A control method, apparatus, device, and storage medium

Abstract

A control method, device, equipment and storage medium are disclosed. The method comprises: acquiring a target path, wherein the target path comprises at least one learning section and at least one test section; if it is determined according to user behavior data and the target path that a current section is a learning section and a next section is a test section, determining a target learning duration corresponding to the current section according to the user behavior data; and if the target learning duration is less than a recommended learning duration, performing the test section. Through the technical solution of the present application, the learning situation of a user can be analyzed in real time, and the subjective learning time of the user can be improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of smart education, and in particular to a control method and device, an electronic device, and a storage medium. BACKGROUND

[0002] In the process of rapid development of the intersection technology in the education field and the Internet field, a smart classroom is generated, which provides a broad space for user autonomous learning. In the past, the smart classroom packs a series of content into a series of courses monotonously for user autonomous learning, and sets a test after the user autonomous learning, and demonstrates the learning results of the user through the test results. However, this solution is unilaterally dependent on the result feedback brought by the test, cannot feedback the learning evaluation data of the user in time, and because the content is monotonous and boring, the user is easy to give up in a long learning state. SUMMARY

[0003] Embodiments of the present application provide a control method, device, electronic device, and storage medium, which can analyze the learning situation of the user in real time and improve the subjective learning time of the user.

[0004] According to an aspect of the present application, a control method is provided, comprising:

[0005] obtaining a target path, wherein the target path comprises at least one learning link and at least one test link;

[0006] if it is determined according to the behavior data of the user and the target path that the current link is a learning link and the next link is a test link, determining a target learning duration corresponding to the current link according to the behavior data of the user;

[0007] if the target learning duration is less than a recommended learning duration, performing the test link.

[0008] According to another aspect of the present application, a control device is provided, comprising:

[0009] an obtaining module configured to obtain a target path, wherein the target path comprises at least one learning link and at least one test link;

[0010] a determining module configured to, if it is determined according to the behavior data of the user and the target path that the current link is a learning link and the next link is a test link, determine a target learning duration corresponding to the current link according to the behavior data of the user;

[0011] a performing module configured to, if the target learning duration is less than a recommended learning duration, perform the test link.

[0012] According to another aspect of the present application, an electronic device is provided, comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the control method according to any one of the embodiments of the present application.

[0016] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the control method according to any one of the embodiments of the present application when executed by the processor.

[0017] The embodiment of the present application acquires a target path, wherein the target path comprises at least one learning link and at least one test link; if it is determined according to the behavior data of the user and the target path that the current link is a learning link and the next link is a test link, the target learning duration corresponding to the current link is determined according to the behavior data of the user; if the target learning duration is less than the recommended learning duration, the test link is performed, which solves the problem that the result feedback caused by unilateral serious dependence on tests cannot instantaneously feedback the learning evaluation condition and other data of the user, solves the problem that the user is prone to give up in a long learning state, and can analyze the learning condition of the user in real time and improve the subjective learning time of the user.

[0018] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 is a flow chart of a control method in the first embodiment of the present application;

[0021] Figure 2 is a structural schematic diagram of a control device in the second embodiment of the present application;

[0022] Figure 3is a structural schematic diagram of an electronic device in embodiment three of the present application. DETAILED DESCRIPTION

[0023] In order for those skilled in the technical field to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] It can be understood that before using the technical solutions disclosed in the embodiments of the present disclosure, the type, scope of use, use scenario, etc. of the personal information involved in the present disclosure should be informed to the user and the authorization of the user should be obtained according to relevant laws and regulations.

[0026] Embodiment one

[0027] Figure 1 is a flowchart of a control method in embodiment one of the present application. The present embodiment can be applied to the case that the user learns deeply in a smart classroom. The method can be executed by the control device in the embodiments of the present application. The device can be realized in the form of software and / or hardware. As shown in Figure 1 , the method specifically includes the following steps:

[0028] S110, acquiring a target path, wherein the target path includes at least one learning link and at least one test link.

[0029] The target path is a learning path selected by a user according to actual needs, and the target path includes at least one learning link and at least one test link. The learning link can be an autonomous learning link, which is a link for subjective initiative learning of the user. The test link is a link for testing the user according to the learning situation of the user. It should be noted that the test link can automatically pop up when it is detected that the user needs to be tested.

[0030] Specifically, the target path can be obtained according to a learning path selected by the user.

[0031] It should be noted that before the target path is obtained, all learning content needs to be obtained, and the learning content is labeled with a label. The label can include a difficulty level, an applicable group, and a content type, and the learning content is uploaded to a corresponding link type according to the label. The link type includes but is not limited to:

[0032] The learning link can include autonomous learning and human-computer interaction, which is a process of subjective initiative learning. The test link is a link for testing the user according to the learning situation of the user. The human-human interaction link is a link for achieving human-human interaction by linking with other intelligent devices. The assessment link is a test link after all learning links, human-human interaction links, and test links corresponding to the preconditions are completed. The review and question answering link is a learning report of the user on the smart classroom generated according to the learning situation, test situation, and assessment situation of the user. The report is linked with a lecturer device, and the lecturer feeds back and reviews the situation of the user in the classroom through video image transmission, and answers questions of the user. The after-class exercise link is homework generated intelligently from a question bank according to the learning situation of the user. The target learning duration is a duration required by the user to complete learning of the current link.

[0033] Through the target path, the user can autonomously select a variety of learning content combinations, and rich learning content can be matched and combined according to the content label, which can better meet the learning needs of the user.

[0034] In S120, if the current link is a learning link and the next link is a test link according to the behavior data of the user and the target path, the target learning duration corresponding to the current link is determined according to the behavior data of the user.

[0035] The behavior data of the user can be online time, stay time, leave time, offline time, link type, learning content data, and heartbeat data of the user. The target learning duration is a duration required by the user to complete learning of the current link.

[0036] Specifically, if the current stage is determined to be a learning stage and the next stage is determined to be a test stage according to the behavior data of the user and the target path, the manner of determining the target learning duration corresponding to the current stage according to the behavior data of the user can be: obtaining all learning stages and test stages in the target path according to the target path, and uninterruptedly obtaining the behavior data of the user at a preset time frequency, if the current stage of the user is determined to be a learning stage and the next stage is determined to be a test stage according to the behavior data of the user and the target path, determining the learning progress of the user according to the behavior data of the user, and then determining the target learning duration of the user in the current stage.

[0037] By determining the target learning duration corresponding to the current stage according to the behavior data of the user, the learning situation of the user can be analyzed in real time.

[0038] S130, if the target learning duration is less than the recommended learning duration, the test stage is performed.

[0039] The recommended learning duration can be a learning duration recommended after intelligent evaluation of the learning content of each learning stage.

[0040] Specifically, if the target learning duration is less than the recommended learning duration, the test stage can be performed in the following manner: obtaining the recommended learning duration of the current stage, if the target learning duration is less than the recommended learning duration, the test stage pops up, the user can perform a test of the content of the test stage, and the learning situation of the user is evaluated through the test of the content of the test stage.

[0041] Optionally, the method further comprises:

[0042] If the target learning duration is greater than or equal to the recommended learning duration, and the target instruction is received, the test stage is skipped and a warning is performed.

[0043] The target instruction can be an instruction of the user to determine not to perform the test stage.

[0044] Specifically, if the target learning duration is greater than or equal to the recommended learning duration, and the target instruction is received, the test stage is skipped and a warning is performed in the following manner: if the target learning duration is greater than or equal to the recommended learning duration, encouragement information can be generated to encourage the user, after the user completes the learning stage, the target instruction is received, the test stage is directly skipped, and the user is warned.

[0045] Optionally, the method further comprises:

[0046] The number of warnings is obtained.

[0047] If the number of warnings is greater than or equal to a warning threshold, the test stage is entered.

[0048] The pre-warning number is a number of pre-warnings to the user after the user skips the test link. The pre-warning number threshold is a preset value.

[0049] Specifically, the pre-warning number can be obtained by a counter.

[0050] Specifically, if the pre-warning number is greater than or equal to the pre-warning number threshold, the user directly enters the test link in the following way: a pre-warning number threshold is preset, and if the obtained pre-warning number is greater than or equal to the pre-warning number threshold, the user directly enters the test link.

[0051] Optionally, the target learning duration corresponding to the current link is determined according to the behavior data of the user, comprising:

[0052] The learning time curve corresponding to the current link is determined according to the behavior data of the user.

[0053] The target learning duration corresponding to the current link is determined according to the learning time curve corresponding to the current link.

[0054] Specifically, the learning time curve corresponding to the current link can be determined according to the behavior data of the user in the following way: the learning time curve of the user in the current link is generated according to the behavior data of the user, wherein the information elements in the learning time curve can include online time, stay time, leave time, offline time and link type. It should be noted that the learning time curve of the user in different links can be generated according to the obtained behavior data of the user.

[0055] Specifically, the target learning duration corresponding to the current link can be determined according to the learning time curve corresponding to the current link in the following way: the learning time curve corresponding to the current link can be intelligently analyzed to determine the target learning duration corresponding to the current link.

[0056] Optionally, it further comprises:

[0057] If the evaluation score corresponding to the test link is less than the score threshold, the target learning link is generated according to the content corresponding to the test link, and the target learning link is performed.

[0058] The score threshold is a preset minimum passing score value. The score threshold of each test link can be the same threshold, or different thresholds.

[0059] Specifically, if the evaluation score corresponding to the test stage is less than the score threshold, a target learning stage is generated based on the content corresponding to the test stage. The method for conducting the target learning stage is as follows: if the user's evaluation score after the test stage is less than the score threshold, it means that the user's evaluation is unqualified and the test content needs to be relearned in a targeted manner. Therefore, a target learning stage is generated based on the content corresponding to the test stage, and the user needs to relearn the content corresponding to the target learning stage.

[0060] Optional, also includes:

[0061] Obtain the recommended duration for each stage;

[0062] Determine the first duration corresponding to the triggered stage based on user behavior data;

[0063] The recommended duration for untriggered stages is adjusted based on the first duration corresponding to the already triggered stages and the recommended duration corresponding to each stage.

[0064] Among them, triggered stages are those that the user has completed or is currently in progress on the target path, while untriggered stages are those that the user has not yet started learning on the target path. The first duration is the time required for the user to spend on the triggered stages.

[0065] Specifically, the recommended duration for each stage can be obtained by pre-evaluating the recommended duration for each stage based on the content of each stage and the historical behavioral data of students learning the content of each stage. It should be noted that when obtaining the recommended duration for each stage, the sum of the recommended durations for each stage along the target path can be determined as the total recommended duration for the target path.

[0066] Specifically, the method for determining the first duration corresponding to the triggered stage based on user behavior data can be as follows: the first duration corresponding to the stage that the user has completed can be determined based on user behavior data, or the first duration corresponding to the stage that the user is currently completing can be determined based on intelligent analysis of user behavior data.

[0067] Specifically, adjusting the recommended duration of untriggered stages based on the initial duration of triggered stages and the recommended duration of each stage can be done as follows: Determine the total recommended duration of the target path based on the recommended duration of each stage, and determine the duration weight of untriggered stages based on their respective recommended durations. Then, adjust the recommended duration of untriggered stages based on the initial duration of triggered stages, the total recommended duration of the target path, and the duration weight of untriggered stages. For example, it could involve calculating the remaining time based on the total recommended duration of the target path and the initial duration of triggered stages, redistributing the remaining time based on the duration weight of untriggered stages, and thus adjusting the recommended duration of untriggered stages.

[0068] Optional, also includes:

[0069] The learning progress corresponding to each stage in the target path is determined based on the user's behavioral data.

[0070] If the learning progress at each stage of the target path exceeds a set threshold, then the assessment stage begins.

[0071] The threshold is set to a preset value, for example, the threshold can be preset to 95%.

[0072] Specifically, the method for determining the learning progress corresponding to each stage in the target path based on user behavior data can be as follows: the learning progress corresponding to each stage in the target path can be intelligently analyzed based on user behavior data.

[0073] Specifically, if the learning progress corresponding to each stage in the target path is greater than the set threshold, the assessment stage can be entered as follows: if the learning progress corresponding to each stage in the target path is greater than the set threshold, it means that the user has basically completed the content corresponding to each stage, and then the assessment stage is entered to test the user.

[0074] Optional, also includes:

[0075] If it is determined from the user's behavior data that the user is in an abandoned state, a reminder message is generated and sent to the target terminal.

[0076] The target terminal can be a user's smartwatch, smartphone, or other smart device.

[0077] Specifically, if it is determined that a user is in an abandoned state based on the user's behavior data, a reminder message is generated and sent to the target terminal. The method for generating the reminder message and sending the reminder message to the target terminal can be as follows: If the user's heartbeat data cannot be detected in the acquired user behavior data, it indicates that the user is in an abandoned state. In this case, a reminder message is generated and sent to the target terminal to remind the user to return to the classroom as soon as possible.

[0078] The technical solution of this embodiment obtains a target path, which includes at least one learning stage and at least one testing stage. If the current stage is determined to be a learning stage and the next stage to be a testing stage based on the user's behavior data and the target path, then the target learning duration corresponding to the current stage is determined based on the user's behavior data. If the target learning duration is less than the recommended learning duration, then the testing stage is performed. This solves the problem that the reliance on test-based feedback leads to an inability to provide timely feedback on the user's learning assessment and other data. It also addresses the issue that users are prone to giving up during long periods of study. This solution enables real-time analysis of the user's learning progress and improves the user's perceived learning time.

[0079] Example 2

[0080] Figure 2 This is a schematic diagram of a control device according to Embodiment 2 of the present invention. This embodiment is applicable to users engaging in deep learning in a smart classroom. The device can be implemented using software and / or hardware, and can be integrated into any device that provides control functions, such as... Figure 2 As shown, the control device specifically includes: an acquisition module 210, a determination module 220, and a performance module 230.

[0081] The acquisition module 210 is used to acquire the target path, wherein the target path includes at least one learning stage and at least one testing stage.

[0082] The determination module 220 is used to determine the target learning duration corresponding to the current stage based on the user's behavior data if the current stage is determined to be a learning stage and the next stage is a testing stage based on the user's behavior data.

[0083] Module 230 is used to conduct a testing phase if the target learning time is less than the recommended learning time.

[0084] Optional, also includes:

[0085] The early warning module is used to skip the testing phase and issue an early warning if the target learning time is greater than or equal to the recommended learning time and a target instruction is received.

[0086] Optionally, the early warning module is also used for:

[0087] Number of alerts received;

[0088] If the number of warnings is greater than or equal to the warning number threshold, then the testing phase begins.

[0089] Optionally, the determining module is specifically used for:

[0090] Determine the learning time curve corresponding to the current stage based on user behavior data;

[0091] Determine the target learning duration for the current stage based on the learning time curve corresponding to the current stage.

[0092] Optional, also includes:

[0093] The generation module is used to generate a target learning stage based on the content of the test stage if the evaluation score corresponding to the test stage is less than the score threshold, and then perform the target learning stage.

[0094] Optionally, the early warning module is also used for:

[0095] Obtain the recommended duration for each stage;

[0096] Determine the first duration corresponding to the triggered stage based on user behavior data;

[0097] The recommended duration for untriggered stages is adjusted based on the first duration corresponding to the already triggered stages and the recommended duration corresponding to each stage.

[0098] Optional, also includes:

[0099] The progress determination module is used to determine the learning progress corresponding to each stage in the target path based on the user's behavior data.

[0100] The assessment module is used to enter the assessment stage if the learning progress corresponding to each stage in the target path is greater than a set threshold.

[0101] Optional, also includes:

[0102] The sending module is used to generate a reminder message and send the reminder message to the target terminal if it is determined that the user is in an abandoned state based on the user's behavior data.

[0103] The above-described products can perform the methods provided in any embodiment of the present invention, and have the corresponding functional modules and beneficial effects for performing the methods.

[0104] The technical solution of this embodiment obtains a target path, which includes at least one learning stage and at least one testing stage. If the current stage is determined to be a learning stage and the next stage to be a testing stage based on the user's behavior data and the target path, then the target learning duration corresponding to the current stage is determined based on the user's behavior data. If the target learning duration is less than the recommended learning duration, then the testing stage is performed. This solves the problem that the reliance on test-based feedback leads to an inability to provide timely feedback on the user's learning assessment and other data. It also addresses the issue that users are prone to giving up during long periods of study. This solution enables real-time analysis of the user's learning progress and improves the user's perceived learning time.

[0105] Example 3

[0106] Figure 3 This is a schematic diagram of an electronic device according to Embodiment 3 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0107] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0108] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0109] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as control methods.

[0110] In some embodiments, the control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to execute the control method by any other suitable means (e.g., by means of firmware).

[0111] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0113] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0114] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0115] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0116] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0117] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0118] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A control method characterized by, The method comprises the following steps: acquiring a target path, wherein the target path comprises at least one learning link and at least one test link; if it is determined according to the behavior data of the user and the target path that the current link is a learning link and the next link is a test link, then determining a target learning time length corresponding to the current link according to the behavior data of the user; if the target learning time length is less than a recommended learning time length, then proceeding to the test link; if the target learning time length is greater than or equal to the recommended learning time length, then generating encouragement information to encourage the user, and after the user completes the learning link, if a target instruction is received, then directly skipping the test link and warning the user; the method further comprises acquiring a warning frequency; if the warning frequency is greater than or equal to a warning frequency threshold, then proceeding to the test link; the acquiring of the warning frequency comprises acquiring the warning frequency by setting a counter; if the warning frequency is greater than or equal to the warning frequency threshold, then the user directly proceeds to the test link; the method further comprises acquiring a recommended time length corresponding to each link; determining a first time length corresponding to a triggered link according to the behavior data of the user; adjusting a recommended time length corresponding to an untriggered link according to the first time length corresponding to the triggered link and the recommended time length corresponding to each link; the acquiring of the recommended time length corresponding to each link comprises intelligently evaluating the recommended time length corresponding to each link in advance according to the content corresponding to each link and historical behavior data of a plurality of students collected when the plurality of students learn the content corresponding to each link; the determining of the first time length corresponding to the triggered link according to the behavior data of the user comprises determining the first time length corresponding to a link completed by the user according to the behavior data of the user, or analyzing the first time length of a link being completed by the user according to intelligent analysis of the behavior data of the user; the adjusting of the recommended time length corresponding to the untriggered link comprises calculating a remaining time according to a total recommended time length of the target path and the first time length corresponding to the triggered link, redistributing the remaining time according to a time length weight corresponding to the untriggered link, and then adjusting the recommended time length corresponding to the untriggered link; wherein the behavior data of the user is online time, stay time, leave time, offline time, link type, learning content data and heartbeat data of the user, the first time length is a time length consumed by the user in the triggered link, and the total recommended time length of the target path is a sum of the recommended time lengths corresponding to each link on the target path; before the acquiring of the target path, all learning content is acquired, the learning content is labeled with a label, and the learning content is uploaded to a corresponding link type according to the label, wherein the label comprises a difficulty level, a suitable group and a content type label.

2. The method of claim 1, wherein, The target learning duration corresponding to the current link is determined according to the behavior data of the user, comprising: The learning time curve corresponding to the current link is determined according to the behavior data of the user. The target learning duration corresponding to the current link is determined according to the learning time curve corresponding to the current link.

3. The method of claim 1, wherein, Further comprising: If the evaluation score corresponding to the test link is less than the score threshold, a target learning link is generated according to the content corresponding to the test link, and the target learning link is performed.

4. The method of claim 1, wherein, Further comprising: The learning progress corresponding to each link in the target path is determined according to the behavior data of the user. If the learning progress corresponding to each link in the target path is greater than a set threshold, the examination link is entered.

5. The method of claim 1, wherein, Further comprising: If it is determined according to the behavior data of the user that the user is in a leaving state, a reminder information is generated, and the reminder information is sent to a target terminal.

6. A control device characterized by comprising: Comprising: An acquisition module is configured to acquire a target path, wherein the target path comprises at least one learning link and at least one test link; A determination module is configured to, if it is determined according to the behavior data of the user and the target path that the current link is a learning link and the next link is a test link, determine a target learning duration corresponding to the current link according to the behavior data of the user; A performance module is configured to, if the target learning duration is less than a recommended learning duration, perform a test link; An early warning module is configured to, if the target learning duration is greater than or equal to the recommended learning duration, generate an encouragement information to encourage the user, and after the user completes the learning link, a target instruction is received, the test link is directly skipped, and the user is warned; The module is further configured to acquire a warning frequency; If the warning frequency is greater than or equal to a warning frequency threshold, the test link is entered; The acquisition of the warning frequency comprises acquiring the warning frequency by setting a counter; If the warning frequency is greater than or equal to the warning frequency threshold, the user directly enters the test link; The module is further configured to acquire a recommended duration corresponding to each link; A first duration corresponding to a triggered link is determined according to the behavior data of the user; A recommended duration corresponding to an untriggered link is adjusted according to the first duration corresponding to the triggered link and the recommended duration corresponding to each link; The acquisition of the recommended duration corresponding to each link comprises pre-intelligently evaluating the recommended duration corresponding to each link according to the content corresponding to each link and collected historical behavior data of a plurality of students when learning the content corresponding to each link; The determination of the first duration corresponding to the triggered link according to the behavior data of the user comprises determining the first duration corresponding to a link completed by the user according to the behavior data of the user, or intelligently analyzing the first duration of a link being completed by the user according to the behavior data of the user. The adjusting the recommended time length corresponding to the untriggered link according to the first time length corresponding to the triggered link and the recommended time length corresponding to each link comprises: calculating a remaining time according to a total recommended time length of the target path and the first time length corresponding to the triggered link, re-distributing the remaining time according to a time weight corresponding to the untriggered link, and adjusting the recommended time length corresponding to the untriggered link; The behavior data of the user is online time, stay time, leave time, offline time, link type, learning content data, and heartbeat data of the user, the first time length is a time length that the user needs to spend in the triggered link, and the total recommended time length of the target path is a sum of the recommended time length corresponding to each link on the target path. The method further comprises: before the obtaining the target path, obtaining all learning contents, labeling the learning contents with labels, and uploading the learning contents to corresponding link types according to the labels, wherein the labels comprise difficulty level, applicable group, and content type label.

7. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the control method in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the control method in any one of claims 1-5 when executed.

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