Big data management system based on 5G network

Abstract

The invention discloses a big data management system based on a 5G network, and relates to the technical field of management, the big data management system comprises a data management center, the data management center is in communication connection with a cloud database, a user terminal module, a data acquisition module, a data analysis module and a reading material pushing module; the cloud database is used for storing reading materials and personal account data of an online reading user; the user terminal module is used for completing related identity verification of a user and browsing related reading materials; the data acquisition module is used for acquiring user feature data and reading material feature data; the data analysis module is used for analyzing the reading interest of the user according to the feature data; the reading material pushing module is used for pushing corresponding reading materials to the user according to the analysis result of the data analysis module; the method has the beneficial effects that when the user reads, the corresponding reading material is recommended, so that the user is not limited in the original reading range.

Description

Technical Field

[0001] The present invention relates to the field of security technology, and in particular to a big data management system based on a 5G network. Background Art

[0002] With the development of computer science and technology and 5G networks, big data has gradually played its role in people's lives, making people's lives more and more convenient. Big data, also known as massive data, refers to the amount of data involved that is so large that it cannot be captured, managed, processed and organized within a reasonable time through mainstream software tools to become information that helps companies make more positive business decisions. Big data management requires three steps: data access, data calculation and data storage. With the rise of the Internet, people's reading of books has gradually shifted from offline to online. When people enter libraries and bookstores offline, they are often attracted by fields they have never known when passing by bookshelves, and thus borrow or buy books. The books they are interested in may have nothing to do with the fields they are good at and interested in, but they are interested in them at first sight; however, with the gradual improvement of the development of big data, the analysis of people's living habits is more thorough, and the recommended books to us are the fields we are usually interested in, which causes our vision to be limited to the fields we are accustomed to, making our knowledge narrower. Therefore, how to avoid being limited by big data is a problem we need to solve. To this end, a big data management system based on 5G network is now provided. Summary of the invention

[0003] In order to solve the above technical problems, the purpose of the present invention is to provide a big data management system based on 5G network.

[0004] The object of the present invention can be achieved by the following technical solutions: A big data management system based on a 5G network, comprising a data management center, wherein the data management center is communicatively connected to a cloud database, a user terminal module, a data acquisition module, a data analysis module, and a reading material push module; The cloud database is used to store reading materials and personal account information of users who use online reading; The user terminal module is used to perform relevant verification of the user's identity, and browse relevant reading materials through the login account after completing the identity verification; The data collection module is used to collect user characteristic data and reading material characteristic data, and upload the collected characteristic data to the data analysis module; The data analysis module is used to analyze the feature data collected by the data collection module, generate feature links, and send the feature links to the reading material push module; The reading material push module is used to receive feature links and push corresponding reading materials to users according to the feature links.

[0005] Furthermore, the cloud database is provided with a storage space unit and a user storage unit. The storage space unit is provided with corresponding reading material storage space according to different types of reading materials, and corresponding types of reading materials are stored in the corresponding reading material storage space; the user storage unit is provided with corresponding user storage space according to the user's personal account information, and the user storage space is used to store the user's personal account information and the user feature data collected by the data collection module.

[0006] Furthermore, the user terminal module is provided with a registration unit and a login unit. The registration unit is used for new users to register and verify their identities, and the login unit is used for users to complete login. The user terminal module is also provided with a reading platform. After completing the login, the user enters the reading platform to read. The reading platform is used for users to read online at any time, wherein a browsing classification window and a reading recommendation window are provided. There are multiple browsing classification windows, which are mutually associated with the storage space units in the cloud database. The corresponding browsing classification windows are set according to the corresponding reading storage space. Multiple sub-windows are provided in the browsing classification window and the reading recommendation window. The sub-windows are used to place the reading name and cover, and the user enters the reading interface of the corresponding reading through the sub-window.

[0007] Furthermore, the data collection module is provided with a reading collection unit and a user collection unit; The reading material collection unit is used to collect all reading material characteristic data in the reading platform, wherein the reading material characteristic data includes click data and reading data, wherein the click data refers to the data of the user entering the corresponding reading interface when using the reading platform. During the collection process, a monitoring time threshold is set. When the time when the user stays in the reading interface of the reading material exceeds the monitoring time threshold, the data is simultaneously determined as reading data, and the collected click data and reading data are uploaded to the data analysis module; The user collection unit is used to collect user characteristic data, which includes the total number, types and genres of readings read by the user when using the reading platform; the user characteristic data collected by the user collection unit is uploaded to the data analysis module.

[0008] Furthermore, the data analysis module is provided with a reading material analysis unit, and the reading material analysis unit is provided with threshold data, and the threshold data are respectively a popular threshold and a common threshold. The reading data obtained by the reading material acquisition unit is compared with the popular threshold and the common threshold, and the types of reading materials are divided into unpopular reading materials, common reading materials and popular reading materials according to the comparison results. The reading material analysis unit also analyzes the proportion of reading data of reading materials in click data, sets a reading data proportion threshold, and compares and analyzes the proportion data with the reading data proportion threshold. According to the comparison results, the reading materials are divided into inferior reading materials and superior reading materials, and a reading feature link is generated for the type of reading material analyzed by the reading material analysis unit, and the reading feature link is uploaded to the reading material push module.

[0009] Furthermore, the data analysis module is also provided with a user analysis unit, which is used to determine the user type based on the user characteristic data, analyze the respective proportions of popular books, ordinary books and unpopular books in the total number of books read by the user, and determine the reader type as a trendy reader, ordinary reader, unpopular reader or casual reader based on the relationship between the proportions of popular books, ordinary books and unpopular books, analyze the proportion of different types of books browsed by the user in the total number of readings, generate a user characteristic link based on the analyzed reading proportions and reader types, and upload the user characteristic link to the reading push module.

[0010] Furthermore, the reading material push module is interconnected with the reading platform, in which a user recommendation unit is provided, and the user recommendation unit is used to push corresponding reading materials to the reading material recommendation window. The user recommendation unit recommends reading materials of corresponding percentage types in 80% of the sub-windows of the reading material recommendation window according to the proportion of different types of reading materials browsed by the user in the total number of readings, and recommends reading materials in the reading storage space that the user has not browsed in 20% of the sub-windows of the reading recommendation window.

[0011] Furthermore, the reading material push module is also provided with a classification recommendation unit, which is used to push corresponding reading materials to the browsing classification window, obtain the user's reader type according to the user feature link uploaded by the data analysis module, and set the delivery ratio of the corresponding reading material type in the sub-window of the browsing classification window according to the user's reader type. In the process of pushing the corresponding reading materials, the reading material push module strictly controls the proportion of the recommended inferior reading materials.

[0012] Compared with the prior art, the beneficial effect of the present invention is: by judging the types of users and books based on the data of users reading books and the data of books being read, and then setting corresponding push ratios according to corresponding types, it avoids limiting the reading scope of readers due to too single pushed content, and expands the possible reading scope of readers. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is the principle diagram of the present invention; DETAILED DESCRIPTION

[0014] like Figure 1 As shown, a big data management system based on a 5G network includes a data management center, and the data management center is communicatively connected to a cloud database, a user terminal module, a data acquisition module, a data analysis module, and a reading material push module; The cloud database is used to store reading materials and personal account information of users who use online reading; it is provided with a user storage unit and a storage space unit; The storage space unit is provided with corresponding reading material storage spaces according to different types of reading materials, and corresponding types of reading materials are stored in the corresponding reading material storage spaces, and the types of reading materials are distinguished according to their contents; The user storage unit is used to store personal account information for online reading. A corresponding user storage space is set according to the user's personal account information. The user storage space is also used to store the user feature data collected by the data collection module when the user uses the reading platform; when the user exits the reading platform, the user feature data is still saved in the user storage space. When the user logs in again and uses the reading platform, corresponding reading materials can be pushed to the user according to the user feature data; The cloud database is provided with a work quality detection unit, which is used to detect the quality of works on the platform, avoid the appearance of some low-quality and vulgar works on the reading platform, and boycott the author who uploaded the work; It should be further explained that, in the specific implementation process, reading materials include but are not limited to books, newspapers and periodicals, and the types of reading materials are divided according to their content, which may include but are not limited to fantasy, urban entertainment, history and military, science and technology, and real-time news, etc.; The user terminal module is used to perform relevant verification of the user's identity, and browse relevant reading materials through the login account after completing the identity verification; The user terminal module is provided with a registration unit and a login unit. The registration unit is used for new users to register and verify their identities. After passing the identity registration and verification, the user reads books with relevant contents through the user terminal module. The new user enters personal basic information through the registration unit, and the personal basic information includes the mobile phone number of the personal real-name authentication, and uploads the personal basic information to the data management center for review. The specific operation process includes: The data management center generates a random number based on the real-name authenticated mobile phone number in the personal basic information input by the user, puts the generated random number into a text message, and sends the text message with the random number to the mobile phone number input by the user. After the user receives the text message, the user enters the random number in the received text message into the registration unit through the registration unit. The data management center verifies whether the random number input by the user matches the generated random number. If the match fails, it indicates that the user has failed the review. If the match succeeds, it indicates that the user has succeeded the review. A corresponding login account and login password are generated based on the real-name authenticated mobile phone number of the user, and the generated login account and login password are sent to the user's mobile phone. After the review is passed, the user can enter the generated login account and login password into the login unit to complete the login; The user terminal module is also provided with a reading platform, which is used for users to read relevant data, newspapers and other reading materials online at any time, wherein a browsing classification window and a reading recommendation window are provided, wherein a plurality of the browsing classification windows are provided, and are mutually associated with the storage space units in the cloud database, and a corresponding browsing classification window is provided according to the corresponding reading storage space, wherein the browsing classification window is named according to the type of reading material therein, and the reader can search for the reading material he needs in the corresponding named browsing classification window according to personal needs, wherein the browsing classification window and the reading recommendation window are both provided with a plurality of sub-windows, wherein the sub-windows are used to place the reading material name and cover, and the user enters the reading interface of the corresponding reading material through the sub-window; It should be further explained that, in the specific implementation process, after the user logs in, the user management module immediately enters the reading platform, and the browsing category window and the reading recommendation window are different. The user enters the corresponding browsing category window according to personal needs, and the recommended readings are of the same content, while the reading recommendation window recommends different types of readings that the user may be interested in; The data collection module is used to collect the reading habits of different users when reading related books, and upload the obtained data to the data analysis module. The specific process includes: The data collection module is provided with a reading collection unit and a user collection unit; The reading material collection unit is used to collect reading material characteristic data of all reading materials in the reading platform, wherein the reading material characteristic data includes click data and reading data, wherein the click data refers to the data of the user entering the corresponding reading interface when using the reading platform. During the collection process, a monitoring time threshold is set. When the time when the user stays in the reading interface of the reading material exceeds the monitoring time threshold, the data is simultaneously determined as reading data, and the collected click data and reading data are uploaded to the data analysis module; The user collection unit is used to collect user characteristic data, wherein the user characteristic data includes the total number, types and genres of readings read by the user when using the reading platform; the user characteristic data collected by the user collection unit is uploaded to the data analysis module; It should be further explained that, in the specific implementation process, there is a difference between the kind of reading material and the type of reading material. The kind of reading material is set according to the content of the reading material, and the type of reading material is determined based on the data analysis left by the user when browsing the relevant reading material on the reading platform; The data analysis module is used to analyze the characteristic data uploaded by the data acquisition module and send the analysis results to the reading material push module. The data analysis module is provided with a reading material analysis unit and a user analysis unit; The reading analysis unit is used to analyze the reading feature data collected by the reading collection module, generate a reading feature link, and upload it to the reading push module. The specific process includes: The reading data of the corresponding reading material collected by the reading material collection unit is recorded as N i , wherein unpopular books, ordinary books and popular books are set, and corresponding classification standards are set for them, and the reading types are classified according to the set corresponding standards and the reading data of the corresponding books collected by the reading collection unit, and threshold data are set, and the threshold data are respectively set with a popular threshold value and a common threshold value, which are respectively recorded as R and P; The reading data detected by the reading collection unit is compared with the threshold data by the reading analysis unit, and then the reading type is determined to be a rare reading, a common reading or a popular reading. The specific process includes: When N i When <P, it means that the reading material is unpopular; When P≤N i When <R, it means that the reading material is ordinary reading material; When R≤N i , it means that the reading material is a popular reading material; The reading data and click data collected by the reading collection unit are analyzed by the reading analysis unit, and a reading proportion threshold is set and recorded as DM. The proportion data D of the reading data of the reading object in the click data is analyzed. The reading proportion and the proportion data are analyzed and compared by the reading analysis unit. When D<DM, it indicates that the reading object is an inferior reading object; when D≥DM, it indicates that the reading object is an excellent reading object; Obtain the corresponding type of the reading material according to the two analysis methods of the reading material analysis unit, generate a reading material feature link, and upload it to the reading material push module; It should be further explained that, in the specific implementation process, the reading material analysis unit analyzes the data collected by the reading material collection unit within a period of time, so the results obtained in different time periods may be different. By analyzing a period of time, the data of the types of reading materials are floating, and more types of reading materials can be seen by the user when the reading material push module pushes reading materials; The user analysis unit is used to analyze user feature data, and the specific process includes: According to the user characteristic data collected by the user collection unit, the total number of books browsed by the user is obtained, and the percentage of popular books, ordinary books and unpopular books browsed by the user in the total number of books read is obtained; and the percentage values ​​of the popular books, ordinary books and unpopular books read by the user are compared; If the proportion of popular books is higher than the sum of the other two proportions, it means that the user may not pay much attention to unpopular books and ordinary books, and the system defines him as a trend reader; If the proportion of ordinary readings is higher than the sum of the other two proportions, it means that the user may not pay much attention to popular readings and unpopular readings, and the system defines him as an ordinary reader; If the proportion of unpopular books is higher than the sum of the proportions of the other two, it means that the user may not pay much attention to popular books and ordinary books, and the system defines him as an unpopular reader; If there are any other proportions, it means that users may have similar levels of interest in reading materials, and the system defines them as casual readers; Analyze the proportion of different types of reading materials browsed by users to the total number of readings, generate user feature links based on the analyzed reading proportions and reader types, and upload the user feature links to the reading material push module; It should be further explained that, in the specific implementation process, the user analysis unit defines users only for the purpose of recommending more suitable reading materials to the corresponding readers. The readers are not informed of the type of readers they are defined as, thereby avoiding problems in the collected data due to the personal inclinations of the users, which results in failure to recommend relevant reading materials. The reading material push module is used to push corresponding reading materials to the user's reading platform according to the reading needs of different users. The specific operation process includes: The reading material push module is associated with the reading platform, wherein a user recommendation unit and a classification recommendation unit are provided, which are used to recommend related reading materials to the reading material recommendation window and the browsing classification window respectively; The user recommendation unit recommends books of corresponding percentage in 80% of the sub-windows of the reading recommendation window according to the proportion of different types of books browsed by the user to the total number of books read, and recommends books in the reading storage space that the user has not browsed in 20% of the sub-windows of the reading recommendation window; The classification recommendation unit sets the proportion of different types of reading materials to be delivered in the subwindows of the browsing classification window for different types of readers according to the user feature link sent by the data analysis module. The specific operation process includes: When the user's reader type is a trend reader, the proportion of popular books placed in the browsing category window is 60%, and the proportion of ordinary books and unpopular books is 40% in total; When the user's reader type is an ordinary reader, the proportion of ordinary reading materials placed in the browsing category window is 60%, and the proportion of popular reading materials and unpopular reading materials is 40% in total; When the user's reader type is a rare reader, the proportion of rare books placed in the browsing category window is 60%, and the proportion of popular books and ordinary books is 40% in total; When the user's reader type is a casual reader, the proportion of common reading materials, popular reading materials and unpopular reading materials placed in the browsing category window is equal; In the process of recommending relevant books, the reading material push module needs to control not only the proportion of the books pushed, but also the quality of the books recommended, in order to ensure the quality of the books read by the readers. Among the recommended books, the proportion of inferior books recommended shall not exceed 20%; It needs to be further explained that, in the specific implementation process, the criteria for judging superior and inferior reading materials are only based on the relationship between click data and reading data over a period of time, and are judged based on the degree of user preference, not the content of the reading materials. In addition, superior and inferior reading materials are only names used within the system to distinguish them when making recommendations. Users are unaware of them, and the relationship is not proportional to the quality of the content of the reading materials.

[0015] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A big data management system based on 5G network, including a data management center, characterized in that: The data management center is communicatively connected to a cloud database, a user terminal module, a data acquisition module, a data analysis module, and a reading material push module; The cloud database is used to store reading materials and personal account information of users who use online reading; The user terminal module is used to perform relevant verification of the user's identity, and browse relevant reading materials through the login account after completing the identity verification; The data collection module is used to collect user characteristic data and reading material characteristic data, and upload the collected characteristic data to the data analysis module; The data analysis module is used to analyze the feature data collected by the data collection module, generate feature links, and send the feature links to the reading material push module; The reading material push module is used to receive feature links and push corresponding reading materials to users according to the feature links.

2. A big data management system based on 5G network according to claim 1, characterized in that: The cloud database is provided with a storage space unit and a user storage unit, wherein the storage space unit is provided with corresponding reading storage spaces according to different types of reading materials, and corresponding types of reading materials are stored in corresponding reading storage spaces; The user storage unit is provided with a corresponding user storage space according to the user's personal account information, and the user storage space is used to store the user's personal account information and the user characteristic data collected by the data collection module.

3. A big data management system based on 5G network according to claim 2, characterized in that: The user terminal module is provided with a registration unit and a login unit. The registration unit is used for new users to register and verify their identities, and the login unit is used for users to complete login. The user terminal module is also provided with a reading platform. After completing the login, the user enters the reading platform to read. The reading platform is used for users to read online at any time, wherein a browsing classification window and a reading recommendation window are provided. There are multiple browsing classification windows, which are mutually associated with the storage space units in the cloud database. The corresponding browsing classification windows are set according to the corresponding reading storage space. Multiple sub-windows are provided in the browsing classification window and the reading recommendation window. The sub-windows are used to place the reading name and cover. The user enters the reading interface of the corresponding reading through the sub-window.

4. A big data management system based on 5G network according to claim 3, characterized in that: The data collection module is provided with a reading collection unit and a user collection unit; The reading material collection unit is used to collect all reading material characteristic data in the reading platform, wherein the reading material characteristic data includes click data and reading data, wherein the click data refers to the data of the user entering the corresponding reading interface when using the reading platform. During the collection process, a monitoring time threshold is set. When the time when the user stays in the reading interface of the reading material exceeds the monitoring time threshold, the data is simultaneously determined as reading data, and the collected click data and reading data are uploaded to the data analysis module; The user collection unit is used to collect user characteristic data, which includes the total number, types and genres of readings read by the user when using the reading platform; the user characteristic data collected by the user collection unit is uploaded to the data analysis module.

5. A big data management system based on 5G network according to claim 4, characterized in that: The data analysis module is provided with a reading material analysis unit, and the reading material analysis unit is provided with threshold data, and the threshold data are respectively a popular threshold and a common threshold. The reading data obtained by the reading material acquisition unit is compared with the popular threshold and the common threshold, and the types of reading materials are divided into unpopular reading materials, common reading materials and popular reading materials according to the comparison results. The reading material analysis unit also analyzes the proportion of reading data of reading materials in click data, sets a reading data proportion threshold, compares and analyzes the proportion data with the reading data proportion threshold, and divides the reading materials into inferior reading materials and superior reading materials according to the comparison results. A reading feature link is generated for the type of reading material analyzed by the reading material analysis unit, and the reading feature link is uploaded to the reading material push module.

6. A big data management system based on 5G network according to claim 5, characterized in that: The data analysis module is also provided with a user analysis unit, which is used to determine the user type based on the user characteristic data, analyze the respective proportions of popular books, ordinary books and unpopular books in the total number of books read by the user, and determine the reader type as a trendy reader, an ordinary reader, an unpopular reader or a casual reader based on the relationship between the proportions of popular books, ordinary books and unpopular books, analyze the proportion of different types of books browsed by the user in the total number of readings, generate a user characteristic link based on the analyzed reading proportions and reader types, and upload the user characteristic link to the reading push module.

7. A big data management system based on 5G network according to claim 6, characterized in that: The reading material push module is associated with the reading platform, and is provided with a user recommendation unit, which is used to push corresponding reading materials to the reading material recommendation window. The user recommendation unit recommends reading materials of corresponding percentage types in 80% of the sub-windows of the reading material recommendation window according to the proportion of different types of reading materials browsed by the user in the total number of readings, and recommends reading materials in the reading storage space that the user has not browsed in 20% of the sub-windows of the reading recommendation window.

8. A big data management system based on 5G network according to claim 7, characterized in that: The reading material push module is also provided with a classification recommendation unit, which is used to push corresponding reading materials to the browsing classification window, obtain the user's reader type according to the user feature link uploaded by the data analysis module, and set the delivery ratio of the corresponding reading material type in the subwindow of the browsing classification window according to the user's reader type. In the process of pushing the corresponding reading materials, the reading material push module strictly controls the proportion of the recommended inferior reading materials.

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