Application management and control method and device, equipment, medium and product

Abstract

The invention discloses an application management and control method, device and equipment, a medium and a product. The method comprises the steps of determining a current learning concentration attribute of a use object corresponding to the wearable device in a learning application running process, and obtaining current behavior interaction data of the use object and the learning application; determining a current learning state of the use object based on the current learning concentration attribute and the current behavior interaction data; determining at least one discrete valid learning time range and invalid learning time range associated with the use object according to the current learning state; when a continuous invalid learning duration corresponding to any invalid learning time range is greater than a first preset duration, determining a resource adjustment duration based on the continuous invalid learning duration, the valid learning time range and the invalid learning time range; and on the basis of the resource adjustment duration and the effective learning time range, updating the available duration of the non-learning application so as to use the non-learning application in the updated available duration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer processing, and in particular to an application management and control method, device, equipment, medium and product. BACKGROUND

[0002] With the wide popularity of smart terminals such as smart phones, tablet computers, watches, etc., more and more users will use learning applications of such smart terminals to help improve their learning performance. However, since such terminals have applications with entertainment functions, parents are worried that such smart terminals will distract learning.

[0003] Currently, the management and control of applications usually includes two ways, one is to set a password for entertainment applications to limit the use of entertainment functions of the terminal, and the other way is to set the available time length of the day for entertainment applications to limit the use of entertainment functions of the terminal.

[0004] However, both ways will cause the reverse psychology of the user and reduce the learning enthusiasm by forcibly controlling the use of entertainment applications. SUMMARY

[0005] The present application provides an application management and control method, device, equipment, medium and product to achieve the use of non-learning applications in exchange for the time length of learning while improving the learning efficiency and autonomy of the user and enhancing the interest and enthusiasm of the user in using the application.

[0006] According to an aspect of the present application, an application management and control method is provided, applied to a wearable device integrated with a learning application and a non-learning application, the method comprising:

[0007] During the running of the learning application, the current learning focus attribute of the user corresponding to the wearable device is determined, and the current behavior interaction data of the user and the learning application is obtained;

[0008] Based on the current learning focus attribute and the current behavior interaction data, the current learning state of the user is determined;

[0009] According to the current learning state, at least one discrete effective learning time range and ineffective learning time range associated with the user is determined;

[0010] When the continuous ineffective learning time length corresponding to any ineffective learning time range is greater than a first preset time length, the resource adjustment time length is determined based on the continuous ineffective learning time length, the effective learning time range and the ineffective learning time range;

[0011] update a usable duration of the non-learning application based on the resource adjustment duration and the effective learning time range, so as to use the non-learning application within the updated usable duration.

[0012] According to another aspect of the present application, an application management device is provided, configured in a wearable device integrated with a learning application and a non-learning application, the device comprising:

[0013] a data acquisition module configured to determine a current learning focus attribute of a user of the wearable device during running of the learning application, and acquire current behavior interaction data of the user and the learning application;

[0014] a current learning state determination module configured to determine a current learning state of the user based on the current learning focus attribute and the current behavior interaction data;

[0015] a learning time range determination module configured to determine at least one discrete effective learning time range and ineffective learning time range associated with the user according to the current learning state;

[0016] a resource adjustment duration determination module configured to determine a resource adjustment duration based on a continuous ineffective learning duration of any ineffective learning time range, the effective learning time range and the ineffective learning time range when the continuous ineffective learning duration is greater than a first preset duration;

[0017] a usable duration update module configured to update a usable duration of the non-learning application based on the resource adjustment duration and the effective learning time range, so as to use the non-learning application within the updated usable duration.

[0018] According to another aspect of the present application, an electronic device is provided, comprising:

[0019] at least one processor; and a memory connected with the at least one processor in communication; wherein,

[0020] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the application management method according to any one of the embodiments of the present application.

[0021] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to execute the application management method according to any one of the embodiments of the present application.

[0022] According to another aspect of the present application, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the application management method as described in any embodiment of the present application.

[0023] The technical solution of the embodiment of the present application determines the current learning focus attribute of the use object corresponding to the wearable device in the process of running the learning application, and acquires the current behavior interaction data of the use object and the learning application; determines the current learning state of the use object based on the current learning focus attribute and the current behavior interaction data; determines at least one discrete effective learning time range and ineffective learning time range associated with the use object according to the current learning state; when the continuous ineffective learning duration corresponding to any ineffective learning time range is greater than a first preset duration, determines a resource adjustment duration based on the continuous ineffective learning duration, the effective learning time range, and the ineffective learning time range; updates the available duration of the non-learning application based on the resource adjustment duration and the effective learning time range, so that the non-learning application is used within the updated available duration, which solves the problem that the use of the entertainment application is controlled based on the password and the set use duration in the prior art, which reduces the user's interest and enthusiasm in use, and achieves the following effects: the current learning focus attribute of the use object corresponding to the wearable device is determined in the process of running the learning application, and the current behavior interaction data of the use object and the learning application is acquired; the current learning state of the use object is determined in combination with the current learning focus attribute and the current behavior interaction data, which improves the accuracy of learning state determination and avoids misjudgment. Further, at least one discrete effective learning time range and ineffective learning time range associated with the use object are determined according to the current learning state; when the continuous ineffective learning duration corresponding to any ineffective learning time range is greater than a first preset duration, a resource adjustment duration for adjusting the use duration is determined based on the continuous ineffective learning duration, the effective learning time range, and the ineffective learning time range. Furthermore, the available duration of the non-learning application is dynamically updated based on the resource adjustment duration and the effective learning time range, which realizes the effect of encouraging the use object to learn to exchange the use duration of the non-learning application, improves the user's autonomy in learning, improves the accuracy, fairness, and reliability of the available duration determination, balances the allocation of learning and entertainment resources, and thus improves the user's interest and enthusiasm in using the application.

[0024] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to make the technical solution in the embodiments of the present application clearer, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0026] Figure 1 is a flow chart of an application management method according to the first embodiment of the present application;

[0027] Figure 2 is a flow chart of an application management method according to the second embodiment of the present application;

[0028] Figure 3 is a flow chart of an application management method according to the third embodiment of the present application

[0029] Figure 4 is a flow chart of an application management method according to the fourth embodiment of the present application;

[0030] Figure 5 is a structural schematic diagram of an application management device according to the fifth embodiment of the present application;

[0031] Figure 6 is a structural schematic diagram of an electronic device implementing the application management method of the present application. DETAILED DESCRIPTION

[0032] In order to make the technical solution in the embodiments of the present application clearer, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0033] It should be noted that the terms "first", "second", and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to include all the steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0034] It should be noted that the data involved in the technical solution (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the relevant laws and regulations and relevant provisions.

[0035] Embodiment one

[0036] Figure 1 It is a flowchart of an application control method according to the first embodiment of the present application. The present embodiment can be applied to the case of controlling the use time of non-learning applications in wearable devices. The method is applied to a wearable device, which integrates learning applications and non-learning applications. The method can be executed by an application control device, which can be realized in the form of hardware and / or software, and can be configured in the wearable device. As shown in the figure, the method comprises: Figure 1

[0037] S110, in the process of running the learning application, determining the current learning concentration attribute of the use object corresponding to the wearable device, and acquiring the current behavior interaction data of the use object and the learning application.

[0038] The wearable device refers to a portable device that can be worn on the user's body or integrated into the user's clothes or accessories. For example, the wearable device includes but is not limited to smart watches, smart bracelets, smart glasses, etc. The learning application can refer to a software application for assisting learning. For example, these applications can include various learning applications in various fields, such as language learning applications, subject knowledge learning applications (mathematics problem solving, physics formula explanation, etc.), skill teaching applications (programming language teaching, design software operation, etc.). Various content can be provided in the learning application, such as learning content, practice questions, tests, learning progress tracking, etc. The use object can be the user of the application wearable device. The learning concentration attribute can be used to represent the attention information of the use object during learning. For example, the learning concentration attribute can be represented by the concentration degree, such as the greater the concentration degree value, the higher the user's learning attention, and vice versa. The learning concentration attribute can be represented by different levels, such as high concentration, medium concentration and low concentration, etc. The current behavior interaction data can be various behavior data generated by the use object when using the learning application. For example, the current behavior interaction data includes the operation record of the use object on the application, such as the number of times the course video play button is clicked, the time spent on the learning page, the frequency of taking notes, the participation in interactive question and answer, etc.

[0039] ​In the embodiment, if the application control method proposed in the embodiment is integrated in the wearable device, the function of acquiring the available time length of the non-learning application can be started when the entertainment time length acquisition module in the wearable device is triggered; or the prompt information of acquiring the entertainment time length can be displayed on the interface of the wearable device, and the function of acquiring the available time length of the non-learning application can be started when the confirmation operation on the prompt information is triggered. Further, the current learning concentration attribute of the use object corresponding to the wearable device when using the learning application can be determined in real time or periodically during the running of the learning application, and the current behavior interaction data of the use object in the learning application is synchronously acquired. Optionally, the current behavior interaction data includes but is not limited to operation behavior data (such as the frequency and time of the user's operation of clicking buttons, links and the like on the application interface, or the user's behavior of sliding the screen to view the content, or the time of the user's staying on each learning page or knowledge point), learning content interaction data (such as the time of the user's staying on each learning page or knowledge point, the difficulty of the exercise, the answering time length, the accuracy, etc.). Based on the current learning concentration attribute and the current behavior interaction data, it is determined whether the use object is learning in the learning application.

[0040] In the embodiment, determining the current learning concentration attribute of the use object corresponding to the wearable device includes: acquiring the voice information corresponding to the use object based on the microphone device in the wearable device, and determining the first concentration degree based on the voice information; acquiring the image information corresponding to the use object based on the camera device in the wearable device, and determining the second concentration degree based on the image information; acquiring the first acceleration information corresponding to the use object based on the accelerometer in the wearable device, acquiring the second acceleration information corresponding to the use object based on the gravity sensor in the wearable device, and determining the third concentration degree based on the first acceleration information and the second acceleration information; and determining the current learning concentration attribute of the use object based on the first concentration degree, the second concentration degree and the third concentration degree.

[0041] Among them, the microphone device, the camera device, the accelerometer and the gravity sensor can be arranged in the wearable device.

[0042] In the present embodiment, the microphone device built-in the wearable device can continuously or intermittently capture the voice of the user and the sound of other people around the wearable device, such as reading, speech, and the sound of people talking around, when the user is authorized and allowed to use. Further, the natural language processing technology can be used to extract key voice features from the voice information, such as speech rate, tone change, and pause frequency, and then, according to the voice features, the first concentration degree of the user is determined. For example, when the user is concentrating on reading or discussing, the speech rate is relatively stable, the tone change is regular, and the pause frequency is low; the more the user is not concentrating on learning (such as daydreaming) may suddenly increase or decrease the tone. The voice information can also be converted into text information, and then the text information is analyzed to determine whether it is related to the learning topic, and according to the text analysis result, the first concentration degree of the user is determined. For example, if the user is learning a history course, the voice content frequently appears the words related to historical events, characters, etc., which indicates that the user is concentrating on learning, and the higher the value of the first concentration degree. Or, the first concentration degree can also be determined based on the voice features in the voice information and the text information analyzed from the voice information.

[0043] The image information including the head, facial expression, and eye direction of the user can be captured by the camera device on the wearable device, the head deflection direction, facial expression, and eye gaze point and eye movement frequency of the user are recognized based on computer vision technology, and whether the user is concentrating on learning is determined according to the head deflection direction, facial expression, and eye gaze point, eye movement frequency, etc., to obtain the second concentration degree. For example, the eye and head have a large swing, such as the eye is wandering, the head is turning, the eye is not looking at the desktop, the eye is deviating from the learning materials for a long time, etc., which can be considered as not concentrating, and the value of the second concentration degree is low. Or, if the eye gaze point is the learning materials (such as books, screens, etc.), and the opened content of the learning materials is consistent with the learning content of the user, the value of the second concentration degree is high. The image information including the user's finger writing with a pen can also be captured, and whether the user is calculating the problem is determined based on the image analysis technology to recognize the image, if yes, the value of the second concentration degree is high, if no, the value of the second concentration degree is low; or, whether the calculation content is consistent with the learning content of the user can also be recognized, if yes, the value of the second concentration degree is high. The head deflection direction, facial expression, and eye gaze point, calculation content, etc. of the user can also be combined to determine the second concentration degree.

[0044] The acceleration of the wearable device in three directions (x, y, z axes) can be measured based on an accelerometer in the wearable device as first acceleration information. The gravitational acceleration of the wearable device is measured using a gravity sensor as second acceleration information. Since the wearable device is worn by the user, the wearable device moves with the limb movement of the user, and thus the first acceleration information and the second acceleration information of the wearable device can be used to represent the acceleration information of the user. Further, the first acceleration information and the second acceleration information are analyzed to determine a third concentration degree. For example, if the user is in a learning process, the first acceleration information is close to the second acceleration information, and the change range of the first acceleration information and the second acceleration information is small, it indicates that the body is relatively static, which means that the user is more concentrated, and the value of the third concentration degree is higher at this time. If the change range of the first acceleration information and the second acceleration information is larger, it indicates that the body of the user shakes more obviously, and it is considered that the user is distracted or disturbed by the outside world, and the value of the third concentration degree is smaller at this time.

[0045] Further, the first concentration degree, the second concentration degree, and the third concentration degree can be weighted and fused, that is, the three concentration degrees are multiplied by corresponding weights and added to obtain the current learning concentration attribute of the user. For example, in a learning scene, the voice information is more important for reflecting the learning concentration degree, and the first concentration degree can be assigned a higher weight, such as 0.4; the weight of the image information is 0.3; and the weight of the acceleration information is 0.3. Alternatively, one of the concentration degrees can be taken as a reference, and the reference concentration degree is corrected based on the other two concentration degrees, and the three concentration degrees after correction are weighted and fused to obtain the current learning concentration attribute. For example, the third concentration degree determined based on the acceleration information is 0.4, but the concentration degrees determined based on the image information and the voice information are higher, and the third concentration degree can be adjusted. It should be noted that the current learning concentration attribute of the user can also be determined based on the voice information, the image information, the first acceleration information, and the second acceleration information.

[0046] The technical solution provided in the embodiment can effectively improve the evaluation accuracy of the current learning state of the user by comprehensively determining the current learning concentration attribute of the user based on the concentration degrees determined by multiple information sources.

[0047] It should be noted that the data (including but not limited to the data itself, acquisition or use of the data) involved in the technical solution provided in the embodiment is performed under the authorization and permission of the user, and the acquisition, storage, use, processing, etc. of the data in the technical solution provided in the embodiment comply with the relevant provisions of the national laws and regulations.

[0048] S120, determine the current learning state of the use object based on the current learning focus attribute and the current behavior interaction data.

[0049] In this embodiment, the learning state of the use object can be evaluated based on the operation behavior (such as clicking to play a video, taking notes, participating in discussions, answering questions, etc.) of the use object in the current behavior interaction data, and the fourth focus degree of the use object is obtained. Based on the current learning focus attribute and the fourth focus degree, the current learning state of the use object is determined. Alternatively, the current learning focus attribute and the current behavior interaction data can be input into a pre-trained evaluation model to output the current learning state of the use object. Alternatively, the current learning focus attribute is taken as a benchmark, and the current learning focus attribute is adjusted in combination with the behavior interaction data, and the current learning state of the use object is determined based on the adjusted current learning focus attribute. For example, the current learning focus attribute is 0.71, and the correct answer rate of the use object in the current behavior interaction data is higher than a preset threshold, the current learning focus attribute can be adjusted, such as 0.71+0.1=0.81. If the current learning focus attribute is greater than a preset attribute value (such as 0.8), it indicates that the use object is in an efficient learning state, otherwise, the use object is in an inefficient learning state or is absent-minded.

[0050] In this embodiment, there are at least three implementation manners for determining the current learning state of the use object. In order to clearly introduce how to determine the current learning state, the at least three implementation manners for determining the current learning state of the use object are explained as follows.

[0051] The first implementation manner can be that when the current learning focus attribute meets a preset first learning condition, the current learning state of the use object is determined to be an effective learning state.

[0052] The preset first learning condition can be a condition for judging whether the current learning focus attribute meets a preset learning requirement.

[0053] In this embodiment, the current learning focus attribute and the preset first learning condition can be compared to determine whether the current learning focus attribute meets the preset first learning condition. If yes, it indicates that the learning state of the user is good, and the current learning state of the use object can be determined to be an effective learning state. If not, the current learning state of the use object can be further determined in combination with the current behavior interaction data. For example, when the learning focus attribute value is greater than or equal to a preset threshold, it is considered that the use object is in an effective learning state.

[0054] Optionally, the current behavior interaction data comprises at least learning content interaction data and learning task interaction data. The learning task interaction data can refer to a task module (such as homework, project, experiment, test, etc.) learned in the learning application. The learning content interaction data can refer to a unit (such as homework question, experiment item, etc.) in the module learned in the learning application.

[0055] Another implementation manner can be: when the current learning focus attribute does not reach the preset first learning condition, determining a content difficulty attribute corresponding to the learning content interaction data and a completion attribute corresponding to the learning task interaction data in the current behavior interaction data; when it is determined that the second learning condition is met based on the content difficulty attribute and / or the completion attribute, determining that the current learning state is an effective learning state.

[0056] The content difficulty attribute refers to the difficulty of the learning content. For example, the content difficulty attribute can be determined according to the complexity of the learning content, the knowledge level, the feedback of the user, etc. The completion attribute refers to the completion of the learning content by the user. For example, the completion attribute can be evaluated by the proportion of the learning task completed by the user, such as the number of completed homework, the number of times of participating in tests, the progress of submitted projects, etc. For example, if the user completes 80% of the learning task, the completion attribute is high. The preset second learning condition refers to a condition for determining whether the content difficulty attribute and / or the completion attribute meet the preset requirement. For example, if the content difficulty attribute is high or the completion attribute is higher than 50%, the preset second learning condition is met.

[0057] In the embodiment, when the current learning focus attribute does not reach the preset first learning condition, the content difficulty attribute corresponding to the learning content interaction data can be determined based on the learning content interaction data in the current behavior interaction data. The completion attribute can be determined based on the learning task interaction data in the current behavior interaction data. For example, the completion attribute can be the proportion of the video watching time to the total learning time, the proportion of the article reading page number to the total learning content, the proportion of the completed homework number to the total task number, the proportion of the test participating number to the total test number, etc. Further, the content difficulty attribute and the completion attribute can be compared with the preset second learning condition respectively to determine whether the content difficulty attribute and the completion attribute reach the preset first learning condition. When any of the attributes meets the preset first learning condition, the current learning state of the user can be determined as an effective learning state. When both the content difficulty attribute and the completion attribute meet the preset first learning condition, the current learning state of the user can be determined as an effective learning state.

[0058] Another implementation manner can be: when the current learning focus attribute does not reach the preset first learning condition, and it is determined that the preset second learning condition is not met based on the content difficulty attribute and the completion attribute, the current learning state of the user is determined as an ineffective learning state.

[0059] That is, when the current learning focus attribute does not reach the preset first learning condition, and the content difficulty attribute and the completion attribute do not meet the second learning condition, it is determined that the current learning state of the use object is an invalid learning state.

[0060] Through the above various ways of determining the current learning state of the use object, the accuracy of determining the current learning state of the use object can be effectively improved, thereby improving the accuracy, fairness and reliability of determining the usable duration of the non-learning application.

[0061] S130, according to the current learning state, determining at least one discrete valid learning time range and invalid learning time range associated with the use object.

[0062] The valid learning time range refers to the continuous time period during which the use object is in the valid learning state in the learning process. For example, from 9:00 to 10:30, the use object is in the valid learning state all the time, and this period of time can be considered as a valid learning time range. The invalid learning time range refers to the continuous time period during which the use object is in the invalid learning state in the learning process. For example, from 10:30 to 10:45, the use object is in the invalid learning state all the time, and this period of time can be defined as an invalid learning time range.

[0063] In this embodiment, the start learning time can be recorded each time the current learning state of the use object changes from the invalid learning state to the valid learning state, and the end learning time can be recorded each time the learning state changes from the valid learning state to the invalid learning state. The time period between the start learning time and the end learning time is the valid learning time range. Similarly, the start distraction time can be recorded each time the current learning state of the use object changes from the valid learning state to the invalid learning state, and the end distraction time can be recorded each time the learning state changes from the invalid learning state to the valid learning state. The time period between the start distraction time and the end distraction time is the invalid continuous learning time range. It can be understood that the valid learning time range and the invalid learning time range are intermittently present, that is, each valid learning time range is discretely distributed, and each invalid learning time range is also discretely distributed. In order to reasonably allocate the usable duration for the non-learning application according to these discrete time ranges, and help the use object to better manage the learning time and improve the learning efficiency.

[0064] For example, the user starts learning at 9:00 am, the learning state is invalid learning state from 9:00 to 9:10, valid learning state from 9:10 to 10:30, and invalid learning state from 10:30 to 10:45. Then, the valid learning time range is from 9:10 to 10:30, and the invalid learning time ranges are from 9:00 to 9:10 and from 10:30 to 10:45.

[0065] It should be noted that, in order to improve the learning efficiency and experience of the user, when the duration of the valid learning time range exceeds a certain duration, a rest reminder information can be generated to avoid learning fatigue. When the duration of the invalid learning time range exceeds a certain duration, a learning reminder information can be generated to remind the user to concentrate or show methods and suggestions for improving concentration, such as taking a short stretch, adjusting the learning environment, etc.

[0066] S140, when the duration of any invalid learning time range is greater than the first preset duration, the resource adjustment duration is determined based on the duration of the invalid learning time range, the valid learning time range, and the invalid learning time range.

[0067] The first preset duration can be a preset threshold for determining whether the duration of the invalid learning time range is too long. For example, the first preset duration can be set to 15 minutes or 30 minutes. The resource adjustment duration can be used to adjust the total learning duration of the learning application and the available duration of the non-learning application. It should be noted that the resource adjustment duration can be positive, indicating positive adjustment of the duration, or negative, indicating negative adjustment of the duration.

[0068] In this embodiment, for each invalid learning time range, the duration of the invalid learning time range can be calculated. For example, if the invalid learning time range is from 10:30 to 10:45, the duration of the invalid learning time range is 15 minutes. Further, the duration of the invalid learning time range and the first preset duration can be compared. If the duration of the invalid learning time range is greater than the first preset duration, it indicates that the user has not been concentrating on learning for a long time. When the duration of the invalid learning time range is greater than the first preset duration, it can be determined whether the invalid learning time range greater than the first preset duration is within a preset rest time period (such as 11:30 to 2:00, 18:00 to 20:30, or 22:00 to 8:00, which can be set in advance according to actual conditions). If not, the resource adjustment duration is determined based on the duration of the invalid learning time range, the valid learning time range, and the invalid learning time range.

[0069] For example, a baseline resource adjustment duration can be determined based on the continuous ineffective learning duration, such as the longer the continuous ineffective learning duration, the shorter the resource adjustment duration, and vice versa, the shorter the continuous ineffective learning duration, the longer the resource adjustment duration, while the resource adjustment duration is within a preset range; or the resource adjustment duration = the baseline adjustment duration + (the continuous ineffective learning duration / the first preset duration) x a preset adjustment coefficient. Further, if the usage object is in an effective learning state for a long time before the ineffective learning time range, it indicates that the learning state of the usage object before is better, and the baseline resource adjustment duration can be adjusted based on the duration of the effective learning state. Or, if the ineffective learning time range appears frequently, it indicates that the usage object is less focused on learning, and the baseline resource adjustment duration can be adjusted at this time.

[0070] S150, based on the resource adjustment duration and the effective learning time range, updating the usable duration of the non-learning application, so as to use the non-learning application within the updated usable duration.

[0071] The usable duration of the non-learning application refers to the length of time that the usage object can use the non-learning application (such as social media, games, etc.). The usable duration is dynamically updated based on the resource adjustment duration and the effective learning time range.

[0072] In this embodiment, all or part of the effective learning time range can be accumulated to obtain the total learning duration. The entertainment duration can be determined based on the total learning duration, for example, the total learning duration and the entertainment duration have a linear or nonlinear relationship, such as the total learning duration is 10 minutes, the entertainment duration is 2 minutes, the total learning duration is 20 minutes, the entertainment duration is 4 minutes, and the linear increasing relationship. Of course, the maximum value of the entertainment duration can also be set, such as two hours, that is, the entertainment duration is at most two hours. The sum of the entertainment duration and part or all of the resource adjustment duration can be used as the usable duration of the non-learning application. For example, if the resource adjustment duration is 10 minutes, and the entertainment duration reaches the preset maximum value (such as 2 hours), a part of the resource adjustment duration (such as 5 minutes) can be allocated to the usable duration of the non-learning application, and if the entertainment duration does not reach the preset maximum value, the entire resource adjustment duration can be allocated to the usable duration of the non-learning application. If the resource adjustment duration is -10 minutes, and the entertainment duration is 1 hour, the sum of the entertainment duration and part of the resource adjustment duration (such as -5 minutes or -10 minutes) can be obtained to allocate the usable duration of the non-learning application.

[0073] In order to ensure the accuracy of the application state of the non-learning application, the application state of the non-learning application can be adjusted to the allowed use state after updating the usable duration of the non-learning application. The application state of the non-learning application refers to whether the application is allowed to be used.

[0074] Specifically, when it is determined that the available usage duration of the non-learning type application is greater than the preset duration (e.g., 0), if the application state of the non-learning type application is the limited usage state, the application state can be adjusted to the allowed usage state. The real-time performance of the application state update of the non-learning type application is achieved. After the application state of the non-learning type application is adjusted to the allowed usage state, the usage subject can use the non-learning type applications within the updated available usage duration.

[0075] In this embodiment, when the starting operation for the non-learning type application is detected, the available usage duration or the application state of the non-learning type application is determined; when the available usage duration is greater than the preset threshold or the application state is the allowed usage state, the non-learning type application is started; and in the process of using the non-learning type application, the available usage duration is updated.

[0076] It can be understood that in the wearable device, the starting operation for the non-learning type application can be considered to be detected when the usage subject triggers the icon of the non-learning type application or triggers the starting link of the non-learning type application. In response to this operation, the available usage duration or the application state of the non-learning type application can be determined at this time. The available usage duration is compared with the preset threshold, and if the available usage duration is greater than the preset threshold or the application state is the allowed usage state, the non-learning type application is started. After the non-learning type application is started, the usage subject can use the non-learning type application, such as browsing a video or playing a game, and in the process of using the non-learning type application, the available usage duration is adjusted based on the usage duration. For example, if the usage subject uses the non-learning type application for 5 minutes, the available usage duration is reduced from 20 minutes to 15 minutes. It should be noted that the timing can be stopped, that is, the adjustment of the available usage duration is paused, when the non-learning type application is exited or closed.

[0077] In this way, the management accuracy of the usage subject using the non-learning type application can be improved, and it is ensured that the usage subject can use the non-learning type application within a reasonable duration, thereby avoiding excessive addiction.

[0078] It should be noted that the used non-learning type application can be limited when the available usage duration is less than or equal to the preset threshold. In this embodiment, when it is detected that the available usage duration of the non-learning type application is less than the preset threshold, the application state of the non-learning type application can be adjusted to the limited usage state, and the prompt information for obtaining the available usage duration can be generated, so that when the confirmation operation for the prompt information is detected, the available usage duration of the non-learning type application is updated in the process of running the learning type application.

[0079] Specifically, when the available usage duration of the non-learning application is less than a preset threshold, the application state of the non-learning application can be adjusted to a limited usage state, and in the limited usage state, the non-learning application is prohibited from being used (e.g., the non-learning application or each application module in the non-learning application is prohibited from being opened). At this time, prompt information for obtaining the available usage duration can be generated. The use object can confirm the prompt information through an input operation (e.g., voice, shortcut key, click operation, etc.). The wearable device responds to the confirmation operation, and in the process of running the learning application, the steps of S110 to S150 are re-executed, and the available usage duration of the non-learning application is updated. When the updated available usage duration reaches a sixth preset duration, or the use object triggers the resource acquisition closing control in the wearable device, or the continuous invalid learning duration corresponding to the invalid learning time range is greater than a seventh preset duration, it is considered that a resource acquisition closing event is detected, and at this time, the steps of S110 to S150 are no longer executed, that is, the acquisition of the available usage duration is stopped.

[0080] The technical scheme of the embodiment of the application determines the current learning focus attribute of the use object corresponding to the wearable device in the process of running the learning application, and acquires the current behavior interaction data of the use object and the learning application; determines the current learning state of the use object based on the current learning focus attribute and the current behavior interaction data; determines at least one discrete effective learning time range and ineffective learning time range associated with the use object according to the current learning state; when the continuous ineffective learning duration corresponding to any ineffective learning time range is greater than a first preset duration, determines a resource adjustment duration based on the continuous ineffective learning duration, the effective learning time range, and the ineffective learning time range; updates the usable duration of the non-learning application based on the resource adjustment duration and the effective learning time range, so that the non-learning application is used within the updated usable duration, which solves the problem that the use of the entertainment application is controlled based on the password and the set use duration in the prior art, which reduces the user's interest and enthusiasm, determines the current learning focus attribute of the use object corresponding to the wearable device and the current behavior interaction data of the use object and the learning application in the process of running the learning application, determines the current learning state of the use object in combination with the current learning focus attribute and the current behavior interaction data, improves the accuracy of learning state determination, and avoids misjudgment. Further, at least one discrete effective learning time range and ineffective learning time range associated with the use object are determined according to the current learning state; when the continuous ineffective learning duration corresponding to any ineffective learning time range is greater than a first preset duration, a resource adjustment duration for adjusting the use duration is determined based on the continuous ineffective learning duration, the effective learning time range, and the ineffective learning time range. Further, the usable duration of the non-learning application is dynamically updated based on the resource adjustment duration and the effective learning time range, which realizes that the use object exchanges the use duration of the non-learning application by learning, improves the user's learning autonomy, improves the accuracy, fairness, and reliability of the determination of the usable duration, and thus improves the user's interest and enthusiasm for using the application.

[0081] Embodiment Two

[0082] Figure 2 is a flowchart of an application control method according to Embodiment Two of the application, which further refines "S140" on the basis of the foregoing embodiment. The specific implementation can be seen from the technical solution of this embodiment. The same or corresponding technical terms as the foregoing embodiments are not described again here.

[0083] As Figure 2 shown, the method specifically includes the following steps:

[0084] S210, in the process of running the learning application, determining the current learning focus attribute of the use object corresponding to the wearable device, and acquiring the current behavior interaction data of the use object and the learning application.

[0085] S220, determining the current learning state of the use object based on the current learning focus attribute and the current behavior interaction data.

[0086] S230, determining at least one discrete effective learning time range and ineffective learning time range associated with the use object according to the current learning state.

[0087] S240, when it is detected that the current time is in the ineffective learning time range, if the effective learning time range with the current time as the effective learning end time is greater than the second preset time length, determining the first incentive attribute based on the continuous effective learning time length corresponding to the effective learning time range.

[0088] The first incentive attribute can be used to represent the incentive given to the use object, such as reward points (which can be exchanged for learning resources, rest time, etc.), rest time, reward use time of non-learning application, etc.

[0089] In this embodiment, the ineffective learning time range can be determined in real time. When it is detected that the learning state of the current time is an ineffective learning state, the current time can be taken as the starting time of the ineffective learning time range, and the ineffective learning time range is continuously recorded. At this time, the effective learning time range with the current time as the effective learning end time can be determined. When the continuous effective learning time length corresponding to the effective learning time range is greater than the second preset time length, it indicates that the use object has a long continuous learning time before the current time, and at this time the incentive mechanism can be triggered. Based on the continuous effective learning time length corresponding to the effective learning time range, the first incentive attribute is determined. For example, the longer the continuous effective learning time length, the higher the value of the first incentive attribute. For example, the continuous effective learning time length is 2 hours, and the first incentive attribute is 25 minutes. The continuous effective learning time length is 3 hours, and the first incentive attribute is 37.5 minutes. Or, the continuous effective learning time length is 30-60 minutes, and the first incentive attribute is 10 minutes; if the continuous effective learning time length is 60-120 minutes, the first incentive attribute is 20 minutes; the continuous effective learning time length is more than 120 minutes, and the first incentive attribute is 40 minutes.

[0090] For example, the learning state at 9:30-10:45 is an effective learning state, and this time period is an effective learning time range. The learning state at 10:45-11:00 is an ineffective learning state, and this time period is an ineffective learning time range. At this time, 10:45 is the learning end time of the effective learning time range. The first incentive attribute in the ineffective learning time range of 10:45-11:00 can be determined based on the effective learning time range of 9:30-10:45.

[0091] In S250, the first loss attribute is determined based on the ineffective learning time range with the start time of the effective learning time range as the ineffective learning end time and the continuous ineffective learning duration.

[0092] The first loss attribute can be used to represent the loss given to the use object, such as reducing the rest duration, reducing the reward use duration of the non-learning application, deducting points, and the like.

[0093] In this embodiment, after determining the effective learning time range with the current time as the effective learning end time, the ineffective learning time range with the start time of the effective learning time range as the ineffective learning end time can also be determined. Further, the first loss attribute can be determined in combination with the continuous ineffective learning duration corresponding to the ineffective learning time range. For example, the longer the continuous ineffective learning duration, the larger the first loss attribute can be set, and the shorter the continuous ineffective learning duration, the smaller the first loss attribute can be set. Alternatively, if the continuous ineffective learning duration is less than a ninth preset duration, the first loss attribute is 0, that is, no loss processing is performed; if the continuous ineffective learning duration is greater than the ninth preset duration and less than a tenth preset duration, the first loss attribute is set to be greater than 0.

[0094] For example, the time period of 9:30-10:45 is an effective learning time range, and 9:30 is the start time of the effective learning time range. The time period of 9:15-9:30 is an ineffective learning time range, and 9:30 is the ineffective learning end time of the ineffective learning time range at this time. At this time, the continuous ineffective learning duration of the ineffective learning time range pair is 15 minutes, and the first loss attribute can be determined based on 15 minutes.

[0095] In this embodiment, the first loss attribute is determined based on the ineffective learning time range with the start time of the effective learning time range as the ineffective learning end time and the continuous ineffective learning duration, including: when the first ineffective learning duration corresponding to the ineffective learning time range with the start time of the effective learning time range as the ineffective learning end time is less than a third preset duration, the continuous ineffective learning duration is adjusted based on the first ineffective learning duration; and the first loss attribute is determined based on the processed continuous ineffective learning duration.

[0096] Specifically, when the first invalid learning duration corresponding to the invalid learning time range with the start moment of the effective learning time range as the invalid learning end moment is less than the third preset duration, the continuous invalid learning duration can be adjusted based on the difference between the first invalid learning duration and the third preset duration, for example, the larger the difference, the higher the adjustment step, and the smaller the difference, the lower the adjustment step. Further, the first loss attribute can be determined based on the processed continuous invalid learning duration.

[0097] In this way, by considering the length of the continuous invalid learning duration before the start of the effective learning time range, the first loss attribute is determined, which can avoid the influence of the rest after the use of the object on the calculation of the available duration, and ensure the rationality of the available duration. At the same time, it promotes the use of objects to reduce the frequency of frequent distraction, and improves the learning efficiency.

[0098] S260, determining the resource adjustment duration corresponding to the effective learning time range based on the first incentive attribute and the first loss attribute.

[0099] In this embodiment, a basic adjustment duration can be set, for example, 5 minutes. According to the first incentive attribute, the basic adjustment duration is adjusted. For example, if the use object obtains 5 minutes of the first incentive attribute, the adjustment duration can be increased by 5 minutes. According to the first loss attribute, the adjusted basic adjustment duration is adjusted to obtain the resource adjustment duration. Or, the first incentive attribute and the first loss attribute are summed to obtain the resource adjustment duration corresponding to the effective learning time range.

[0100] S270, for each effective learning time range, determining the to-be-allocated learning duration based on the continuous effective learning duration and the resource adjustment duration corresponding to the effective learning time range.

[0101] In this embodiment, the continuous effective learning duration and the resource adjustment duration corresponding to the effective learning time range can be summed to obtain the to-be-allocated learning duration corresponding to the continuous effective learning duration.

[0102] S280, updating the available duration of the non-learning application based on all the to-be-allocated learning durations, and using the non-learning application within the updated available duration.

[0103] In the embodiment, the total time length can be obtained by summing up all the to-be-allocated learning time lengths. Further, based on the total time length, the to-be-used adjustment time length is determined, and the to-be-adjusted time length and the original available time length of the non-learning application are accumulated to obtain the new available time length of the non-learning application. For example, the total time length and the to-be-used adjustment time length are positively correlated. For example, if the total time length is 60-120 minutes, the to-be-used adjustment time length is increased by 10 minutes. If the to-be-allocated learning time length exceeds 120 minutes, the to-be-used adjustment time length is increased by 30 minutes. Alternatively, the to-be-used adjustment time length is increased by 5 minutes for each 20-minute increase in the total time length, until the to-be-used adjustment time length is increased to a preset limit value.

[0104] The technical solution of the embodiment determines the first incentive attribute based on the continuous effective learning time length corresponding to the effective learning time range when the current time is detected as an invalid learning time range and the invalid learning time range does not reach the preset rest condition, and the effective learning time range with the current time as the effective learning end time is greater than the second preset time length; determines the first loss attribute based on the invalid learning time range with the starting time of the effective learning time range as the invalid learning end time and the continuous invalid learning time length; and determines the resource adjustment time length corresponding to the effective learning time range based on the first incentive attribute and the first loss attribute. Further, for each effective learning time range, the to-be-allocated learning time length is determined based on the continuous effective learning time length and the resource adjustment time length corresponding to the effective learning time range; and the available time length of the non-learning application is updated based on all the to-be-allocated learning time lengths, so that the time length of using the non-learning application can be exchanged for learning, and the use object is encouraged to better manage the learning time, to encourage the use object to maintain an effective learning state, to improve the learning efficiency and autonomy, and to improve the interest and enthusiasm of the user in using the application.

[0105] Embodiment Three

[0106] Figure 3 is a flowchart of an application management method according to Embodiment Three of the present application, which further refines "S140" on the basis of the foregoing embodiments. The specific implementation can be seen from the technical solution of the embodiment. The technical terms that are the same as or corresponding to those in the foregoing embodiments will not be described herein.

[0107] As shown in Figure 3 , the method specifically includes the following steps:

[0108] S310, in the process of running the learning application, determining the current learning focus attribute of the use object corresponding to the wearable device, and obtaining the current behavior interaction data of the use object and the learning application.

[0109] S330, based on the current learning focus attribute and the current behavior interaction data, determining the current learning state of the use object.

[0110] S330, determine at least one discrete effective learning time range and ineffective learning time range associated with the use object according to the current learning state.

[0111] S340, when the duration of ineffective learning corresponding to any ineffective learning time range is greater than the first preset duration, determine a second loss attribute based on the duration of ineffective learning greater than the fourth preset duration among the durations of ineffective learning corresponding to all ineffective learning time ranges.

[0112] In this embodiment, when the duration of ineffective learning corresponding to any ineffective learning time range is greater than the first preset duration, the duration of ineffective learning greater than the fourth preset duration can be selected from the durations of ineffective learning corresponding to all ineffective learning time ranges. Furthermore, the second loss attribute is determined based on the selected duration of ineffective learning. It should be noted that the way of determining the second loss attribute based on the selected duration of ineffective learning is similar to the way of determining the first loss attribute, which will not be repeated here.

[0113] S350, determine a second incentive attribute based on the duration of effective learning greater than the fifth preset duration among the durations of effective learning corresponding to all effective learning time ranges.

[0114] In this embodiment, the duration of effective learning greater than the fifth preset duration can be selected from the durations of effective learning corresponding to all effective learning time ranges. Furthermore, the second incentive attribute is determined based on the selected duration of effective learning. It should be noted that the way of determining the second incentive attribute based on the selected duration of effective learning is similar to the way of determining the first incentive attribute, which will not be repeated here.

[0115] S360, determine a resource adjustment duration based on the second loss attribute and the second incentive attribute.

[0116] In this embodiment, a basic adjustment duration can be set. According to the second incentive attribute, the basic adjustment duration is adjusted. According to the second loss attribute, the adjusted basic adjustment duration is adjusted to obtain the resource adjustment duration. Or, the second incentive attribute and the second loss attribute are summed to obtain the resource adjustment duration.

[0117] S370, in response to a learning end event, sum the durations of effective learning corresponding to all effective learning time ranges to obtain a total duration.

[0118] In the embodiment, the updated available time length can be considered to reach the sixth preset time length, or the resource acquisition closing control in the wearable device is triggered by the use object, or the continuous invalid learning time length corresponding to the total invalid learning time range is greater than the seventh preset time length, and it is considered that the learning end event is detected. At this time, the steps S310 to S370 are no longer executed, that is, the acquisition of the available time length is stopped. The continuous valid learning time lengths corresponding to the total valid learning time range are summed to obtain the total time length.

[0119] S380, determining the acquired time length based on the resource adjustment time length and the total time length.

[0120] In the embodiment, the resource adjustment time length and the total time length are summed to obtain the acquired time length.

[0121] S390, updating the available time length of the non-learning application based on the acquired time length, so as to use the non-learning application within the updated available time length.

[0122] In the embodiment, the acquired time length and the original available time length of the non-learning application can be accumulated to obtain the new available time length of the non-learning application. The acquired time length can also be processed, such as multiplying the acquired time length by a preset proportion, and then the processed acquired time length and the original available time length of the non-learning application are accumulated to obtain the new available time length of the non-learning application.

[0123] The technical scheme of the embodiment, when the continuous invalid learning time length corresponding to any invalid learning time range is greater than the first preset time length, determines the second loss attribute based on the continuous invalid learning time length greater than the fourth preset time length among the continuous invalid learning time lengths corresponding to all invalid learning time ranges; determines the second incentive attribute based on the continuous valid learning time length greater than the fifth preset time length among the continuous valid learning time lengths corresponding to all valid learning time ranges; and determines the resource adjustment time length based on the second loss attribute and the second incentive attribute. Further, in response to the learning end event, the continuous valid learning time lengths corresponding to all valid learning time ranges are summed to obtain the total time length; the acquired time length is determined based on the resource adjustment time length and the total time length; and the available time length of the non-learning application is updated based on the acquired time length, so as to realize the exchange of the use time length of the non-learning application by learning, encourage the use object to better manage the learning time, encourage the use object to maintain the valid learning state, improve the learning efficiency and autonomy, and improve the interest and enthusiasm of the user in using the application.

[0124] Embodiment Four

[0125] As an optional embodiment of the above-mentioned embodiments, in order to make the skilled in the art further clear the technical scheme of the embodiments of the present application, a specific application scenario example is given. Specifically, refer to the following specific content.

[0126] Referring to Figure 4 A control object (e.g., a guardian) of a user (e.g., a child) starts a focus mode of a wearable device (e.g., a smart watch) through a mobile terminal (e.g., a mobile phone).

[0127] After starting the focus mode, the useable duration of a non-learning application integrated in the wearable device can be detected, and the control object can freely set the useable duration of the non-learning application during non-learning time and the non-learning application that can be opened only within the useable duration.

[0128] When starting the non-learning application, it can be determined whether the useable duration of the non-learning application is greater than a preset threshold (e.g., 0). If not, the starting of the non-learning application is limited, i.e., the user cannot start the non-learning application. If yes, the non-learning application is started, and the useable duration is updated. After the preset threshold, the current preset threshold is automatically closed, and a prompt message that the useable duration is 0 and learning is required to obtain the useable duration is sent to the user. The user can start the calculation of the useable duration by the wearable device by starting the learning function of the wearable device.

[0129] During the running of the learning application, the behavior of the user can be detected by elements such as a camera, a microphone, an accelerometer, and a gravity sensor, and the current learning focus attribute can be obtained under the authorization and permission of the user. For example, the microphone can receive the surrounding environment of the user, and the learning focus of the user can be determined by voiceprint, such as self-talk, which is not determined as distraction, and the user's conversation, which is determined as distraction, etc., to obtain a first focus. The camera can monitor the focus of the user's eyes, and the learning situation of the user can be determined by the dictionary and search function of the wearable device. At this time, the user can gaze at the wearable device, and the camera only needs to monitor whether the eyes and head of the user have a large swing that may not be focused, such as wandering eyes and turning head to look away from the desktop, etc., to obtain a second focus. The hand movement of the user is detected by the accelerometer and gravity sensor, such as a large range of swing of the user's hand, which cannot correctly determine that the user is currently learning, i.e., determined as distraction, etc., to obtain a third focus. Further, based on the first focus, the second focus, and the third focus, the current learning focus attribute of the user is determined.

[0130] When it is determined, based on the current learning focus attribute and the current behavior interaction data of the user with the learning class application, that the current learning state of the user is an invalid learning state, the timing can be stopped, and the user is reminded to return to reality in time. When the user completes a certain length of learning and turns off the focus mode, the resource adjustment duration can be determined based on the continuous invalid learning duration, the valid learning time range, and the invalid learning time range, and the usable duration of the non-learning class application can be updated based on the resource adjustment duration and the valid learning time range, so that the non-learning class application is used within the updated usable duration.

[0131] In this way, the length of time for using the non-learning class application can be exchanged for learning, while encouraging the user to better manage learning time, motivating the user to maintain an effective learning state, improving learning efficiency and autonomy, and enhancing the user's interest and enthusiasm for using the application.

[0132] Embodiment Five

[0133] Figure 5 is a structural schematic diagram of an application management device according to Embodiment Five of the present application. The device is configured in a wearable device that integrates a learning class application and a non-learning class application, as shown in Figure 5 The device includes a data acquisition module 410, a current learning state determination module 420, a learning time range determination module 430, a resource adjustment duration determination module 440, and a usable time updating module 450.

[0134] The data acquisition module 410 is configured to determine the current learning focus attribute of the user corresponding to the wearable device during the running of the learning class application, and to acquire the current behavior interaction data of the user with the learning class application. The current learning state determination module 420 is configured to determine the current learning state of the user based on the current learning focus attribute and the current behavior interaction data. The learning time range determination module 430 is configured to determine at least one discrete valid learning time range and invalid learning time range associated with the user according to the current learning state. The resource adjustment duration determination module 440 is configured to determine a resource adjustment duration based on the continuous invalid learning duration, the valid learning time range, and the invalid learning time range when the continuous invalid learning duration corresponding to any invalid learning time range is greater than a first preset duration. The usable time updating module 450 is configured to update the usable duration of the non-learning class application based on the resource adjustment duration and the valid learning time range, so that the non-learning class application is used within the updated usable duration.

[0135] The technical scheme of the embodiment solves the problem of the prior art that the use of an entertainment application is controlled based on a password and a set use duration, which reduces the interest and enthusiasm of a user, and achieves the following: in the process of running a learning application, a current learning focus attribute of a use object corresponding to a wearable device is determined, and current behavior interaction data of the use object and the learning application is determined; the current learning focus attribute and the current behavior interaction data are combined to determine a current learning state of the use object, which improves the accuracy of learning state determination and avoids misjudgment. Further, at least one discrete effective learning time range and an ineffective learning time range associated with the use object are determined according to the current learning state; when a continuous ineffective learning duration corresponding to any ineffective learning time range is greater than a first preset duration, a resource adjustment duration for adjusting the use duration is determined based on the continuous ineffective learning duration, the effective learning time range, and the ineffective learning time range. Then, the useable duration of a non-learning application is dynamically updated based on the resource adjustment duration and the effective learning time range, which realizes that the use object can exchange the use duration of the non-learning application by learning, improves the autonomy of the user in learning, improves the accuracy, fairness, and reliability of the determination of the useable duration, and balances the allocation of learning and entertainment resources, thereby improving the interest and enthusiasm of the user in using the application.

[0136] On the basis of the above device, optionally, the data acquisition module 410 comprises:

[0137] A first focus degree determination unit is configured to acquire voice information corresponding to the use object based on a microphone device in the wearable device, and determine a first focus degree based on the voice information.

[0138] A second focus degree determination unit is configured to acquire image information corresponding to the use object based on a camera device in the wearable device, and determine a second focus degree based on the image information.

[0139] A third focus degree determination unit is configured to acquire first acceleration information corresponding to the use object based on an accelerometer in the wearable device, acquire second acceleration information corresponding to the use object based on a gravity sensor in the wearable device, and determine a third focus degree based on the first acceleration information and the second acceleration information.

[0140] A current learning focus attribute determination unit is configured to determine a current learning focus attribute of the use object based on the first focus degree, the second focus degree, and the third focus degree.

[0141] On the basis of the above device, optionally, the current behavior interaction data at least comprises learning content interaction data and learning task interaction data, and the current learning state determination module 420 comprises:

[0142] a first unit configured to determine that the current learning state of the user is a valid learning state when the current learning focus attribute reaches a preset first learning condition;

[0143] a second unit configured to determine a content difficulty attribute corresponding to learning content interaction data in the current behavior interaction data and a completion attribute corresponding to the learning task interaction data when the current learning focus attribute does not reach the preset first learning condition, and determine that the current learning state is a valid learning state when it is determined that a preset second learning condition is met based on the content difficulty attribute and / or the completion attribute;

[0144] a third unit configured to determine that the current learning state of the user is an invalid learning state when the current learning focus attribute does not reach the preset first learning condition and it is determined that the preset second learning condition is not met based on the content difficulty attribute and the completion attribute.

[0145] On the basis of the above device, optionally, the resource adjustment duration determination module 440 comprises:

[0146] a first incentive attribute determination unit configured to, when it is detected that the current time is an invalid learning time range and the invalid learning time range does not reach a preset rest condition, determine a first incentive attribute based on a continuous valid learning duration corresponding to a valid learning time range with the current time as a valid learning end time if the valid learning time range is greater than a second preset duration;

[0147] a first loss attribute determination unit configured to determine a first loss attribute based on an invalid learning time range with a starting time of the valid learning time range as an invalid learning end time and the continuous invalid learning duration;

[0148] a resource adjustment duration determination first unit configured to determine a resource adjustment duration corresponding to the valid learning time range based on the first incentive attribute and the first loss attribute.

[0149] On the basis of the above device, optionally, the first loss attribute determination unit comprises

[0150] a reduction processing unit configured to, when a first invalid learning duration corresponding to the invalid learning time range with the starting time of the valid learning time range as the invalid learning end time is less than a third preset duration, perform reduction processing on the continuous invalid learning duration based on the first invalid learning duration;

[0151] a first loss attribute determination subunit configured to determine the first loss attribute based on the processed continuous invalid learning duration.

[0152] On the basis of the above device, optionally, the available time updating module 450 comprises:

[0153] To-be-allocated learning duration determination unit, for each of the valid learning time range, based on the valid learning time range corresponding to the duration of the valid learning time and the resource adjustment duration, determine the to-be-allocated learning duration;

[0154] Available time updating unit, for updating the available time of the non-learning class application based on all the to-be-allocated learning duration.

[0155] On the basis of the above device, optionally, the resource adjustment duration determination module 440 comprises:

[0156] Second loss attribute determination unit, for when any invalid learning time range corresponding to the duration of the invalid learning time is greater than the first preset duration, based on all the duration of the invalid learning time range corresponding to the duration of the invalid learning time greater than the fourth preset duration, determine the second loss attribute;

[0157] Second incentive attribute determination unit, for based on all the duration of the valid learning time range corresponding to the duration of the valid learning time greater than the fifth preset duration, determine the second incentive attribute;

[0158] Resource adjustment duration determination second unit, for based on the second loss attribute and the second incentive attribute, determine the resource adjustment duration.

[0159] On the basis of the above device, optionally, the available time updating module 450 comprises:

[0160] Total duration determination unit, for responding to the learning end event, summing up all the duration of the valid learning time range corresponding to the duration of the valid learning time, to get the total duration;

[0161] Has acquired duration determination unit, for based on the resource adjustment duration and the total duration, determine the acquired duration;

[0162] Available time determination unit, for based on the acquired duration, update the available time of the non-learning class application.

[0163] On the basis of the above device, optionally, the device further comprises:

[0164] State adjustment determination unit, for adjusting the application state of the non-learning class application to the allowed use state.

[0165] On the basis of the above device, optionally, the device further comprises:

[0166] An information obtaining unit is configured to determine a usable duration or an application state of the non-learning type application when a starting operation for the non-learning type application is detected.

[0167] A starting unit is configured to start the non-learning type application when the usable duration is greater than a preset threshold or the application state is an allowed use state.

[0168] A usable duration adjusting unit is configured to update the usable duration during use of the non-learning type application.

[0169] On the basis of the above device, optionally, the device further comprises:

[0170] A prompt information generating unit is configured to adjust the application state of the non-learning type application to a limited use state when the usable duration of the non-learning type application is less than a preset threshold, and generate prompt information for obtaining the usable duration, so as to update the usable duration of the non-learning type application during running of the learning type application when a confirmation operation for the prompt information is detected.

[0171] The application management and control device provided in the embodiments of the present application can execute the application management and control method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0172] Embodiment six

[0173] Figure 6 is a structural schematic diagram of an electronic device for implementing the application management and control method of the embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0174] As Figure 6As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory 12, a random access memory 13, etc., communicatively connected to the at least one processor 11, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory 12 or loaded into the random access memory 13 from the storage unit 18. Various programs and data required for the operation of the electronic device 10 can also be stored in the random access memory 13. The processor 11, the read-only memory 12, and the random access memory 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0175] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, speakers, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0176] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the application governance method.

[0177] In some embodiments, the application governance method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the read-only memory 12 and / or the communication unit 19. When the computer program is loaded onto the random access memory 13 and executed by the processor 11, one or more steps of the application governance method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the application governance method by any other appropriate means, such as by means of firmware.

[0178] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip systems (SOCs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0179] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program

[0180] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory, read-only memory, erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0181] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0182] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0183] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0184] In particular, the processes described above with reference to the flow charts can be implemented as computer software programs. For example, embodiments of the application include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program comprising program code for performing the methods illustrated in the flow charts. In such embodiments, the computer program can be downloaded and installed from a network via the communication unit 19, or installed from the storage unit 18, or installed from the read only memory 12. When the computer program is executed by the processor 11, the above-described functions defined in the methods of the embodiments of the application are performed.

[0185] The embodiment of the present application further provides a computer program product comprising a computer program which, when executed by a processor, implements the application management and control method provided by any of the embodiments of the present application.

[0186] The computer program product, in the implementation, can be written in one or more programming languages or combinations thereof to implement computer program codes for performing the operations of the present application, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" language or similar programming languages. The program codes can be executed completely on a user computer, partially on a user computer, as an independent software package, partially on a user computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet by using an Internet service provider).

[0187] It should be understood that the steps shown above can be reordered, added or deleted, using various forms of flow. For example, the steps described in the present application can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0188] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An application management and control method, characterized in that: Applied to a wearable device, the wearable device integrating learning applications and non-learning applications, the method includes: During the operation of the learning application, determining a current learning focus attribute of a user corresponding to the wearable device, and obtaining current behavioral interaction data between the user and the learning application; Determining a current learning state of the user based on the current learning focus attribute and the current behavior interaction data; determining, according to the current learning state, at least one discrete valid learning time range and invalid learning time range associated with the usage object; When the continuous invalid learning time length corresponding to any invalid learning time range is greater than a first preset time length, determining the resource adjustment time length based on the continuous invalid learning time length, the valid learning time range, and the invalid learning time range; Based on the resource adjustment duration and the effective learning time range, the usable duration of the non-learning application is updated, so that the non-learning application can be used within the updated usable duration.

2. The method according to claim 1, characterized in that The determining of the current learning focus attribute of the user corresponding to the wearable device includes: acquiring, based on a microphone device in the wearable device, voice information corresponding to the user, and determining a first concentration level based on the voice information; acquiring, based on a camera device in the wearable device, image information corresponding to the user object, and determining a second degree of concentration based on the image information; Acquiring first acceleration information corresponding to the user based on an accelerometer in the wearable device, acquiring second acceleration information corresponding to the user based on a gravity sensor in the wearable device, and determining a third concentration based on the first acceleration information and the second acceleration information; A current learning concentration attribute of the user is determined based on the first concentration, the second concentration, and the third concentration.

3. The method according to claim 1, characterized in that The current behavior interaction data includes at least learning content interaction data and learning task interaction data. The determining of the current learning state of the user based on the current learning focus attribute and the current behavior interaction data includes: When the current learning concentration attribute reaches a preset first learning condition, determining to make the current learning state of the user object a valid learning state; When the current learning focus attribute does not meet the preset first learning condition, determining the content difficulty attribute corresponding to the learning content interaction data in the current behavior interaction data and the completion attribute corresponding to the learning task interaction data, and when it is determined that the preset second learning condition is met based on the content difficulty attribute and / or the completion attribute, determining that the current learning state is a valid learning state; When the current learning focus attribute does not meet the preset first learning condition and it is determined based on the content difficulty attribute and the completion attribute that the preset second learning condition is not met, the current learning state of the user is determined to be an invalid learning state.

4. The method according to claim 1, wherein When the continuous invalid learning duration corresponding to any invalid learning time range is greater than a first preset duration, determining the resource adjustment duration based on the continuous invalid learning duration, the valid learning time range, and the invalid learning time range includes: When it is detected that the current moment is within an invalid learning time range and the invalid learning time range does not meet the preset rest condition, if the effective learning time range with the current moment as the effective learning end time is greater than a second preset duration, determining a first incentive attribute based on the continuous effective learning duration corresponding to the effective learning time range; determining a first loss attribute based on an invalid learning time range with a start time of the valid learning time range as an end time of the invalid learning and the duration of the continuous invalid learning; A resource adjustment duration corresponding to the effective learning time range is determined based on the first incentive attribute and the first loss attribute.

5. The method according to claim 4, characterized in that The first loss attribute is determined based on the invalid learning time range with the starting moment of the valid learning time range as the invalid learning end moment and the continuous invalid learning duration, including When a first invalid learning duration corresponding to an invalid learning time range with the start time of the valid learning time range as the invalid learning end time is less than a third preset duration, the continuous invalid learning duration is reduced based on the first invalid learning duration; A first loss attribute is determined based on the processed continuous ineffective learning duration.

6. The method according to claim 4, characterized in that The updating of the usable duration of the non-learning application based on the resource adjustment duration and the effective learning time range includes: For each of the effective learning time ranges, determining the learning time to be allocated based on the continuous effective learning time corresponding to the effective learning time range and the resource adjustment time; Based on all the learning time to be allocated, the available time of the non-learning application is updated.

7. The method according to claim 6, characterized in that When the continuous invalid learning duration corresponding to any invalid learning time range is greater than a first preset duration, determining the resource adjustment duration based on the continuous invalid learning duration, the valid learning time range, and the invalid learning time range includes: When the continuous invalid learning time period corresponding to any invalid learning time range is greater than the first preset time period, determining the second loss attribute based on the continuous invalid learning time period greater than the fourth preset time period among the continuous invalid learning time periods corresponding to all the invalid learning time ranges; Determining a second incentive attribute based on a continuous effective learning duration greater than a fifth preset duration among the continuous effective learning durations corresponding to all the effective learning time ranges; A resource adjustment duration is determined based on the second loss attribute and the second incentive attribute.

8. The method according to claim 4, characterized in that The updating of the usable duration of the non-learning application based on the resource adjustment duration and the effective learning time range includes: In response to a learning end event, summing up the continuous effective learning durations corresponding to all the effective learning time ranges to obtain a total duration; Determining the acquired duration based on the resource adjustment duration and the total duration; Based on the acquired duration, the usable duration of the non-learning application is updated.

9. The method according to claim 1, characterized in that After updating the usable duration of the non-learning application, the method further includes: The application status of the non-learning application is adjusted to a usage-allowed state.

10. The method according to claim 1 or 9, characterized in that The method further comprises: When a startup operation for the non-learning application is detected, determining the usable duration or application status of the non-learning application; When the usable time is greater than a preset threshold, or the application state is in a state where use is permitted, starting the non-learning application; During the use of the non-learning application, the usable time is updated.

11. The method according to claim 10, characterized in that The method further comprises: When it is detected that the usable time of the non-learning application is less than a preset threshold, the application state of the non-learning application is adjusted to a restricted usage state, and a prompt message for obtaining the usable time is generated, so that when a confirmation operation of the prompt message is detected, the usable time of the non-learning application is updated during the operation of the learning application.

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