Health online time length determination method and device for computer, equipment and medium

Abstract

The invention belongs to the technical field of health management, and particularly relates to a health online time length determination method and device for a computer, equipment, a medium and a product. According to the method, the current face video of the user is acquired regularly according to the acquisition interval duration; determining a previous face video of the user according to the acquisition moment of the current face video and the acquisition interval duration; determining the current fatigue degree and the current fatigue change rate of the user according to the previous face video and the current face video; and predicting the remaining health online time length of the user according to the current fatigue change rate and the current fatigue degree. According to the method, the fatigue detection and the fatigue change rate of the user during online work or entertainment are realized, the residual health online duration of the user is predicted through the fatigue change rate and the fatigue detection, the body health of the user can be better protected, and sudden diseases caused by fatigue are avoided as much as possible.

Description

Technical Field

[0001] The invention belongs to the technical field of health management, and specifically relates to a method, device, equipment, medium and product for determining the healthy online time of a computer. Background Art

[0002] With the popularization and development of computers, computers can be used as office supplies or entertainment supplies, and people spend more and more time with computers. When using computers for work or entertainment, people's minds are in a highly concentrated state. In this state, people cannot effectively feel physical or mental fatigue. When the human body is in a state of fatigue, the secretion of hormones such as adrenaline and norepinephrine increases. These hormones can enhance myocardial contractility and increase cardiac output, so that people can maintain a high state of mental concentration, which makes cardiovascular and cerebrovascular diseases prone to sudden occurrence. Therefore, we need to detect people's fatigue state when using computers and other electronic devices, and manage the health of users. Summary of the invention

[0003] In response to the above technical problems, the present invention proposes a method, device, equipment, medium and product for determining the healthy online time of a computer. The present application obtains the current facial video of the user according to the acquisition interval time; determines the previous facial video of the user according to the acquisition time of the current facial video and the acquisition interval time; determines the current fatigue level and the current fatigue change rate of the user according to the previous facial video and the current facial video; and predicts the remaining healthy online time of the user according to the current fatigue change rate and the current fatigue level. The fatigue detection and fatigue change rate of the user when working or entertaining online are realized. The remaining healthy online time of the user is predicted by the fatigue change rate and fatigue detection, which can better protect the user's physical health and avoid the occurrence of sudden illnesses caused by fatigue as much as possible.

[0004] To solve the above technical problems, the technical solution provided by the present invention includes five aspects.

[0005] In a first aspect, the present application provides a method for determining the healthy online time of a computer, comprising: obtaining a current facial video of a user at regular intervals according to a collection interval; determining a previous facial video of the user according to the time of obtaining the current facial video and the collection interval; determining a current fatigue level and a current fatigue change rate of the user according to the previous facial video and the current facial video; and predicting a remaining healthy online time of the user according to the current fatigue change rate and the current fatigue level.

[0006] In some embodiments, determining the current fatigue level and current fatigue change rate of the user based on the previous facial video and the current facial video includes: determining the current RPPG signal of the user based on the current facial video; obtaining a preset reference RPPG signal; determining the momentary fatigue level at each moment based on the reference RPPG signal and the current RPPG signal; determining the current fatigue level of the user in the current facial video based on the momentary fatigue level at each moment; determining whether the users in the two videos are the same user based on the previous facial video and the current facial video; when it is determined that they are the same user, obtaining the previous fatigue level of the previous facial video based on the previous facial video; determining the fatigue change level based on the current fatigue level and the previous fatigue level; determining the current fatigue change rate based on the fatigue change level and the acquisition interval duration.

[0007] In some embodiments, determining the current fatigue level and the current fatigue change rate of the user based on the previous facial video and the current facial video also includes: when determined to be different users, determining the minimum fatigue level and the maximum fatigue level among the fatigue levels at each moment; obtaining the duration of the current facial video; and determining the current fatigue change rate based on the duration, the minimum fatigue level and the maximum fatigue level.

[0008] In some embodiments, predicting the remaining healthy online time of the user based on the current fatigue change rate and the current fatigue level includes: obtaining the current online time of the user; obtaining the historical fatigue change rate determined within the current online time based on the current online time; determining the fatigue change trend of the user based on the historical fatigue change rate and the current fatigue change rate; obtaining a preset fatigue level threshold; and determining the remaining healthy online time based on the fatigue change trend and the fatigue level threshold.

[0009] In some embodiments, the method further includes: obtaining a preset change rate interval and collection interval comparison table, wherein the collection interval comparison table includes collection interval durations corresponding to different change rate intervals; determining the collection interval duration for the next facial video collection based on the fatigue change rate, the change rate interval and the collection interval comparison table.

[0010] In some embodiments, the method of periodically acquiring the current facial video of the user according to the acquisition interval duration includes: performing face detection on the current facial video; if the current facial video does not contain a face, determining the previous acquisition interval duration as the next acquisition interval duration; if the current facial video does not contain a face for two consecutive times, clearing the current online time of the user, and acquiring the current facial video according to the shortest acquisition interval duration.

[0011] In the second aspect, the present application proposes a device for determining the healthy online time of a computer, comprising: a first acquisition module, used to periodically acquire a current facial video of a user according to a collection interval duration; a first determination module, used to determine the previous facial video of the user according to the acquisition time of the current facial video and the collection interval duration; a second determination module, used to determine the current fatigue level and the current fatigue change rate of the user according to the previous facial video and the current facial video; a first prediction module, used to predict the remaining healthy online time of the user according to the current fatigue change rate and the current fatigue level.

[0012] In a third aspect, the present application proposes a computer electronic device, characterized in that it includes a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of any one of the methods described in the first aspect.

[0013] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

[0014] In a fifth aspect, the present application proposes a computer program product, comprising a computer program / instructions, characterized in that the computer program is executed by a processor to perform the steps of any method described in the first aspect.

[0015] Beneficial effects created by the present invention: The present application acquires the current facial video of the user according to the acquisition interval time; determines the previous facial video of the user according to the acquisition time of the current facial video and the acquisition interval time; determines the current fatigue level and the current fatigue change rate of the user according to the previous facial video and the current facial video; and predicts the remaining healthy online time of the user according to the current fatigue change rate and the current fatigue level. The fatigue detection and fatigue change rate of the user when working or entertaining online are realized. The remaining healthy online time of the user is predicted by the fatigue change rate and fatigue detection, which can better protect the user's physical health and avoid the occurrence of sudden illnesses caused by fatigue as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. The drawings included are:

[0017] Figure 1 An overall flow chart of a method for determining the healthy online time of a computer provided in an embodiment of the present application;

[0018] Figure 2A structural block diagram of a method for determining the healthy online time of a computer provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0021] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0023] Embodiment 1:

[0024] With the popularization and development of computers, computers can be used as office supplies or entertainment supplies, and people spend more and more time with computers. When using computers for work or entertainment, people's minds are in a highly concentrated state. In this state, people cannot effectively feel physical or mental fatigue. When the human body is in a state of fatigue, the secretion of hormones such as adrenaline and norepinephrine increases. These hormones can enhance myocardial contractility and increase cardiac output, so that people can maintain a high state of mental concentration, which makes cardiovascular and cerebrovascular diseases prone to sudden occurrence. Therefore, we need to detect people's fatigue state when using computers and other electronic devices, and manage the health of users.

[0025] In view of the problems existing in the prior art, such as Figure 1As shown, the present application provides a method for determining the healthy online duration of a computer, the method system is in an electronic device, and the electronic device can be a server, a mobile terminal, a computer, a cloud platform, etc. The function implemented by the device data processing provided in the embodiment of the present application can be implemented by calling a program code by a processor of the electronic device, wherein the program code can be stored in a computer storage medium, and the method for determining the healthy online duration of a computer includes:

[0026] Step S1: acquiring the user's current facial video regularly according to the acquisition interval.

[0027] In some embodiments, the method further comprises:

[0028] Step S11: obtaining a preset change rate interval and collection interval comparison table, wherein the collection interval comparison table includes collection interval durations corresponding to different change rate intervals.

[0029] Step S12: determining the duration of the next facial video acquisition interval according to the fatigue change rate, the change rate interval and the acquisition interval comparison table.

[0030] In some embodiments, step S1 "regularly obtaining the user's current facial video according to the acquisition interval duration" includes:

[0031] Step S13: performing face detection on the current facial video.

[0032] Step S14: If the current facial video does not contain a human face, the duration of the last acquisition interval is determined as the duration of the next acquisition interval.

[0033] Step S15: If the current facial video contains no human face for two consecutive times, the current online time of the user is cleared, and the current facial video is collected according to the shortest collection interval time.

[0034] Although the detection of fatigue status in the prior art also determines the user's current fatigue status by identifying images or videos, if a long-term real-time monitoring method is adopted, it will cause a lot of resource occupation for electronic equipment and is not very meaningful. Of course, you can also choose to collect images or videos at a fixed time, and the time interval for collecting images or videos in the prior art is also fixed. But in fact, for the user, the increase in his fatigue will change with the change of fatigue, that is, the fatigue change rate mentioned in this application is changing. If a fixed interval duration is used for collection, more meaningless data will be generated at a certain stage, and the monitoring effect will be insufficient at some stages. Moreover, in the stage where the monitoring effect is insufficient, it is easy for the user to be overly fatigued and cause sudden illness. Therefore, it is very important to set the collection interval duration. The appropriate collection interval duration can improve the monitoring effect while also improving the data quality.

[0035] Therefore, the method proposed in step S11-step S12 of the present application can well determine the collection interval length according to the fatigue change rate of the current user. The fatigue change rate is a parameter affected by many factors, among which it will be affected by the overall fatigue level, and it will also be affected by the interaction between the current user and the electronic device. For example, when using electronic devices to watch dramas, read novels, and other interactions that do not require high mental concentration, the fatigue change rate will be much lower than the fatigue change rate when the user works on the electronic device or plays high-intensity games. Of course, the reason why it is called the fatigue change rate instead of the fatigue growth rate in this application is that not all interactions with electronic devices will increase fatigue. Some interactive behaviors, such as listening to music and other relaxing behaviors, will reduce the user's fatigue, so it is referred to as the fatigue change rate in this application.

[0036] In addition, because the purpose of this application is to predict the user's healthy online time, and the prediction needs to be based on certain data support. After a period of rest, the user's fatigue level will change significantly, but these changes are not caused by interaction with electronic devices, so the fatigue change rate calculated at this time will greatly interfere with the prediction, and the user's interaction behavior with the computer may also change after the user rests. At this time, using the data before the rest to predict the user's remaining healthy online time, there will be a huge error. Therefore, in order to reduce the error, face detection will also be performed on facial videos in this application. When no face is detected in the facial video for two consecutive times, it means that the user may have gone to rest on his own. At this time, the current online time needs to be cleared to ensure that the next time the user is predicted, it will not be affected by other online time data, so that the prediction result is more accurate.

[0037] Step S2: Determine the previous facial video of the user according to the acquisition time of the current facial video and the acquisition interval duration.

[0038] Step S3: determining the current fatigue degree and current fatigue change rate of the user according to the previous facial video and the current facial video.

[0039] In an embodiment, step S3 "determining the current fatigue degree and the current fatigue change rate of the user according to the previous facial video and the current facial video" includes:

[0040] Step S31: Determine the current RPPG signal of the user according to the current facial video.

[0041] Step S32: Acquire a preset reference RPPG signal.

[0042] Step S33: determining the momentary fatigue degree at each moment according to the reference rppg signal and the current rppg signal.

[0043] Step S34: determining the current fatigue degree of the user in the current facial video according to the moment fatigue degree at each moment.

[0044] Step S35: Determine whether the users in the previous facial video and the current facial video are the same user.

[0045] Step S36: When it is determined to be the same user, the previous fatigue level of the previous facial video is obtained according to the previous facial video.

[0046] Step S37: determining the fatigue change degree according to the current fatigue degree and the previous fatigue degree.

[0047] Step S38: determining the current fatigue change rate according to the fatigue change degree and the acquisition interval duration.

[0048] In some embodiments, step S3 "determining the current fatigue degree and the current fatigue change rate of the user according to the previous facial video and the current facial video" further includes:

[0049] Step S391: when it is determined that they are different users, the minimum fatigue degree and the maximum fatigue degree are determined in the fatigue degrees at each moment.

[0050] Step S392: Obtain the duration of the current facial video.

[0051] Step S393: Determine the current fatigue change rate according to the duration, the minimum fatigue degree and the maximum fatigue degree.

[0052] The main purpose of this application is to predict the remaining healthy online time, and the prediction is mainly carried out through the fatigue change rate. The change rate reflects the speed of change of a certain data in a section of data, so the premise of calculating the change rate is to calculate the fatigue difference, and the calculation of the fatigue difference is inseparable from the current fatigue and the historical fatigue closest to the current fatigue time. Therefore, in this application, it is necessary to obtain the last facial video, determine the corresponding last fatigue through the last facial video, calculate the fatigue change through the last fatigue and the current fatigue, and then use the quotient of the fatigue change and the acquisition interval as the current fatigue change rate. Among them, Remote Photoplethysmography (rPPG) uses reflected ambient light to measure subtle brightness changes in the skin. Subtle changes in the brightness of the skin are caused by blood flow caused by heartbeats. Therefore, after extracting the current rppg signal from the current facial video, the standard rppg signal and the current rppg signal can be used to obtain the gap between the user at each moment and the standard rppg signal, and then the moment fatigue degree at each moment can be clarified. Since the change rate between the moment fatigue degrees is not large, it is easy to be overfitted in the prediction process, so the moment fatigue degrees at each moment in a facial video are averaged to obtain the current fatigue degree. Of course, the standard rppg signal in this application can be obtained by acquiring facial videos of healthy people through big data, and individual differences can also be considered. The rppg signal of the same user when he is in the best state can be determined as the standard rppg signal through long-term tracking of the same user.

[0053] Generally, the user's facial video is obtained by shooting at a fixed position through a camera or other fixed device, but there is no guarantee that the captured facial video is the same person as the user in the previous facial video. Therefore, in this application, it is also necessary to determine whether the users in the two facial videos are the same person. If they are the same person, follow steps S31-S38. If they are different people, it means that the user of the current facial video has just started to interact with the current computer. At this time, there is no relevant data about him, and the fatigue change rate can only be determined by the fatigue at each moment in the current facial video. Of course, if you want to better consider individual differences, you can choose to set up face recognition login before collecting facial videos to obtain the standard RPPG signal corresponding to the user.

[0054] Step S4: predicting the remaining healthy online time of the user according to the current fatigue change rate and the current fatigue degree.

[0055] In some embodiments, step S4 "predicting the remaining healthy online time of the user according to the current fatigue change rate and the current fatigue degree" includes:

[0056] Step S41: Obtain the current online time of the user.

[0057] Step S42: acquiring the historical fatigue change rate determined within the current online duration according to the current online duration.

[0058] Step S43: determining the fatigue change trend of the user according to the historical fatigue change rate and the current fatigue change rate.

[0059] Step S44: Obtain a preset fatigue threshold.

[0060] Step S45: predicting the remaining healthy online time according to the fatigue change trend, the current fatigue level and the fatigue level threshold.

[0061] If a user interacts with a computer for a long continuous time, a lot of historical fatigue and historical fatigue change rates will inevitably be generated. Through the numerous historical fatigue change rates, the fatigue change trend of the user during this period, that is, the current online time mentioned in this application, can be obtained. The fatigue change trend and the current fatigue can be used to predict the fatigue of the target user at various moments in the future. When the predicted fatigue reaches the fatigue threshold, it means that the user is in urgent need of a rest. When the predicted fatigue is greater than or equal to the fatigue threshold, the time required from the current fatigue to the predicted fatigue is the remaining healthy time. Compared with the fixed time setting in the prior art, this time length calculation method is more targeted and can better protect the health of the user while meeting the user's interaction needs with the electronic device as much as possible.

[0062] Therefore, compared with the prior art, the technical solution of the present application can more accurately predict the remaining healthy time, so that the corresponding strategies specified according to the remaining healthy time can be better implemented, and the life and health of the user can be better protected.

[0063] Embodiment 2:

[0064] like Figure 2As shown, based on the aforementioned embodiment 1, the embodiment of the present application provides a device for determining the healthy online time of a computer, and the modules included in the device, and the units included in each module, can be implemented by a processor in a computer device; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0065] A device for determining the healthy online duration of a computer includes: a first acquisition module 1, a first determination module 2, a second determination module 3 and a first prediction module 4.

[0066] The first acquisition module 1 is used to periodically acquire the current facial video of the user according to the acquisition interval duration. The first determination module 2 is used to determine the previous facial video of the user according to the acquisition time of the current facial video and the acquisition interval duration. The second determination module 3 is used to determine the current fatigue level and the current fatigue change rate of the user according to the previous facial video and the current facial video. The first prediction module 4 is used to predict the remaining healthy online time of the user according to the current fatigue change rate and the current fatigue level.

[0067] Each module in the above-mentioned device for determining the healthy online duration of a computer can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the device in the form of hardware, or can be stored in the memory in the processing device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0068] Embodiment 3:

[0069] In a third aspect, the present application provides a computer electronic device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any method described in the first aspect.

[0070] Embodiment 4:

[0071] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of any one of the methods described in the first aspect.

[0072] Embodiment 5:

[0073] In a fifth aspect, the present application proposes a computer program product, comprising a computer program / instructions, characterized in that the computer program is executed by a processor to implement the steps of any one of the methods described in the first aspect.

[0074] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0075] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0076] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0077] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0078] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit. The above integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0080] Those skilled in the art can understand that all or part of the steps of the above method embodiments can be completed by hardware related to program instructions, and the above program can be stored in a computer-readable storage medium, and when the program is executed, the steps of the above method embodiments are executed. The above storage medium includes: a mobile storage device, a read-only memory (ROM), a magnetic disk or an optical disk, and other media that can store program codes.

[0081] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0082] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for determining the healthy online duration of a computer, characterized in that: include: Obtain the user's current facial video regularly according to the acquisition interval; Determining the previous facial video of the user according to the acquisition time of the current facial video and the acquisition interval duration; Determining a current fatigue degree and a current fatigue change rate of the user according to the previous facial video and the current facial video; The remaining healthy online time of the user is predicted according to the current fatigue change rate and the current fatigue degree.

2. The method according to claim 1, characterized in that The determining the current fatigue degree and the current fatigue change rate of the user according to the previous facial video and the current facial video includes: Determine a current RPPG signal of the user according to the current facial video; Obtain a preset reference rppg signal; Determine the moment fatigue degree at each moment according to the reference rppg signal and the current rppg signal; Determining the current fatigue degree of the user in the current facial video according to the moment fatigue degree at each moment; Determining whether users in the previous facial video and the current facial video are the same user; When it is determined that the user is the same user, obtaining a previous fatigue level of the previous facial video according to the previous facial video; Determining a fatigue change degree according to the current fatigue degree and the previous fatigue degree; The current fatigue change rate is determined according to the fatigue change degree and the acquisition interval duration.

3. The method according to claim 2, characterized in that The determining of the current fatigue degree and the current fatigue change rate of the user according to the previous facial video and the current facial video further includes: When it is determined that they are different users, determining the minimum fatigue degree and the maximum fatigue degree among the fatigue degrees at each moment; Obtaining the duration of the current facial video; The current fatigue change rate is determined according to the duration, the minimum fatigue degree and the maximum fatigue degree.

4. The method according to claim 1, characterized in that: The predicting the remaining healthy online time of the user according to the current fatigue change rate and the current fatigue degree includes: Obtain the current online time of the user; Acquire, according to the current online duration, a historical fatigue change rate determined within the current online duration; Determining the fatigue change trend of the user according to the historical fatigue change rate and the current fatigue change rate; Obtaining a preset fatigue threshold; The remaining healthy online time is predicted according to the fatigue change trend, the current fatigue level and the fatigue level threshold.

5. The method according to claim 4, characterized in that The method further comprises: Obtaining a preset change rate interval and collection interval comparison table, wherein the collection interval comparison table includes collection interval durations corresponding to different change rate intervals; The duration of the collection interval for collecting facial video next time is determined according to the fatigue change rate, the change rate interval and the collection interval comparison table.

6. The method according to claim 5, characterized in that The step of obtaining the current facial video of the user according to the acquisition interval time period includes: Performing face detection on the current facial video; If the current facial video does not contain a human face, the duration of the last acquisition interval is determined as the duration of the next acquisition interval; If the current facial video contains no human face for two consecutive times, the current online time of the user is cleared, and the current facial video is collected according to the shortest collection interval time.

7. A device for determining the healthy online duration of a computer, characterized in that: include: The first acquisition module is used to periodically acquire the current facial video of the user according to the acquisition interval duration; A first determination module, configured to determine a previous facial video of the user according to a time at which the current facial video is acquired and a duration of an acquisition interval; A second determination module, configured to determine the current fatigue degree and the current fatigue change rate of the user according to the previous facial video and the current facial video; The first prediction module is used to predict the remaining healthy online time of the user according to the current fatigue change rate and the current fatigue degree.

8. A computer electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that The computer program is executed by a processor to implement the steps of the method according to any one of claims 1 to 6.

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