A method and corresponding device for study partner recommendation

Abstract

The application discloses a method for recommending a study partner, comprising the following steps: a terminal device sends a study partner request to a cloud device in response to a study partner request operation of a user for at least one knowledge point in target learning content; the cloud device determines information of at least one group of study partners according to identification information of the at least one knowledge point and user identification information; the terminal device receives the information of the at least one group of study partners, and displays the identification information of the at least one knowledge point and the information of the at least one group of study partners. The scheme provided in the embodiment of the application can recommend a study partner for a knowledge point, and improves the accuracy of study partner recommendation.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a method and corresponding equipment for recommending study partners. Background Technology

[0002] Online education allows learners to learn more flexibly without spatial or temporal limitations, improving the convenience of learning. However, online education also presents several challenges, such as a lack of social interaction and a sense of collective learning, leading to less sustained learning and difficulty in guaranteeing learning outcomes. Online learning partners, similar to classmates in traditional offline classes, can facilitate interaction, compensate for the lack of social interaction in online learning, increase user engagement, promote communication among users, and effectively improve learning outcomes.

[0003] Existing online learning partners typically include virtual and real-person partners. Because current artificial intelligence technology cannot yet fully approach human levels in semantic understanding and reasoning, virtual partners often fail to answer questions directly or clearly understand the user's intent. In contrast, real-person partners offer a better user experience. However, due to varying learning progress among users, the matching degree of real-person partners recommended by the system is not high. Summary of the Invention

[0004] This application provides a method for recommending study partners, which recommends suitable study partners to users based on knowledge points, thereby improving the accuracy of study partner recommendations. Embodiments of this application also provide corresponding devices, computer-readable storage media, and computer program products.

[0005] The first aspect of this application provides a method for recommending learning partners, comprising: a terminal device responding to a user's request for a learning partner for at least one knowledge point in a target learning content, sending a learning partner request to a cloud device, the learning partner request including identification information of at least one knowledge point and user identification information; the terminal device receiving at least one set of learning partner information sent by the cloud device based on the identification information of at least one knowledge point and the user identification information, the at least one set of learning partner information corresponding one-to-one with the identification information of at least one knowledge point; and the terminal device displaying the identification information of at least one knowledge point and the at least one set of learning partner information.

[0006] In this application, a learning partner refers to a person with whom you can study online. The target learning content can be a main heading (e.g., "functions"), and knowledge points can be subheadings under that main heading (e.g., "different types of functions"). The identification information for a knowledge point can be its title, identifier, or name. User identification information can be the user's registered account, mobile phone number, or a unique identifier assigned to the user by the cloud device during registration, as well as indexes uniquely identifying the user recorded by the cloud device during other user registrations. The learning partner's information can include their username and contact information.

[0007] In this application, the learning partners correspond to the knowledge points, and each knowledge point corresponds to a group of learning partners. Each group of learning partners may include one or more learning partners. In this application, "multiple" includes two or more.

[0008] As can be seen from the first aspect above, when users are learning target learning content, they can request learning partners from cloud devices for the knowledge points in the target learning content. The cloud devices can recommend suitable learning partners to users for the knowledge points, which improves the accuracy of learning partner recommendations. Users can learn together with suitable learning partners for the knowledge points, which is conducive to improving learning efficiency and obtaining better learning results.

[0009] In one possible implementation of the first aspect, the first group of learning partners includes at least one of guiding learning partners and accompanying learning partners. The first group of learning partners is any one of at least one group of learning partners, and the first group of learning partners corresponds to a first knowledge point among at least one knowledge point. Among them, the guiding learning partner is a learning partner who has the ability to guide the user regarding the first knowledge point; the accompanying learning partner is a learning partner who has the ability to accompany the user regarding the first knowledge point.

[0010] In this possible implementation, the learning partners recommended by the cloud device include not only companion learning partners who can accompany users in their studies and exchange learning content, but also mentor learning partners who can provide assistance and guidance to users. This approach considers users' needs from multiple perspectives and improves the comprehensiveness of learning partner recommendations.

[0011] In one possible implementation of the first aspect, the mentoring learning partner is determined by the cloud device based on a first difference and a first learning feature similarity. The first difference is the difference between the first learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The first learning partner is a learning partner in the candidate set of mentoring learning partners for the first knowledge point. The first learning feature similarity is the similarity of the learning features between the first learning partner and the user. The first learning partner's mastery of the first knowledge point is higher than a first threshold.

[0012] In this possible implementation, learning features can include multiple aspects such as grade level, region, textbook version, historical learning records, learning interests, and learning style. Learning feature similarity refers to the degree of similarity in learning features between two students or two users. The candidate set of mentoring partners for the first knowledge point is a collection of multiple users stored in the cloud device who have the ability to provide guidance for the first knowledge point. Users in this set have a mastery level of the first knowledge point higher than a first threshold. In this application, the greater the difference between the mentoring partner's mastery level of the first knowledge point and the user's mastery level, the better; the closer the similarity of the learning features between the mentoring partner and the user, the better. This ensures that the recommended mentoring partners are more accurate, more suitable for guiding users, and conducive to users achieving better learning outcomes.

[0013] In one possible implementation of the first aspect, the companion learning partner is determined by the cloud device based on the absolute value of the second difference and the second learning feature similarity. The second difference is the difference between the second learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The second learning partner is a learning partner in the candidate set of companion learning partners for the first knowledge point. The second learning feature similarity is the similarity of the learning features between the second learning partner and the user. The second learning partner's mastery of the first knowledge point is lower than the first threshold.

[0014] In this possible implementation, the candidate set of learning companions for the first knowledge point is a collection of multiple users stored in the cloud device who have the ability to provide companionship for the first knowledge point. The users in this set have a level of mastery of the first knowledge point below a first threshold. In this application, the smaller the difference between the learning companion's level of mastery of the first knowledge point and the user's level of mastery, the better; and the closer the similarity between the learning companion's and the user's learning characteristics, the better. This ensures that the recommended learning companions are more accurate, more suitable for the user, and conducive to the user achieving better learning results.

[0015] In one possible implementation of the first aspect, the method further includes: the terminal device receiving a knowledge point learning path sent by the cloud device, the knowledge point learning path including multiple knowledge points arranged in a recommended learning order, the multiple knowledge points being knowledge points in the target learning content whose user mastery level is less than a first threshold.

[0016] In this possible implementation, the knowledge point learning path is used to prompt users on the learning order of multiple knowledge points they are not yet proficient in. Users can learn in this order, which helps them achieve better learning results.

[0017] In one possible implementation of the first aspect, when the learning partner request is a learning partner sequence request for a knowledge point learning path, the terminal device receiving at least one set of learning partner information sent by the cloud device based on the identification information of at least one knowledge point and the user identification information specifically includes: the terminal device receiving a learning partner sequence sent by the cloud device, wherein the learning partner sequence includes multiple sets of learning partner information arranged in the order of knowledge points in the knowledge point learning path.

[0018] In this possible implementation, when a user requests a learning partner sequence for a knowledge point learning path, the cloud device determines a set of learning partners for each knowledge point according to the order of multiple knowledge points in the learning path, thus obtaining a learning partner sequence corresponding to the knowledge point learning path. When learning each knowledge point, the user can obtain the corresponding learning partner from the learning partner sequence. This eliminates the need for the user to send a learning partner request for each knowledge point separately, improving the efficiency of learning partner recommendation.

[0019] In one possible implementation of the first aspect, before the terminal device responds to the user's request for a learning partner for at least one knowledge point in the target learning content, the method further includes: the terminal device sending an assessment request for the target learning content to a cloud device, the assessment request including identification information of the target learning content and user identification information; the terminal device receiving assessment content sent by the cloud device based on the identification information of the target learning content; the terminal device sending the user's response information input for the assessment content to the cloud device; and the terminal device receiving information from the cloud device regarding the user's mastery level of each knowledge point in the target learning content, sent by the cloud device based on the response information.

[0020] In this possible implementation, users can take an assessment before learning the target learning content. The cloud device will determine the user's mastery of each knowledge point in the target learning content based on the user's answers to the assessment, so that the user can learn more effectively on the knowledge points they have not mastered.

[0021] In one possible implementation of the first aspect, the method further includes: a terminal device receiving a first query message sent by a cloud device, the first query message being used to ask the user whether they are willing to become a mentoring learning partner for knowledge points in the target learning content where their mastery level is greater than a first threshold; and the terminal device responding to the user's reply to the first query message by sending a reply message to the cloud device in response to the first query message.

[0022] In this possible implementation, after determining the user's mastery level of each knowledge point in the target learning content, the cloud device can classify the user into a candidate set of mentoring learning partners for knowledge points where the user's mastery level is greater than a first threshold. In order to determine which users are willing to become mentoring learning partners, the cloud device sends an inquiry message to the terminal device. Only after the user agrees to become a mentoring learning partner will the user be classified into the candidate set of mentoring learning partners. In this way, more responsible mentoring learning partners can be recommended when recommending learning partners, which is conducive to users obtaining better learning results.

[0023] In one possible implementation of the first aspect, the method further includes: the terminal device receiving a second query message sent by the cloud device, the second query message being used to ask the user whether they are willing to become a companion learning partner for knowledge points in the target learning content where their mastery level is less than a first threshold; and the terminal device responding to the user's reply to the second query message by sending a reply message to the cloud device in response to the second query message.

[0024] In this possible implementation, after determining the user's mastery level of each knowledge point in the target learning content, the cloud device can categorize the user into a candidate set of learning companions for knowledge points where the user's mastery level is less than a first threshold. To determine which users are willing to become learning companions, the cloud device sends an inquiry message to the terminal device. Only after the user agrees to become a learning companion will the user be included in the candidate set of learning companions. In this way, more responsible learning companions can be recommended when recommending learning companions, which is conducive to users obtaining better learning results.

[0025] In one possible implementation of the first aspect, the method further includes: the terminal device sending the user's learning content for at least one knowledge point to the cloud device, the learning content for at least one knowledge point being used by the cloud device to update the user's mastery of at least one knowledge point.

[0026] In this possible implementation, as users learn, their mastery of knowledge points will change. The cloud device acquires the user's learning content and then re-evaluates the user's mastery of the corresponding knowledge points, thereby updating the user's mastery of the knowledge points. If the user's mastery of a knowledge point that was originally below the first threshold has exceeded the first threshold after learning, the cloud device will change the user's role to a mentoring learning partner, that is, move the user from the set of accompanying learning partners for the corresponding knowledge point to the set of mentoring learning partners.

[0027] A second aspect of this application provides a method for recommending study partners, comprising: a cloud device receiving a study partner request sent by a terminal device, the study partner request including identification information of at least one knowledge point in the target learning content and user identification information; the cloud device determining at least one set of study partner information for the user based on the identification information of at least one knowledge point and the user identification information, wherein the information of at least one set of study partners corresponds one-to-one with the identification information of at least one knowledge point; and the cloud device sending the information of at least one set of study partners to the terminal device.

[0028] In one possible implementation of the second aspect, the first group of learning partners includes at least one of guiding learning partners and accompanying learning partners. The first group of learning partners is any one of at least one group of learning partners, and the first group of learning partners corresponds to a first knowledge point among at least one knowledge point. Among them, the guiding learning partner is a learning partner who has the ability to guide the user regarding the first knowledge point; the accompanying learning partner is a learning partner who has the ability to accompany the user regarding the first knowledge point.

[0029] In one possible implementation of the second aspect, the mentoring learning partner is determined by the cloud device based on a first difference and a first learning feature similarity. The first difference is the difference between the first learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The first learning partner is a learning partner in the candidate set of mentoring learning partners for the first knowledge point. The first learning feature similarity is the similarity of the learning features between the first learning partner and the user. The first learning partner's mastery of the first knowledge point is higher than a first threshold.

[0030] In one possible implementation of the second aspect, the companion learning partner is determined by the cloud device based on the absolute value of the second difference and the second learning feature similarity. The second difference is the difference between the second learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The second learning partner is a learning partner in the candidate set of companion learning partners for the first knowledge point. The second learning feature similarity is the similarity of the learning features between the second learning partner and the user. The second learning partner's mastery of the first knowledge point is lower than the first threshold.

[0031] In one possible implementation of the second aspect, the method further includes: the cloud device identifying multiple knowledge points in the target learning content whose user mastery level is less than a first threshold, and determining the recommended learning order of the multiple knowledge points to generate a knowledge point learning path; the cloud device sending the knowledge point learning path to the terminal device.

[0032] In one possible implementation of the second aspect, when the learning partner request is a learning partner sequence request for a knowledge point learning path, the cloud device determines at least one set of learning partner information for the user based on the identification information of at least one knowledge point and the user identification information. Specifically, the cloud device determines a learning partner sequence for the knowledge point learning path, and the learning partner sequence includes multiple sets of learning partner information arranged in the order of the knowledge points in the knowledge point learning path.

[0033] In one possible implementation of the second aspect, before the cloud device receives the learning companion request sent by the terminal device, the method further includes: the cloud device receiving an assessment request sent by the terminal device, the assessment request including identification information of the target learning content and user identification information; the cloud device sending the assessment content to the terminal device based on the identification information of the target learning content; the cloud device receiving the user's answer information input by the terminal device regarding the assessment content; the cloud device determining the user's mastery level of each knowledge point in the target learning content based on the answer information; and the cloud device sending the user's mastery level of each knowledge point in the target learning content to the terminal device.

[0034] In one possible implementation of the second aspect, the method further includes: the cloud device sending a first inquiry message to the terminal device, the first inquiry message being used to inquire whether the user is willing to become a mentoring learning partner for knowledge points in the target learning content where the user's mastery level is greater than a first threshold; the cloud device receiving a response message from the terminal device to the first inquiry message; if the response message to the first inquiry message is an agreement to become a mentoring learning partner, the cloud device adding the user's identification information to the mentoring learning partner candidate set for knowledge points where the user's mastery level is greater than the first threshold.

[0035] In one possible implementation of the second aspect, the method further includes: the cloud device sending a second inquiry message to the terminal device, the second inquiry message being used to inquire whether the user is willing to become a companion learning partner for knowledge points in the target learning content where the user's mastery level is less than a first threshold; the cloud device receiving the terminal device's response to the second inquiry message; if the response to the second inquiry message is an agreement to become a companion learning partner, the cloud device adding the user's identification information to the candidate set of companion learning partners for knowledge points where the user's mastery level is less than the first threshold.

[0036] In one possible implementation of the second aspect, the method further includes: the cloud device receiving learning content for at least one knowledge point sent by the terminal device; and the cloud device updating the user's mastery of at least one knowledge point based on the learning content for at least one knowledge point.

[0037] In a third aspect, this application provides a terminal device for executing the method described in the first aspect or any possible implementation thereof. Specifically, the terminal device includes modules or units for executing the method described in the first aspect or any possible implementation thereof, such as a display unit, a sending unit, and a receiving unit.

[0038] In a fourth aspect, this application provides a cloud device for performing the method described in the second aspect or any possible implementation thereof. Specifically, the cloud device includes modules or units for performing the method described in the second aspect or any possible implementation thereof, such as a receiving unit, a processing unit, and a transmitting unit.

[0039] The fifth aspect of this application provides a terminal device including a transceiver, a processor, and a memory. The transceiver and the processor are coupled to the memory, which is used to store programs or instructions. When the programs or instructions are executed by the processor, the terminal device performs the methods in the first aspect or any possible implementation thereof.

[0040] A sixth aspect of this application provides a cloud device. The cloud device may include at least one processor, a memory, and a communication interface. The processor is coupled to the memory and the communication interface. The memory is used to store instructions, the processor is used to execute the instructions, and the communication interface is used to communicate with other network elements under the control of the processor. When executed by the processor, the instructions cause the processor to perform the methods of the second aspect or any possible implementation thereof.

[0041] The seventh aspect of this application provides a chip system including one or more interface circuits and one or more processors; the interface circuits and processors are interconnected via lines; the interface circuits are used to receive signals from the memory of a terminal device and send signals to the processor, the signals including computer instructions stored in the memory; when the processor executes the computer instructions, the terminal device executes the method in the first aspect or any possible implementation of the first aspect.

[0042] The eighth aspect of this application provides a chip system including one or more interface circuits and one or more processors; the interface circuits and processors are interconnected via lines; the interface circuits are used to receive signals from the memory of a cloud device and send signals to the processor, the signals including computer instructions stored in the memory; when the processor executes the computer instructions, the cloud device executes the method in the second aspect or any possible implementation thereof.

[0043] The ninth aspect of this application provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed on a computer, cause the computer to perform the method of the first aspect or any possible implementation thereof.

[0044] The tenth aspect of this application provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed on a computer, cause the computer to perform the methods of the second aspect or any possible implementation thereof.

[0045] The eleventh aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0046] The twelfth aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to perform the methods of the second aspect or any possible implementation thereof.

[0047] The thirteenth aspect of this application provides a learning buddy recommendation system, which includes a terminal device and a cloud device. The terminal device is used to execute the method in the first aspect or any possible implementation thereof, and the cloud device is used to execute the method in the second aspect or any possible implementation thereof.

[0048] The technical effects of the second to thirteenth aspects or any of their possible implementations can be found in the first aspect or the technical effects of different possible implementations of the first aspect, and will not be repeated here. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the architecture of the learning companion recommendation system provided in the embodiments of this application;

[0050] Figure 2A This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application;

[0051] Figure 2B This is a schematic diagram of the structure of a cloud device provided in an embodiment of this application;

[0052] Figure 3 This is a schematic diagram of an embodiment of the learning buddy recommendation method provided in this application;

[0053] Figure 4A This is a schematic diagram of a main interface provided in an embodiment of this application;

[0054] Figure 4B This is a schematic diagram of a login interface provided in an embodiment of this application;

[0055] Figure 4C This is a schematic diagram of a learning interface provided in an embodiment of this application;

[0056] Figure 4D This is a schematic diagram of a learning buddy classification provided in an embodiment of this application;

[0057] Figure 4E This is a schematic diagram of a display interface provided in an embodiment of this application;

[0058] Figure 4F This is another schematic diagram of a display interface provided in an embodiment of this application;

[0059] Figure 4G This is another schematic diagram of a display interface provided in an embodiment of this application;

[0060] Figure 5 This is a schematic diagram of the architecture of a learning companion recommendation provided in an embodiment of this application;

[0061] Figure 6A This is another schematic diagram of a display interface provided in an embodiment of this application;

[0062] Figure 6B This is another schematic diagram of a learning interface provided in an embodiment of this application;

[0063] Figure 6C This is another schematic diagram of a learning interface provided in an embodiment of this application;

[0064] Figure 7 This is a schematic diagram of a learning buddy recommendation provided in an embodiment of this application;

[0065] Figure 8A This is another schematic diagram of a learning interface provided in an embodiment of this application;

[0066] Figure 8B This is another schematic diagram of a learning interface provided in an embodiment of this application;

[0067] Figure 8C This is another schematic diagram of a learning interface provided in an embodiment of this application;

[0068] Figure 9A This is an example schematic diagram of a knowledge point learning path provided in an embodiment of this application;

[0069] Figure 9B This is another schematic diagram of a learning interface provided in an embodiment of this application;

[0070] Figure 9C This is an example schematic diagram of the learning buddy sequence recommendation provided in the embodiments of this application;

[0071] Figure 9D This is an example schematic diagram of the learning buddy sequence provided in the embodiments of this application;

[0072] Figure 9E This is another schematic diagram of a learning interface provided in an embodiment of this application;

[0073] Figure 10 This is another structural schematic diagram of the terminal device provided in the embodiments of this application;

[0074] Figure 11This is another structural schematic diagram of the cloud device provided in the embodiments of this application. Detailed Implementation

[0075] The embodiments of this application are described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. As those skilled in the art will recognize, with the development of technology and the emergence of new scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0076] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0077] This application provides a method for recommending study partners, which recommends suitable study partners to users based on knowledge points, thereby improving the accuracy of study partner recommendations. This application also provides corresponding devices, computer-readable storage media, and computer program products, etc., which are described in detail below.

[0078] Figure 1 This is a schematic diagram of the architecture of the learning companion recommendation system provided in the embodiments of this application.

[0079] like Figure 1 As shown in the embodiment of this application, the learning partner recommendation system includes a cloud device and multiple terminal devices. The cloud device can communicate with the multiple terminal devices via a network. Users can learn online through the terminal devices. In response to a user's request for a learning partner for at least one knowledge point in the target learning content, the terminal device sends a learning partner request to the cloud device. The learning partner request includes identification information of at least one knowledge point and user identification information. The terminal device receives at least one set of learning partner information sent by the cloud device based on the identification information of at least one knowledge point and the user identification information. The information of at least one set of learning partners corresponds one-to-one with the identification information of at least one knowledge point. The terminal device displays the identification information of at least one knowledge point and the information of at least one set of learning partners.

[0080] In this embodiment of the application, the cloud device can be a physical machine, a virtual machine (VM), or a container.

[0081] This terminal device (also known as user equipment, UE) is a device with wireless transceiver capabilities. It can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; it can also be deployed on water (such as on ships); and it can be deployed in the air (such as on airplanes, balloons, and satellites). The terminal can be a mobile phone, tablet, computer with wireless transceiver capabilities, virtual reality (VR) terminal, augmented reality (AR) terminal, wireless terminal in industrial control, wireless terminal in self-driving, wireless terminal in remote medical care, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, and wireless terminal in the Internet of Things (IoT), etc. This application's embodiments only describe the terminal device as a mobile phone.

[0082] Please refer to Figure 2A This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Figure 2A As shown, the terminal device may include a processor 210, an external memory interface 220, an internal memory 221, a universal serial bus (USB) interface 230, a charging management module 240, a power management module 241, a battery 242, an antenna 1, an antenna 2, a mobile communication module 250, a wireless communication module 260, an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, a headphone jack 270D, a sensor module 280, buttons 290, a motor 291, an indicator 292, a camera 293, a display screen 294, and a subscriber identification module (SIM) card interface 295, etc. The sensor module 280 may include a pressure sensor 280A, a gyroscope sensor 280B, a barometric pressure sensor 280C, a magnetic sensor 280D, an accelerometer sensor 280E, a distance sensor 280F, a proximity sensor 280G, a fingerprint sensor 280H, a temperature sensor 280J, a touch sensor 280K, an ambient light sensor 280L, a bone conduction sensor 280M, a motion sensor 280N, etc.

[0083] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the terminal device. In other embodiments, the terminal device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0084] Processor 210 may include one or more processing units, such as application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, memory, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0085] The controller can serve as the nerve center and command center of a terminal device. Based on the instruction opcode and timing signals, the controller generates operation control signals to control the fetching and execution of instructions.

[0086] The processor 210 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 210 is a cache memory. This memory can store instructions or data that the processor 210 has just used or that are used repeatedly. If the processor 210 needs to use the instruction or data again, it can directly retrieve it from the memory. This avoids repeated accesses, reduces the waiting time of the processor 210, and thus improves the efficiency of the system.

[0087] In some embodiments, the processor 210 may include one or more interfaces. Interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0088] It is understood that the interface connection relationships between the modules illustrated in this embodiment are merely illustrative and do not constitute a structural limitation on the terminal device. In other embodiments, the terminal device may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.

[0089] The charging management module 240 receives charging input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 240 receives charging input from the wired charger via a USB interface 230. In some wireless charging embodiments, the charging management module 240 receives wireless charging input via the wireless charging coil of the terminal device. While charging the battery 242, the charging management module 240 can also supply power to the terminal device via the power management module 241.

[0090] The power management module 241 connects the battery 242, the charging management module 240, and the processor 210. The power management module 241 receives input from the battery 242 and / or the charging management module 240, providing power to the processor 210, internal memory 221, external memory, display screen 294, camera 293, and wireless communication module 260. The power management module 241 can also monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage current, impedance). In some other embodiments, the power management module 241 may also be located within the processor 210. In other embodiments, the power management module 241 and the charging management module 240 may be housed in the same device.

[0091] The wireless communication function of the terminal device can be implemented through antenna 1, antenna 2, mobile communication module 250, wireless communication module 260, modem processor, and baseband processor.

[0092] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the terminal device can be used to cover one or more communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other embodiments, the antennas can be used in conjunction with a tuning switch.

[0093] The mobile communication module 250 can provide solutions for wireless communication applications including 2G / 3G / 4G / 5G on terminal devices. The mobile communication module 250 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 250 can receive electromagnetic waves via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The mobile communication module 250 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the mobile communication module 250 may be housed in processor 210. In some embodiments, at least some functional modules of the mobile communication module 250 and at least some modules of the processor 210 may be housed in the same device.

[0094] The modem processor may include a modulator and a demodulator. The modulator modulates the low-frequency baseband signal to be transmitted into a mid-to-high frequency signal. The demodulator demodulates the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After processing by the baseband processor, the low-frequency baseband signal is transmitted to the application processor. The application processor outputs sound signals through an audio device (not limited to speaker 270A, receiver 270B, etc.) or displays images or videos through the display screen 294. In some embodiments, the modem processor may be a separate device. In other embodiments, the modem processor may be independent of the processor 210 and may be housed in the same device as the mobile communication module 250 or other functional modules.

[0095] The wireless communication module 260 can provide solutions for wireless communication applications on terminal devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. The wireless communication module 260 can be one or more devices integrating at least one communication processing module. The wireless communication module 260 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to processor 210. The wireless communication module 260 can also receive signals to be transmitted from processor 210, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0096] In some embodiments, antenna 1 of the terminal device is coupled to mobile communication module 250, and antenna 2 is coupled to wireless communication module 260, enabling the terminal device to communicate with networks and other devices via wireless communication technology. The wireless communication technology may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TDSCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies. The GNSS may include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou Navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS), and / or Satellite Based Augmentation Systems (SBAS).

[0097] The terminal device implements display functions through a GPU, a display screen 294, and an application processor. The GPU is a microprocessor for image processing, connecting the display screen 294 and the application processor. The GPU performs mathematical and geometric calculations and is used for graphics rendering. The processor 210 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0098] Display screen 294 is used to display images, videos, etc. Display screen 294 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a Mini LED, a MicroLED, a Micro-OLED, a quantum dot light-emitting diode (QLED), etc.

[0099] Terminal devices can achieve shooting functions through ISP, camera 293, video codec, GPU, display 294 and application processor.

[0100] The ISP (Image Signal Processor) is used to process data fed back from the camera 293. For example, when taking a picture, the shutter is opened, and light is transmitted through the lens to the camera's photosensitive element. The light signal is converted into an electrical signal, and the camera's photosensitive element transmits the electrical signal to the ISP for processing, transforming it into an image visible to the naked eye. The ISP can also perform algorithmic optimization on image noise, brightness, and skin tone. The ISP can also optimize parameters such as exposure and color temperature of the shooting scene. In some embodiments, the ISP can be set in the camera 293.

[0101] Camera 293 is used to capture still images or videos. An object is projected onto a photosensitive element by generating an optical image through the lens. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then passed to an ISP for conversion into a digital image signal. The ISP outputs the digital image signal to a DSP for processing. The DSP converts the digital image signal into image signals in standard RGB, YUV, or other formats. In some embodiments, the terminal device may include one or N cameras 293, where N is a positive integer greater than 1.

[0102] The camera 293 can also be used to provide users with personalized and contextualized business experiences based on the perceived external environment and user actions. Specifically, the camera 293 can acquire rich and accurate information, enabling the terminal device to perceive the external environment and user actions. In this embodiment, the camera 293 can be used to identify whether the user of the terminal device is a first user or a second user.

[0103] Digital signal processors (DSPs) are used to process digital signals. Besides digital image signals, they can also process other digital signals. For example, when a terminal device selects a frequency, a DSP can perform a Fourier transform on the frequency energy.

[0104] Video codecs are used to compress or decompress digital video. Terminal devices can support one or more video codecs. This allows the terminal device to play or record video in various encoding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0105] NPU stands for Neural Network (NN) Computing Processor. By borrowing the structure of biological neural networks, such as the transmission patterns between neurons in the human brain, it can rapidly process input information and continuously learn on its own. NPUs enable intelligent cognitive applications in terminal devices, such as image recognition, facial recognition, speech recognition, and text understanding.

[0106] The external storage interface 220 can be used to connect an external storage card, such as a Micro SD card, to expand the storage capacity of the terminal device. The external storage card communicates with the processor 210 through the external storage interface 220 to perform data storage functions. For example, music, video, and other files can be saved on the external storage card.

[0107] Internal memory 221 can be used to store computer executable program code, which includes instructions. Processor 210 executes various functional applications and data processing of the terminal device by running the instructions stored in internal memory 221. For example, in this embodiment, processor 210 can display corresponding content on the display screen 294 in response to user operations by executing instructions stored in internal memory 221. Internal memory 221 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc. The data storage area may store data created during the use of the terminal device (such as audio data, phone book, etc.). In addition, internal memory 221 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0108] The terminal device can implement audio functions such as music playback and recording through an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, a headphone jack 270D, and an application processor.

[0109] Audio module 270 is used to convert digital audio information into analog audio signal output, and also to convert analog audio input into digital audio signal. Audio module 270 can also be used for encoding and decoding audio signals. In some embodiments, audio module 270 can be located in processor 210, or some functional modules of audio module 270 can be located in processor 210. Speaker 270A, also called a "loudspeaker," is used to convert audio electrical signals into sound signals. The terminal device can listen to music or hands-free calls through speaker 270A. Receiver 270B, also called a "handpiece," is used to convert audio electrical signals into sound signals. When the terminal device answers a phone call or voice message, the receiver 270B can be brought close to the user's ear to listen to the voice. Microphone 270C, also called a "microphone" or "voice transducer," is used to convert sound signals into electrical signals. When making a phone call, sending a voice message, or needing to trigger certain functions of the terminal device through a voice assistant, the user can bring their mouth close to microphone 270C to input sound signals into microphone 270C. The terminal device may be equipped with at least one microphone 270C. In some embodiments, the terminal device may be equipped with two microphones 270C, which, in addition to collecting sound signals, can also perform noise reduction. In other embodiments, the terminal device may be equipped with three, four or more microphones 270C, which can collect sound signals, reduce noise, identify the sound source, and perform directional recording, etc.

[0110] The headphone jack 270D is used to connect wired headphones. The headphone jack 270D can be a USB 230 interface or a 3.5mm Open Mobile Terminal Platform (OMTP) standard interface, a CTIA (Cellular Telecommunications Industry Association of the USA) standard interface.

[0111] Pressure sensor 280A is used to sense pressure signals and convert them into electrical signals. In some embodiments, pressure sensor 280A can be disposed on display screen 294. There are many types of pressure sensors 280A, such as resistive pressure sensors, inductive pressure sensors, and capacitive pressure sensors. A capacitive pressure sensor may include at least two parallel plates with conductive material. When force is applied to pressure sensor 280A, the capacitance between the electrodes changes. The terminal device determines the pressure intensity based on the change in capacitance. When a touch operation is applied to display screen 294, the terminal device detects the intensity of the touch operation based on pressure sensor 280A. The terminal device can also calculate the touch position based on the detection signal from pressure sensor 280A. In some embodiments, touch operations applied to the same touch position but with different touch operation intensities can correspond to different operation commands. For example, when a touch operation with an intensity less than the pressure threshold is applied to the SMS application icon, a command to view an SMS is executed. When a touch operation with an intensity greater than or equal to the pressure threshold is applied to the SMS application icon, a command to create a new SMS is executed.

[0112] The gyroscope sensor 280B can be used to determine the motion posture of a terminal device. In some embodiments, the gyroscope sensor 280B can determine the angular velocity of the terminal device around three axes (i.e., the x, y, and z axes). The gyroscope sensor 280B can be used for image stabilization. For example, when the shutter is pressed, the gyroscope sensor 280B detects the angle of the terminal device's shake, calculates the distance the lens module needs to compensate based on the angle, and allows the lens to counteract the shake of the terminal device through reverse movement, thus achieving image stabilization. The gyroscope sensor 280B can also be used in navigation and motion-sensing game scenarios. In addition, the gyroscope sensor 280B can also be used to measure the rotation amplitude or movement distance of the terminal device.

[0113] The barometric pressure sensor 280C is used to measure air pressure. In some embodiments, the terminal device calculates altitude using the air pressure value measured by the barometric pressure sensor 280C to assist in positioning and navigation.

[0114] The magnetic sensor 280D includes a Hall sensor. The terminal device can use the magnetic sensor 280D to detect the opening and closing of the flip cover. In some embodiments, when the terminal device is a flip phone, the terminal device can detect the opening and closing of the flip cover using the magnetic sensor 280D. Then, based on the detected opening and closing state of the cover or the flip cover, features such as automatic flip unlocking can be set.

[0115] The 280E accelerometer sensor can detect the magnitude of acceleration in various directions (typically three axes) of a terminal device. When the terminal device is stationary, it can detect the magnitude and direction of gravity. It can also be used to identify the terminal device's posture, applicable to screen orientation switching, pedometers, and other applications. Additionally, the 280E accelerometer sensor can also be used to measure the terminal device's orientation (i.e., the direction vector of orientation).

[0116] A distance sensor 280F is used to measure distance. The terminal device can measure distance via infrared or laser. In some embodiments, during a shooting scene, the terminal device can utilize the distance sensor 280F to measure distance for rapid focusing.

[0117] The proximity sensor 280G may include, for example, a light-emitting diode (LED) and a light detector, such as a photodiode. The LED may be an infrared LED. The terminal device emits infrared light outward through the LED. The terminal device uses the photodiode to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that an object is near the terminal device. When insufficient reflected light is detected, the terminal device can determine that no object is near the terminal device. The terminal device can use the proximity sensor 280G to detect when a user holds the terminal device close to their ear for a call, so as to automatically turn off the screen to save power. The proximity sensor 280G can also be used in holster mode and pocket mode for automatic unlocking and screen locking.

[0118] The ambient light sensor 280L is used to detect ambient light intensity. The terminal device can adaptively adjust the brightness of the display screen 294 based on the detected ambient light intensity. The ambient light sensor 280L can also be used to automatically adjust the white balance when taking photos. The ambient light sensor 280L can also work with the proximity sensor 280G to detect whether the terminal device is in a pocket, preventing accidental touches.

[0119] The fingerprint sensor 280H is used to collect fingerprints. Terminal devices can use the collected fingerprint characteristics to achieve fingerprint unlocking, app access lock, fingerprint photography, fingerprint answering of calls, etc.

[0120] Temperature sensor 280J is used to detect temperature. In some embodiments, the terminal device uses the temperature detected by temperature sensor 280J to execute a temperature handling strategy. For example, when the temperature reported by temperature sensor 280J exceeds a threshold, the terminal device reduces the performance of the processor located near temperature sensor 280J to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is below another threshold, the terminal device heats battery 242 to prevent abnormal shutdown of the terminal device due to low temperature. In still other embodiments, when the temperature is below yet another threshold, the terminal device boosts the output voltage of battery 242 to prevent abnormal shutdown due to low temperature.

[0121] Touch sensor 280K, also known as a "touch panel," can be located on display screen 294. The touch sensor 280K and display screen 294 together form a touchscreen, also known as a "touchscreen." Touch sensor 280K detects touch operations applied to or near it. The touch sensor can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through display screen 294. In other embodiments, touch sensor 280K may also be located on the surface of the terminal device, in a different position than display screen 294.

[0122] The bone conduction sensor 280M can acquire vibration signals. In some embodiments, the bone conduction sensor 280M can acquire vibration signals from the vibrating bone segments of the human vocal cords. The bone conduction sensor 280M can also contact the human pulse to receive blood pressure signals. In some embodiments, the bone conduction sensor 280M can also be incorporated into headphones to form bone conduction headphones. The audio module 270 can parse the voice signals from the vibrating bone segments of the vocal cords acquired by the bone conduction sensor 280M to realize voice functionality. The application processor can parse heart rate information from the blood pressure signals acquired by the bone conduction sensor 280M to realize heart rate detection functionality.

[0123] The 280N motion sensor can acquire operational data.

[0124] Buttons 290 include a power button, volume buttons, etc. Buttons 290 can be mechanical buttons or touch-sensitive buttons. The terminal device can receive button input and generate key signal inputs related to user settings and function control of the terminal device.

[0125] The terminal device identifies the operations (including the first operation, the second operation, the third operation, etc.) proposed in the embodiments of this application through various sensors in the sensor module 280, buttons 290, and / or cameras 293.

[0126] Motor 291 can generate vibration alerts. Motor 291 can be used for incoming call vibration alerts or for touch vibration feedback. For example, different vibration feedback effects can be corresponding to touch operations applied to different applications (such as taking photos, playing audio, etc.). Motor 291 can also correspond to different vibration feedback effects for touch operations applied to different areas of the display screen 294. Different application scenarios (such as time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also be customized.

[0127] Indicator 292 can be an indicator light, which can be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

[0128] The SIM card interface 295 is used to connect a SIM card. The SIM card can be inserted into or removed from the SIM card interface 295 to achieve contact and separation with the terminal device. The terminal device can support one or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 295 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 295 simultaneously. The multiple cards can be of the same or different types. The SIM card interface 295 is also compatible with different types of SIM cards. The SIM card interface 295 is also compatible with external memory cards. The terminal device interacts with the network through the SIM card to achieve functions such as calls and data communication. In some embodiments, the terminal device uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the terminal device and cannot be separated from the terminal device.

[0129] above Figure 2A The structure of the terminal device has been introduced. The following section will combine... Figure 2B Introduce the structure of cloud devices.

[0130] Figure 2B A schematic diagram of a possible logical structure of a cloud device provided for an embodiment of this application. For example... Figure 2B As shown in the embodiments of this application, the cloud device 20 includes: a processor 201, a communication interface 202, a memory 203, and a bus 204. The processor 201, the communication interface 202, and the memory 203 are interconnected via the bus 204. In the embodiments of this application, the processor 201 is used to control and manage the actions of the cloud device 20; for example, the processor 201 is used to execute the process of determining a learning partner. The communication interface 202 is used to support communication by the cloud device 20; for example, the communication interface 202 can execute the steps of sending and receiving data in the above method embodiments. The memory 203 is used to store the program code and data of the cloud device 20.

[0131] The processor 201 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc. The bus 204 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 2B The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0132] The method for recommending study partners provided in the embodiments of this application is described below. The content executed by the terminal device in this method can be executed by the terminal device itself, or by components of the terminal device (such as a processor, chip, or chip system).

[0133] Figure 3 This is a schematic diagram of an embodiment of the learning companion recommendation method provided in this application.

[0134] like Figure 3 As shown, one embodiment of the learning buddy recommendation method provided in this application includes:

[0135] 301. In response to a user's request for a learning partner for at least one knowledge point in the target learning content, the terminal device sends a learning partner request to the cloud device, and the cloud device receives the learning partner request sent by the terminal.

[0136] The learning buddy request includes identification information for at least one knowledge point, as well as user identification information.

[0137] In this embodiment, a learning partner refers to a person with whom you can study online. The target learning content can be a main heading (e.g., "functions"), and knowledge points can be subheadings under that main heading (e.g., "functions of different types"). The identification information for a knowledge point can be its title, identifier, or name. User identification information can be the user's registered account, mobile phone number, or a unique identifier assigned to the user by the cloud device during registration, as well as indexes recorded by the cloud device during other user registrations that uniquely identify the user.

[0138] 302. The cloud device determines at least one set of learning partners' information based on the identification information of at least one knowledge point and the user identification information.

[0139] At least one set of learning partners' information corresponds one-to-one with the identification information of at least one knowledge point. The learning partner's information may include the learning partner's username and contact information, etc.

[0140] In this embodiment, the correspondence between the knowledge point identification information and the learning partner information can be understood by referring to Table 1. As shown in Table 1:

[0141] Table 1: Correspondence between knowledge points and learning partners

[0142] Knowledge Point A Information on the first group of study buddies Knowledge Point B Information on the second group of study buddies … … Knowledge Point N Information on the Nth group of study partners

[0143] As shown in Table 1, each knowledge point corresponds to a set of learning partners, and the information of each set of learning partners can include information of one or more learning partners. That is to say, learning partners correspond to knowledge points, each knowledge point corresponds to a set of learning partners, and each set of learning partners can include one or more learning partners. In this application, "multiple" includes two or more.

[0144] 303. The cloud device sends at least one set of learning partner information to the terminal device. Correspondingly, the terminal device receives at least one set of learning partner information.

[0145] 304. The terminal device displays identification information for at least one knowledge point and information for at least one set of learning partners.

[0146] In this embodiment, when a user is learning target learning content, they can request a learning partner from a cloud device for the knowledge points within the target learning content. The cloud device can recommend suitable learning partners to the user based on the knowledge points, thereby improving the accuracy of learning partner recommendations. Users can learn together with suitable learning partners for specific knowledge points, which helps improve learning efficiency and achieve better learning results.

[0147] In this embodiment, each group of learning partners may include at least one of a mentoring learning partner and a companion learning partner. The number of mentoring learning partners can be one or more, and the number of companion learning partners can also be one or more. In other words, the learning partners recommended by the cloud device not only include companion learning partners who can accompany the user in learning and exchange learning content, but also mentoring learning partners who can provide assistance and guidance. This considers the user's needs from multiple perspectives and improves the comprehensiveness of the learning partner recommendations.

[0148] In this embodiment of the application, the first group of learning partners includes at least one of the guiding learning partners and the accompanying learning partners. The first group of learning partners is any one of the at least one group of learning partners, and the first group of learning partners corresponds to a first knowledge point among at least one knowledge point. Among them, the guiding learning partner is a learning partner who has the ability to guide the user on the first knowledge point; the accompanying learning partner is a learning partner who has the ability to accompany the user on the first knowledge point.

[0149] The mentoring partner for the first knowledge point is selected by the cloud device from the candidate set of mentoring partners for the first knowledge point, and the accompanying partner for the first knowledge point is selected by the cloud device from the candidate set of accompanying partners for the first knowledge point. In this embodiment of the application, each knowledge point may have one candidate set of mentoring partners and one candidate set of accompanying partners. Alternatively, the candidate set of mentoring partners may include multiple knowledge points, and each knowledge point may have a subset, which is the candidate set of mentoring partners for the corresponding knowledge point. Similarly, the candidate set of accompanying partners may include multiple knowledge points, and each knowledge point may have a subset, which is the candidate set of accompanying partners for the corresponding knowledge point.

[0150] The process of obtaining the candidate sets of mentoring and companion learning partners is described below with reference to the accompanying diagrams.

[0151] like Figure 4A As shown, taking a registered user X as an example, user X can start learning by clicking the "Education Center" application 401 on the phone's main screen. After user X clicks "Education Center," the phone can display the following... Figure 4B The login screen shown above displays the following message: After the user enters their login password, the phone will then display... Figure 4C The learning interface shown. In actual use, it may not be necessary to enter a login password every time; it can be accessed from, for example... Figure 4A The interface shown will take you directly to... Figure 4C The learning interface shown is illustrated. It should be noted that, in this embodiment, the learning interface is not limited to... Figure 4A The main interface shown requires several steps to access. Figure 4C The learning interface shown is not limited to any particular form of presentation. Figure 4C The example shown is just one example; all other interfaces that can provide users with a learning entry point belong to the learning interfaces in this application.

[0152] Before a user begins learning the target learning content, the solution may further include: the terminal device sending an assessment request for the target learning content to the cloud device, the assessment request containing identification information of the target learning content and user identification information; the cloud device sending assessment content to the terminal device based on the identification information of the target learning content; the terminal device sending the user's responses to the assessment content to the cloud device; and the cloud device determining the user's level of mastery of each knowledge point in the target learning content based on the user's responses to the assessment content. The terminal device receives information from the cloud device regarding the user's level of mastery of each knowledge point in the target learning content, sent based on the responses. This allows users to more effectively target their learning of knowledge points where their understanding is weak.

[0153] Figure 4C In the learning interface shown, taking the "functions" course that user X wants to learn as an example, the system can first diagnose user X's level of understanding of functions. User X can click... Figure 4C The "Diagnose Mastery Level" button 402 is shown in the image. After user X clicks the "Diagnose Mastery Level" button 402, the phone will send an assessment request to the cloud device. This assessment request may include the identification information of the target learning content, which in this example is the identification information of the function, as well as user X's user identification information.

[0154] After receiving a function assessment request from user X, the cloud device can send the assessment content to user X's mobile phone. It can then diagnose the user's responses to the assessment content returned by the phone. Furthermore, the cloud device can also analyze user X's historical learning records for the function, providing a diagnostic result for the level of mastery of each knowledge point within the function. For example... Figure 4D As shown, this function includes multiple knowledge points, such as knowledge point A, knowledge point B, knowledge point C, knowledge point D, and knowledge point E. User X's level of mastery of each knowledge point can be understood by referring to Table 2 below:

[0155] Table 2: Correspondence between knowledge points and the degree of mastery of knowledge points

[0156] Knowledge Point A 0.32 Knowledge Point B 0.47 Knowledge Point C 0.88 Knowledge Point D 0.92 Knowledge Point E 0.63 … …

[0157] After the cloud device determines the numerical value of user X's mastery of each knowledge point in the function, it can send the assessment results to user X's mobile phone, such as... Figure 4E As shown, after the mobile phone receives the assessment results, it will display the numerical value of user X's mastery of each knowledge point.

[0158] As shown in Table 2, the mastery level of each knowledge point is represented by a normalized value. In fact, there are many ways to represent the mastery level of knowledge points, such as a percentage system, or using "poor", "average", "pass", "good", "excellent" to represent different mastery levels.

[0159] In this embodiment, the user's mastery of knowledge points is illustrated in Table 2. When dividing users into candidate sets in the cloud device, a threshold for the mastery of each knowledge point is set. This threshold can be the same or different for each knowledge point. In this embodiment, a threshold of 0.8 is used as an example. Comparing the mastery values ​​of each knowledge point with this threshold of 0.8 reveals that user X's mastery of knowledge points A, B, and E is all below 0.8. Therefore, for these three knowledge points, user X can only be a companion / study partner. However, user X's mastery of knowledge points C and D exceeds 0.8, so for knowledge points C and D, user X can be a mentor / study partner.

[0160] The cloud device determines the candidate set to which user X belongs for each knowledge point of the function, and can directly add user X to the candidate set of mentoring or companion learning partners for each knowledge point. Considering that different users may have different preferences, the cloud device usually sends a query message to the terminal device, and only adds user X to the candidate set of mentoring or companion learning partners for each knowledge point after obtaining user X's consent. Users can receive rewards provided by the education center when acting as mentoring or companion learning partners, such as learning beans or points. The specific form of the reward is not limited in this embodiment. Taking learning beans as an example, a user receives some learning beans for each time they accompany another user in learning, and some learning beans for each time they guide another user. After accumulating a certain number of learning beans, they can redeem gifts or paid learning courses, etc.

[0161] In this embodiment of the application, the process of inquiring whether the user is willing to become a mentor-type learning partner may include: a cloud device sending a first inquiry message to a terminal device, the first inquiry message being used to inquire whether the user is willing to become a mentor-type learning partner for knowledge points in the target learning content where their mastery level is greater than a first threshold; the terminal device responding to the user's reply to the first inquiry message sending a reply message to the cloud device. If the reply message to the first inquiry message indicates willingness to become a mentor-type learning partner, the cloud device will classify the user into a mentor-type learning partner candidate set. This allows for the recommendation of more responsible mentor-type learning partners, which is beneficial for the user to achieve better learning results.

[0162] In this embodiment of the application, the process of inquiring whether the user is willing to become a learning companion may include: a second inquiry message sent by the cloud device to the terminal device, the second inquiry message being used to inquire whether the user is willing to become a learning companion for knowledge points in the target learning content where their mastery level is less than a first threshold; the terminal device responding to the user's reply to the second inquiry message by sending a reply message to the cloud device. If the reply message to the second inquiry message indicates willingness to become a learning companion, the cloud device will classify the user into a candidate set of learning companions. This allows for the recommendation of more responsible learning companions, which is beneficial for the user to achieve better learning outcomes.

[0163] like Figure 4F As shown, for knowledge points A, B, and E, the cloud device will send an inquiry message to the terminal device. This inquiry message asks user X whether they are willing to become a companion learning partner for knowledge points A, B, and E. The specific form of the content on the interface is not limited in this application. If user X clicks the "Agree" button, the cloud device will then... Figure 4D As shown in the figure, for knowledge points A, B, and E, user X is added to the corresponding candidate set of companion learning partners.

[0164] like Figure 4G As shown, for knowledge points C and D, the cloud device will send an inquiry message to the terminal device. This inquiry message asks user X whether they are willing to become a mentoring learning partner for knowledge points C and D. The specific form of the content on the interface is not limited in this application. If user X clicks the "Agree" button, the cloud device will then... Figure 4D As shown in the figure, for knowledge points C and D, user X is added to the corresponding mentoring buddy candidate set.

[0165] The above explanation uses the example of user X joining the candidate sets of companion and mentor learning partners for each knowledge point of a function. Any other user can execute the process performed by user X before learning the function, thus ensuring a sufficient number of mentor and companion learning partners for each knowledge point. Furthermore, the cloud device can diagnose the learning content completed by user X and add user X to the candidate sets of companion and mentor learning partners for each knowledge point based on the diagnostic results. After user X or another user sends a learning partner request for a specific knowledge point, the cloud device will determine suitable mentor and companion learning partners for user X.

[0166] In this embodiment, the cloud device can build a student feature database for each user. This allows for the selection of study partners based on features from the student feature database when recommending learning partners, further improving the accuracy of the recommendations. The following section will illustrate this further. Figure 5 This section describes the process of collecting user characteristics.

[0167] like Figure 5 As shown, taking user Y as an example, the cloud device collects user Y's personal attribute information, historical learning data, and learning progress data. Personal attribute information may include user Y's grade level, region, and the publisher of the textbooks used. Historical learning data may include past class attendance data and test-taking data. Learning progress data may include learning interests and learning styles.

[0168] Personal attribute information can be such as user registration information. Figure 4A The "Education Center" App shown above is where user Y fills in the information during registration. Figure 6A The information shown indicates that after user Y fills in all the registration information and clicks the "Submit" button, the mobile phone will send this personal attribute information to the cloud device.

[0169] Historical learning data can be historical learning data collected from the network by cloud devices based on user Y's personal attribute information from other applications, or it can be based on user Y's learning records after using the "Education Center" App.

[0170] Learning interests and styles can be derived from cloud-based analysis of user Y's historical learning records, such as study duration and preferred subjects. These characteristics can also be continuously updated based on user Y's learning records after using the "Education Center" App.

[0171] In this embodiment, each user can take an assessment when learning a specific learning content. For example, if user Y chooses to learn about "functions," the cloud device will assess user Y's performance on the "functions" learning content. The assessment process can be found in the previous section. Figures 4A to 4G To understand the process.

[0172] In this embodiment of the application, after the cloud device assesses user Y's learning content on "function", the assessment result is as follows: Figure 6B As shown, user Y has a poor grasp of knowledge point A, with a mastery score of only 0.2. Taking knowledge point A as a linear function as an example, after user Y clicks "Start Learning," the following will occur: Figure 6C On the learning page shown, if user Y clicks the "Find a Study Partner" button, the phone will send a study partner request to the cloud device. The study partner request will contain user Y's user identification information and the identification information of "Knowledge Point A".

[0173] After receiving a learning partner request from user Y via mobile phone for knowledge point A, the cloud device will assign a mentoring partner and a companion partner for user Y. The mentoring partner is determined by the cloud device based on a first difference and a first learning feature similarity. The first difference is the difference between the mentor's mastery of the first knowledge point and the user's mastery of the first knowledge point; the mentor is from the candidate set of mentoring partners for the first knowledge point; the first learning feature similarity is the similarity between the mentor's and the user's learning features; and the mentor's mastery of the first knowledge point is higher than a first threshold. The companion partner is determined by the cloud device based on the absolute value of a second difference and a second learning feature similarity. The second difference is the difference between the second mentor's and the user's mastery of the first knowledge point; the companion is from the candidate set of companion partners for the first knowledge point; the second learning feature similarity is the similarity between the second mentor's and the user's learning features; and the companion's mastery of the first knowledge point is lower than a first threshold.

[0174] The process of using cloud devices to determine mentoring and companionship partners for knowledge point A can be understood by referring to Figure 7.

[0175] like Figure 7 As shown, when a cloud device selects a mentoring learning partner for user Y for knowledge point A, it calculates the gap in the user's mastery of the knowledge point and the similarity of learning features.

[0176] The difference in the degree of mastery of knowledge points is calculated as the difference between the degree of mastery of knowledge point A by users in the candidate set of the teaching partner and the degree of mastery of knowledge point A by user Y.

[0177] For example, in the candidate set of mentoring study partners, user Z's mastery of knowledge point A is 0.85, and user Y's mastery of knowledge point A is 0.2. Then the difference between the two is 0.85 - 0.2 = 0.65.

[0178] Calculating learning feature similarity means calculating the similarity between users in the candidate set of mentoring learning partners and user Y in terms of personal attribute information, historical learning data, and learning situation data, based on the student feature database.

[0179] Similarly, when cloud devices select a learning companion for user Y for knowledge point A, they also calculate the gap in the level of knowledge mastery and the similarity of learning features.

[0180] The difference lies in the approach: when choosing a mentoring study partner, the user Y will be selected based on a significant difference in their understanding of knowledge point A, while when choosing a companion study partner, the user Y will be selected based on a smaller difference in their understanding of knowledge point A. Both mentoring and companion study partners will strive to select partners with high similarity in learning characteristics for the user Y.

[0181] In other words, when recommending learning partners, cloud devices will prioritize partners with similar learning characteristics to user Y (whose mastery of knowledge points differs significantly) for mentoring-type learning partners, while prioritizing partners with similar learning characteristics to user Y (whose mastery of knowledge points differs significantly). This approach helps users achieve better learning outcomes.

[0182] Cloud device execution Figure 7 The process shown allows us to designate user Z as user Y's mentoring study partner and user X as user Y's companion study partner. This designation process will be explained further below.

[0183] In this embodiment, users Z, Y, and X have mastered 85%, 20%, and 25% of the knowledge point A, respectively. For knowledge point A, user Z belongs to the mentoring-type learning partner candidate set, while users X and Y both belong to the companion-type learning partner candidate set.

[0184] In this embodiment of the application, the candidate set of mentoring study partners can be represented by T (teachers), and the candidate set of accompanying study partners can be represented by S (students).

[0185] The process of calculating the mentoring buddy for user Y can be as follows:

[0186] 1. Calculate the knowledge gap between each user (e.g., user Z) and user Y in T.

[0187] For example: master_gap(user Y, user Z) = 0.85 - 0.2 = 0.65;

[0188] 2. Calculate the similarity between each user in T (e.g., user Z) and user Y in terms of learning features (e.g., personal attributes, learning history, learning data).

[0189] For example: sim(user Y, user Z) = 0.8;

[0190] 3. The final matching score is obtained by weighting the gap in knowledge mastery and the similarity of learning characteristics.

[0191] For example: match_score(user Y, user Z) = α * master_gap + β * sim;

[0192] Here, α and β are weights used to adjust the emphasis on the difference in knowledge mastery between recommended learning partners and the similarity of learning features.

[0193] Then, using the calculation process described above for users Z and Y, the final matching score between each user in T and user Y can be calculated. The user with the highest matching score is then selected as user Y's mentor. If user Z has the highest matching score, then user Z is selected as user Y's mentor.

[0194] The process of calculating user Y's companion learning partner can be as follows:

[0195] 1. Calculate the absolute value of the difference in knowledge mastery between each user (e.g., user X) and user Y in S.

[0196] For example: master_gap(user Y, user X) = |0.2 - 0.25| = 0.05;

[0197] 2. Calculate the similarity between each user (e.g., user X) and user Y in S on learning features (e.g., personal attributes, learning history, learning data).

[0198] For example: sim(user Y, user X) = 0.9;

[0199] 3. The final matching score is obtained by weighting the absolute value of the gap in knowledge mastery with the similarity of learning features.

[0200] For example: match_score(user Y, user X) = γ*(1-master_gap) + β*sim;

[0201] Here, γ and β are weights used to adjust the emphasis on the gap in knowledge point mastery between recommended learning partners and the similarity of learning features. Unlike recommended mentoring learning partners, the gap in knowledge point mastery between companion learning partners should be as small as possible; therefore, the knowledge point gap should be taken as (1-master_gap).

[0202] Then, using the calculation process described above for users X and Y, the final matching score between each user in S and user Y can be calculated. The user with the highest matching score is then selected as user Y's study buddy. If user X has the highest matching score, then user X is selected as user Y's study buddy.

[0203] It should be noted that, in this embodiment of the application, the recommendation of mentoring and companion learning partners is not limited to only one. Multiple mentoring and companion learning partners can be recommended to user Y according to the final matching score, and user Y can choose.

[0204] After the cloud device identifies a mentoring buddy and a companion buddy for user Y, it sends information about these buddies to the user's mobile phone. The mobile phone receives and displays this information; the content displayed on the phone can be found in the attached image. Figure 8A To understand.

[0205] like Figure 8A As shown, on User Y's study buddy recommendation interface, User Z is ranked first among the mentoring study buddies. If User Y wants to select other mentoring study buddies, he can click "More" in the mentoring study buddy section, and information about other mentoring study buddies will be displayed on his phone.

[0206] Similarly, if user X is ranked first among the companion learning partners, and wants to select other companion learning partners, user Y can click "More" in the companion learning partners section, and information about other companion learning partners will be displayed on the phone.

[0207] User Y can use various methods such as email, telephone, online voice or video to communicate with companion or mentor learning partners online.

[0208] When user Z acts as user Y's mentor, user Z's mobile phone interface can be viewed as follows: Figure 8B To understand, such as Figure 8B As shown, user Z can be tutoring other users like user Y and user X for knowledge point A. The phone interface can also display tutoring achievements for knowledge point A, such as... Figure 8B In the example, user Z previously provided guidance to users A and B, among others.

[0209] When user X acts as a study companion for user Y, user X's mobile phone interface can be viewed as follows. Figure 8C To understand, such as Figure 8C As shown, user X can have user Y as a study partner accompanying user X on knowledge point A.

[0210] It should be noted that the companionship-type study partners are sometimes not symmetrical: for user Y, the most suitable companionship-type study partner is user X; but for user X, the most suitable study partner is not always user Y. In this case, user Y will not appear first in user X's companionship-type study partners, but will appear in more of them.

[0211] The above describes a learning partner recommendation scheme for a single knowledge point. In reality, for multiple unmastered knowledge points involved in the user's selected target learning content, the cloud device can first plan a learning path.

[0212] This process can be found in [reference]. Figure 5 The learning path planning in the text is divided into 40 parts for understanding.

[0213] like Figure 5As shown in section 40 of the learning path planning, for multiple knowledge points that user Y has not mastered, the cloud device will plan learning paths for these unmastered knowledge points (i.e., knowledge points whose mastery level is below a first threshold). This learning path planning determines the learning order of these multiple unmastered knowledge points to generate a knowledge point learning path. The knowledge point learning path includes multiple knowledge points arranged in a recommended learning order, where these knowledge points are those whose user mastery level is below the first threshold within the target learning content. Then, the cloud device sends the knowledge point learning path to the terminal device. This allows the user to learn according to the order of the knowledge points in the learning path, which is beneficial for the user to obtain better learning results.

[0214] After receiving the knowledge point learning path, the terminal device can send a request for a learning partner sequence to the cloud device. The cloud device then determines a learning partner sequence for this request and sends it to the terminal device. The learning partner sequence includes information on multiple sets of learning partners arranged in the order of the knowledge points in the learning path. This eliminates the need for the user to send a learning partner request for each knowledge point separately, thus improving the efficiency of learning partner recommendation.

[0215] like Figure 9A As shown, taking user Y as an example, after user Y assesses the learning content of "functions," it is determined that the knowledge points it has not mastered include linear functions, inverse proportional functions, trigonometric functions, and comprehensive applications of functions. The cloud device can then determine the learning order for these knowledge points that user Y has not mastered, such as... Figure 9A As shown, the cloud device determines the following learning path for user Y: linear function → inverse proportional function → trigonometric function → comprehensive application of functions. This learning path is then sent to the corresponding terminal device.

[0216] The learning path for this knowledge point will be displayed on user Y's terminal device, such as... Figure 9B As shown, if a user starts learning according to the learning path of this knowledge point and clicks the "Study Buddy Sequence Request" button, the terminal device will send a study buddy request to the cloud device. The request may include the identifier of the learning path of this knowledge point and the information of user Y. After receiving the study buddy sequence request, the cloud device will determine the corresponding study buddy sequence for the learning path of this knowledge point.

[0217] The process by which cloud devices determine the appropriate learning partner sequence for this knowledge point's learning path can be found in [reference needed]. Figure 9C To understand, such as Figure 9C As shown, cloud devices can assign corresponding mentoring and companion learning partners to user Y for each knowledge point individually, or they can assign these partners in parallel for each knowledge point. The principles behind assigning mentoring and companion learning partners can be found in the previous sections. Figure 7The description in part explains the principles behind determining mentoring and companionship learning partners based on knowledge point A (linear function). This will not be repeated here.

[0218] The cloud device provides a guide-type learning partner sequence and a companion-type learning partner sequence for each knowledge point along the learning path. (See also...) Figure 9D To understand. For example Figure 9D As shown, the mentoring buddy sequence determined for user Y is user Z→user W→user P→user Q, and the companion buddy sequence determined for user Y is user X→user B→user C→user D.

[0219] Cloud devices will Figure 9D The guided learning partner sequence and the accompanying learning partner sequence shown are sent to user Y's terminal device. This terminal device will display the learning partner sequence corresponding to the knowledge point learning path. This learning partner sequence can include both guided and accompanying learning partner sequences. The content displayed on the terminal can be found in [reference needed]. Figure 9E To understand.

[0220] like Figure 9E As shown, in the mentored learning buddy sequence, the mentored learning buddy for linear functions is user Z, for inverse proportional functions it is user W, for trigonometric functions it is user P, and for comprehensive function applications it is user Q. In the companion learning buddy sequence, the companion learning buddy for linear functions is user X, for inverse proportional functions it is user B, for trigonometric functions it is user C, and for comprehensive function applications it is user D.

[0221] When learning various knowledge points, User Y can communicate with corresponding mentoring and supportive learning partners. It should be noted that, although... Figure 9D and Figure 9E In the previous example, only one mentoring buddy and one companion buddy were shown for each knowledge point, but this application does not limit the number of mentoring buddies and companion buddies for each knowledge point.

[0222] The above introduces Figure 5 The document includes several sections: User Feature Collection (10), Ability Level Diagnosis (20), User Role Transformation (30), Learning Path Planning (40), and Learning Partner Sequence Recommendation (50). Please refer to the relevant documentation. Figure 5 This application embodiment also provides a real-time tracking solution for users. In this application embodiment, the cloud device will update the learning characteristics of each user in real time, and will also update the user's mastery of knowledge points in real time based on the user's learning results. For example, the terminal device sends the user's learning content for at least one knowledge point to the cloud device, and the learning content for at least one knowledge point is used by the cloud device to update the user's mastery of at least one knowledge point.

[0223] In addition, in this embodiment of the application, if a user’s previously unmastered knowledge point has become a well-mastered knowledge point through real-time tracking, then the user’s role can be changed, that is, for that knowledge point, the user can be transferred from the companion learning partner candidate set to the guidance learning partner candidate set.

[0224] The above describes the method for recommending study partners. The following describes the terminal device and cloud device provided in the embodiments of this application with reference to the accompanying drawings.

[0225] like Figure 10 As shown, one embodiment of the terminal device 100 provided in this application includes:

[0226] The sending unit 1001 is used to send a learning partner request to the cloud device in response to a user's learning partner request operation for at least one knowledge point in the target learning content. The learning partner request includes identification information of at least one knowledge point and user identification information.

[0227] The receiving unit 1002 is used to receive at least one set of learning partner information sent by the cloud device based on the identification information of at least one knowledge point and the user identification information, wherein the information of at least one set of learning partners corresponds one-to-one with the identification information of at least one knowledge point.

[0228] Display unit 1003 is used to display identification information of at least one knowledge point and information of at least one set of learning partners.

[0229] In this embodiment, when a user is learning target learning content, they can request a learning partner from a cloud device for the knowledge points within the target learning content. The cloud device can recommend suitable learning partners to the user based on the knowledge points, thereby improving the accuracy of learning partner recommendations. Users can learn together with suitable learning partners for specific knowledge points, which helps improve learning efficiency and achieve better learning results.

[0230] Optionally, the first group of learning partners includes at least one of the guiding learning partners and the accompanying learning partners. The first group of learning partners is any one of the at least one group of learning partners, and the first group of learning partners corresponds to the first knowledge point in at least one knowledge point. Among them, the guiding learning partner is the learning partner who has the ability to guide the user on the first knowledge point; the accompanying learning partner is the learning partner who has the ability to accompany the user on the first knowledge point.

[0231] Optionally, the guided learning partner is determined by the cloud device based on a first difference and a first learning feature similarity. The first difference is the difference between the first learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The first learning partner is a learning partner in the candidate set of guided learning partners for the first knowledge point. The first learning feature similarity is the similarity of the learning features between the first learning partner and the user. The first learning partner's mastery of the first knowledge point is higher than a first threshold.

[0232] Optionally, the companion learning partner is determined by the cloud device based on the absolute value of the second difference and the second learning feature similarity. The second difference is the difference between the second learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The second learning partner is a learning partner in the candidate set of companion learning partners for the first knowledge point. The second learning feature similarity is the similarity of the learning features between the second learning partner and the user. The second learning partner's mastery of the first knowledge point is lower than the first threshold.

[0233] Optionally, the receiving unit 1002 is also used to receive a knowledge point learning path sent by the cloud device. The knowledge point learning path includes multiple knowledge points arranged in the recommended learning order, and the multiple knowledge points are knowledge points in the target learning content whose user mastery level is less than a first threshold.

[0234] Optionally, the receiving unit 1002 is specifically used to receive a learning partner sequence sent by the cloud device when the learning partner request is a learning partner sequence request for a knowledge point learning path. The learning partner sequence includes information on multiple groups of learning partners arranged in the order of knowledge points in the knowledge point learning path.

[0235] Optionally, the sending unit 1001 is further configured to send an evaluation request for the target learning content to the cloud device. The evaluation request includes the identification information of the target learning content and the user identification information.

[0236] The receiving unit 1002 is also used to receive the evaluation content sent by the cloud device based on the identification information of the target learning content.

[0237] The sending unit 1001 is also used to send the user's answer information for the evaluation content to the cloud device.

[0238] The receiving unit 1002 is also used to receive information from the cloud device regarding the user's mastery of each knowledge point in the target learning content, sent based on the answer information.

[0239] Optionally, the receiving unit 1002 is also used to receive a first inquiry message sent by the cloud device. The first inquiry message is used to ask the user whether they are willing to become a mentoring learning partner for knowledge points in the target learning content where their mastery level is greater than a first threshold.

[0240] The sending unit 1001 is also used to send a response message to the cloud device in response to the user's reply to the first query message.

[0241] Optionally, the receiving unit 1002 is also used to receive a second inquiry message sent by the cloud device. The second inquiry message is used to ask the user whether they are willing to become a companion learning partner for knowledge points in the target learning content where their mastery level is less than a first threshold.

[0242] The sending unit 1001 is also used to send a response message to the cloud device in response to the user's reply to the second query message.

[0243] Optionally, the sending unit 1001 is also used to send the user's learning content for at least one knowledge point to the cloud device, and the learning content for at least one knowledge point is used by the cloud device to update the user's mastery of at least one knowledge point.

[0244] In this embodiment, the operations performed by each unit in the terminal device are the same as those described above. Figures 3 to 9E The embodiments shown are similar and will not be repeated here.

[0245] like Figure 11 As shown, one embodiment of the cloud device 110 provided in this application includes:

[0246] The receiving unit 1101 is used to receive a learning buddy request sent by the terminal device. The learning buddy request includes the user's identification information for at least one knowledge point in the target learning content, as well as the user's identification information.

[0247] The processing unit 1102 is used to determine at least one set of learning partners for a user based on the identification information of at least one knowledge point and the user identification information, wherein the information of the at least one set of learning partners corresponds one-to-one with the identification information of at least one knowledge point.

[0248] The sending unit 1103 is used to send at least one set of learning partner information to the terminal device.

[0249] In this embodiment, when a user is learning target learning content, they can request a learning partner from a cloud device for the knowledge points within the target learning content. The cloud device can recommend suitable learning partners to the user based on the knowledge points, thereby improving the accuracy of learning partner recommendations. Users can learn together with suitable learning partners for specific knowledge points, which helps improve learning efficiency and achieve better learning results.

[0250] Optionally, the first group of learning partners includes at least one of the guiding learning partners and the accompanying learning partners. The first group of learning partners is any one of the at least one group of learning partners, and the first group of learning partners corresponds to the first knowledge point in at least one knowledge point. Among them, the guiding learning partner is the learning partner who has the ability to guide the user on the first knowledge point; the accompanying learning partner is the learning partner who has the ability to accompany the user on the first knowledge point.

[0251] Optionally, the companion learning partner is determined by the cloud device based on the absolute value of the second difference and the second learning feature similarity. The second difference is the difference between the second learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The second learning partner is a learning partner in the candidate set of companion learning partners for the first knowledge point. The second learning feature similarity is the similarity of the learning features between the second learning partner and the user. The second learning partner's mastery of the first knowledge point is lower than the first threshold.

[0252] Optionally, the processing unit 1102 is further configured to identify multiple knowledge points in the target learning content whose user mastery level is less than a first threshold, and to determine the recommended learning order of the multiple knowledge points in order to generate a knowledge point learning path.

[0253] The sending unit 1103 is also used to send the knowledge point learning path to the terminal device.

[0254] Optionally, the processing unit 1102 is specifically used to determine a learning partner sequence for a knowledge point learning path when the learning partner request is a learning partner sequence request for a knowledge point learning path. The learning partner sequence includes information on multiple sets of learning partners arranged in the order of knowledge points in the knowledge point learning path.

[0255] Optionally, the receiving unit 1101 is further configured to receive an assessment request sent by the terminal device, the assessment request including identification information of the target learning content and user identification information. The sending unit 1103 is further configured to send assessment content to the terminal device according to the identification information of the target learning content. The receiving unit 1101 is further configured to receive user response information input by the user regarding the assessment content sent by the terminal device; the cloud device determines the user's level of mastery of each knowledge point in the target learning content based on the response information.

[0256] The sending unit 1103 is also used to send information to the terminal device about the user's mastery of each knowledge point in the target learning content.

[0257] Optionally, the sending unit 1103 is further configured to send a first inquiry message to the terminal device. The first inquiry message is used to ask the user whether they are willing to become a mentoring learning partner for knowledge points in the target learning content where their mastery level is greater than a first threshold.

[0258] The receiving unit 1101 is also used to receive the response information from the terminal device in response to the first query information.

[0259] The processing unit 1102 is further configured to add the user identification information to the candidate set of mentoring learning partners for knowledge points whose mastery level is greater than the first threshold if the response information to the first inquiry is agreement to serve as a mentoring learning partner.

[0260] Optionally, the sending unit 1103 is further configured to send a second query message to the terminal device, the second query message being used to ask the user whether they are willing to become a companion learning partner for knowledge points in the target learning content where their mastery level is less than a first threshold.

[0261] The receiving unit 1101 is also used to receive the response information from the terminal device in response to the second query information.

[0262] The processing unit 1102 is further configured to add the user identification information to the candidate set of companion learning points whose mastery level is less than the first threshold if the response information to the second inquiry is agreement to act as a companion learning partner.

[0263] Optionally, the receiving unit 1101 is also configured to receive learning content sent by the terminal device for at least one knowledge point.

[0264] The processing unit 1102 is also used to update the user's mastery of at least one knowledge point based on the learning content of at least one knowledge point.

[0265] In this embodiment, the operations performed by each unit in the terminal device are the same as those described above. Figures 3 to 9E The embodiments shown are similar and will not be repeated here.

[0266] In another embodiment of this application, a computer-readable storage medium is also provided, which stores computer-executable instructions. When the processor of the terminal executes the computer-executable instructions, the terminal device performs the above-mentioned... Figures 3 to 9E The steps performed by the terminal device.

[0267] In another embodiment of this application, a computer-readable storage medium is also provided, which stores computer-executable instructions. When the processor of the terminal executes the computer-executable instructions, the cloud device performs the aforementioned actions. Figures 3 to 9E The steps performed by the cloud computing device.

[0268] In another embodiment of this application, a computer program product is also provided, which includes computer program code. When the computer program code is executed on a computer, the computer device performs the above-described... Figures 3 to 9E The steps performed by the terminal.

[0269] In another embodiment of this application, a chip system is also provided, the chip system including one or more interface circuits and one or more processors; the interface circuits and processors are interconnected via lines; the interface circuits are used to receive signals from the terminal's memory and send signals to the processors, the signals including computer instructions stored in the memory; when the processor executes the computer instructions, the terminal performs the aforementioned actions. Figures 3 to 9EThe steps performed by the terminal device. In one possible design, the chip system may also include a memory for storing program instructions and data necessary for controlling the device. This chip system may consist of chips or may include chips and other discrete components.

[0270] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and 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 through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0271] 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.

[0272] Furthermore, 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. The integrated units described above can be implemented wholly or partially through software, hardware, firmware, or any combination thereof.

[0273] When the integrated unit is implemented using software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

Claims

1. A method for recommending study partners, characterized in that, include: In response to a user's request for a learning partner for at least one knowledge point in the target learning content, the terminal device sends a learning partner request to the cloud device. The learning partner request includes the identification information of the at least one knowledge point and the user identification information. The terminal device receives at least one set of learning companion information sent by the cloud device based on the identification information of the at least one knowledge point and the user identification information. The information of the at least one set of learning companions corresponds one-to-one with the identification information of the at least one knowledge point. The at least one set of learning companions includes a first set of learning companions, which includes a guidance learning companion and a companion learning companion. The first set of learning companions corresponds to a first knowledge point among the at least one knowledge point. The guidance learning companion is a learning companion who has the ability to guide the user on the first knowledge point. The companion learning companion is a learning companion who has the ability to accompany the user on the first knowledge point. If the companion learning companion's mastery of the first knowledge point is higher than a first threshold, it can be transformed into the guidance learning companion. The terminal device displays the identification information of the at least one knowledge point and the information of the at least one group of learning partners.

2. The method according to claim 1, characterized in that, The mentoring learning partner is determined by the cloud device based on a first difference and a first learning feature similarity. The first difference is the difference between the first learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The first learning partner is a learning partner in the mentoring learning partner candidate set for the first knowledge point. The first learning feature similarity is the similarity of the learning features between the first learning partner and the user. The first learning partner's mastery of the first knowledge point is higher than the first threshold.

3. The method according to claim 1, characterized in that, The accompanying learning partner is determined by the cloud device based on the absolute value of a second difference and a second learning feature similarity. The second difference is the difference between the second learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The second learning partner is a learning partner in the candidate set of accompanying learning partners for the first knowledge point. The second learning feature similarity is the similarity of the learning features between the second learning partner and the user. The second learning partner's mastery of the first knowledge point is lower than the first threshold.

4. The method according to any one of claims 1-3, characterized in that, The method further includes: The terminal device receives a knowledge point learning path sent by the cloud device. The knowledge point learning path includes multiple knowledge points arranged in a recommended learning order. The multiple knowledge points are knowledge points in the target learning content whose user mastery level is less than a first threshold.

5. The method according to claim 4, characterized in that, When the learning buddy request is a learning buddy sequence request for the learning path of the knowledge point, the terminal device receives at least one set of learning buddy information sent by the cloud device based on the identification information of the at least one knowledge point and the user identification information, specifically including: The terminal device receives a learning partner sequence sent by the cloud device. The learning partner sequence includes information on multiple groups of learning partners arranged in the order of knowledge points in the knowledge point learning path.

6. The method according to any one of claims 1-3, characterized in that, Before the terminal device responds to a user's request for a learning partner for at least one knowledge point in the target learning content, the method further includes: The terminal device sends an evaluation request for the target learning content to the cloud device. The evaluation request includes the identification information of the target learning content and the user identification information. The terminal device receives the evaluation content sent by the cloud device based on the identification information of the target learning content; The terminal device sends the user's response information to the assessment content to the cloud device; The terminal device receives information from the cloud device, based on the answer information, regarding the user's mastery of each knowledge point in the target learning content.

7. The method according to claim 6, characterized in that, The method further includes: The terminal device receives a first inquiry message sent by the cloud device. The first inquiry message is used to ask the user whether they are willing to become a mentoring learning partner for knowledge points in the target learning content where their mastery level is greater than a first threshold. In response to the user's reply to the first query, the terminal device sends a reply to the cloud device.

8. The method according to claim 6, characterized in that, The method further includes: The terminal device receives a second inquiry message sent by the cloud device. The second inquiry message is used to ask the user whether they are willing to become a companion learning partner for knowledge points in the target learning content where their mastery level is less than a first threshold. In response to the user's reply to the second query, the terminal device sends a reply to the cloud device.

9. The method according to any one of claims 1-3, characterized in that, The method further includes: The terminal device sends the user's learning content for the at least one knowledge point to the cloud device, and the learning content for the at least one knowledge point is used by the cloud device to update the user's mastery of the at least one knowledge point.

10. A method for recommending study partners, characterized in that, include: The cloud device receives a learning companion request sent by the terminal device. The learning companion request includes the user's identification information for at least one knowledge point in the target learning content, as well as the user's identification information. The cloud device determines at least one set of learning partners for the user based on the identification information of the at least one knowledge point and the user identification information. The information of the at least one set of learning partners corresponds one-to-one with the identification information of the at least one knowledge point. The at least one set of learning partners includes a first set of learning partners, which includes a mentoring learning partner and a companion learning partner. The first set of learning partners corresponds to a first knowledge point among the at least one knowledge point. The mentoring learning partner is a learning partner who has the ability to guide the user on the first knowledge point. The companion learning partner is a learning partner who has the ability to accompany the user on the first knowledge point. If the companion learning partner's mastery of the first knowledge point is higher than a first threshold, it can be transformed into a mentoring learning partner. The cloud device sends the information of at least one group of study partners to the terminal device.

11. The method according to claim 10, characterized in that, The mentoring learning partner is determined by the cloud device based on a first difference and a first learning feature similarity. The first difference is the difference between the first learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The first learning partner is a learning partner in the mentoring learning partner candidate set for the first knowledge point. The first learning feature similarity is the similarity of the learning features between the first learning partner and the user. The first learning partner's mastery of the first knowledge point is higher than the first threshold.

12. The method according to claim 10, characterized in that, The accompanying learning partner is determined by the cloud device based on the absolute value of a second difference and a second learning feature similarity. The second difference is the difference between the second learning partner's mastery of the first knowledge point and the user's mastery of the first knowledge point. The second learning partner is a learning partner in the candidate set of accompanying learning partners for the first knowledge point. The second learning feature similarity is the similarity of the learning features between the second learning partner and the user. The second learning partner's mastery of the first knowledge point is lower than the first threshold.

13. The method according to any one of claims 10-12, characterized in that, The method further includes: The cloud device identifies multiple knowledge points in the target learning content whose user mastery level is less than a first threshold, and determines the recommended learning order for the multiple knowledge points to generate a knowledge point learning path. The cloud device sends the knowledge point learning path to the terminal device.

14. The method according to claim 13, characterized in that, When the learning buddy request is a learning buddy sequence request for the learning path of the knowledge point, the cloud device determines at least one set of learning buddy information for the user based on the identification information of the at least one knowledge point and the user identification information, specifically including: The cloud device determines a learning partner sequence for the knowledge point learning path, and the learning partner sequence includes information on multiple groups of learning partners arranged in the order of the knowledge points in the knowledge point learning path.

15. The method according to any one of claims 10-12, characterized in that, Before the cloud device receives the learning buddy request sent by the terminal device, the method further includes: The cloud device receives an evaluation request sent by the terminal device. The evaluation request includes the identification information of the target learning content and the user identification information. The cloud device sends the evaluation content to the terminal device based on the identification information of the target learning content; The cloud device receives the user's response information regarding the assessment content sent by the terminal device; The cloud device determines, based on the answer information, the user's level of mastery of each knowledge point in the target learning content; The cloud device sends information to the terminal device about the user's level of mastery of each knowledge point in the target learning content.

16. The method according to claim 15, characterized in that, The method further includes: The cloud device sends a first query message to the terminal device. The first query message is used to ask the user whether they are willing to become a mentoring learning partner for the knowledge points in the target learning content where their mastery level is greater than a first threshold. The cloud device receives the response information from the terminal device in response to the first query information; If the response to the first inquiry is an agreement to serve as a mentoring learning partner, the cloud device will add the user identification information to the mentoring learning partner candidate set for the knowledge points whose mastery level is greater than the first threshold.

17. The method according to claim 15, characterized in that, The method further includes: The cloud device sends a second query message to the terminal device. The second query message is used to ask the user whether they are willing to become a companion learning partner for knowledge points in the target learning content where their mastery level is less than a first threshold. The cloud device receives the response information from the terminal device in response to the second query information; If the response to the second inquiry is an agreement to serve as a companion learning partner, the cloud device will add the user identification information to the candidate set of companion learning partners for the knowledge points whose mastery level is less than the first threshold.

18. The method according to any one of claims 10-12, characterized in that, The method further includes: The cloud device receives learning content from the terminal device for at least one knowledge point sent by the user. The cloud device updates the user's mastery of the at least one knowledge point based on the learning content of the at least one knowledge point.

19. A terminal device, characterized in that, include: A transceiver, a processor, and a memory, wherein the transceiver and the processor are coupled to the memory, the memory being used to store a program or instructions that, when executed by the processor, cause the terminal device to perform the method as described in any one of claims 1 to 9.

20. A cloud device, characterized in that, include: A communication interface, a processor, and a memory, wherein the communication interface and the processor are coupled to the memory, the memory being used to store programs or instructions that, when executed by the processor, cause the cloud device to perform the method as described in any one of claims 10 to 18.

21. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a terminal device, cause the terminal device to perform the method as described in any one of claims 1 to 9.

22. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a cloud device, cause the cloud device to perform the method as described in any one of claims 10 to 18.

23. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 9.

24. A computer program product, characterized in that, The computer program product includes computer program code that, when run on a computer, causes the computer to perform the method as described in any one of claims 10 to 18.

25. A chip system, characterized in that, The chip system includes one or more interface circuits and one or more processors; the interface circuits and processors are interconnected via lines; the interface circuits are used to receive signals from the memory of the terminal device and send signals to the processor, the signals including computer instructions stored in the memory; when the processor executes the computer instructions, the terminal device performs the method as described in any one of claims 1 to 9.

26. A chip system, characterized in that, The chip system includes one or more interface circuits and one or more processors; the interface circuits and processors are interconnected via lines; the interface circuits are used to receive signals from the memory of the cloud device and send signals to the processor, the signals including computer instructions stored in the memory; when the processor executes the computer instructions, the cloud device performs the method as described in any one of claims 10 to 18.

27. A learning buddy recommendation system, characterized in that, include: A terminal device and a cloud device, wherein the terminal device is used to perform the method according to any one of claims 1-9, and the cloud device is used to perform the method according to any one of claims 10-18.

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