A data collection and analysis method, system and terminal for a cloud education platform

Abstract

The present application relates to the field of cloud education and learning, and in particular to a data collection and analysis method, system and terminal for a cloud education platform, wherein the method comprises obtaining the current time; judging whether the current time is in the learning time based on the current time; if it is in the learning time, obtaining the facial information of the student; judging whether the student is in the learning state based on the facial information; if it is judged that the student is not in the learning state, obtaining the change in the number of pulse beats of the student; judging whether the student is in the learning state based on the change in the number of pulse beats; if the change in the number of pulse beats exceeds the change threshold, confirming that the student is not in the learning state; if the change in the number of pulse beats does not exceed the change threshold, confirming that the student is in the learning state. It is convenient for teachers to grasp the learning state of students in real time.

Description

Technical Field

[0001] The present application relates to the field of cloud education and learning, and in particular to a data collection and analysis method, system and terminal for a cloud education platform. Background Art

[0002] Online education refers to a method of content dissemination and rapid learning through the application of information technology and Internet technology. With the rapid development of the Internet, online education has become popular among each of us. Online education is popular among people because of its flexible teaching time and rich resources.

[0003] In the related technology, when primary and secondary school students use the cloud education learning platform at home, teachers cannot supervise students in a timely manner like in the classroom, and due to the limited screen space, teachers cannot always see everyone's learning status.

[0004] Regarding the above-mentioned related technologies, the inventor believes that due to the lack of supervision on the cloud-based education and learning platform, some unconscious students are prone to being absent-minded when studying on the cloud-based education and learning platform, and teachers are unable to grasp the students' learning situation. Summary of the invention

[0005] In order to facilitate teachers to understand students' learning situation on the cloud education platform, this application provides a data collection and analysis method, system and terminal for a cloud education platform.

[0006] The data collection and analysis method, system and terminal of a cloud education platform provided in this application adopt the following technical solutions:

[0007] A data collection and analysis method for a cloud education platform, comprising:

[0008] Get the current time;

[0009] Based on the current time, determining whether the current time is within the learning time;

[0010] If it is during the learning time, obtaining the student's facial information;

[0011] Based on the facial information, preliminarily determine whether the student is in a learning state;

[0012] If it is preliminarily determined that the student is not in a learning state, the change in the student's pulse beat frequency is obtained;

[0013] Based on the change in the pulse beat frequency, further determining whether the student is in a learning state;

[0014] If the change in the pulse beat frequency exceeds the change threshold, it is determined that the student is not in a learning state;

[0015] If the change in the pulse beat frequency does not exceed the change threshold, it is confirmed that the student is in a learning state.

[0016] By adopting the above technical solution, there is a fixed time on the cloud education and learning platform. According to whether the current time is in the learning time, it is judged whether the student is currently on the cloud education and learning platform. If it is in the learning time, the student's facial information is obtained, and a preliminary judgment is made based on the facial information whether the student is in a serious learning state. If not, it is preliminarily judged that the student is not in a learning state. In order to prevent misjudgment, the student's pulse beats are combined to further judge whether the student is really in a serious learning state. For example, when the student is playing games, the pulse beats will not remain relatively stable. If the student's pulse is not in a stable state, it is determined that the student is not in a learning state. If it is in a stable state, it proves that an error has occurred in judging only based on facial information. The student's learning state when on the cloud education and learning platform is judged based on the student's facial information and pulse beats when on the cloud education and learning platform, so that teachers can understand the student's learning situation on the cloud education platform.

[0017] Optionally, the preliminarily judging whether the student is in a learning state based on the facial information includes:

[0018] Analyze the facial information to obtain the student's eye focus direction;

[0019] Determining whether the eye focusing direction is toward the screen;

[0020] If the eye focus direction is toward the screen, a screenshot is obtained to determine whether the student is in a learning state;

[0021] If the eye focus direction is not facing the screen, the student's head posture is obtained to determine whether the student is in a learning state.

[0022] By adopting the above technical solution, the student's facial information is analyzed to obtain whether the student's eyes are fixed on the computer screen. If the student is staring at the computer screen, it is necessary to combine the screenshot to determine whether the student is in a learning state. If the eyes are not fixed on the screen, it cannot be simply assumed that the student is not studying seriously. Whether the student is in a learning state is determined based on the student's head posture. When judging whether the student is in a learning state, it is necessary to combine multiple situations to improve the accuracy of the judgment result.

[0023] Optionally, obtaining the head posture of the student to determine whether the student is in a learning state includes:

[0024] Based on the head posture of the student, determining whether the head posture of the student is bowing;

[0025] If the head posture is head down, then obtain the head down time;

[0026] Determine whether the head-down time is greater than the head-down threshold;

[0027] If the head-down time is greater than the head-down threshold, it is determined that the student is not in a learning state.

[0028] By adopting the above technical solution, when the eyes are not staring at the screen, the student's head posture is used to determine whether the student is in a learning state. If the student lowers his head for a long time, he is likely to be doing other things that are not related to the learning state. At this time, the judgment result is that the student is not currently in a learning state.

[0029] Optionally, obtaining a screenshot to determine whether the student is in a learning state includes:

[0030] Based on the screenshots, determining whether the student is in a learning state;

[0031] If there is only the Cloud Education Learning Platform page in the screenshot, the student is in the learning state;

[0032] If there are other pages in the screenshot, the student is not in the learning state.

[0033] By adopting the above technical solution, when a student's eyes are fixed on the screen, it cannot be simply determined that the student is currently in a student state. The student may be staring at the screen of a computer, mobile phone or other smart product but is doing other things. At this time, by obtaining a screenshot of the computer device used by the student in class, the content in the screenshot is analyzed. If there are other pages in the screenshot except the cloud education learning platform, then it is judged that the student is not studying. If there is only one page of the cloud education learning platform in the screenshot, it is further judged whether the student is studying based on the time the student stares at the computer screen, so as to prevent the situation where the student just stares at the screen for a short time without actually studying.

[0034] Optionally, obtaining the focusing time of the eye focusing direction facing the screen to determine whether the student is in a learning state includes:

[0035] Obtaining the focusing time of the eye focusing direction facing the screen;

[0036] Determining whether the focusing time is greater than a time threshold;

[0037] If the focus time is greater than the time threshold, it is determined that the student is in a learning state;

[0038] If the focusing time is not greater than the time threshold, the time when the eye focusing direction leaves the screen is obtained to determine whether the student is in a learning state.

[0039] By adopting the above technical solution, when only the cloud education learning platform page exists on the screen, whether the student is studying is judged based on the time the student stares at the computer screen. If the student only stares at the computer screen occasionally when learning through the cloud education platform, it cannot be determined that the student is studying. The time of staring at the screen must reach a certain time threshold to determine that the student’s current state is indeed learning. If the student’s eyes leave the screen, it is necessary to combine the time the student’s eyes leave the screen to judge whether the student is really not studying when leaving the screen. It is possible that the student’s eyes are away from the screen for a short period of time or for reasons such as being distracted or going to get something, and the probability of making a wrong judgment is reduced as much as possible.

[0040] Optionally, obtaining the time when the eye focus direction leaves the screen to determine whether the student is in a learning state includes:

[0041] Determine whether the time when the eye focus direction leaves the screen is greater than the distraction threshold;

[0042] If the time when the eye focus direction leaves the screen is greater than the distraction threshold, it is determined that the student is not in a learning state;

[0043] If the time that the eye focus direction leaves the screen is not greater than the distraction threshold, it is determined that the student is in a learning state.

[0044] By adopting the above technical solution, if the time when the student's eyes are away from the screen is greater than the distraction threshold, it proves that the student is no longer studying on the cloud education platform at this moment, but is doing other things instead. Therefore, it is determined that the student is not in a learning state. If the student's eyes are away from the screen for a short time, then the student is also in a learning state at this time.

[0045] Optionally, the step of confirming that the student is not in a learning state includes:

[0046] Get student account number;

[0047] Based on the student account, the student name is obtained and the deduction record is made.

[0048] By adopting the above technical solution, the student account used to log in to the cloud education learning platform is obtained, the student name is matched according to the student's login account, and points are deducted from students who are not in the learning state, so that teachers can evaluate students.

[0049] In a second aspect, the present application provides a data collection and analysis system for a cloud education platform.

[0050] A data collection and analysis system for a cloud education platform, comprising:

[0051] Acquisition module, used to obtain the current time;

[0052] A first judging module, used for judging whether the current time is within the learning time based on the current time;

[0053] A second acquisition module is used to acquire the student's facial information if it is during the learning time;

[0054] A second judgment module is used to preliminarily judge whether the student is in a learning state based on the facial information;

[0055] The third acquisition module is used to obtain the change in the student's pulse beat frequency if it is preliminarily determined that the student is not in a learning state;

[0056] A third judgment module is used to further judge whether the student is in a learning state based on the change in the pulse beat frequency;

[0057] A first execution module is used to confirm that the student is not in a learning state if the change in the pulse beat frequency exceeds a change threshold;

[0058] The second execution module is used to confirm that the student is in a learning state if the change in the pulse beat frequency does not exceed a change threshold.

[0059] By adopting the above technical solution, the first acquisition module acquires the current time, the first judgment module determines whether the current time is within the learning time, the second acquisition module acquires the student's facial information within the learning time, the second judgment module determines whether the student is learning based on the facial information, the third acquisition module acquires the pulse beat change when the student is not in the learning state, the third judgment module determines whether the student is not in the learning state based on the pulse beat change, the first execution module confirms that the student is not in the learning state when the pulse beat change exceeds the change threshold, and the second execution module confirms that the student is in the learning state when the pulse beat change does not exceed the change threshold. It is convenient for teachers to grasp the learning situation of students on the cloud education platform.

[0060] In a third aspect, the present application provides a terminal device, which adopts the following technical solution:

[0061] A terminal device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor loads and executes the computer program, the data collection and analysis method of the cloud education platform mentioned above is adopted.

[0062] By adopting the above technical solution, a computer program is generated by the data collection and analysis method of the above-mentioned cloud education platform, and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0063] In summary, this application includes the following beneficial technical effects:

[0064] There is a fixed time on the cloud education learning platform. Whether the current time is in the learning time is determined based on whether the student is currently on the cloud education learning platform. If it is in the learning time, the student's facial information is obtained, and a preliminary judgment is made based on the facial information whether the student is in a serious learning state. If not, it is preliminarily judged that the student is not in a learning state. In order to prevent misjudgment, the student's pulse beats are combined to further judge whether the student is really in a serious learning state. For example, when a student is playing games, the pulse beats will not remain relatively stable. If the student's pulse is not in a stable state, then it is determined that the student is not in a learning state. If it is in a stable state, it proves that there is an error in judging only based on facial information. The student's learning state when on the cloud education learning platform is judged based on the student's facial information and pulse beating when the student is on the cloud education learning platform. Reminders are given when the learning state is not good to improve the student's learning efficiency through the cloud education platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a method flow chart of a data collection and analysis method for a cloud education platform according to an embodiment of the present application;

[0066] Figure 2 is a flow chart of a method for preliminarily judging whether a student is in a learning state based on facial information according to an embodiment of the present application;

[0067] Figure 3 is a flow chart of a method for obtaining a student's head posture to determine whether the student is in a learning state according to an embodiment of the present application;

[0068] Figure 4 is a flow chart of a method for obtaining a screenshot to determine whether a student is in a learning state in an embodiment of the present application;

[0069] Figure 5 This is a flow chart of a method for obtaining the focus time of the eye focus direction facing the screen to determine whether the student is in a learning state according to an embodiment of the present application;

[0070] Figure 6 is a flow chart of a method for obtaining the time when the eye focus direction leaves the screen to determine whether the student is in a learning state according to an embodiment of the present application;

[0071] Figure 7 is a flow chart of the method after confirming that the student is not in the learning state in the embodiment of the present application;

[0072] Figure 8 It is a system block diagram of a data collection and analysis system of a cloud education platform in an embodiment of the present application.

[0073] Explanation of the accompanying drawings: 1. First acquisition module; 2. First judgment module; 3. Second acquisition module; 4. Second judgment module; 5. Third acquisition module; 6. Third judgment module; 7. First execution module; 8. Second execution module. DETAILED DESCRIPTION

[0074] The present application is further described in detail below in conjunction with all the accompanying drawings.

[0075] The present application embodiment discloses a data collection and analysis method for a cloud education platform, referring to Figure 1 ,include:

[0076] S100: Get current time.

[0077] Specifically, the current time is Beijing time.

[0078] S110. Based on the current time, determine whether the current time is within the learning time.

[0079] Specifically, online learning through the cloud education platform has set learning time periods. For example, 8 to 10 in the morning is the time for Chinese learning. For example, the current time is 8:10 in the background time, so this moment is within the learning time.

[0080] S120: If it is during study time, obtain the student's facial information.

[0081] Specifically, the facial information is the facial image of the student taken by a camera when the student accesses the cloud education and learning platform.

[0082] S130: Based on the facial information, preliminarily determine whether the student is in a learning state.

[0083] Specifically, by analyzing facial information, we can obtain information such as the student's eye gaze direction, head posture, and facial expression, and the learning status is whether the student is currently studying seriously.

[0084] S140. If it is preliminarily determined that the student is not in a learning state, the change in the student's pulse beat rate is obtained.

[0085] Specifically, the change in the pulse beats is the change in the pulse beats within a certain period of time. For example, a student's pulse is 80 beats / minute, and at a certain moment his pulse becomes 110 beats / minute, then the change in the pulse is 30 beats / minute. The pulse beats can be detected by wearing relevant electronic equipment for pulse detection, such as an electronic watch with pulse detection function.

[0086] S150. Based on the change in the pulse beat frequency, further determine whether the student is in a learning state.

[0087] Specifically, in order to prevent inaccurate judgment of whether a student is in a learning state based solely on facial information, the student's pulse changes are taken into account and combined to judge whether the student is in a learning state, thereby improving the accuracy of the judgment.

[0088] S160: If the change in the pulse beat rate exceeds the change threshold, it is determined that the student is not in a learning state.

[0089] Specifically, the threshold value is changed to be pre-set in the execution subject to determine whether the student's emotions fluctuate greatly. For example, when playing games, a person's pulse will not remain stable. When studying on the cloud education platform, the pulse suddenly changes greatly, which proves that the student is not in a learning state, but is doing other things, such as playing games.

[0090] S170: If the change in the pulse beat rate does not exceed the change threshold, it is confirmed that the student is in a learning state.

[0091] The implementation principle of the embodiment of the present application is as follows: the cloud-based education and learning platform has a fixed time. Whether the current time is in the learning time is determined based on whether the student is currently on the cloud-based education and learning platform. If it is in the learning time, the student's facial information is obtained, and a preliminary judgment is made based on the facial information as to whether the student is in a serious learning state. If not, a preliminary judgment is made that the student is not in a learning state. In order to prevent misjudgment, the student's pulse rate is combined to further determine whether the student is really in a serious learning state. For example, when a student is playing games, the pulse rate will not remain relatively stable. If the student's pulse is not in a stable state, then it is determined that the student is not in a learning state. If it is in a stable state, it proves that an error has occurred in judging based solely on facial information. The student's learning state when on the cloud-based education and learning platform is judged based on the student's facial information and pulse rate when the student is on the cloud-based education and learning platform, so that teachers can understand the student's learning situation on the cloud-based education platform.

[0092] Reference Figure 2 Based on facial information, the initial judgment of whether the student is in a learning state includes:

[0093] S200, analyzing facial information to obtain the student's eye focus direction.

[0094] Specifically, when studying on the cloud education platform, people's line of sight should be looking forward. By analyzing the facial image in the video, it can be obtained whether the student's eyes are focused on the screen.

[0095] S210: Determine whether the eye focus direction is toward the screen.

[0096] Specifically, if the student's eyes are not looking at the screen during the study time, then there is a high probability that the student is not in a learning state.

[0097] S220: If the eyes are focused toward the screen, a screenshot is obtained to determine whether the student is in a learning state.

[0098] Specifically, the screenshot is the screen of each student who studies through the cloud education platform, and can be a screenshot of a computer, tablet, mobile phone or other smart terminal. When the eyes are focused on the screen, if the student stares at the computer screen but does not do anything related to learning, such as opening the cloud education learning platform to study and opening an online TV series at the same time, in this case, although the student is staring at the computer screen, he is not studying, so it is necessary to judge based on the specific content of the screenshot.

[0099] S230: If the eye focus direction is not facing the screen, obtain the student's head posture to determine whether the student is in a learning state.

[0100] Specifically, when students' eyes are not fixed on the computer screen, they may be studying, such as practicing arithmetic or bending down to pick up something. Therefore, we can judge whether they are studying based on their head posture. The head posture refers to the posture of the student's head at this time, such as lowering the head or tilting the head.

[0101] The implementation principle of the embodiment of the present application for preliminarily judging whether a student is in a learning state based on facial information is as follows: analyzing the facial information of the student to obtain whether the student's eyes are fixed on the computer screen; if the student is fixed on the computer screen, it is necessary to combine the screenshot to judge whether the student is in a learning state; if the student's eyes are not fixed on the screen, it cannot be simply assumed that the student is not studying seriously. It is judged whether the student is in a learning state based on the student's head posture. When judging whether the student is in a learning state, it is judged based on multiple situations to improve the accuracy of the judgment result.

[0102] Reference Figure 3 , obtaining the student's head posture to determine whether the student is in the learning state includes:

[0103] S300: Based on the head posture of the student, determine whether the head posture of the student is lowering the head.

[0104] Specifically, head posture can also be obtained through camera capture. When a student is not looking at the screen but lowering his head, he may not be in a learning state. The time of lowering his head can be used to further determine whether he is not in a learning state.

[0105] S310: If the head posture is lowering, obtain the lowering time.

[0106] Specifically, the head-down time is the time from when the student was detected to now.

[0107] S320: Determine whether the head-down time is greater than a head-down threshold.

[0108] Specifically, the head-down threshold is a preset maximum time for judging that the student is not in a learning state. For example, the head-down threshold is 10 minutes. When it is detected that the student has lowered his head for more than 10 minutes, it is judged that the student is not studying.

[0109] S330: If the head-down time is greater than the head-down threshold, it is determined that the student is not in a learning state.

[0110] S340: If the head-down time is not greater than the head-down threshold, it is determined that the student is in a learning state.

[0111] The implementation principle of the embodiment of the present application for obtaining the student's head posture to determine whether the student is in a learning state is as follows: when the eyes are not staring at the screen, the student's head posture is used to determine whether the student is in a learning state. If the student lowers his head and keeps his head down for a long time, he is likely to be doing other things that are not related to the learning state. At this time, a judgment result is obtained that the student is not currently in a learning state.

[0112] Reference Figure 4 , taking screenshots to determine whether students are in learning status includes:

[0113] S400: Based on the screenshot, determine whether the student is in a learning state.

[0114] Specifically, the screenshots are taken through smart terminal devices such as computers, mobile phones and tablets when students are using the cloud education and learning platform. The screenshots are taken at irregular times and the number of times they are taken is also random. The screenshot function will only be triggered when the students are staring at the computer screen.

[0115] S410. If there is only the cloud education learning platform page in the screenshot, the focusing time of the eye focusing direction facing the screen is obtained to determine whether the student is in a learning state.

[0116] Specifically, when learning through the cloud education platform, either through the pre-installed running software or through the web link, no matter which method is used, there will only be one page of the cloud education learning platform on the screen. Compare the captured image with the preset image. If there is only the icon of the cloud education platform in the taskbar icon, it means that there is only one page of the cloud education platform. When there is only one page of the cloud education platform, it is combined with the time that the student stares at the screen to further judge whether he is in a learning state, rather than simply assuming that he is studying seriously without opening other things.

[0117] S420. If there are other pages in the screenshot, the student is not in the learning state.

[0118] Specifically, other pages are pages unrelated to the cloud education learning platform, such as watching TV series, playing online games, etc. During class, when pages unrelated to the cloud education platform appear in the screenshot, it is determined that the student is not in a learning state.

[0119] The implementation principle of obtaining screenshots to determine whether students are in a learning state in the embodiment of the present application is as follows: when a student's eyes are staring at the screen, it cannot be simply determined that the student is currently in a student state. The student may be staring at the screen of a computer, mobile phone or other smart product but is doing other things. At this time, a screenshot of the computer device used by the student in class is obtained, and the content in the screenshot is analyzed. If there are pages other than the cloud education learning platform in the screenshot, then it is determined that the student is not studying. If there is only one page of the cloud education learning platform in the screenshot, it is further determined whether the student is studying based on the time the student stares at the computer screen to prevent the student from just staring at the screen for a short time without actually studying.

[0120] Reference Figure 5 , obtaining the focus time of the eye focus direction facing the screen to determine whether the student is in the learning state includes:

[0121] S510: Obtain the focusing time of the eye focusing direction facing the screen.

[0122] Specifically, the focus time is the time that the students' eyes continue to stare at the computer screen. The focus time starts to be calculated after the person's gaze is detected as staring at the computer screen.

[0123] S520: Determine whether the focusing time is greater than a time threshold.

[0124] Specifically, the time threshold is the shortest time for judging that a student has entered a learning state. For example, when a student is studying through a cloud education platform, he only stares at the computer screen for a short time and then looks away. At this time, it proves that the student has not entered a learning state, but is just completing a learning task. For example, the time threshold is set to 5 minutes, but the longest time the student stares at the screen is only 1 minute, and he cannot even stare at the computer screen continuously, so naturally he is not studying seriously. However, if the student has been studying, but his eyes have briefly left the computer screen because of other things in the middle, it cannot be judged that the student is not studying seriously. It is necessary to combine the time when the eyes leave the screen in the direction of focus to further judge whether they are studying.

[0125] S530: If the focus time is greater than the time threshold, it is determined that the student is in a learning state.

[0126] Specifically, when the time spent staring at the screen is greater than the time threshold, the student has entered the learning state and is learning.

[0127] S540: If the focusing time is not greater than the time threshold, the time when the eye focusing direction leaves the screen is obtained to determine whether the student is in a learning state.

[0128] Specifically, when the focus time is less than the time threshold, in order to exclude the possibility that the student's eyes are away from the screen for a short period of time due to other things, it is necessary to make a more specific judgment on whether the student is in a learning state based on the time when the eyes are away from the screen in the focus direction.

[0129] The implementation principle of the embodiment of the present application for obtaining the focus time of the eyes in the focus direction facing the screen to determine whether the student is in a learning state is as follows: when only the cloud education learning platform page exists on the screen, whether the student is learning is determined based on the time the student stares at the computer screen. If the student only occasionally stares at the computer screen when learning through the cloud education platform, it cannot be determined that the student is learning. The time of staring at the screen must reach a certain time threshold to determine that the student is indeed learning. If the student's eyes leave the screen, it is necessary to determine whether the student is really not studying when leaving the screen based on the time the student's eyes leave the screen. It is possible that the student's eyes have left the screen for a short period of time due to distraction or going to get something, etc., and the probability of making a wrong judgment is reduced as much as possible.

[0130] Reference Figure 6 , obtaining the time when the eye focus direction leaves the screen to determine whether the student is in a learning state includes:

[0131] S600: Determine whether the time in which the eyes are focused away from the screen is greater than a distraction threshold.

[0132] Specifically, the distraction threshold is preset in the execution entity, which is the maximum time allowed for a student to look away. For example, if the distraction threshold is set to 1 minute, a student was staring at the screen to study, but suddenly his eyes left the screen. The time it takes for the eye focus to leave the screen is calculated from the time the student's eyes leave the screen. If the student's eyes are away from the screen for more than one minute, it is determined that the student is not in a learning state.

[0133] S610: If the time when the eyes are focused away from the screen is greater than the distraction threshold, it is determined that the student is not in a learning state.

[0134] S620: If the time that the eyes are focused away from the screen is not greater than the distraction threshold, it is determined that the student is in a learning state.

[0135] Specifically, the student's eyes leave the screen and then return to the screen within the distraction threshold time. This may be because the student suddenly went to pick up something or take something and his eyes left the screen for a short time. The student is still in a learning state.

[0136] The implementation principle of the embodiment of the present application for obtaining the time when the eye focus direction leaves the screen to determine whether the student is in a learning state is as follows: if the time when the student's eyes leave the screen is greater than the distraction threshold, it proves that the student is no longer studying on the cloud education platform at this moment, but is doing other things instead. Therefore, it is determined that the student is not in a learning state. If the student's eyes only leave the screen for a short time, then the student is also in a learning state at this time.

[0137] Reference Figure 7 , after confirming that the student is not in the learning state, the steps include:

[0138] S700. Obtain student account number.

[0139] Specifically, the student account is a personal account used by students when they enter the cloud education platform to study. The account includes information such as the student's name, gender, and class.

[0140] S710. Based on the student account, obtain the student name and make a point deduction record.

[0141] The implementation principle after confirming that the student is not in the learning state as described in the embodiment of the present application is: obtaining the student account used by the student to log in to the cloud education learning platform, matching the student's name according to the student's login account, and deducting points from the student who is not in the learning state, so as to facilitate the teacher to evaluate the students.

[0142] The above describes in detail a data collection and analysis method for a cloud education platform. The following describes in detail a data collection and analysis system for a cloud education platform based on a data collection and analysis method for a cloud education platform.

[0143] A data collection and analysis system for a cloud education platform, comprising:

[0144] Acquisition module, used to obtain the current time;

[0145] A first judgment module is used to judge whether the current time is within the learning time based on the current time;

[0146] The second acquisition module is used to acquire the facial information of the student if it is during the study time;

[0147] The second judgment module is used to preliminarily judge whether the student is in a learning state based on facial information;

[0148] The third acquisition module is used to obtain the change in the student's pulse beat frequency if it is preliminarily determined that the student is not in a learning state;

[0149] The third judgment module is used to further judge whether the student is in a learning state based on the change in the pulse beat frequency;

[0150] The first execution module is used to confirm that the student is not in the learning state if the change in the pulse beat frequency exceeds the change threshold;

[0151] The second execution module is used to confirm that the student is in a learning state if the change in the pulse beat frequency does not exceed the change threshold.

[0152] By adopting the above technical solution, the first acquisition module acquires the current time, the first judgment module determines whether the current time is within the learning time, the second acquisition module acquires the student's facial information within the learning time, the second judgment module determines whether the student is learning based on the facial information, the third acquisition module acquires the pulse beat change when the student is not in the learning state, the third judgment module determines whether the student is not in the learning state based on the pulse beat change, the first execution module confirms that the student is not in the learning state when the pulse beat change exceeds the change threshold, and the second execution module confirms that the student is in the learning state when the pulse beat change does not exceed the change threshold. It is convenient for teachers to grasp the learning situation of students on the cloud education platform.

[0153] An embodiment of the present application also discloses a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, a data collection and analysis method for a cloud education platform in the above embodiment is adopted.

[0154] The terminal device may be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device may also include input and output devices, a network access device and a bus.

[0155] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0156] Among them, the memory can be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device, or it can be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the terminal device, and the memory can also be a combination of an internal storage unit and an external storage device of the terminal device. The memory is used to store computer programs and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0157] Among them, through this terminal device, a data collection and analysis method of a cloud education platform in the above-mentioned embodiment is stored in the memory of the terminal device, and is loaded and executed on the processor of the terminal device for easy use.

[0158] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A data collection and analysis method for a cloud education platform, characterized in that: include: Get the current time; Based on the current time, determining whether the current time is within the learning time; If it is during the learning time, obtaining the student's facial information; Based on the facial information, preliminarily determine whether the student is in a learning state; If it is preliminarily determined that the student is not in a learning state, the change in the student's pulse beat frequency is obtained; Based on the change in the pulse beat frequency, further determining whether the student is in a learning state; If the change in the pulse beat frequency exceeds the change threshold, it is determined that the student is not in a learning state; If the change in the pulse beat frequency does not exceed the change threshold, it is confirmed that the student is in a learning state; Wherein, the preliminary determination of whether the student is in a learning state based on the facial information includes: Analyze the facial information to obtain the student's eye focus direction; Determining whether the eye focusing direction is toward the screen; If the eye focus direction is toward the screen, a screenshot is obtained to determine whether the student is in a learning state; If the eye focus direction is not facing the screen, the head posture of the student is obtained to determine whether the student is in a learning state; Wherein, obtaining the head posture of the student to determine whether the student is in a learning state includes: Based on the head posture of the student, determining whether the head posture of the student is bowing; If the head posture is head down, then obtain the head down time; Determine whether the head-down time is greater than the head-down threshold; If the head-down time is greater than the head-down threshold, it is determined that the student is not in a learning state.

2. The data collection and analysis method of a cloud education platform according to claim 1 is characterized in that: The obtaining of a screenshot to determine whether the student is in a learning state includes: Based on the screenshots, determining whether the student is in a learning state; If there is only the cloud education learning platform page in the screenshot, the focusing time of the eye focusing direction facing the screen is obtained to determine whether the student is in a learning state; If there are other pages in the screenshot, the student is not in the learning state.

3. The data collection and analysis method of a cloud education platform according to claim 2 is characterized in that: The step of obtaining the focusing time of the eye focusing direction facing the screen to determine whether the student is in a learning state comprises: Obtaining the focusing time of the eye focusing direction facing the screen; Determining whether the focusing time is greater than a time threshold; If the focus time is greater than the time threshold, it is determined that the student is in a learning state; If the focusing time is not greater than the time threshold, the time when the eye focusing direction leaves the screen is obtained to determine whether the student is in a learning state.

4. The data collection and analysis method of a cloud education platform according to claim 3 is characterized in that: The step of obtaining the time when the eye focus direction leaves the screen to determine whether the student is in a learning state comprises: Determine whether the time when the eye focus direction leaves the screen is greater than the distraction threshold; If the time when the eye focus direction leaves the screen is greater than the distraction threshold, it is determined that the student is not in a learning state; If the time that the eye focus direction leaves the screen is not greater than the distraction threshold, it is determined that the student is in a learning state.

5. The data collection and analysis method of a cloud education platform according to claim 1 is characterized in that: The confirmation that the student is not in the learning state includes: Get student account number; Based on the student account, the student name is obtained and the deduction record is made.

6. A data collection and analysis system for a cloud education platform, characterized in that: include: The first acquisition module is used to obtain the current time; A first judging module, used for judging whether the current time is within the learning time based on the current time; A second acquisition module is used to acquire the student's facial information if it is during the learning time; A second judgment module is used to preliminarily judge whether the student is in a learning state based on the facial information; The third acquisition module is used to obtain the change in the student's pulse beat frequency if it is preliminarily determined that the student is not in a learning state; A third judgment module is used to further judge whether the student is in a learning state based on the change in the pulse beat frequency; A first execution module is used to confirm that the student is not in a learning state if the change in the pulse beat frequency exceeds a change threshold; A second execution module is used to confirm that the student is in a learning state if the change in the pulse beat frequency does not exceed the change threshold; Wherein, the preliminary determination of whether the student is in a learning state based on the facial information includes: Analyze the facial information to obtain the student's eye focus direction; Determining whether the eye focusing direction is toward the screen; If the eye focus direction is toward the screen, a screenshot is obtained to determine whether the student is in a learning state; If the eye focus direction is not facing the screen, the head posture of the student is obtained to determine whether the student is in a learning state; Wherein, obtaining the head posture of the student to determine whether the student is in a learning state includes: Based on the head posture of the student, determining whether the head posture of the student is bowing; If the head posture is head down, then obtain the head down time; Determine whether the head-down time is greater than the head-down threshold; If the head-down time is greater than the head-down threshold, it is determined that the student is not in a learning state.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor loads and executes the computer program, the method according to any one of claims 1 to 5 is adopted.

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