CN112150330A - Internet-based supervised learning method and system - Google Patents
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Abstract
The invention discloses a supervised learning method and system based on the Internet, wherein a first learning plan of a first user is obtained through first electronic equipment; obtaining a daily learning plan according to the first learning plan; obtaining a predetermined learning time according to the daily learning plan; obtaining a first spare time and a second spare time of the first user, wherein the first spare time and the second spare time are both larger than a preset learning time; inputting the first idle time and the second idle time into a training model to obtain output information of the training model, wherein the output information comprises learning state grade information of the first user at different times; and acquiring the spare time with higher learning state grade of the first user, and supervising the first user to learn in the spare time. The technical problem that a supervision system for assisting the user in learning, supervising the user and exciting the learning interest of the user is lacked in the prior art is solved.
Description
Technical Field
The invention relates to the field of supervised learning, in particular to a supervised learning method and system based on the Internet.
Background
The standing flag seems to be the normal state of the young people at the present stage, and the face marked by the flag which is set up by the user is also seemingly irreparable. How to select a proper target and keep the same with constant practice is an urgent problem to be solved.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the prior art is lack of a supervising system which assists the user in learning, supervises the user for learning and stimulates the learning interest of the user.
Disclosure of Invention
The embodiment of the application solves the technical problem that a monitoring system for assisting the user in learning, supervising the user and stimulating the learning interest of the user is lacked in the prior art by providing the internet-based supervised learning method and system, and achieves the technical effects of supervising and stimulating the user to learn according to the learning plan and the characteristics of the user.
In view of the foregoing problems, embodiments of the present application provide a supervised learning method and system based on the internet.
In a first aspect, an embodiment of the present application provides an internet-based supervised learning method, which is applied to a supervised learning system, where the supervised learning system is in communication connection with a first monitoring camera and a first electronic device, where the method includes: obtaining, by the first electronic device, a first learning plan for a first user; obtaining a daily learning plan according to the first learning plan; obtaining a predetermined learning time according to the daily learning plan; obtaining a first spare time and a second spare time of the first user, wherein the first spare time and the second spare time are both larger than a preset learning time; inputting the first idle time and the second idle time into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: a first free time, a second free time, and identification information identifying a learning state level of the first user; obtaining output information of the training model, wherein the output information comprises learning state grade information of the first user at different times; and acquiring the spare time with higher learning state grade of the first user, and supervising the first user to learn in the spare time.
In another aspect, the present application further provides an internet-based supervised learning system, wherein the system includes: a first obtaining unit, configured to obtain, by the first electronic device, a first learning plan of a first user; a second obtaining unit configured to obtain a daily learning plan according to the first learning plan; a third obtaining unit for obtaining a predetermined learning time according to the daily learning plan; a fourth obtaining unit, configured to obtain a first free time and a second free time of the first user, where the first free time and the second free time are both greater than a predetermined learning time; a first input unit, configured to input the first idle time and the second idle time into a training model, where the training model is obtained by training multiple sets of training data, and each of the multiple sets of training data includes: a first free time, a second free time, and identification information identifying a learning state level of the first user; a fifth obtaining unit, configured to obtain output information of the training model, where the output information includes learning state level information of the first user at different times; a sixth obtaining unit, configured to obtain a free time with a higher learning state level of the first user, and supervise the first user to learn in the free time.
In a third aspect, the present invention provides an internet-based supervised learning system, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the first learning plan is decomposed every day according to the first learning plan of the first user to obtain the daily learning plan, the preset learning time of the daily learning plan is obtained, the first free time and the second free time of the first user are input into the training model, the free time with a higher learning state level of the first user is obtained based on the continuous self-correction and adjustment logic of the training model, and the free time is recommended to the first user for learning, so that the technical effect of supervising the first user to learn in the better free time of the learning state according to the characteristic of the self learning state of the first user is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
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Fig. 1 is a schematic flowchart of a supervised learning method based on the internet according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an internet-based supervised learning system according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a first input unit 15, a fifth obtaining unit 16, a sixth obtaining unit 17, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the application solves the technical problem that a monitoring system for assisting the user in learning, supervising the user and stimulating the learning interest of the user is lacked in the prior art by providing the internet-based supervised learning method and system, and achieves the technical effects of supervising and stimulating the user to learn according to the learning plan and the characteristics of the user. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
The standing flag seems to be the normal state of the young people at the present stage, and the face marked by the flag which is set up by the user is also seemingly irreparable. How to select a proper target and keep the same with constant practice is an urgent problem to be solved. The prior art is lack of a supervising system which assists the user in learning, supervises the user for learning and stimulates the learning interest of the user.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides an internet-based supervised learning method, which is applied to a supervised learning system, wherein the supervised learning system is in communication connection with a first monitoring camera and a first electronic device, and the method comprises the following steps: obtaining, by the first electronic device, a first learning plan for a first user; obtaining a daily learning plan according to the first learning plan; obtaining a predetermined learning time according to the daily learning plan; obtaining a first spare time and a second spare time of the first user, wherein the first spare time and the second spare time are both larger than a preset learning time; inputting the first idle time and the second idle time into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: a first free time, a second free time, and identification information identifying a learning state level of the first user; obtaining output information of the training model, wherein the output information comprises learning state grade information of the first user at different times; and acquiring the spare time with higher learning state grade of the first user, and supervising the first user to learn in the spare time.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides an internet-based supervised learning method, which is applied to a supervised learning system, where the supervised learning system is in communication connection with a first monitoring camera and a first electronic device, and the method includes:
step S100: obtaining, by the first electronic device, a first learning plan for a first user;
specifically, the first electronic device is an electronic device of the first user, which has a communication function and a learning plan, and may be a mobile phone, a computer, or a tablet, which is not specifically limited herein, the first user is a user who wants to perform supervised learning by a supervised learning system, and the first learning plan is a target plan of the first user, that is, a total target plan.
Step S200: obtaining a daily learning plan according to the first learning plan;
specifically, the daily learning plan is a plan for arranging reasonable daily learning contents and total learning time obtained by comprehensively considering the time arrangement, learning ability and other factors of the first user. By giving a reasonable daily learning plan based on the target of the first user and the learning ability, time and the like of the first user, the situation that the user is worried and shy because the total target is too far away is avoided, and the technical effects that the problem of complexity and high difficulty is decomposed into simple problems, and the water drops penetrate through stones and gather into the sea are achieved.
Step S300: obtaining a predetermined learning time according to the daily learning plan;
specifically, the predetermined learning time is predetermined learning time period information for completing the daily goal, and further, the predetermined learning time is the minimum learning time given by the system for completing the daily plan.
Step S400: obtaining a first spare time and a second spare time of the first user, wherein the first spare time and the second spare time are both larger than a preset learning time;
specifically, the first free time and the second free time are free times which can meet the preset learning time in a day of the first user, and the free time which is most suitable for the first user to learn is obtained by performing analysis processing according to the obtained free time which meets the requirement of the first user.
Step S500: inputting the first idle time and the second idle time into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: a first free time, a second free time, and identification information identifying a learning state level of the first user;
specifically, the first and second idle times are input into a training model, which is a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely connecting a large number of simple processing units (called neurons), reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. Training based on a large amount of training data, wherein each set of training data of the training data comprises: the first idle time, the second idle time and identification information for identifying the learning state grade of the first user, the neural network model continuously self-corrects and adjusts, and when the output information of the neural network model reaches a preset accuracy rate/reaches a convergence state, the supervised learning process is ended. Through data training of the neural network model, the neural network model can process the input data more accurately, and the output idle time information is more suitable for the first user to learn. And acquiring output information of the training model, and selecting accurate and appropriate free time for the first user to learn based on the characteristic that the data is more accurate after the training model is trained.
Step S600: obtaining output information of the training model, wherein the output information comprises learning state grade information of the first user at different times;
specifically, the learning state grade is a grade division for evaluating the learning state of the first user. The evaluation condition can be obtained according to comprehensive consideration of evaluation results in multiple aspects. Specifically, the learning result may be obtained by comprehensively considering the examination result of the first user, the quality of the learning perception of the first user, and the like.
Step S700: and acquiring the spare time with higher learning state grade of the first user, and supervising the first user to learn in the spare time.
Specifically, the free time with a higher learning state level of the first user is obtained, and the first user is supervised to learn in the free time. Through the judgment of the learning states of the first user at different time periods, the optimal learning state suitable for the first user is obtained for supervised learning, and the technical effect of supervising the first user to learn in the better free time of the learning state according to the characteristics of the learning state of the first user is achieved.
Further, the embodiment of the present application further includes:
step S810: obtaining a learning insight of the first user;
step S820: inputting the learning insight of the first user into a convolutional neural network model to obtain output information of the convolutional neural network model, wherein the output information comprises the ranking information of the learning insight;
step S830: and generating a first correction parameter according to the rating information, and carrying out supervision and adjustment processing on the first user according to the first correction parameter.
Specifically, there is cloud in the ancient language, learning but not thinking deceive, in order to ensure the learning effect of the first user, obtaining the thinking and comprehension of the first user on the learning course of the current day, inputting the learning comprehension into a convolutional neural network model, analyzing the learning comprehension of the first user according to the convolutional neural network model to obtain an analysis result, judging the learning state of the first user according to the grade judgment of the learning comprehension of the first user, obtaining a first correction parameter when the learning state of the first user is not good, and performing intensive supervision on the first user according to the first correction parameter.
Further, in the step S830 of generating the first correction parameter according to the rating information in this embodiment of the present application further includes:
step S831: judging whether the rating information meets a first preset threshold value or not;
step S832: when the rating information meets a first preset threshold value, sharing the learning perception to a supervised learning system;
step S833: a first incentive scheme is generated by the supervised learning system.
Specifically, the first preset threshold is a level at which the perception rating is excellent, when the perception rating is excellent, it indicates that the first user has not only learned what floats on the surface of knowledge, but also has performed a deep thought on the knowledge, and really performs a deep analysis and digestion on the knowledge, so as to convert the knowledge into self understanding.
Further, step S900 in the embodiment of the present application further includes:
step S910: obtaining first eye action information of the first user through the first monitoring camera;
step S920: obtaining learning concentration degree information of the first user according to the first eye action;
step S930: when the learning concentration degree is lower than a first preset threshold value, first reminding information is obtained;
step S940: and reminding the first user to concentrate on learning according to the first reminding information.
Specifically, the first camera is a camera which can monitor the first user after the first user performs learning, first eye movement information of the first user is obtained through the first monitoring camera, and concentration degree of the first user in the learning process is obtained by judging eye movement of the first user. The first preset threshold is a learning state threshold obtained according to the learning state of the first user, when the first monitoring camera judges that the concentration degree of the first user is lower than the first preset threshold, first reminding information is obtained, and the first user is reminded to concentrate on learning according to the first reminding information.
Further, the embodiment of the present application further includes:
step S950: acquiring small action information of the first user through the first monitoring camera;
step S960: judging whether the small action information has positive effect on the class attending state of the first user according to the learning perception;
step S970: when the small action information generates positive action, the small action is updated to the non-negative influence action of the first user.
Specifically, the monitoring camera is used for obtaining small action information of the first user, and people can have some small actions belonging to the first user in the learning work, the small actions have good or bad effects, some small actions can improve the learning efficiency, otherwise, the small action information of the first user is obtained, the generation and duration of the small actions are obtained, whether the small actions have positive effects on the learning process is judged according to the learning perception conditions of the small action generation time and the small action generation time, and when the small actions have positive effects, the small actions are updated to non-negative effect actions of the first user. And judging whether the small action should be stopped or not through the influence of the small action on the learning effect so as to achieve the technical effect of ensuring the learning effect of the first user.
Further, said determining whether the small action information has a positive effect on the first user' S lesson listening status according to the learning comprehension, step S960 in this embodiment of the present application further includes:
step S961: when the small action information generates negative action, updating the small action information into the negative influence action of the first user;
step S962: when the first monitoring camera detects that the first user has the small action information, a first reminding instruction is obtained;
step S963: and reminding the first user to stop the small action and concentrate on learning according to the first reminding instruction.
Specifically, when the small motion has a negative effect and cannot help the first user to better enter the learning state and maintain the learning state, the small motion information is added to the negative effect motion of the first user. When the first monitoring camera detects that the first user has the small action information, a first reminding instruction is generated, and the first user is reminded to stop the small action and concentrate on learning according to the first reminding instruction.
In summary, the supervised learning method and system based on the internet provided by the embodiment of the present application have the following technical effects:
1. the first learning plan is decomposed every day according to the first learning plan of the first user to obtain the daily learning plan, the preset learning time of the daily learning plan is obtained, the first free time and the second free time of the first user are input into the training model, the free time with a higher learning state level of the first user is obtained based on the continuous self-correction and adjustment logic of the training model, and the free time is recommended to the first user for learning, so that the technical effect of supervising the first user to learn in the better free time of the learning state according to the characteristic of the self learning state of the first user is achieved.
2. Due to the fact that the mode that the concentration degree of the first user in the learning process is obtained by judging the eye movement of the first user is adopted, when the first monitoring camera judges that the concentration degree of the first user is lower than a first preset threshold value, first reminding information is obtained, and the technical effect that the first user is reminded to concentrate on learning according to the first reminding information is achieved.
3. Whether the small action is to be stopped or not is judged according to whether the small action has a positive influence on the learning state of the first user or not, so that the technical effect of ensuring the learning effect of the first user is achieved.
Example two
Based on the same inventive concept as the internet-based supervised learning method in the foregoing embodiment, the present invention further provides an internet-based supervised learning system, as shown in fig. 2, the system includes:
a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining a first learning plan of a first user through the first electronic equipment;
a second obtaining unit 12, the second obtaining unit 12 being configured to obtain a daily learning plan according to the first learning plan;
a third obtaining unit 13, wherein the third obtaining unit 13 is used for obtaining a preset learning time according to the daily learning plan;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to obtain a first free time and a second free time of the first user, where the first free time and the second free time are both greater than a predetermined learning time;
a first input unit 15, where the first input unit 15 is configured to input the first free time and the second free time into a training model, where the training model is obtained by training multiple sets of training data, and each of the multiple sets of training data includes: a first free time, a second free time, and identification information identifying a learning state level of the first user;
a fifth obtaining unit 16, where the fifth obtaining unit 16 is configured to obtain output information of the training model, where the output information includes learning state level information of the first user at different times;
a sixth obtaining unit 17, where the sixth obtaining unit 17 is configured to obtain a free time with a higher learning state level of the first user, and recommend the first user to learn in the free time.
Further, the system further comprises:
a seventh obtaining unit for obtaining learning insight of the first user;
a second input unit, configured to input the learning insight of the first user into a convolutional neural network model, to obtain output information of the convolutional neural network model, where the output information includes ranking information of the learning insight;
and the first supervision unit is used for generating a first correction parameter according to the rating information and carrying out supervision and adjustment processing on the first user according to the first correction parameter.
Further, the system further comprises:
the first judging unit is used for judging whether the rating information meets a first preset threshold value or not;
an eighth obtaining unit, configured to share the learning insight into a supervised learning system when the rating information satisfies a first preset threshold;
a ninth obtaining unit for generating a first incentive scheme by the supervised learning system.
Further, the system further comprises:
a tenth obtaining unit, configured to obtain first eye movement information of the first user through the first monitoring camera;
an eleventh obtaining unit configured to obtain learning concentration information of the first user according to the first eye movement;
a twelfth obtaining unit, configured to obtain first reminding information when the learning concentration degree is lower than a first preset threshold;
and the first reminding unit is used for reminding the first user to concentrate on learning according to the first reminding information.
Further, the system further comprises:
a thirteenth obtaining unit configured to obtain, by the first monitoring camera, small motion information of the first user;
a second judging unit, configured to judge whether the small motion information has an active effect on the first user listening state according to the learning comprehension;
a first updating unit, configured to update the small action to a non-negative impact action of the first user when the small action information generates a positive action.
Further, the system further comprises:
a second updating unit, configured to update the small action information into a negative influence action of the first user when the small action information generates a negative action;
a fourteenth obtaining unit, configured to obtain a first reminding instruction when the first monitoring camera detects that the first user has the small action information;
and the second reminding unit is used for reminding the first user to stop the small action and concentrate on learning according to the first reminding instruction.
Various changes and specific examples of the internet-based supervised learning method in the first embodiment of fig. 1 are also applicable to the internet-based supervised learning system in the present embodiment, and those skilled in the art can clearly understand the implementation method of the internet-based supervised learning system in the present embodiment through the foregoing detailed description of the internet-based supervised learning method, so that the detailed description is omitted here for the sake of brevity of the description.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the internet-based supervised learning method as described in the previous embodiments, the present invention further provides an internet-based supervised learning system, on which a computer program is stored, which, when being executed by a processor, implements the steps of any one of the above-described internet-based supervised learning methods.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the invention provides an internet-based supervised learning method, which is applied to a supervised learning system, wherein the supervised learning system is in communication connection with a first monitoring camera and first electronic equipment, and the method comprises the following steps: obtaining, by the first electronic device, a first learning plan for a first user; obtaining a daily learning plan according to the first learning plan; obtaining a predetermined learning time according to the daily learning plan; obtaining a first spare time and a second spare time of the first user, wherein the first spare time and the second spare time are both larger than a preset learning time; inputting the first idle time and the second idle time into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: a first free time, a second free time, and identification information identifying a learning state level of the first user; obtaining output information of the training model, wherein the output information comprises learning state grade information of the first user at different times; and acquiring the spare time with higher learning state grade of the first user, and supervising the first user to learn in the spare time. The technical problem that a monitoring system for assisting the user in learning, supervising the user and exciting the learning interest of the user is lacked in the prior art is solved, and the technical effects of supervising and exciting the user to learn according to the learning plan and the characteristics of the user are achieved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A supervised learning method based on the Internet is applied to a supervised learning system, and the supervised learning system is in communication connection with a first monitoring camera and first electronic equipment, wherein the method comprises the following steps:
obtaining, by the first electronic device, a first learning plan for a first user;
obtaining a daily learning plan according to the first learning plan;
obtaining a predetermined learning time according to the daily learning plan;
obtaining a first spare time and a second spare time of the first user, wherein the first spare time and the second spare time are both larger than a preset learning time;
inputting the first idle time and the second idle time into a training model, wherein the training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: a first free time, a second free time, and identification information identifying a learning state level of the first user;
obtaining output information of the training model, wherein the output information comprises learning state grade information of the first user at different times;
and acquiring the spare time with higher learning state grade of the first user, and supervising the first user to learn in the spare time.
2. The method of claim 1, wherein the method further comprises:
obtaining a learning insight of the first user;
inputting the learning insight of the first user into a convolutional neural network model to obtain output information of the convolutional neural network model, wherein the output information comprises the ranking information of the learning insight;
and generating a first correction parameter according to the rating information, and carrying out supervision and adjustment processing on the first user according to the first correction parameter.
3. The method of claim 2, wherein a first modification parameter is generated based on the rating information, the method comprising:
judging whether the rating information meets a first preset threshold value or not;
when the rating information meets a first preset threshold value, sharing the learning perception to a supervised learning system;
a first incentive scheme is generated by the supervised learning system.
4. The method of claim 1, wherein the method further comprises:
obtaining first eye action information of the first user through the first monitoring camera;
obtaining learning concentration degree information of the first user according to the first eye action;
when the learning concentration degree is lower than a first preset threshold value, first reminding information is obtained;
and reminding the first user to concentrate on learning according to the first reminding information.
5. The method of claim 1, wherein the method comprises:
acquiring small action information of the first user through the first monitoring camera;
judging whether the small action information has positive effect on the class attending state of the first user according to the learning perception;
when the small action information generates positive action, the small action is updated to the non-negative influence action of the first user.
6. The method of claim 5, wherein said determining from said learning insight whether said small-action information has a positive effect on said first user-attending-class status comprises:
when the small action information generates negative action, updating the small action information into the negative influence action of the first user;
when the first monitoring camera detects that the first user has the small action information, a first reminding instruction is obtained;
and reminding the first user to stop the small action and concentrate on learning according to the first reminding instruction.
7. An internet-based supervised learning system, wherein the system comprises:
a first obtaining unit, configured to obtain, by the first electronic device, a first learning plan of a first user;
a second obtaining unit configured to obtain a daily learning plan according to the first learning plan;
a third obtaining unit for obtaining a predetermined learning time according to the daily learning plan;
a fourth obtaining unit, configured to obtain a first free time and a second free time of the first user, where the first free time and the second free time are both greater than a predetermined learning time;
a first input unit, configured to input the first idle time and the second idle time into a training model, where the training model is obtained by training multiple sets of training data, and each of the multiple sets of training data includes: a first free time, a second free time, and identification information identifying a learning state level of the first user;
a fifth obtaining unit, configured to obtain output information of the training model, where the output information includes learning state level information of the first user at different times;
a sixth obtaining unit, configured to obtain a free time with a higher learning state level of the first user, and recommend the first user to learn during the free time.
8. An internet-based supervised learning system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any one of claims 1 to 6 are implemented when the program is executed by the processor.
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