Academic information recommendation method and device based on data flow management

Abstract

The invention relates to an academic information recommendation method and device based on data flow management, and the method comprises the steps: sending an academic information recommendation request to a data relay end, so as to indicate the data relay end to capture academic information from a cloud end or a database; dividing the academic information into a plurality of sub-data streams, dividing the academic information into a plurality of sub-data streams based on the number of the data relay ends and the number of the data recommendation ends, and determining an original effective sub-data stream set; dividing effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams, and controlling a data recommendation end to recommend the recommended data streams based on the academic information recommendation request; and monitoring the update of the recommendation amount in real time, and converting the recommendation data stream and the to-be-recommended data stream according to the update. On the basis, the recommendation data flow is accurately recognized, and meanwhile the data processing amount is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of information data management, and in particular to a method and device for recommending academic information based on data stream management. Background Art

[0002] Academic information recommendation refers to services that provide researchers with the latest academic research findings, conference information, journal developments, and other information. These services are typically delivered through specific platforms or websites, helping researchers stay up-to-date on the latest developments in their field, thereby promoting academic exchange and research progress.

[0003] With the development of recommendation data platforms, recommendation algorithms, and AI algorithms, a growing number of data-driven approaches to recommending academic information are emerging. Current academic information management primarily relies on data-driven approaches to capture academic resources from the internet or specific databases and make recommendations accordingly. However, due to the numerous sources and types of academic information, as well as the large volume of data, directly capturing and recommending academic information requires extensive computational effort and often results in inaccurate results. Summary of the Invention

[0004] In view of the above analysis, the embodiments of the present invention aim to provide an academic information recommendation method and device based on data flow management, so as to solve the problem in the existing technology implementation results that there are many sources and types of data for current academic information, and the amount of data of academic information is large, resulting in a large amount of data calculation for directly capturing and recommending academic information, and the accuracy is often not high enough.

[0005] A method for recommending academic information based on data stream management is applied to a data platform including a data relay terminal and a data recommendation terminal, comprising the following steps: Sending an academic information recommendation request to the data relay terminal to instruct the data relay terminal to capture academic information from the cloud or database; Dividing the academic information into a plurality of sub-data streams, dividing the academic information into a plurality of sub-data streams based on the number of data relay terminals and data recommendation terminals, and determining an original effective sub-data stream set; Dividing the effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams, and controlling the data recommendation terminal to recommend the recommended data streams based on the academic information recommendation request; The update of the recommendation amount is monitored in real time, and the recommended data stream and the to-be-recommended data stream are converted according to the update.

[0006] The academic information recommendation method based on data stream management of the embodiment of the present application sends an academic information recommendation request to the data relay terminal to instruct the data relay terminal to capture academic information from the cloud or database. The academic information is divided into multiple sub-data streams, and the academic information is divided into multiple sub-data streams based on the number of data relay terminals and data recommendation terminals, and the original effective sub-data stream set is determined; the effective sub-data streams of the original effective sub-data stream set are divided into recommended data streams and data streams to be recommended, and the data recommendation terminal is controlled to recommend the recommended data stream based on the academic information recommendation request; the update of the recommendation amount is monitored in real time, and the recommended data stream and the data stream to be recommended are converted according to the update. Based on this, the recommended data stream is accurately identified while reducing the data processing amount.

[0007] As one of the optional embodiments, the academic information is divided into a plurality of sub-data streams, and the sub-data streams are allocated based on the number of data relay terminals and data recommendation terminals, and the process of determining the original effective sub-data stream set includes the steps of: The academic information is divided into a set number of sub-data streams; wherein the number of the sub-data streams is the product of the data relay end and the data recommendation end.

[0008] As one of the optional embodiments, the process of dividing the effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams includes the steps of: Calculate the data volume according to the academic information recommendation request; Determining the number of valid sub-data streams in the original valid sub-data stream set according to the data volume; The recommended data streams and the data streams to be recommended are divided according to the quantity.

[0009] As one of the optional embodiments, the process of monitoring the update of the recommendation amount in real time and converting the recommended data stream and the to-be-recommended data stream according to the update includes the steps of: weighting the recommended data streams according to the time taken to capture the academic information; Recommending the recommended data stream in real time according to weight sorting; Calculating the data proportion of the recommended data stream according to the update of the recommendation amount after the recommended data stream is recommended; The recommended data stream having a data proportion less than the set proportion is converted into the data stream to be recommended.

[0010] As one of the optional embodiments, according to the update of the recommendation amount of the recommended data stream after it is recommended, the process of calculating the data proportion of the recommended data stream is as follows: in, Indicates the proportion of the data; Indicates the recommended amount of the recommended data stream after being recommended, Indicates the recommendation amount of the recommended data stream before it is recommended.

[0011] As one of the optional embodiments, the method further includes the steps of: recommending the recommended data stream again to obtain updated vector data of the two recommended amounts; and performing dimensionality reduction processing on the vector data to update the data proportion.

[0012] As one of the optional embodiments, the following formula is provided: in, Indicates the updated data percentage. Indicates the proportion of data for the first time, Indicates the proportion of the second data. The Euclidean norm of the first recommendation. The Euclidean norm of the second recommendation.

[0013] An academic information recommendation device based on data stream management is applied to a data platform including a data relay terminal and a data recommendation terminal, comprising: The information capture module is used to send an academic information recommendation request to the data relay terminal to instruct the data relay terminal to capture academic information from the cloud or database; An information segmentation module is used to segment the academic information into multiple sub-data streams, segment the academic information into multiple sub-data streams based on the number of data relay terminals and data recommendation terminals, and determine an original effective sub-data stream set; A data stream recommendation module, configured to divide the effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams, and control the data recommendation terminal to recommend the recommended data streams based on the academic information recommendation request; The data stream updating module is used to monitor the update of the recommendation amount in real time and convert the recommended data stream and the to-be-recommended data stream according to the update.

[0014] The academic information recommendation device based on data stream management of the embodiment of the present application sends an academic information recommendation request to the data relay terminal to instruct the data relay terminal to capture academic information from the cloud or database. The academic information is divided into multiple sub-data streams, and the academic information is divided into multiple sub-data streams based on the number of data relay terminals and data recommendation terminals, and the original effective sub-data stream set is determined; the effective sub-data streams of the original effective sub-data stream set are divided into recommended data streams and data streams to be recommended, and the data recommendation terminal is controlled to recommend the recommended data stream based on the academic information recommendation request; the update of the recommendation amount is monitored in real time, and the recommended data stream and the data stream to be recommended are converted according to the update. Based on this, the recommended data stream is accurately identified while reducing the data processing volume.

[0015] At least one embodiment of the present application further provides a data control device, including: one or more memories non-transitorily storing computer-executable instructions; One or more processors are configured to run computer-executable instructions, wherein the computer-executable instructions, when executed by the one or more processors, implement the academic information recommendation method based on data stream management according to any embodiment of the present application.

[0016] The above-mentioned data control device sends an academic information recommendation request to the data relay terminal to instruct the data relay terminal to capture academic information from the cloud or database. The academic information is divided into multiple sub-data streams, and the academic information is divided into multiple sub-data streams based on the number of data relay terminals and data recommendation terminals, and the original effective sub-data stream set is determined; the effective sub-data streams of the original effective sub-data stream set are divided into recommended data streams and to-be-recommended data streams, and the data recommendation terminal is controlled to recommend the recommended data stream based on the academic information recommendation request; the update of the recommendation amount is monitored in real time, and the recommended data stream and the to-be-recommended data stream are converted according to the update. Based on this, the recommended data stream is accurately identified while reducing the amount of data processing.

[0017] At least one embodiment of the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, an academic information recommendation method based on data flow management according to any embodiment of the present application is implemented.

[0018] The above-mentioned non-transient computer-readable storage medium sends the academic information recommendation request to the data relay terminal to instruct the data relay terminal to capture the academic information from the cloud or database. The academic information is divided into multiple sub-data streams, and the academic information is divided into multiple sub-data streams based on the number of data relay terminals and data recommendation terminals, and the original effective sub-data stream set is determined; the effective sub-data streams of the original effective sub-data stream set are divided into recommended data streams and to-be-recommended data streams, and the data recommendation terminal is controlled to recommend the recommended data stream based on the academic information recommendation request; the update of the recommendation amount is monitored in real time, and the recommended data stream and the to-be-recommended data stream are converted according to the update. Based on this, the recommended data stream is accurately identified while reducing the amount of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of an academic information recommendation method based on data stream management according to an embodiment of the present invention; Figure 2 This is a module structure diagram of an academic information recommendation device based on data stream management according to an embodiment of the application; Figure 3A schematic block diagram of a data control device provided by the present invention; Figure 4 A schematic diagram of a non-transitory computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0020] In order to better understand the purpose, technical solutions and technical effects of the present invention, the present invention is further explained below with reference to the accompanying drawings and embodiments. It is also stated that the embodiments described below are only used to illustrate the present invention and are not intended to limit the present invention.

[0021] An embodiment of the present invention provides an academic information recommendation method based on data stream management.

[0022] Figure 1 This is a flow chart of an academic information recommendation method based on data stream management according to an embodiment of the application. Figure 1 As shown, an academic information recommendation method based on data flow management of an embodiment of the application is applied to a data platform including a data relay terminal and a data recommendation terminal, including steps S100 to S103: S100, sending an academic information recommendation request to a data relay terminal to instruct the data relay terminal to capture academic information from a cloud or a database; S101, dividing the academic information into multiple sub-data streams, dividing the academic information into multiple sub-data streams based on the number of data relay terminals and data recommendation terminals, and determining an original effective sub-data stream set; S102, dividing the effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams, and controlling the data recommendation terminal to recommend the recommended data streams based on the academic information recommendation request; S103: Monitor the update of the recommendation amount in real time, and convert the recommended data stream and the to-be-recommended data stream according to the update.

[0023] During the deployment of the data platform, the data relay terminal serves as a centralized processing center, distributing links to various data recommendation terminals. The data relay terminal captures academic information from the cloud or database and synchronizes it to each data recommendation terminal.

[0024] As one of the embodiments, in step S101, the academic information is divided into a plurality of sub-data streams, and the sub-data streams are allocated based on the number of data relay terminals and data recommendation terminals, and the process of determining the original effective sub-data stream set includes the steps of: The academic information is divided into a set number of sub-data streams; wherein the number of the sub-data streams is the product of the data relay end and the data recommendation end.

[0025] Academic information exists in the form of data packets in the data relay terminal. The data packets are parsed and distributed according to the set number, and repackaged to form sub-data streams.

[0026] Preferably, the content of the data stream is adjusted according to the independent header in the parsed data packet to ensure that each sub-data stream stores one or more complete data with independent headers, wherein the data with independent headers is used to express an independent and complete piece of academic information.

[0027] Preferably, the process of dividing the effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams in step S102 includes the following steps: Calculate the data volume according to the academic information recommendation request; Determining the number of valid sub-data streams in the original valid sub-data stream set according to the data volume; The recommended data streams and the data streams to be recommended are divided according to the quantity.

[0028] Among them, the data relay end is also used to capture the user's academic information request, calculate the data volume of the academic information information to be pushed based on the academic information request, so as to determine the number of effective sub-data streams in the original effective sub-data stream set, and divide the recommended data stream and the data stream to be recommended.

[0029] Preferably, the number of recommended data streams is the same as the number of valid sub-data streams.

[0030] Preferably, the process of monitoring the update of the recommended amount in real time in step S103 and converting the recommended data stream and the to-be-recommended data stream according to the update includes the following steps: weighting the recommended data streams according to the time taken to capture the academic information; Recommending the recommended data stream in real time according to weight sorting; Calculating the data proportion of the recommended data stream according to the update of the recommendation amount after the recommended data stream is recommended; The recommended data stream having a data proportion less than the set proportion is converted into the data stream to be recommended.

[0031] Preferably, a binary exponential backoff algorithm is used to convert the recommended data stream into the pending data stream. The backoff time is determined based on the data percentage, i.e., backoff time = set time * data percentage. The pending data stream must wait for the backoff time to be converted into the recommended data stream. The initial conversion is relatively short to test whether the conversion is working properly. The backoff time is subsequently reduced to ensure conversion efficiency.

[0032] Preferably, according to the update of the recommendation amount after the recommended data stream is recommended, the process of calculating the data proportion of the recommended data stream is as follows: in, Indicates the proportion of the data; Indicates the recommended amount of the recommended data stream after being recommended, Indicates the recommendation amount of the recommended data stream before it is recommended.

[0033] Preferably, the method further comprises the steps of: The recommended data stream is recommended again to obtain updated vector data of the two recommended amounts; and dimensionality reduction processing is performed on the vector data to update the data proportion.

[0034] Preferably, as follows: in, Indicates the updated data percentage. Indicates the proportion of data for the first time, Indicates the proportion of the second data. The Euclidean norm of the first recommendation. The Euclidean norm of the second recommendation.

[0035] Through dimensionality reduction processing, the similarity between the updated data proportion and the actual proportion is improved.

[0036] The academic information recommendation method based on data stream management of any of the above embodiments sends an academic information recommendation request to the data relay terminal to instruct the data relay terminal to capture academic information from the cloud or database. The academic information is divided into multiple sub-data streams, and the academic information is divided into multiple sub-data streams based on the number of data relay terminals and data recommendation terminals, and the original effective sub-data stream set is determined; the effective sub-data streams of the original effective sub-data stream set are divided into recommended data streams and data streams to be recommended, and the data recommendation terminal is controlled to recommend the recommended data stream based on the academic information recommendation request; the update of the recommendation amount is monitored in real time, and the recommended data stream and the data stream to be recommended are converted according to the update. Based on this, the recommended data stream is accurately identified while reducing the amount of data processing.

[0037] An embodiment of the present invention also provides an academic information recommendation device based on data stream management.

[0038] Figure 2 This is a module structure diagram of an academic information recommendation device based on data stream management according to an embodiment of the application. Figure 2 As shown, an academic information recommendation device based on data stream management according to an embodiment of the application includes: The information capture module 100 is used to send an academic information recommendation request to the data relay terminal to instruct the data relay terminal to capture academic information from the cloud or database; An information segmentation module 101 is configured to segment the academic information into a plurality of sub-data streams, segment the academic information into a plurality of sub-data streams based on the number of data relay terminals and data recommendation terminals, and determine an original effective sub-data stream set; A data stream recommendation module 102, configured to divide the effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams, and control the data recommendation terminal to recommend the recommended data streams based on the academic information recommendation request; The data stream updating module 103 is configured to monitor the update of the recommended amount in real time, and convert the recommended data stream and the to-be-recommended data stream according to the update.

[0039] The above-mentioned academic information recommendation device based on data stream management sends an academic information recommendation request to the data relay terminal to instruct the data relay terminal to capture academic information from the cloud or database. The academic information is divided into multiple sub-data streams, and the academic information is divided into multiple sub-data streams based on the number of data relay terminals and data recommendation terminals, and the original effective sub-data stream set is determined; the effective sub-data streams of the original effective sub-data stream set are divided into recommended data streams and to-be-recommended data streams, and the data recommendation terminal is controlled to recommend the recommended data stream based on the academic information recommendation request; the update of the recommendation amount is monitored in real time, and the recommended data stream and the to-be-recommended data stream are converted according to the update. Based on this, the recommended data stream is accurately identified while reducing the amount of data processing.

[0040] At least one embodiment of the present application further provides a data control device. Figure 3 A schematic block diagram of a data control device provided in at least one embodiment of the present application. Figure 3 As shown, the data control device 20 may include one or more memories 200 and one or more processors 201. The memories 200 are used to store computer-executable instructions non-transiently; the processor 201 is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor 201, the processor 201 may execute one or more steps of the academic information recommendation method based on data stream management according to any embodiment of the present application.

[0041] The specific implementation and related explanation of each step of the academic information recommendation method based on data stream management can be found in the relevant content of the embodiment of the academic information recommendation method based on data stream management, which will not be repeated here. Figure 3 The components of the data control device 20 shown are merely exemplary and non-limiting. The data control device 20 may further include other components according to actual application requirements.

[0042] In one embodiment, the processor 201 and the memory 200 can communicate with each other directly or indirectly. For example, the processor 201 and the memory 200 can communicate via a network connection. The network can include a wireless network, a wired network, and / or any combination of wireless and wired networks. This application does not limit the type and function of the network. For another example, the processor 201 and the memory 200 can also communicate via a bus connection. The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industrial Standard Architecture (EISA) bus. For example, the processor 201 and the memory 200 can be located on a remote data server (cloud) or a distributed energy system (local), or on a client (e.g., a mobile device such as a mobile phone). For example, the processor 201 can be a device with data processing capabilities and / or instruction execution capabilities, such as a central processing unit (CPU), a tensor processing unit (TPU), or a graphics processing unit (GPU), and can control other components in the data control device 20 to perform the desired functions. The central processing unit (CPU) can be an X86 or ARM architecture, etc.

[0043] In one embodiment, the memory 200 may include any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, a flash memory, etc. One or more computer-executable instructions may be stored on the computer-readable storage medium, and the processor 201 may execute the computer-executable instructions to implement various functions of the data control device 20. Various applications and various data, as well as various data used and / or generated by the applications, may also be stored in the memory 200.

[0044] It should be noted that the data control device 20 can achieve technical effects similar to the aforementioned academic information recommendation method based on data flow management, and the repeated parts will not be repeated.

[0045] At least one embodiment of the present application also provides a non-transitory computer-readable storage medium. Figure 4 A schematic diagram of a non-transitory computer-readable storage medium provided for at least one embodiment of the present application. For example, Figure 4As shown, one or more computer-executable instructions 301 may be non-transitory stored on a non-transitory computer-readable storage medium 30. For example, when the computer-executable instructions 301 are executed by a computer, the computer may execute one or more steps of the academic information recommendation method based on data stream management according to any embodiment of the present application.

[0046] In one embodiment, the non-transitory computer-readable storage medium 30 may be applied to the above-mentioned data control device 20 , for example, it may be the memory 200 in the data control device 20 .

[0047] In one embodiment, the description of the non-transitory computer-readable storage medium 30 may refer to the description of the memory 200 in the embodiment of the data control device 20 , and the repeated parts will be omitted.

[0048] It should be noted that the memory 200 stores different non-transient computer-executable instructions, and the data control device 20 corresponds to a firmware upgrade device. When the computer-executable instructions are executed by the processor 201, the processor 201 can execute one or more steps in the academic information recommendation method based on data flow management according to any embodiment of the present application.

[0049] Regarding this application, the following points need to be explained: (1) The drawings of the embodiments of this application only relate to the structures related to the embodiments of this application. Other structures can refer to the general design.

[0050] (2) For the sake of clarity, the thickness and size of layers or structures in the drawings used to describe the embodiments of the present invention are exaggerated. It will be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element can be "directly on" or "under" the other element, or intervening elements may be present.

[0051] (3) Unless there is a conflict, the embodiments of this application and the features therein may be combined to form new embodiments. The above are only specific implementation methods of this application, but the scope of protection of this application is not limited thereto. The scope of protection of this application shall be based on the scope of protection of the claims.

[0052] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0053] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for recommending academic information based on data stream management, characterized in that: The method is applied to a data platform including a data relay terminal and a data recommendation terminal, and includes the following steps: Sending an academic information recommendation request to a data relay terminal to instruct the data relay terminal to capture academic information from a cloud or a database; Divide the academic information into a plurality of sub-data streams, divide the academic information into a plurality of sub-data streams based on the number of data relay terminals and data recommendation terminals, and determine an original effective sub-data stream set; Dividing the effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams, and controlling the data recommendation end to recommend the recommended data stream based on the academic information recommendation request; The update of the recommended amount is monitored in real time, and the recommended data stream and the to-be-recommended data stream are converted according to the update.

2. The academic information recommendation method based on data stream management according to claim 1 is characterized in that: The process of dividing the academic information into a plurality of sub-data streams, allocating the sub-data streams based on the number of data relay terminals and data recommendation terminals, and determining the original effective sub-data stream set comprises the steps of: The academic information is divided into a set number of sub-data streams; wherein the number of the sub-data streams is the product of the data relay end and the data recommendation end.

3. The academic information recommendation method based on data stream management according to claim 1 is characterized in that: The process of dividing the effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams comprises the steps of: Calculate the data volume according to the academic information recommendation request; Determine the number of valid sub-data streams of the original valid sub-data stream set according to the data volume; The recommended data streams and the to-be-recommended data streams are divided according to the quantity.

4. The academic information recommendation method based on data stream management according to claim 1 is characterized in that: The process of monitoring the update of the recommended amount in real time and converting the recommended data stream and the to-be-recommended data stream according to the update comprises the steps of: According to the time consumption of capturing the academic information, the recommended data streams are weighted and sorted; Recommending the recommended data stream in real time according to weight sorting; Calculating the data proportion of the recommended data stream according to the update of the recommended amount after the recommended data stream is recommended; The recommended data stream whose data proportion is less than the set proportion is converted into the data stream to be recommended.

5. The academic information recommendation method based on data stream management according to claim 4 is characterized in that: The process of calculating the data proportion of the recommended data stream according to the update of the recommended amount after the recommended data stream is recommended is as follows: in, Indicates the proportion of the data; represents the recommended amount of the recommended data stream after being recommended, Indicates the recommendation amount of the recommended data stream before it is recommended.

6. The academic information recommendation method based on data stream management according to claim 5 is characterized in that: Also includes the steps: Recommending the recommended data stream again, and obtaining updated vector data of the recommended quantities twice before and after; Perform dimensionality reduction processing on the vector data to update the data proportion.

7. The academic information recommendation method based on data stream management according to claim 6 is characterized in that: As follows: in, Indicates the updated data percentage. Indicates the proportion of data for the first time, Indicates the proportion of the second data. The Euclidean norm of the recommendation amount for the first recommendation, The Euclidean norm of the recommendation amount for the second recommendation.

8. An academic information recommendation device based on data stream management, characterized in that: Applicable to data platforms including data relay and data recommendation terminals, including: An information capture module is used to send an academic information recommendation request to a data relay terminal to instruct the data relay terminal to capture academic information from a cloud or a database; An information segmentation module, used to segment the academic information into a plurality of sub-data streams, segment the academic information into a plurality of sub-data streams based on the number of data relay terminals and data recommendation terminals, and determine an original effective sub-data stream set; A data stream recommendation module, used for dividing the effective sub-data streams of the original effective sub-data stream set into recommended data streams and to-be-recommended data streams, and controlling the data recommendation terminal to recommend the recommended data stream based on the academic information recommendation request; The data stream updating module is used to monitor the update of the recommended amount in real time, and convert the recommended data stream and the to-be-recommended data stream according to the update.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the academic information recommendation method based on data stream management as described in any one of claims 1 to 7.

10. A data control device, characterized in that: include: one or more memories non-transitorily storing computer-executable instructions; One or more processors are configured to run computer executable instructions, wherein the computer executable instructions, when executed by one or more processors, implement the academic information recommendation method based on data stream management as described in any one of claims 1 to 7.

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