment of recommendation preferences, not only improves the efficiency and security of data management, but also significantly enhances user experience and work efficiency.

[0055] In some embodiments of the present application, the different databases include:

[0056] A relational database for storing structured data in terahertz data; wherein the structured data includes terahertz spectrum data and material property data;

[0057] A non-relational database is used to store semi-structured data or unstructured data; wherein the semi-structured data includes emails, XML files and JSON files; the unstructured data includes text files, images and videos.

[0058] It is understandable that relational databases are used to store structured data, such as terahertz spectral data and material property data, which makes data query and retrieval more efficient and accurate because structured data can take advantage of the powerful query language and indexing mechanism of relational databases. Non-relational databases are used to process semi-structured data (such as emails, XML files, and JSON files) and unstructured data (such as text files, images, and videos), which provides flexibility and scalability for storing and accessing this data because non-relational databases do not require a fixed table structure and can better adapt to the diversity and complexity of data formats. This combined use of relational and non-relational databases not only optimizes the efficiency of data management, but also increases the flexibility of data processing, thereby providing strong support for terahertz data analysis and applications.

[0059] In some embodiments of the present application, the login verification module is configured to obtain login personnel information, and perform security verification based on the login personnel information, including:

[0060] The login personnel information includes identity ID, user name and password;

[0061] The security verification includes verifying whether the identity ID matches the password and verifying whether the identity ID has the authority to access the database.

[0062] In some embodiments of the present application, the login verification module is configured to determine whether to authorize access based on the verification result, including:

[0063] If the identity ID and the password match, and the identity ID has permission to access the database, access is authorized;

[0064] If the identity ID and the password do not match, or the identity ID does not have permission to access the database, access is denied and a log record of access failure is generated.

[0065] It is understandable that the login verification module is configured to obtain the login person's information and, when performing security verification, includes a matching check of the identity ID, user name, and password, as well as confirmation of whether the identity ID has permission to access the database. This ensures that only users who have passed the verification can obtain authorized access, thereby enhancing the security of the system. Specifically, if the identity ID and password match, and the identity ID has access rights, the user will be granted access; conversely, if the identity ID and password do not match, or the identity ID does not have access rights, access will be denied, and the system will record a log of the access failure to facilitate subsequent security audits and problem tracking. The advantage of this verification mechanism is that it not only ensures data security, but also provides detailed access records, which helps to promptly detect and deal with potential security threats.

[0066] In some embodiments of the present application, the fuzzy recommendation module is configured to obtain the information of the person authorized to access and obtain the historical browsing information of the person in the historical browsing database, including:

[0067] According to the identity ID in the authorized access personnel information, determine whether the authorized access personnel has historical browsing records;

[0068] If there is a historical browsing record, extract the historical browsing record, obtain recommended preferences based on the historical browsing record, and make fuzzy recommendations to authorized access personnel based on the recommended preferences;

[0069] If there is no historical browsing record, fuzzy recommendations are made to authorized access personnel based on the historical browsing database.

[0070] In some embodiments of the present application, if there is a historical browsing record, extracting the historical browsing record, obtaining a recommended preference based on the historical browsing record, and making a fuzzy recommendation to the authorized access person based on the recommended preference includes:

[0071] Obtaining the browsing data formats of the authorized access personnel in the historical browsing records, counting the number of views of each browsing data format, and arranging the browsing data formats in descending order of the number of views; wherein the browsing data formats include text format, image format, audio format, and video format;

[0072] The result of arranging the browsing data format in descending order from high to low is the result of arranging the recommended preferences in descending order from high to low;

[0073] The search terms of the authorized access personnel are obtained, the search results corresponding to the search terms are filtered, and the search results are arranged in descending order according to the recommendation preferences for recommendation.

[0074] In some embodiments of the present application, if there is no historical browsing record, making a fuzzy recommendation for authorized access personnel based on the historical browsing database includes:

[0075] Obtaining a search term of an authorized access person, filtering search results corresponding to the search term, and obtaining a total number of views of the search results in different viewing data formats;

[0076] Arrange the browsing data formats in descending order according to the total number of browsing times;

[0077] The search results are arranged in descending order according to the total number of views and recommended from first to last.

[0078] As you can understand, the fuzzy recommendation module aims to provide personalized content recommendations by analyzing historical user behavior data. First, the fuzzy recommendation module checks whether the authorized user has any browsing history. If so, the system analyzes this history to determine the user's preferences and then makes fuzzy recommendations. Specifically, the system counts the number of times the user has viewed different data formats (such as text, images, audio, and video) and sorts these formats in descending order by number of views, using this as a basis for user preferences. The system then obtains the user's search terms and filters relevant search results based on these terms. Finally, these results are ranked and recommended based on the user's preferences. This approach effectively transforms user history into personalized recommendations, thereby improving user experience and satisfaction. If the user has no browsing history, the system adopts a different fuzzy recommendation strategy. In this case, the system analyzes the user's search terms and filters the corresponding search results. The system then counts the total number of views across different data formats in these results and sorts the formats in descending order based on this total number of views. Finally, the system ranks the search results based on total number of views and recommends them to the user. Although this method does not have direct personal historical data as a reference, it can still provide relatively personalized recommendations by analyzing the preferences of the entire user group.

[0079] Overall, this fuzzy recommendation mechanism flexibly adapts to the needs of different users, whether they have extensive user history or are new to the platform. By analyzing historical data and overall user behavior, the system can provide more accurate and personalized recommendations, thereby improving user satisfaction, increasing user stickiness, and potentially increasing the commercial value of the platform. Furthermore, this recommendation mechanism can help users discover content they may be interested in but have not yet actively searched for, thereby broadening their horizons and increasing content diversity.

[0080] In some embodiments of the present application, the data adjustment module is configured to obtain operation information of the authorized access personnel, and adjust the recommendation preferences of the authorized access personnel according to the operation information, including:

[0081] The operation information includes the browsing time of the data format search results and whether the browsing data format search results are blocked;

[0082] Adjust the recommended preferences of authorized visitors based on the browsing time and blocking behavior.

[0083] In some embodiments of the present application, adjusting the recommended preferences of authorized access personnel based on the browsing duration and blocking behavior further includes:

[0084] If a blocking action is set, the browsing data format corresponding to the blocking action is lowered by one level in the recommended preference ranking;

[0085] Otherwise, a browsing time threshold is set. If the browsing time corresponding to the browsing data format is greater than or equal to the browsing time threshold, the browsing data format is moved up one level in the recommended preference ranking.

[0086] It is understandable that the data adjustment module can intelligently adjust its recommendation preferences by obtaining the operation information of authorized access personnel. Specifically, the module analyzes the length of time users browse data format search results and whether they block certain search results. Through this operation information, the system can optimize the recommendation algorithm so that users are more likely to receive content they are interested in. When a user blocks a specific type of search result, the system will correspondingly lower the priority of such content in the recommendation list. Conversely, if the user's browsing time for a certain type of search result exceeds the set threshold, the system will increase the recommendation priority of such content. Such a mechanism not only improves user satisfaction, but also enhances the personalization and accuracy of the recommendation system, thereby bringing users a more accurate and considerate service experience.

[0087] On the other hand, see Figure 2 As shown, the present application also provides a data fuzzy recommendation method for a database, which is applied to the data fuzzy recommendation system of the above database, comprising the following steps:

[0088] S100, storing terahertz data by combining different databases;

[0089] S200, obtaining login personnel information, performing security verification based on the login personnel information, and determining whether to authorize access based on the verification result;

[0090] S300, obtaining information of personnel authorized to access, and obtaining historical browsing information of the personnel information in a historical browsing database, and making fuzzy recommendations for authorized personnel based on the historical browsing information;

[0091] S400: Acquire operation information of the authorized access personnel, and adjust the recommended preferences of the authorized access personnel according to the operation information.

[0092] It can be understood that the present invention can improve user experience and usage efficiency. First, by combining different databases to store terahertz data (S100), efficient data management and rapid retrieval are achieved. This multi-database approach not only increases data storage capacity but also enhances the flexibility and scalability of data processing, providing a solid foundation for subsequent data processing and recommendations. Second, obtaining login user information and performing security verification (S200) is a key step in ensuring data security and privacy protection. By verifying the login user's identity, unauthorized access can be effectively prevented, ensuring that only authorized users can access sensitive data. This security mechanism not only protects user information security but also enhances user trust in the system. Third, fuzzy recommendations for authorized users based on historical browsing information (S300) are key to improving user experience. By analyzing users' historical behavior and preferences, relevant data can be intelligently recommended, reducing the time and effort users spend searching for the information they need. This personalized recommendation mechanism can significantly improve user satisfaction and increase user stickiness. Finally, obtaining authorized user operation information and adjusting recommendation preferences based on this operation information (S400) further optimizes recommendation accuracy. By tracking user behavior in real time, we can dynamically adjust recommendation strategies to ensure that recommended content always meets the user's latest needs and interests. This dynamic adjustment mechanism makes the recommendation system more intelligent and humane, and can better adapt to user changes.

[0093] In summary, the database data fuzzy recommendation method provided by the present invention not only improves the efficiency and security of data processing by combining technical means such as multi-database storage, security verification, personalized recommendation and dynamic adjustment, but also greatly enhances the user experience, allowing users to obtain the required information more conveniently and efficiently.

[0094] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A data fuzzy recommendation system for a database, characterized in that: include: A database construction module is configured to store terahertz data by combining different databases; A login verification module is configured to obtain login personnel information, perform security verification based on the login personnel information, and determine whether to authorize access based on the verification result; A fuzzy recommendation module is configured to obtain information of personnel authorized to access and obtain historical browsing information of the personnel information in a historical browsing database, and make fuzzy recommendations to the authorized personnel based on the historical browsing information; The data adjustment module is configured to obtain operation information of the authorized access personnel and adjust the recommendation preferences of the authorized access personnel according to the operation information.

2. The data fuzzy recommendation system for a database according to claim 1, characterized in that: The different databases include: A relational database for storing structured data in terahertz data; wherein the structured data includes terahertz spectrum data and material property data; A non-relational database is used to store semi-structured data or unstructured data; wherein the semi-structured data includes emails, XML files and JSON files; the unstructured data includes text files, images and videos.

3. The data fuzzy recommendation system for a database according to claim 1, characterized in that: The login verification module is configured to obtain login personnel information and perform security verification based on the login personnel information, including: The login personnel information includes identity ID, user name and password; The security verification includes verifying whether the identity ID matches the password and verifying whether the identity ID has the authority to access the database.

4. The data fuzzy recommendation system for a database according to claim 3, characterized in that: The login verification module is configured to determine whether to authorize access based on the verification result, including: If the identity ID and the password match, and the identity ID has permission to access the database, access is authorized; If the identity ID and the password do not match, or the identity ID does not have permission to access the database, access is denied and a log record of access failure is generated.

5. The database data fuzzy recommendation system according to claim 4, characterized in that: The fuzzy recommendation module is configured to obtain the information of the person authorized to access and obtain the historical browsing information of the person in the historical browsing database, including: According to the identity ID in the authorized access personnel information, determine whether the authorized access personnel has historical browsing records; If there is a historical browsing record, extract the historical browsing record, obtain recommended preferences based on the historical browsing record, and make fuzzy recommendations to authorized access personnel based on the recommended preferences; If there is no historical browsing record, fuzzy recommendations are made to authorized access personnel based on the historical browsing database.

6. The data fuzzy recommendation system for a database according to claim 5, characterized in that: If there is a historical browsing record, extracting the historical browsing record, obtaining a recommended preference based on the historical browsing record, and making a fuzzy recommendation to the authorized access person based on the recommended preference includes: Obtaining the browsing data formats of the authorized access personnel in the historical browsing records, counting the number of views of each browsing data format, and arranging the browsing data formats in descending order of the number of views; wherein the browsing data formats include text format, image format, audio format, and video format; The result of arranging the browsing data format in descending order from high to low is the result of arranging the recommended preferences in descending order from high to low; The search terms of the authorized access personnel are obtained, the search results corresponding to the search terms are filtered, and the search results are arranged in descending order according to the recommendation preferences for recommendation.

7. The data fuzzy recommendation system for a database according to claim 5, characterized in that: If there is no historical browsing record, then the fuzzy recommendation of authorized access personnel based on the historical browsing database includes: Obtaining a search term of an authorized access person, filtering search results corresponding to the search term, and obtaining a total number of views of the search results in different viewing data formats; Arrange the browsing data formats in descending order according to the total number of browsing times; The search results are arranged in descending order according to the total number of views and recommended from first to last.

8. The database data fuzzy recommendation system according to claim 7, characterized in that: The data adjustment module is configured to obtain operation information of the authorized access personnel and adjust the recommendation preferences of the authorized access personnel according to the operation information, including: The operation information includes the browsing time of the data format search results and whether the browsing data format search results are blocked; Adjust the recommended preferences of authorized visitors based on the browsing time and blocking behavior.

9. The database data fuzzy recommendation system according to claim 8, characterized in that: When adjusting the recommended preferences of authorized access personnel based on the browsing time and blocking behavior, it also includes: If a blocking action is set, the browsing data format corresponding to the blocking action is lowered by one level in the recommended preference ranking; Otherwise, a browsing time threshold is set. If the browsing time corresponding to the browsing data format is greater than or equal to the browsing time threshold, the browsing data format is moved up one level in the recommended preference ranking.

10. A method for fuzzy recommendation of database data, applied to the fuzzy recommendation system for database data according to any one of claims 1 to 9, characterized in that: include: Using a combination of different databases to store terahertz data; Obtaining login personnel information, performing security verification based on the login personnel information, and determining whether to authorize access based on the verification result; Obtaining information of personnel authorized to access, and obtaining historical browsing information of the personnel information in a historical browsing database, and making fuzzy recommendations for authorized access personnel based on the historical browsing information; The operation information of the authorized access personnel is obtained, and the recommendation preferences of the authorized access personnel are adjusted according to the operation information.

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