Atmography machine control method and device, electronic equipment and storage medium

Abstract

The invention provides a control method of an auction learning machine, and the method comprises the steps: displaying a first knowledge card corresponding to a target object on a main interface of the auction learning machine when the target object is determined, and determining whether an interface sliding operation is detected or not; if the interface sliding operation is detected, determining whether the interface sliding operation is a first direction sliding operation; when the interface sliding operation is a first direction sliding operation, the shooting learning machine displays according to a first second knowledge card in a preset card sequence, and the second knowledge card is a knowledge card of the same category as the target object; and controlling the beat learning machine to output the second knowledge card. According to the invention, the problem that the control requirement of a user cannot be met due to the fact that the user is difficult to accurately control an existing apex learning machine which has a small display interface and cannot set more interactive keys is solved.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and in particular to a control method, device, electronic device, and storage medium for a learning machine. Background Technology

[0002] Learning cameras are used to photograph objects, identify them, and output relevant information. However, due to their size, learning cameras cannot have a large display interface or many interactive buttons, making it difficult for users to precisely control them and failing to meet their control needs. Therefore, there is an urgent need for a control method for learning cameras that meets users' control requirements, addressing the issues of small display interfaces, limited interactive buttons, and the resulting inability to precisely control the learning camera and meet user needs. Summary of the Invention

[0003] This invention provides a control method for a learning machine, aiming to solve the problem that existing learning machines have small display interfaces and cannot accommodate a large number of interactive buttons, making it difficult for users to accurately control the machine and thus failing to meet user control needs. This invention, by identifying a target object, displays the corresponding first knowledge card on the main interface of the learning machine. It then determines whether a sliding operation is detected. If a sliding operation is detected, it determines whether the sliding operation is a first-direction sliding operation. If the sliding operation is a first-direction sliding operation, it controls the learning machine to display the second knowledge card that is first in a preset card sequence and outputs the second knowledge card. This solves the problem that existing learning machines have small display interfaces and cannot accommodate a large number of interactive buttons, making it difficult for users to accurately control the machine and thus failing to meet user control needs.

[0004] In a first aspect, embodiments of the present invention provide a control method for a learning machine, the method comprising the following steps:

[0005] When a target object is identified, the first knowledge card corresponding to the target object is displayed on the main interface of the learning machine to determine whether an interface swiping operation is detected.

[0006] If the interface swipe operation is detected, determine whether the interface swipe operation is a first-direction swipe operation;

[0007] When the interface sliding operation is the first direction sliding operation, the learning machine is controlled to display the second knowledge card that is ranked first in the preset card sequence. The second knowledge card is a knowledge card of the same category as the target object.

[0008] The learning machine is controlled to output the second knowledge card.

[0009] Optionally, when the target object is determined, the first knowledge card corresponding to the target object is displayed on the main interface of the learning machine, including:

[0010] The camera takes pictures of objects to obtain image data;

[0011] The object image data is used for object recognition to identify the target object;

[0012] Based on the target object, a first knowledge card corresponding to the target object is determined, and the first knowledge card is displayed on the main interface of the learning machine.

[0013] Optionally, determining the first knowledge card corresponding to the target object based on the target object includes:

[0014] Based on the target object, the object type of the target object is determined;

[0015] Based on the correspondence between the object type and the knowledge base, a target knowledge base is determined, and a first knowledge card corresponding to the target object is determined in the target knowledge base. Different object types correspond to different knowledge bases.

[0016] Optionally, when the interface sliding operation is a sliding operation in the first direction, before controlling the learning machine to display the second knowledge card that is first in the preset card sequence, the method further includes:

[0017] Obtain the historical learning path of the target object;

[0018] Based on the historical learning path, the knowledge cards of the target object are sorted to obtain a preset card sequence.

[0019] Optionally, after the method controls the learning machine to display the second knowledge card, which is ranked first in a preset card sequence, the method further includes:

[0020] During the presentation of the second knowledge card, user learning feedback is obtained;

[0021] Based on the learning feedback, the second knowledge cards are sorted in a second order within a preset card sequence.

[0022] Optionally, obtaining the user's learning feedback includes:

[0023] Obtain the user's browsing time and learning level;

[0024] Based on the browsing time and the level of learning, the user's learning feedback is determined.

[0025] Optionally, after determining whether the interface swipe operation is a first-direction swipe operation, the method further includes:

[0026] If it is determined that the interface sliding operation is not a first-direction sliding operation, then it is determined whether the interface sliding operation is a second-direction sliding operation.

[0027] When the interface sliding operation is a second-direction sliding operation, the learning machine is controlled to display the knowledge cards shown in the previous round. The first direction and the second direction are opposite directions.

[0028] Secondly, embodiments of the present invention provide a control device for a learning machine, the control device comprising:

[0029] The first determining module is used to display the first knowledge card corresponding to the target object on the main interface of the learning machine when the target object is determined, and to determine whether the interface sliding operation is detected.

[0030] The second determining module is used to determine whether the interface sliding operation is a first direction sliding operation if the interface sliding operation is detected.

[0031] The first control module is used to control the learning machine to display the second knowledge card that is first in the preset card sequence when the interface sliding operation is the first direction sliding operation. The second knowledge card is a knowledge card of the same category as the target object.

[0032] The second control module is used to control the learning machine to output the second knowledge card.

[0033] Thirdly, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the control method of the learning machine provided in the embodiments of the present invention.

[0034] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the control method of the learning machine provided in the embodiments of the present invention.

[0035] In this embodiment of the invention, when a target object is identified, the first knowledge card corresponding to the target object is displayed on the main interface of the learning machine, and it is determined whether an interface sliding operation is detected. If an interface sliding operation is detected, it is determined whether the interface sliding operation is a first-direction sliding operation. If the interface sliding operation is a first-direction sliding operation, the learning machine displays the second knowledge card, which is the first in a preset card sequence, and the second knowledge card is a knowledge card of the same category as the target object. The learning machine is then controlled to output the second knowledge card. This invention solves the problem that existing learning machines have small display interfaces, cannot accommodate many interactive buttons, and thus cannot accurately control the learning machine, failing to meet user control needs. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a control method for a learning machine provided in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the structure of a control device for a learning machine provided in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] like Figure 1 As shown, Figure 1This is a flowchart of a control method for a learning machine provided in an embodiment of the present invention. The control method for the learning machine includes the following steps:

[0042] 101. When the target object is identified, the first knowledge card corresponding to the target object is displayed on the main interface of the learning machine to determine whether the interface swiping operation is detected.

[0043] In this embodiment of the invention, the control method of the learning machine described above can be applied to a server, and the server and the learning machine communicate with each other. The learning machine includes a voice acquisition device and an image acquisition device. The voice acquisition device is used to acquire voice data, and the voice acquisition device can be a microphone, etc.; the image acquisition device is used to acquire image data, and the image acquisition device can be a camera, etc.

[0044] The target object mentioned above can be understood as an object that needs to be identified or explained by the learning machine, such as building blocks, milk, bananas, etc.

[0045] The aforementioned first knowledge card can be understood as a knowledge card corresponding to the target object. A knowledge card can be understood as a method of visualizing knowledge by creating cards representing knowledge about an object. Specifically, the first knowledge card corresponding to the target object can be obtained from the corresponding knowledge base. This knowledge base is a pre-set knowledge base used to store and manage knowledge, including knowledge bases for toys, fruits, daily necessities, school supplies, food, etc.

[0046] The main interface described above can be understood as the main display screen of the learning machine.

[0047] The above-mentioned interface swiping operation can be understood as the user swiping on the main interface.

[0048] It should be noted that when a target object is identified, the first knowledge card corresponding to the target object is displayed on the main interface of the learning machine to determine whether a screen swipe operation is detected, so that the learning machine can perform the corresponding operation based on the screen swipe operation.

[0049] 102. If a screen swipe operation is detected, determine whether the screen swipe operation is a first-direction swipe operation.

[0050] In this embodiment of the invention, the aforementioned directional sliding can be understood as the interface sliding direction performed by the user on the main interface of the learning machine.

[0051] The aforementioned first-direction swipe operation can be understood as the swipe action performed on the interface when the first knowledge card is displayed on the main screen of the learning machine. The first-direction swipe operation can be a swipe to the left, a swipe to the right, etc.

[0052] Specifically, when a sliding operation is detected on the interface, the direction and distance of the sliding are recorded. When the sliding stops or the sliding distance exceeds a certain threshold, the current sliding direction is determined, and based on the current sliding direction, it is determined whether the current sliding direction is the first direction sliding operation.

[0053] 103. When the interface sliding operation is a first-direction sliding operation, the learning machine is controlled to display the second knowledge card that is first in the preset card sequence.

[0054] In this embodiment of the invention, the second knowledge card is a knowledge card of the same category as the target object.

[0055] When the interface swipe operation is in the first direction, the learning machine can be controlled to display the second knowledge card that is first in the preset card sequence.

[0056] The above card sequence can be understood as a card sequence that sets the order in which knowledge cards appear.

[0057] The aforementioned preset card sequence can be obtained by sorting the knowledge cards of the target object according to the user's historical learning path, from high importance to significant learning effect, and from low importance to insignificant learning effect, in descending order. The aforementioned historical learning path can be understood as the learning path of the target object being learned and studied during the user's historical learning process, including the knowledge points and skills learned.

[0058] The "first" mentioned above can be understood as the first position in the preset card sequence. Specifically, when the interface swipe operation is detected as a first-direction swipe operation, the learning machine displays the second knowledge card, which is the first in the preset card sequence, on the main interface.

[0059] The aforementioned second knowledge card can be understood as the knowledge card that is in the first position in the preset card sequence.

[0060] In one possible embodiment, for example, when the first direction is to the left, and the interface sliding operation is to the left, the learning machine can be controlled to display the second knowledge card that is first in the preset card sequence; when the first direction is to the right, and the interface sliding operation is to the right, the learning machine can be controlled to display the second knowledge card that is first in the preset card sequence, etc.

[0061] 104. Control the learning machine to output the second knowledge card.

[0062] In this embodiment of the invention, the above-mentioned output can be understood as the output format of the learning device, which is voice output. The above-mentioned voice output can be understood as the learning device converting the second knowledge card into audible voice information and outputting it.

[0063] Specifically, the learning machine can be controlled to convert the second knowledge card into audible voice information for output.

[0064] In this embodiment of the invention, when a target object is identified, the first knowledge card corresponding to the target object is displayed on the main interface of the learning machine, and it is determined whether an interface sliding operation is detected. If an interface sliding operation is detected, it is determined whether the interface sliding operation is a first-direction sliding operation. If the interface sliding operation is a first-direction sliding operation, the learning machine displays the second knowledge card, which is the first in a preset card sequence, and the second knowledge card is a knowledge card of the same category as the target object. The learning machine is then controlled to output the second knowledge card. This invention solves the problem that existing learning machines have small display interfaces, cannot accommodate many interactive buttons, and thus cannot accurately control the learning machine, failing to meet user control needs.

[0065] It is understood that in the specific implementation of this application, data related to the main object, voice, question, knowledge, and answer are involved. When the embodiments in this application are applied to specific products or technologies, user permission or consent is required. Furthermore, the collection, use, and processing of related data, as well as the training, deployment, and invocation of algorithm models, must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0066] Optionally, in the step of displaying the first knowledge card corresponding to the target object on the main interface of the learning machine when the target object is identified, the learning machine can take a picture of the object to obtain image data; perform object recognition on the object image data to identify the target object; and based on the target object, determine the first knowledge card corresponding to the target object and display the first knowledge card on the main interface of the learning machine.

[0067] In this embodiment of the invention, the above-mentioned image data is acquired by the image acquisition device of the learning machine.

[0068] The object recognition described above can be understood as the process of identifying target objects in image data. Specifically, an image recognition model can be used to identify target objects within image data. This model is trained using sample image data and corresponding target object annotations. The untrained image recognition model can be based on deep learning or machine learning, such as ResNet or AlexNet. Specifically, the untrained model is trained using sample image data and corresponding target object annotations. During training, the parameters of the image recognition model are adjusted using a minimum loss function to obtain a trained model. The target object annotations can be information classifying and describing target objects, such as toys, fruits, and everyday items. The training can be supervised training, which uses a set of known labeled data to train the model. By optimizing the model parameters, the model can predict the label of new data or make decisions based on the characteristics of existing data. The loss function is used to evaluate and optimize model performance by comparing the model's predicted values ​​with the true values. The loss function mentioned above can be the mean squared error loss function, the cross-entropy loss function, etc.

[0069] The target objects mentioned above can be understood as objects that need to be identified or explained by the learning machine, such as building blocks, milk, bananas, etc.

[0070] The first knowledge card mentioned above is a knowledge card corresponding to the target object. A knowledge card can be understood as a method of visualizing knowledge by making the knowledge about an object into the form of a card.

[0071] The above-mentioned main interface of the learning machine can be understood as the main display screen of the learning machine.

[0072] It should be noted that by taking pictures of objects with the learning camera, image data is obtained. The image recognition model can then be used to identify the target object and determine the corresponding first knowledge card based on it. This first knowledge card is then displayed on the main interface of the learning camera so that users can view and understand information about the target object.

[0073] Optionally, in the step of determining the first knowledge card corresponding to the target object based on the target object, the object type of the target object can be determined based on the target object; the target knowledge base can be determined based on the correspondence between the object type and the knowledge base; and the first knowledge card corresponding to the target object can be determined in the target knowledge base, with different object types corresponding to different knowledge bases.

[0074] In this embodiment of the invention, the above-mentioned object type can be understood as the classification type of the target object.

[0075] Furthermore, based on the target object, the object type can be determined. For example, if the target object is a building block toy, which belongs to the category of toys, then the object type of the building block toy is determined to be a toy; if the target object is a banana, which belongs to the category of fruits, then the object type of the banana is determined to be a fruit, and so on.

[0076] The correspondence between the object types and the knowledge base mentioned above is a pre-set correspondence by the system, with different object types corresponding to different knowledge bases. For example, toys can correspond to the toy knowledge base, fruits to the fruit knowledge base, and daily necessities to the daily necessities knowledge base, etc.

[0077] The aforementioned knowledge base is a pre-set knowledge base of the system. A knowledge base is used to store and manage knowledge, including toy knowledge bases, fruit knowledge bases, daily necessities knowledge bases, school supplies knowledge bases, etc.

[0078] The aforementioned target knowledge base can be a knowledge base corresponding to the object type of the target object.

[0079] The first knowledge card mentioned above is a knowledge card corresponding to the target object. A knowledge card can be understood as a method of visualizing knowledge by making the knowledge about the object into the form of cards.

[0080] In one possible implementation, for example, when the target object is a building block toy, the knowledge base corresponding to the building block toy can be determined as the toy knowledge base, and the toy knowledge base can be used as the target knowledge base, and the first knowledge card corresponding to the building block toy can be determined in the toy knowledge base; when the target object is an apple, the knowledge base corresponding to the apple can be determined as the fruit knowledge base, and the fruit knowledge base can be used as the target knowledge base, and the first knowledge card corresponding to the apple can be determined in the fruit knowledge base, etc.

[0081] Optionally, when the interface sliding operation is a first-direction sliding operation, before the step of controlling the learning machine to display the second knowledge card that is first in the preset card sequence, the historical learning path of the target object can also be obtained; based on the historical learning path, the knowledge cards of the target object are sorted to obtain the preset card sequence.

[0082] In this embodiment of the invention, the target object can be an object that needs to be identified or explained by the learning machine.

[0083] The learning path described above can be understood as a continuous learning process through which users acquire knowledge and skills, forming a systematic understanding and developing skills. The learning path can be represented as: Learning Path = Goal Setting + Task Decomposition + Event Management + Resource Allocation + Progress Tracking + Strategy Adjustment. Goal setting involves clearly defining specific learning objectives, such as mastering a concept, skill, or reaching a certain level of knowledge. Task decomposition involves breaking down a large goal into a series of smaller tasks or sub-goals, each with clearly defined learning content and expected outcomes. Time management involves setting completion times for each task to ensure learning progresses according to plan. Resource allocation involves allocating necessary learning resources, such as books. Progress tracking involves regularly checking learning progress and assessing whether expected goals have been achieved. Strategy adjustment involves adjusting learning methods and strategies based on progress and results to ensure efficient learning.

[0084] The aforementioned historical learning path can be understood as the learning path of a target object during a user's historical learning process, including the learned knowledge points and skills. Specifically, a dynamic knowledge graph can record the user's learning progress data on the target object. This progress data can include mastered knowledge points, unmastered knowledge points, learning difficulty, etc., and a learning path for the target object is generated based on this data. This dynamic knowledge graph can be updated in real time based on the target object's learning progress data, reflecting the user's latest cognitive state regarding the target object. This dynamic knowledge graph is a graph that adds a time dimension to traditional knowledge graphs, used to analyze and capture changes in knowledge over time. Traditional knowledge graphs primarily focus on the representation and reasoning of static knowledge, while dynamic knowledge graphs further consider the changes in knowledge over time. Dynamic knowledge graphs utilize graph data structures to store and represent entities, relationships, and the changes in entities and relationships over time, thus forming a continuously updated knowledge network.

[0085] The knowledge cards mentioned above can be understood as a method of visualizing knowledge by creating cards representing knowledge about objects.

[0086] The above sorting can be understood as the process of arranging knowledge cards according to the target learning path.

[0087] The aforementioned preset card sequence can be understood as a card sequence that sets the order in which knowledge cards appear. The preset card sequence can be obtained by sorting the knowledge cards of the target object according to the user's historical learning path.

[0088] Specifically, by analyzing a user's historical learning path, metrics such as interaction, dwell time, and re-access to knowledge cards related to the target object can be identified during the learning process. Knowledge cards with long interaction times, high dwell times, and high re-access are designated as high-importance cards with significant learning outcomes, while those with short interaction times, low re-access are designated as low-importance cards with insignificant learning outcomes. These knowledge cards are then arranged in descending order of importance to obtain a card sequence representing the order in which the knowledge cards of the target object appear. This descending order can be understood as following the order from high importance and significant learning outcomes to low importance and insignificant learning outcomes.

[0089] Optionally, in the step of controlling the learning machine to display the second knowledge card that is ranked first in the preset card sequence, the user's learning feedback can also be obtained during the display of the second knowledge card; based on the learning feedback, the second knowledge card can be sorted in the preset card sequence in a second order.

[0090] In this embodiment of the invention, the knowledge card in the first position of the preset card sequence of the second knowledge card is mentioned above.

[0091] The learning feedback from the users mentioned above can be understood as the opinions and suggestions expressed by users regarding the learning content, learning methods, and learning effects during the learning process. For example, whether the user understands the content of the second knowledge card, or how the user evaluates the second knowledge card.

[0092] The aforementioned preset card sequence can be a card sequence that sets the order in which knowledge cards appear. The preset card sequence can be obtained by sorting the knowledge cards of the target object in order of high importance and obvious learning effect to low importance and insignificant learning effect through the user's historical learning path.

[0093] The aforementioned second sorting can be understood as a process of reordering the second knowledge cards in a preset card sequence.

[0094] In one possible implementation, for example, if a user spends very little time on a knowledge card and gives it a low rating, it can be determined that the user's learning effect on the knowledge card is poor, and the knowledge card can be ranked lower; if a user interacts with the knowledge card, spends a long time on it, and has a high rate of repeated visits, it can be determined that the user's learning effect on the knowledge card is good, and the knowledge card can be ranked higher.

[0095] Specifically, during the presentation of the second knowledge card, based on the user's learning feedback, the second knowledge card can be sorted in a preset card sequence. This allows for better adjustment and improvement of the user's learning strategy, thereby enhancing the user's learning effectiveness and quality.

[0096] Optionally, in the step of obtaining user learning feedback, the user's browsing time and learning level can be obtained; based on the browsing time and learning level, the user's learning feedback can be determined.

[0097] In this embodiment of the invention, the aforementioned browsing time can be understood as the time a user spends on the second knowledge card, the number of pages accessed, the frequency of access, etc.

[0098] The aforementioned learning level can be understood as the user's mastery and comprehension of the knowledge in the second knowledge card. The user's learning level can be obtained through tests, questionnaires, and other methods.

[0099] It should be noted that user learning feedback can be determined based on browsing time and learning progress. Based on this feedback, recommended knowledge cards can be further optimized to improve user satisfaction.

[0100] Optionally, after determining whether the interface sliding operation is a first-direction sliding operation, if it is determined that the interface sliding operation is not a first-direction sliding operation, then it is determined whether the interface sliding operation is a second-direction sliding operation; if the interface sliding operation is a second-direction sliding operation, then the learning machine is controlled to display the knowledge card displayed in the previous round, with the first direction and the second direction being opposite directions.

[0101] In the embodiments of the present invention, the above-mentioned directional sliding can be understood as the interface sliding direction performed by the user on the main interface of the learning machine.

[0102] The aforementioned first-direction swipe operation can be understood as the directional swipe action performed by the user on the learning machine interface when the first knowledge card is displayed on the main interface. The first-direction swipe operation can be a swipe to the left, a swipe to the right, etc.

[0103] The aforementioned second-direction swipe operation can be understood as the directional swipe action performed by the user on the learning device interface when the interface swipe operation is not the first-direction swipe operation. The first and second directions are opposite directions. For example, when the first-direction swipe operation is a leftward swipe operation, the second-direction swipe operation is a rightward swipe operation; when the first-direction swipe operation is a rightward swipe operation, the second-direction swipe operation is a leftward swipe operation.

[0104] The knowledge cards displayed in the previous round can be understood as the knowledge cards displayed to the user in the previous round of object recognition. It's understandable that after a user completes an object recognition task, the system will record the object knowledge cards displayed on the main interface of the learning device.

[0105] In one possible implementation, for example, the first directional swipe operation is a left swipe operation, and the second directional swipe operation is a right swipe operation. If it is determined that the interface swipe operation is not a left swipe operation, then it is detected whether the interface swipe operation is a right swipe operation. If the interface swipe operation is a right swipe operation, then the learning machine is controlled to display the knowledge card displayed in the previous round.

[0106] In another possible embodiment, for example, the first direction is to the right and the second direction is to the left. When the first knowledge card is displayed on the main interface of the learning machine, the direction and distance of the swipe are recorded when the user swipes the screen. When the swipe stops or the swipe distance exceeds a certain threshold, the current swipe direction is determined. If the current swipe direction is not to the right, it is determined whether it is to the left. If it is determined to be to the left, the learning machine is controlled to display the knowledge card displayed in the previous round on the main interface.

[0107] like Figure 2 As shown, an embodiment of the present invention provides a control device for a learning machine, the control device for the learning machine comprising:

[0108] The first determining module 201 is used to display the first knowledge card corresponding to the target object on the main interface of the learning machine when the target object is determined, and to determine whether the interface sliding operation is detected.

[0109] The second determining module 202 is used to determine whether the interface sliding operation is a first direction sliding operation if the interface sliding operation is detected.

[0110] The first control module 203 is used to control the learning machine to display the second knowledge card that is first in the preset card sequence when the interface sliding operation is the first direction sliding operation. The second knowledge card is a knowledge card of the same category as the target object.

[0111] The second control module 204 is used to control the learning machine to output the second knowledge card.

[0112] Optionally, the first determining module 201 is further configured to take a picture of an object to obtain image data; perform object recognition on the object image data to determine the target object; determine the first knowledge card corresponding to the target object based on the target object, and display the first knowledge card on the main interface of the learning machine.

[0113] Optionally, the first determining module 201 is further configured to determine the object type of the target object based on the target object; determine the target knowledge base based on the correspondence between the object type and the knowledge base; and determine the first knowledge card corresponding to the target object in the target knowledge base, with different object types corresponding to different knowledge bases.

[0114] Optionally, the device is further configured to acquire the historical learning path of the target object; and based on the historical learning path, sort the knowledge cards of the target object to obtain a preset card sequence.

[0115] Optionally, the device is further configured to obtain user learning feedback during the display of the second knowledge card; and based on the learning feedback, to perform a second sorting of the second knowledge card in a preset card sequence.

[0116] Optionally, the device is further configured to acquire the user's browsing time and learning level; and determine the user's learning feedback based on the browsing time and learning level.

[0117] Optionally, the device is further configured to determine whether the interface sliding operation is a second-direction sliding operation if it is determined that the interface sliding operation is not a first-direction sliding operation; and when the interface sliding operation is a second-direction sliding operation, control the learning machine to display the knowledge card displayed in the previous round, wherein the first direction and the second direction are opposite directions.

[0118] like Figure 3 As shown, this embodiment of the invention also provides an electronic device, including a processor, which can execute any of the above-described control methods for a learning machine.

[0119] Specifically, it includes a processor 301 and a memory 302, as well as a computer program stored in the memory 302 and capable of running on the processor 301, which executes the control method for the learning machine, wherein:

[0120] The processor 301 executes the calculator program containing the control method of the learning machine stored in the memory 302, and performs the following steps:

[0121] When a target object is identified, the first knowledge card corresponding to the target object is displayed on the main interface of the learning machine to determine whether an interface swiping operation is detected.

[0122] If the interface swipe operation is detected, determine whether the interface swipe operation is a first-direction swipe operation;

[0123] When the interface sliding operation is the first direction sliding operation, the learning machine is controlled to display the second knowledge card that is ranked first in the preset card sequence. The second knowledge card is a knowledge card of the same category as the target object.

[0124] The learning machine is controlled to output the second knowledge card.

[0125] Optionally, when the processor 301 determines the target object, it displays the first knowledge card corresponding to the target object on the main interface of the learning machine, including:

[0126] The camera takes pictures of objects to obtain image data;

[0127] The object image data is used for object recognition to identify the target object;

[0128] Based on the target object, a first knowledge card corresponding to the target object is determined, and the first knowledge card is displayed on the main interface of the learning machine.

[0129] Optionally, the step of determining the first knowledge card corresponding to the target object based on the target object, executed by the processor 301, includes:

[0130] Based on the target object, the object type of the target object is determined;

[0131] Based on the correspondence between the object type and the knowledge base, a target knowledge base is determined, and a first knowledge card corresponding to the target object is determined in the target knowledge base. Different object types correspond to different knowledge bases.

[0132] Optionally, when the interface sliding operation is a sliding operation in the first direction, before controlling the learning machine to display the second knowledge card that is first in the preset card sequence, the method executed by the processor 301 further includes:

[0133] Obtain the historical learning path of the target object;

[0134] Based on the historical learning path, the knowledge cards of the target object are sorted to obtain a preset card sequence.

[0135] Optionally, after the processor 301 displays the second knowledge card, which is ranked first in a preset card sequence, the method further includes:

[0136] During the presentation of the second knowledge card, user learning feedback is obtained;

[0137] Based on the learning feedback, the second knowledge cards are sorted in a second order within a preset card sequence.

[0138] Optionally, the process of obtaining the user's learning feedback, performed by processor 301, includes:

[0139] Obtain the user's browsing time and learning level;

[0140] Based on the browsing time and the level of learning, the user's learning feedback is determined.

[0141] Optionally, after determining whether the interface swipe operation is a first-direction swipe operation, the method executed by the processor 301 further includes:

[0142] If it is determined that the interface sliding operation is not a first-direction sliding operation, then it is determined whether the interface sliding operation is a second-direction sliding operation.

[0143] When the interface sliding operation is a second-direction sliding operation, the learning machine is controlled to display the knowledge cards shown in the previous round. The first direction and the second direction are opposite directions.

[0144] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the control method for the learning machine provided in this invention and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0145] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0146] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. Therefore, equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A control method for a learning machine, characterized in that, The method includes the following steps: When a target object is identified, the first knowledge card corresponding to the target object is displayed on the main interface of the learning machine to determine whether an interface swiping operation is detected. If the interface swipe operation is detected, determine whether the interface swipe operation is a first-direction swipe operation; When the interface sliding operation is the first direction sliding operation, the learning machine is controlled to display the second knowledge card that is ranked first in the preset card sequence. The second knowledge card is a knowledge card of the same category as the target object. The learning machine is controlled to output the second knowledge card.

2. The control method for the learning machine as described in claim 1, characterized in that, When the target object is identified, the first knowledge card corresponding to the target object is displayed on the main interface of the learning machine, including: The camera takes pictures of objects to obtain image data; The object image data is used for object recognition to identify the target object; Based on the target object, a first knowledge card corresponding to the target object is determined, and the first knowledge card is displayed on the main interface of the learning machine.

3. The control method for the learning machine as described in claim 2, characterized in that, The step of determining the first knowledge card corresponding to the target object based on the target object includes: Based on the target object, the object type of the target object is determined; Based on the correspondence between the object type and the knowledge base, a target knowledge base is determined, and a first knowledge card corresponding to the target object is determined in the target knowledge base. Different object types correspond to different knowledge bases.

4. The control method for the learning machine as described in claim 1, characterized in that, When the interface swiping operation is a swiping operation in the first direction, before controlling the learning machine to display the second knowledge card that is first in the preset card sequence, the method further includes: Obtain the historical learning path of the target object; Based on the historical learning path, the knowledge cards of the target object are sorted to obtain a preset card sequence.

5. The control method for the learning machine as described in claim 1, characterized in that, After the learning machine displays the second knowledge card, which is ranked first in a preset card sequence, the method further includes: During the presentation of the second knowledge card, user learning feedback is obtained; Based on the learning feedback, the second knowledge cards are sorted in a second order within a preset card sequence.

6. The control method for the learning machine as described in claim 5, characterized in that, The process of obtaining the user's learning feedback includes: Obtain the user's browsing time and learning level; Based on the browsing time and the level of learning, the user's learning feedback is determined.

7. The control method for the learning machine as described in claim 1, characterized in that, After determining whether the interface swipe operation is a first-direction swipe operation, the method further includes: If it is determined that the interface sliding operation is not a first-direction sliding operation, then it is determined whether the interface sliding operation is a second-direction sliding operation. When the interface sliding operation is a second-direction sliding operation, the learning machine is controlled to display the knowledge cards shown in the previous round. The first direction and the second direction are opposite directions.

8. A control device for a learning machine, characterized in that, The control device for the learning machine includes: The first determining module is used to display the first knowledge card corresponding to the target object on the main interface of the learning machine when the target object is determined, and to determine whether the interface sliding operation is detected. The second determining module is used to determine whether the interface sliding operation is a first direction sliding operation if the interface sliding operation is detected. The first control module is used to control the learning machine to display the second knowledge card that is first in the preset card sequence when the interface sliding operation is the first direction sliding operation. The second knowledge card is a knowledge card of the same category as the target object. The second control module is used to control the learning machine to output the second knowledge card.

9. An electronic device, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the control method for the learning machine as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the control method for the learning machine as described in any one of claims 1 to 7.

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