Learning data processing methods, devices, equipment, storage media, and program products

Abstract

This application provides a method, apparatus, device, storage medium, and program product for processing learning data. The method includes: establishing a data pool and backup database for multiple aggregation units in a learning platform, wherein each aggregation unit corresponds to a data pool and a backup database. The data pool stores learning data of courses bound to and associated with the aggregation unit, and the backup database stores learning data of courses unassociated from the aggregation unit. When any course is unassociated from any aggregation unit, the learning data of the course is stored in the backup database corresponding to that aggregation unit. The method also includes receiving processing instructions and determining a target aggregation unit based on these instructions. Finally, the method executes the processing instructions based on the backup database and data pool corresponding to the target aggregation unit to obtain the learning data of the target aggregation unit. This method improves data integrity, ensures no data loss, and enhances the accuracy of the processed learning data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a learning quantity data processing method and device, equipment, a storage medium and a program product. BACKGROUND

[0002] With the deepening of enterprise digital transformation, online learning platforms have become the core tools for organizational training and knowledge management, and need to support training effectiveness evaluation, course optimization and operation decision-making through learning platform data processing and analysis.

[0003] At present, learning platforms generally use a "course-data strong binding" data storage logic, that is, learning quantity data is directly associated with the survival state of a course. Data processing mainly uses a mode of processing learning quantity data based on the current survival course.

[0004] However, the data integrity in the data processing of the prior art is insufficient, resulting in low accuracy of the processing result. SUMMARY

[0005] The present application provides a learning quantity data processing method, device, equipment, storage medium and program product to improve the integrity of learning quantity data and the accuracy of learning quantity data processing result.

[0006] In a first aspect, the present application provides a learning quantity data processing method, comprising:

[0007] establishing a data pool and a backup database of a plurality of aggregation units in a learning platform, wherein each aggregation unit corresponds to a data pool and a backup database, the data pool is used to store learning quantity data of a course associated with the aggregation unit, and the backup database is used to store learning quantity data of a course disassociated with the aggregation unit;

[0008] when any course is disassociated with any aggregation unit, storing the learning quantity data of the course into the backup database corresponding to the any aggregation unit;

[0009] receiving a processing instruction sent by a user end, and determining a target aggregation unit according to the processing instruction;

[0010] executing the processing instruction according to the backup database and the data pool corresponding to the target aggregation unit to obtain course learning quantity of the target aggregation unit;

[0011] sending the course learning quantity of the target aggregation unit to the user end.

[0012] In one possible implementation, the step of storing the learning data of a course in the backup database corresponding to the aggregation unit when any course is de-associated with any aggregation unit includes: real-time monitoring of change events in the association relationship between multiple courses and any aggregation unit; when a de-association event between any course and any aggregation unit is detected, parsing the de-association event to obtain a course identifier; extracting the learning data corresponding to the course identifier from the data pool corresponding to the aggregation unit based on the course identifier; storing the learning data in the backup database corresponding to the aggregation unit; and deleting the learning data from the data pool.

[0013] In one possible implementation, after storing the learning data of the course in the backup database corresponding to any aggregation unit, the method further includes: real-time monitoring of change events in the association between multiple courses and any aggregation unit; when a binding association event between any course and any aggregation unit is detected, parsing the binding association event to obtain a course identifier; matching the course identifier from the backup database corresponding to any aggregation unit to obtain a matching result; if the matching result is not empty, extracting the learning data corresponding to the course identifier from the backup database; storing the learning data in the data pool corresponding to any aggregation unit; and deleting the learning data from the backup database.

[0014] In one possible implementation, the processing instruction further includes a processing mode, which includes a current processing mode or a full processing mode. Accordingly, executing the processing instruction based on the backup database and data pool corresponding to the target aggregation unit to obtain the course learning volume of the target aggregation unit includes: obtaining the target backup database and target data pool corresponding to the target aggregation unit; if the processing mode is the current processing mode, obtaining the course learning volume of the target aggregation unit based on the learning volume data in the target data pool; if the processing mode is the full processing mode, obtaining the course learning volume of the target aggregation unit based on the learning volume data in the target data pool and the learning volume data in the target backup database.

[0015] In one possible implementation, the processing instruction further includes a processing time range; correspondingly, obtaining the course learning volume of the target aggregation unit based on the learning volume data in the target data pool and the learning volume data in the target backup database includes: obtaining a first course learning volume for all courses in the target data pool within the processing time range based on the learning volume data in the target data pool; obtaining a second course learning volume for all courses in the target backup database within the processing time range based on the learning volume data in the target backup database; and calculating the sum of the first course learning volume and the second course learning volume to obtain the course learning volume of the target aggregation unit.

[0016] In one possible implementation, the learning data includes course name, course identifier, number of times the course is studied, start time of each study session, end time of each study session, and duration of each study session.

[0017] Secondly, this application provides a learning data processing apparatus, comprising:

[0018] A module is established to create data pools and backup databases for multiple aggregation units in the learning platform. Each aggregation unit corresponds to a data pool and a backup database. The data pool is used to store the learning volume data of courses that are bound to and associated with the aggregation unit, and the backup database is used to store the learning volume data of courses that are unassociated from the aggregation unit.

[0019] The storage module is used to store the learning data of any course into the backup database corresponding to any aggregation unit when any course is de-associated with any aggregation unit.

[0020] The receiving module is used to receive processing instructions sent by the user terminal and determine the target aggregation unit according to the processing instructions;

[0021] The execution module is used to execute the processing instructions according to the backup database and data pool corresponding to the target aggregation unit, so as to obtain the course learning volume of the target aggregation unit;

[0022] The sending module is used to send the course learning volume of the target aggregation unit to the user terminal.

[0023] Thirdly, this application provides a learning data processing device, including: a memory and a processor;

[0024] The memory stores computer-executed instructions;

[0025] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0026] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0027] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0028] The learning data processing method, apparatus, equipment, storage medium, and program products provided in this application improve data integrity and ensure no data loss by constructing an independent data pool for the aggregation unit and adding an independent backup database. This physically isolates and stores the learning data of courses to be removed from the backup database. Furthermore, processing the learning data through the backup database and data pool improves the accuracy of the processing results. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0030] Figure 1 A schematic diagram illustrating a scenario for the learning data processing method provided in an embodiment of this application;

[0031] Figure 2 A flowchart illustrating the learning data processing method provided in this application embodiment;

[0032] Figure 3 This is a schematic diagram of the learning data processing device provided in the embodiments of this application;

[0033] Figure 4 This is a schematic diagram of the learning data processing device provided in an embodiment of this application.

[0034] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0035] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0036] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0037] Currently, in existing technologies, enterprise learning platforms use a "course-data strong binding" storage logic. When a course is removed, its associated learning data is simultaneously deleted or isolated, resulting in insufficient data integrity. The accuracy of the learning data processing results in the learning zone is low, and it cannot reflect the course's historical contribution.

[0038] The learning data processing method provided in this application addresses a core issue arising from practical needs: prior art, due to the strong binding between courses and data, directly compromises data integrity during the removal process. To resolve this, the inventors propose the following technical approach: by constructing an independent data pool for each aggregation unit and adding a separate backup database, the learning data of courses being removed is physically isolated and stored in the backup database, thus improving data integrity and ensuring no data loss. Furthermore, processing the learning data using the backup database and the data pool improves the accuracy of the processing results.

[0039] Figure 1 This is a schematic diagram of a scenario for the learning data processing method provided in the embodiments of this application, such as... Figure 1 As shown, the scenario is a computer device, including: a user terminal 101 and a service device 102.

[0040] Among them, the user terminal 101 can be a mobile phone or a computer.

[0041] The service device 102 can be a server. Optionally, it can be a single server or a cluster of multiple servers.

[0042] Specifically, the service device 102 receives the processing instruction sent by the user terminal 101; the service device 102 executes the above processing instruction to obtain the course learning amount of the target aggregation unit; and the service device 102 sends the course learning amount of the target aggregation unit to the user terminal 101.

[0043] Furthermore, the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0044] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0045] Figure 2 This is a flowchart illustrating a learning data processing method provided in one embodiment of this application. The execution entity of this embodiment can be... Figure 1 The computer equipment shown is not specifically limited in this embodiment. Figure 2 As shown, the method includes:

[0046] S201: Establish data pools and backup databases for multiple aggregation units in the learning platform. Each aggregation unit corresponds to a data pool and a backup database. The data pool is used to store the learning volume data of courses that are bound to the aggregation unit, and the backup database is used to store the learning volume data of courses that are unbound from the aggregation unit.

[0047] Optionally, the learning data includes course name, course identifier, number of times the course is studied, start time of each study session, end time of each study session, and duration of each study session.

[0048] Optionally, the aggregation unit can be a learning zone or an online class.

[0049] S202: When any course is unassociated from any aggregation unit, the course's learning data is stored in the backup database corresponding to that aggregation unit.

[0050] Optionally, when any course is unassociated from any aggregation unit, the course's learning data is stored in the backup database corresponding to that aggregation unit. This includes: real-time monitoring of change events in the association between multiple courses and any aggregation unit; when a deassociation event between any course and any aggregation unit is detected, parsing the deassociation event to obtain the course identifier; extracting the learning data corresponding to the course identifier from the data pool corresponding to that aggregation unit based on the course identifier; storing the learning data in the backup database corresponding to that aggregation unit; and deleting the learning data from the data pool.

[0051] By combining association change event monitoring with data migration logic, real-time response to course unassociation events is achieved, ensuring the integrity of learning data when association status changes. This avoids data omissions caused by manual intervention or system delays, while the automated migration process reduces the risk of manual operation and improves data management efficiency and data processing accuracy.

[0052] Optionally, real-time monitoring of changes in the relationships between multiple courses and any aggregation unit includes: configuring data identification rules for changes in the relationships between courses and aggregation units; and, based on the data identification rules, capturing changes in the relationships in real-time using a lightweight event monitoring component via a message queue.

[0053] By enabling real-time perception and precise capture of changes in the association between courses and various aggregation units, the delay and omission of changed data are eliminated, thereby ensuring the migration of learning data for courses that have been decoupled, ensuring data integrity, and thus improving the accuracy of data processing.

[0054] Optionally, after storing the course learning data in the backup database corresponding to any aggregation unit, the method further includes: real-time monitoring of change events in the association between multiple courses and any aggregation unit; when a binding association event between any course and any aggregation unit is detected, parsing the binding association event to obtain the course identifier; matching the course identifier from the backup database corresponding to any aggregation unit to obtain the matching result; if the matching result is not empty, extracting the learning data corresponding to the course identifier from the backup database; storing the learning data in the data pool corresponding to any aggregation unit; and deleting the learning data from the backup database.

[0055] When it is detected that any course has re-established a binding relationship with a previously unlinked aggregation unit, the data recovery process is automatically triggered, ensuring data integrity and consistency, improving resource efficiency, and supporting the tracking of learning volume processing results of aggregation units at different stages (such as when courses have not been moved out, or after some courses have been moved out).

[0056] S203: Receive the processing instruction sent by the user terminal and determine the target aggregation unit according to the processing instruction.

[0057] Optionally, in one embodiment of this application, receiving a processing instruction sent by a user terminal includes: receiving a processing instruction in response to a user's input operation on a processing page.

[0058] Optionally, the processing instructions include the target aggregation unit identifier.

[0059] Accordingly, determining the target aggregation unit based on the processing instructions includes: parsing the processing instructions to obtain multiple field information; performing text recognition on the multiple field information to obtain the target aggregation unit identifier; and determining the target aggregation unit based on the target aggregation unit identifier.

[0060] S204: Based on the backup database and data pool corresponding to the target aggregation unit, execute the processing instructions to obtain the course learning volume of the target aggregation unit.

[0061] Optionally, the processing instruction may also include a processing mode, which may include the current processing mode or the full processing mode.

[0062] Accordingly, based on the backup database and data pool corresponding to the target aggregation unit, processing instructions are executed to obtain the course learning volume of the target aggregation unit, including: obtaining the target backup database and target data pool corresponding to the target aggregation unit; if the processing mode is the current processing mode, then the course learning volume of the target aggregation unit is obtained based on the learning volume data in the target data pool; if the processing mode is the full processing mode, then the course learning volume of the target aggregation unit is obtained based on the learning volume data in the target data pool and the learning volume data in the target backup database.

[0063] By employing a dual-mode processing approach—the current processing mode and the full processing mode—it achieves adaptation to the needs of different scenarios, significantly improving the practicality of the processing results and the ability to cover various scenarios.

[0064] Optionally, the processing instructions may also include a processing time range.

[0065] Accordingly, based on the learning data in the target data pool and the learning data in the target backup database, the course learning volume of the target aggregation unit is obtained, including: obtaining the first course learning volume of all courses in the target data pool within the processing time range based on the learning data in the target data pool; obtaining the second course learning volume of all courses in the target backup database within the processing time range based on the learning data in the target backup database; and calculating the sum of the first course learning volume and the second course learning volume to obtain the course learning volume of the target aggregation unit.

[0066] Optionally, the processing time range includes the processing start time and the processing end time.

[0067] Accordingly, based on the learning data in the target data pool, the first course learning quantity of all courses in the target data pool within the processing time range is obtained, including: comparing the processing start time and processing end time with the learning start time and learning end time in the target data pool to determine the number of course learnings and the duration of each learning within the processing time range; and determining the first course learning quantity of the target aggregation unit based on the number of course learnings and the duration of each learning within the processing time range.

[0068] Accordingly, based on the learning data in the target backup database, the second course learning volume of all courses in the target backup database within the processing time range is obtained, including: comparing the processing start time and processing end time with the learning start time and learning end time in the target backup database to determine the number of course learnings and the duration of each learning within the processing time range; and determining the second course learning volume of the target aggregation unit based on the number of course learnings and the duration of each learning within the processing time range.

[0069] S205: Send the course learning amount of the target aggregation unit to the user terminal.

[0070] The learning data processing method provided in this application improves data integrity and ensures no data loss by constructing an independent data pool for the aggregation unit and adding an independent backup database. This physically isolates and stores the learning data of courses to be removed from the backup database. Furthermore, processing the learning data through the backup database and the data pool improves the accuracy of the processed data.

[0071] In one embodiment of this application, another learning data processing method is provided. The execution subject of this embodiment can be... Figure 1 The computer device shown is not specifically limited in this embodiment. The method includes:

[0072] Establish backup databases for each learning zone on the learning platform. The attributes of these backup databases include: course name, course identifier, the date the course was last added to the learning zone, the date the course was last removed from the learning zone, the number of times the course was studied within the zone in the latest learning cycle, and the duration of study. The learning cycle can be the time between the date the course was last added to the learning zone and the date it was last removed from the learning zone.

[0073] It should be noted that adding a course to the learning zone means that the course is bound to the aggregation unit; removing a course from the learning zone means that the course is unbound from the aggregation unit.

[0074] When a course is moved out of the learning area, the course's learning data is stored in the backup database.

[0075] Optionally, when any course is removed from the learning zone, a record is automatically triggered, that is, a record is automatically added to the backup database to record information such as the course identifier, course name, the time when the course was last removed from the learning zone, the number of times the course was studied in the zone in the latest learning cycle, and the study duration.

[0076] When a course that was previously removed is added back to the learning area, the course's learning data is restored.

[0077] Optionally, in the backup database, a match is performed based on the course identifier. If the match result is not empty, the learning data of the course is obtained, including the number of times and the duration of learning, and the data is stored in the data pool. At the same time, the corresponding record is deleted in the backup database.

[0078] Process the learning data to obtain the course learning volume in the learning zone.

[0079] Optionally, to address the learning volume data processing needs of the learning zone, and to balance data integrity with flexibility in practical use cases, two flexibly switchable processing modes are provided: a full processing mode and a current processing mode. The full processing mode processes learning volume data including currently active courses and historical learning volume data for courses that have been removed. The current processing mode only processes learning volume data for currently active courses. Simultaneously, it supports processing learning volume data within different time ranges in the learning zone. A processing time range is introduced for description, and the learning volume data within this range is processed to obtain the course learning volume for the learning zone. Course learning volume includes learning duration and number of learning sessions, as detailed below:

[0080] A: Full processing mode.

[0081] Optionally, in the above mode, the formula for calculating the course learning volume within the processing time range is:

[0082]

[0083]

[0084] In the formula, SD t Indicates study time; Indicates the number of times the course has been studied; j is the course index in the data pool, m is the number of courses in the data pool; k is the course index in the backup database, n is the number of courses in the backup database; SD j,t SD represents the learning duration of the j-th course in the data pool within the processing time range.k,t To back up the learning duration of the k-th course in the database within the processing time range; This represents the number of times the j-th course in the data pool has been studied within the processing time range; To back up the number of times the k-th course in the database has been studied within the processing time range; It can take the value 1 or 0. When the value is 1, it indicates that the duration of the k-th course in the learning area in the backup database overlaps with the processing time range.

[0085] in, In the formula, This indicates the last time the course was added to the learning area; This indicates the last time the course was removed from the learning area; Indicates the start time of processing; Indicates the processing end time.

[0086] B: Current processing mode.

[0087] Optionally, in the above mode, the formula for calculating the course learning volume within the processing time range is:

[0088]

[0089] .

[0090] The learning data processing method provided in this application improves data integrity and ensures no data loss by constructing an independent data pool for the aggregation unit and adding an independent backup database. This physically isolates and stores the learning data of courses to be removed from the backup database. Furthermore, processing the learning data through the backup database and the data pool improves the accuracy of the processed data.

[0091] Figure 3 This is a schematic diagram of the structure of the learning data processing device provided in the embodiments of this application, as shown below. Figure 3 As shown, the learning data processing device provided in this embodiment includes: a creation module 301, a storage module 302, a receiving module 303, an execution module 304, and a sending module 305.

[0092] Module 301 is established to create data pools and backup databases for multiple aggregation units in the learning platform. Each aggregation unit corresponds to a data pool and a backup database. The data pool is used to store the learning volume data of courses that are bound to the aggregation unit, and the backup database is used to store the learning volume data of courses that are unbound from the aggregation unit.

[0093] Storage module 302 is used to store the learning data of any course into the backup database corresponding to any aggregation unit when any course is de-associated with any aggregation unit.

[0094] The receiving module 303 is used to receive processing instructions sent by the user terminal and determine the target aggregation unit according to the processing instructions;

[0095] The execution module 304 is used to execute processing instructions based on the backup database and data pool corresponding to the target aggregation unit to obtain the course learning volume of the target aggregation unit.

[0096] The sending module 305 is used to send the course learning volume of the target aggregation unit to the user terminal.

[0097] In one possible implementation, the storage module 302 is specifically used for: real-time monitoring of changes in the association between multiple courses and any aggregation unit; when a deassociation event between any course and any aggregation unit is detected, parsing the deassociation event to obtain the course identifier; extracting the learning amount data corresponding to the course identifier from the data pool corresponding to any aggregation unit based on the course identifier; storing the learning amount data in the backup database corresponding to any aggregation unit; and deleting the learning amount data from the data pool.

[0098] In one possible implementation, after storing the course learning data in the backup database corresponding to any aggregation unit, the storage module 302 is further configured to: monitor in real time changes in the association between multiple courses and any aggregation unit; when a binding association event between any course and any aggregation unit is detected, parse the binding association event to obtain the course identifier; match the course identifier with the backup database corresponding to any aggregation unit to obtain a matching result; if the matching result is not empty, extract the learning data corresponding to the course identifier from the backup database; store the learning data in the data pool corresponding to any aggregation unit; and delete the learning data from the backup database.

[0099] In one possible implementation, the processing instruction further includes a processing mode, which includes a current processing mode or a full processing mode. Accordingly, the execution module 304 is specifically used to: obtain the target backup database and the target data pool corresponding to the target aggregation unit; if the processing mode is the current processing mode, then obtain the course learning volume of the target aggregation unit based on the learning volume data in the target data pool; if the processing mode is the full processing mode, then obtain the course learning volume of the target aggregation unit based on the learning volume data in the target data pool and the learning volume data in the target backup database.

[0100] In one possible implementation, the processing instructions also include a processing time range; correspondingly, when the execution module 304 obtains the course learning amount of the target aggregation unit based on the learning amount data in the target data pool and the learning amount data in the target backup database, it is specifically used to: obtain the first course learning amount of all courses in the target data pool within the processing time range based on the learning amount data in the target data pool; obtain the second course learning amount of all courses in the target backup database within the processing time range based on the learning amount data in the target backup database; and calculate the sum of the first course learning amount and the second course learning amount to obtain the course learning amount of the target aggregation unit.

[0101] In one possible implementation, the learning data includes course name, course identifier, number of times the course is studied, start time of each study session, end time of each study session, and duration of each study session.

[0102] The learning data processing device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0103] Figure 4 This is a schematic diagram of the learning data processing device provided in an embodiment of this application. Figure 4 As shown, the learning data processing device provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0104] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0105] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0106] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0107] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0108] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0109] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0110] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0111] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0112] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0113] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0114] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0115] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0116] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0118] Finally, it should be noted that other embodiments of this application will readily conceive of by those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and alterations may be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for processing learning data, characterized in that, include: Establish a data pool and backup database for multiple aggregation units in the learning platform. Each aggregation unit corresponds to a data pool and a backup database. The data pool is used to store the learning volume data of courses that are bound to and associated with the aggregation unit, and the backup database is used to store the learning volume data of courses that are unassociated from the aggregation unit. When any course is unassociated from any aggregation unit, the learning data of the course is stored in the backup database corresponding to the aggregation unit. Receive processing instructions sent by the user terminal, and determine the target aggregation unit according to the processing instructions; Based on the backup database and data pool corresponding to the target aggregation unit, the processing instructions are executed to obtain the course learning volume of the target aggregation unit; Send the course learning volume of the target aggregation unit to the user terminal.

2. The method according to claim 1, characterized in that, When any course is unassociated with any aggregation unit, the learning data of the course is stored in the backup database corresponding to the aggregation unit, including: Monitor in real time for changes in the relationships between multiple courses and any aggregation unit; When a disconnection event is detected between any course and any of the aggregation units, the disconnection event is parsed to obtain the course identifier; Based on the course identifier, extract the learning volume data corresponding to the course identifier from the data pool corresponding to any of the aggregation units; The learning data is stored in the backup database corresponding to any of the aggregation units; The learning data is deleted from the data pool.

3. The method according to claim 1, characterized in that, After storing the learning data of the course into the backup database corresponding to any of the aggregation units, the method further includes: Monitor in real time for changes in the relationships between multiple courses and any aggregation unit; When a binding association event between any course and any of the aggregation units is detected, the binding association event is parsed to obtain the course identifier; Matching is performed from the backup database corresponding to any of the aggregation units based on the course identifier to obtain the matching result; If the matching result is not empty, then the learning volume data corresponding to the course identifier is extracted from the backup database; The learning data is stored in the data pool corresponding to any of the aggregation units; The learning data is deleted from the backup database.

4. The method according to any one of claims 1 to 3, characterized in that, The processing instruction also includes a processing mode, which includes the current processing mode or the full processing mode. Accordingly, the step of executing the processing instructions based on the backup database and data pool corresponding to the target aggregation unit to obtain the course learning volume of the target aggregation unit includes: Obtain the target backup database and target data pool corresponding to the target aggregation unit; If the processing mode is the current processing mode, then the course learning volume of the target aggregation unit is obtained based on the learning volume data in the target data pool; If the processing mode is the full processing mode, then the course learning volume of the target aggregation unit is obtained based on the learning volume data in the target data pool and the learning volume data in the target backup database.

5. The method according to claim 4, characterized in that, The processing instructions also include a processing time range; Accordingly, obtaining the course learning volume of the target aggregation unit based on the learning volume data in the target data pool and the learning volume data in the target backup database includes: Based on the learning data in the target data pool, the first course learning volume of all courses in the target data pool within the processing time range is obtained; Based on the learning data in the target backup database, the second course learning volume of all courses in the target backup database within the processing time range is obtained; Calculate the sum of the learning volume of the first course and the learning volume of the second course to obtain the learning volume of the target aggregation unit.

6. The method according to any one of claims 1 to 3, characterized in that, The learning data includes course name, course identifier, number of times the course is studied, start time of each study session, end time of each study session, and duration of each study session.

7. A learning data processing device, characterized in that, include: A module is established to create data pools and backup databases for multiple aggregation units in the learning platform. Each aggregation unit corresponds to a data pool and a backup database. The data pool is used to store the learning volume data of courses that are bound to and associated with the aggregation unit, and the backup database is used to store the learning volume data of courses that are unassociated from the aggregation unit. The storage module is used to store the learning data of any course into the backup database corresponding to any aggregation unit when any course is de-associated with any aggregation unit. The receiving module is used to receive processing instructions sent by the user terminal and determine the target aggregation unit according to the processing instructions; The execution module is used to execute the processing instructions according to the backup database and data pool corresponding to the target aggregation unit, so as to obtain the course learning volume of the target aggregation unit; The sending module is used to send the course learning volume of the target aggregation unit to the user terminal.

8. A learning data processing device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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