Stay-up health monitoring method, smart television, medium and product

Abstract

The invention relates to a staying-up health monitoring method, a smart television, a medium and a product. The staying-up health monitoring method is used for the smart television, and comprises the following steps: receiving a staying-up health index of a user acquired by monitoring equipment in a preset staying-up time period when the smart television is in an on state; analyzing the staying-up health index of the user, and determining the staying-up health state of the user; and if the staying up health state of the user is abnormal, sending out a prompt. And when the staying-up health state is abnormal, a prompt is given out, so that a staying-up user can pay attention conveniently, the user can take measures in time conveniently, and damage to the body caused by staying-up is reduced. And the smart television is used as a household core electronic device, more activity areas of the user at night are in front of the smart television, and the smart television is generally in an on state. Therefore, based on the smart television, the staying-up health state of the user can be monitored more effectively.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of smart televisions, in particular to a method for monitoring the health of a user who stays up late, a smart television, a medium and a product. BACKGROUND

[0002] In modern society, as the pace of life accelerates, the number of life and work things increases, people are increasingly busy, and the work and rest are increasingly irregular. Many people will voluntarily or involuntarily stay up late for a long time. Long-term staying up late will lead to reduced visual function, skin problems, affect reproductive health, increase the risk of cardiovascular disease, cause digestive system problems, affect cognitive ability, increase the risk of cancer, endocrine disorders, neurasthenia and other problems. SUMMARY

[0003] Therefore, it is necessary to provide a method for monitoring the health of a user who stays up late.

[0004] In one aspect, the present application provides a method for monitoring the health of a user who stays up late, for a smart television, the method comprising the steps of:

[0005] receiving a user's staying-up-late health index acquired by a monitoring device during a preset staying-up-late time period and when the smart television is in an on state;

[0006] analyzing the user's staying-up-late health index to determine the user's staying-up-late health status;

[0007] if the user's staying-up-late health status is abnormal, issuing a reminder.

[0008] Optionally, the monitoring device comprises an image acquisition device; the user's staying-up-late health index acquired by the image acquisition device comprises a user's face image.

[0009] The step of analyzing the user's staying-up-late health index to determine the user's staying-up-late health status comprises:

[0010] extracting a staying-up-late skin index from the user's face image; the staying-up-late skin index comprises at least one of skin color, skin quality, dark circles, eye bags, iris blood vessel density of the eyes, and facial symmetry;

[0011] analyzing the staying-up-late skin index to determine the user's staying-up-late skin health status; wherein the user's staying-up-late health status comprises the user's staying-up-late skin health status.

[0012] Optionally, after analyzing the user's staying-up-late skin index to determine the user's staying-up-late skin health status, if the user's staying-up-late health status is abnormal, the method further comprises the step of:

[0013] performing face recognition on the face image of the user and matching the face image of the user with a face image of a user in an archive;

[0014] If the matching is unsuccessful, a new user archive is established based on the face image, and the sleep-deprivation health status of the user is stored in the user archive; if the matching is successful, the sleep-deprivation health status of the user is stored in the user archive.

[0015] Optionally, the sleep-deprivation health indicators of the user obtained by the image acquisition device further include a body posture image of the user.

[0016] The step of analyzing the sleep-deprivation health indicators of the user to determine the sleep-deprivation health status of the user further includes:

[0017] According to the body posture image of the user, a sleep-deprivation posture indicator is extracted; the sleep-deprivation posture indicator includes a spine state.

[0018] The sleep-deprivation posture indicator is analyzed to determine a sleep-deprivation posture health status of the user; wherein the sleep-deprivation health status of the user includes the sleep-deprivation posture health status of the user.

[0019] Optionally, the sleep-deprivation health indicators of the user obtained by the image acquisition device include a face video of the user.

[0020] The step of analyzing the sleep-deprivation health indicators of the user to determine the sleep-deprivation health status of the user further includes:

[0021] The face video of the user is subjected to photoelectric volume pulse wave information extraction to obtain target photoelectric volume pulse wave information of the user, and a sleep-deprivation cardiovascular indicator of the user is determined; the sleep-deprivation cardiovascular indicator includes at least one of blood pressure and heart rate.

[0022] The sleep-deprivation cardiovascular indicator is analyzed to determine a sleep-deprivation cardiovascular health status of the user; wherein the sleep-deprivation health status of the user includes the sleep-deprivation cardiovascular health status of the user.

[0023] Optionally, the image acquisition device is fixedly arranged on a display of the smart television.

[0024] Optionally, the monitoring device further includes a wearable device, and the sleep-deprivation health indicators of the user obtained by the wearable device include one or more of heart rate, pulse, and blood pressure.

[0025] The method further includes:

[0026] establishing a communication connection with the wearable device, receiving an instruction of matching a specified user, and matching the wearable device with the specified user in the database; wherein the specified user in the instruction of matching a specified user is an archived user in the database;

[0027] analyzing the overnight health indicators of the user, and determining the overnight health status of the user, further comprising:

[0028] analyzing the heart rate, pulse and / or blood pressure of the user, and determining whether the heart rate, pulse and / or blood pressure of the user exceeds a preset threshold; wherein the overnight health status of the user includes the status of the heart rate, pulse and / or blood pressure of the user.

[0029] Optionally, in the step of matching the wearable device with the specified user in the database, the wearable device is identified by an electronic device serial code.

[0030] Optionally, the method further comprises:

[0031] receiving data of the overnight health indicators of the user outside the overnight time period;

[0032] after the step of receiving the overnight health indicators of the user obtained by the monitoring device, further comprising:

[0033] analyzing the gap between the overnight health indicators of the user in the overnight time period and the non-overnight time period;

[0034] if the gap is greater than a preset threshold, an alert is issued.

[0035] Optionally, the monitoring device comprises a wearable device, and the overnight health indicators of the user obtained by the wearable device include one or more of heart rate, pulse and blood pressure.

[0036] the method further comprises:

[0037] establishing a communication connection with the wearable device, receiving an instruction of establishing a new user profile, and establishing a new user profile in the profile library;

[0038] analyzing the overnight health indicators of the user, and determining the overnight health status of the user, further comprising:

[0039] analyzing the heart rate, pulse and / or blood pressure of the user, and determining whether the heart rate, pulse and / or blood pressure of the user exceeds a preset threshold; wherein the overnight health status of the user includes the status of the heart rate, pulse and / or blood pressure of the user.

[0040] Optionally, the monitoring device comprises an image acquisition device; the overnight health index of the user acquired by the image acquisition device comprises a face image, a body posture image and / or a face video of the user;

[0041] The method further comprises:

[0042] If the overnight health status of the user is abnormal, performing face recognition on the face image of the user and matching the face image of the user with the face images of users in the archive;

[0043] If the matching is unsuccessful and an instruction of matching the face image of the user with a specified user is received, matching the face image of the user with the specified user.

[0044] Optionally, the method further comprises:

[0045] Generating an overnight health status report according to a preset time period;

[0046] Sending the overnight health status report to the display of the smart television and / or a specified peripheral terminal device.

[0047] Optionally, the method further comprises:

[0048] When a new user archive is established, recording the archive establishment time of the user;

[0049] Since the archive establishment time of the user, if the monitoring time of the user exceeds a preset monitoring period and the overnight health status of the user remains normal for at least a preset observation time period, deleting the archive of the user in the database.

[0050] Optionally, if the overnight health data of the user is abnormal, the reminding manner in the step of issuing a reminder comprises generating a pop-up window on the display of the smart television.

[0051] Optionally, the preset overnight time period is adjustable.

[0052] In another aspect, the application also provides a smart television, comprising:

[0053] a display module;

[0054] a receiving module, configured to receive the overnight health index of the user acquired by the monitoring device during a preset overnight time period and when the smart television is in an open state;

[0055] an analysis module, configured to analyze the overnight health index of the user and determine the overnight health status of the user;

[0056] a reminder pushing module, configured to push a reminder information to the display module and / or a specified peripheral terminal device when the overnight health status of the user is abnormal.

[0057] In another aspect, the present application provides an intelligent television, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the method for monitoring health status of staying up late.

[0058] In another aspect, the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to perform the steps of the method for monitoring health status of staying up late.

[0059] In another aspect, the present application provides a computer program product, comprising a computer program, wherein the computer program is executed by a processor to perform the steps of the method for monitoring health status of staying up late.

[0060] The embodiments of the present application can remind the user when the health status of staying up late is abnormal, so as to attract the attention of the user, and help the user take measures in time to reduce the damage to the body caused by staying up late. As the core electronic device of the family, the user's activity area at night is in front of the intelligent television, and the intelligent television is usually in an open state. Therefore, based on the intelligent television, the health status of staying up late of the user can be monitored more effectively. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 FIG. 1 is a flowchart of a method for monitoring health status of staying up late according to an embodiment of the present application.

[0062] Figure 2 FIG. 2 is a flowchart of analyzing the health status of staying up late of a user according to an embodiment of the present application.

[0063] Figure 3 FIG. 3 is a flowchart of storing the health status of staying up late of a user according to an embodiment of the present application.

[0064] Figure 4 FIG. 4 is a flowchart of analyzing the health status of staying up late of a user according to an embodiment of the present application.

[0065] Figure 5 FIG. 5 is a flowchart of analyzing the health status of staying up late of a user according to an embodiment of the present application.

[0066] Figure 6 FIG. 6 is a flowchart of analyzing the influence of staying up late on the health index of staying up late according to an embodiment of the present application.

[0067] Figure 7 FIG. 7 is a flowchart of generating and sending a health status report of staying up late according to an embodiment of the present application.

[0068] Figure 8A flowchart of a process for deleting a user's profile in a database in an embodiment of the present application.

[0069] Figure 9 A structural block diagram of a smart television in an embodiment of the present application.

[0070] Figure 10 A structural block diagram of a smart television in an embodiment of the present application. DETAILED DESCRIPTION

[0071] In order to make the technical solutions and advantages of the present application more obvious and easy to understand, the following will be described in detail by way of listing specific embodiments. Unless otherwise defined, the technical and scientific terms used in the present application have the same meaning as the technical and scientific terms in the technical field to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application.

[0072] Referring to Figure 1 The overwork health monitoring method provided by some embodiments of the present application is used for a smart television, and the method comprises the following steps:

[0073] Step 100: receiving an overwork health index of a user acquired by a monitoring device when a preset overwork time period and the smart television are in an open state;

[0074] Step 200: analyzing the overwork health index of the user to determine an overwork health state of the user;

[0075] Step 300: issuing a reminder if the overwork health state of the user is abnormal.

[0076] It can be understood that the damage of overwork to the human body is a gradual accumulation process. Specifically, when overwork accumulates to a certain extent, it first causes some health indexes of the human body to be abnormal; and continued overwork will cause the health indexes to continuously deteriorate and further damage the body. In the present application, the overwork health index refers to a health index that will be abnormal due to overwork.

[0077] It should be noted that in the present application, if at least one overwork health index of the user is abnormal after analysis, it is determined that the overwork health state of the user is abnormal.

[0078] The overwork health monitoring method described above issues a reminder when the overwork health state is abnormal, which facilitates the overworked user to pay attention, so as to facilitate the user to take measures in time and reduce the damage of overwork to the body. Moreover, the smart television is a core electronic device in a family, and the user's activity area at night is more in front of the smart television, and usually the smart television is in an open state. Therefore, based on the smart television, the overwork health state of the user can be more effectively monitored.

[0079] In addition, based on the smart television, the health status of the user staying up late is monitored, without the need to additionally set up electronic devices, reducing the increase in the number of home electronic devices, reducing the occupation of home space, and reducing the cost.

[0080] Furthermore, generally, the abnormality of the health index of staying up late usually occurs first in the staying up late time period. The health monitoring method of staying up late provided in the application is monitored in the preset staying up late time period, so that the abnormality of the health index of staying up late can be monitored as early as possible.

[0081] Furthermore, the monitoring time of the health monitoring method of staying up late provided in the application is in the preset staying up late time period, and the time or frequency of execution is reduced or not executed in the non-preset staying up late time period, so as to avoid invalid monitoring, reduce the monitoring cost, reduce the occupation of the storage space of the memory of the smart television, and reduce the working time of the processor.

[0082] Furthermore, the health monitoring method of staying up late provided in the application is not executed even in the preset staying up late time period if the smart television is not in the open state. In most cases, in the staying up late time period, if the smart television is not in the open state, the user has rested or will rest in a short time, and has no or little influence on the health index of staying up late. In this case, the health monitoring of staying up late is not executed, so as to avoid invalid monitoring, reduce the monitoring cost, reduce the occupation of the storage space of the memory of the smart television, and reduce the working time of the processor.

[0083] In some embodiments, the monitoring device comprises an image acquisition device; the health index of staying up late of the user acquired by the image acquisition device comprises a face image of the user.

[0084] Based on this, referring to Figure 2 , the step of analyzing the health index of staying up late of the user to determine the health status of the user staying up late comprises:

[0085] Step 211, extracting the staying up late skin index according to the face image of the user; the staying up late skin index comprises at least one of skin color, skin quality, dark circles, eye bags, iris blood vessel density of the eyes, and face symmetry.

[0086] Step 212, analyzing the staying up late skin index to determine the health status of the staying up late skin of the user; wherein the health status of the user staying up late comprises the health status of the staying up late skin of the user.

[0087] It can be understood that the staying up late skin index belongs to a type of health index of staying up late. Generally, when the body is damaged by staying up late, the staying up late skin index will usually change in a short time. Therefore, by monitoring and analyzing the staying up late skin index of the user, the abnormality of the health status of staying up late can be monitored more timely.

[0088] Among them, the skin color in the sleep-deprived skin index is used to determine whether the user's face has phenomena such as sallow and / or color spots. The skin quality in the sleep-deprived skin index is used to determine whether the user's face has phenomena such as facial edema. The other indicators in the sleep-deprived skin index can be determined according to the preset standard, which will not be described here.

[0089] In some embodiments, referring to Figure 3 After analyzing the sleep-deprived skin index of the user and determining the sleep-deprived health status of the user, if the sleep-deprived health status of the user is abnormal, the method further comprises the steps of:

[0090] Step 401, performing face recognition on the face image of the user and matching with the face image of the user in the archive library;

[0091] Step 402, if the matching is unsuccessful, establishing a new user archive based on the face image and storing the sleep-deprived health status of the user in the user archive; if the matching is successful, storing the sleep-deprived health status of the user in the user archive.

[0092] The settings of steps 401 and 402 can confirm different users through face recognition and monitor and store the sleep-deprived health status of different users. It can be understood that steps 401 and 402, which are used for archiving the user and storing the sleep-deprived health status of the user, can be executed simultaneously with step 300, or before or after step 300.

[0093] It can be understood that if the face image of the user matches the face image of the user in the database, the user is a user who has established archive information in the database; otherwise, the user is a user who has not established archive information in the database.

[0094] It can be understood that face recognition is a kind of identity recognition technology based on biological characteristics. For face images or videos, face features (such as facial contours, textures, distances, etc.) are extracted by using computer vision and deep learning algorithms, and compared with templates in the database, so as to complete identity recognition.

[0095] For example, in some embodiments, the step of face recognition includes:

[0096] Inputting the face image of the user into a face recognition model, calling the face recognition model to extract the face features corresponding to the face image;

[0097] Comparing the face features with the feature information of the face image of the user in the database to obtain a feature similarity;

[0098] The feature similarity of the feature information is compared with a preset threshold value. If the feature similarity of the feature information is at least partially greater than the corresponding threshold value, the matching is unsuccessful. If the feature similarity of the feature information is all less than the corresponding threshold value, the matching is successful.

[0099] It can be understood that the face recognition model can be obtained by a server through training a large number of face images and corresponding data. The face recognition model can perform feature extraction on the face image based on a face recognition algorithm. The face recognition algorithm can be one of a plurality of recognition algorithms. For example, the face recognition algorithm can be a recognition algorithm based on face feature points, or an algorithm for recognizing by using a neural network, or an algorithm for recognizing by using a support vector machine.

[0100] In some embodiments, referring to Figure 4 The overnight health index of the user acquired by the image acquisition device further includes a human body posture image of the user.

[0101] The step of analyzing the overnight health index of the user to determine the overnight health state of the user further includes:

[0102] Step 221, extracting an overnight posture index according to the human body posture image of the user; the overnight posture index includes a spine state.

[0103] Step 222, analyzing the overnight posture index to determine the overnight posture health state of the user; wherein the overnight health state of the user includes the overnight posture health state of the user.

[0104] It can be understood that during the overnight time period, the user's body is more in a tired state, and the user's nervous system has a decreased precision control ability on muscles. This is manifested as a slow posture adjustment response (such as difficulty in maintaining a straight spine when sitting for a long time), and a tendency to develop a hunched back, a forward-leaning neck, and other unhealthy postures. The method provided in the present application can remind the user when the posture is abnormal, facilitate the user to pay attention to the change in the posture in a timely manner, and adjust in a timely manner, so as to reduce the risk of further deterioration of the posture caused by time accumulation, and even cause other diseases.

[0105] It can be understood that step 211 and step 212 are for analyzing the skin state, and step 221 and step 222 are for analyzing the human body posture. The analysis of the skin state and the analysis of the human body posture can be performed simultaneously, or can be performed alternately or sequentially, which is not limited here.

[0106] In some embodiments, the overnight health index of the user acquired by the image acquisition device includes a face video of the user.

[0107] In some embodiments, referring to Figure 5The step of analyzing the health index of the user staying up late to determine the health status of the user staying up late further includes:

[0108] Step 231, photoelectric plethysmogram information extraction is performed on the facial video of the user to obtain target photoelectric plethysmogram information of the user and determine a cardiovascular index of the user staying up late; the cardiovascular index of the user staying up late includes at least one of blood pressure and heart rate.

[0109] Step 232, the cardiovascular index of the user staying up late is analyzed to determine the cardiovascular health status of the user staying up late; wherein the health status of the user staying up late includes the cardiovascular health status of the user staying up late.

[0110] The photoelectric plethysmogram information can be a remote photo plethysmogram (rPPG) signal. The rPPG signal can be obtained by performing ROI (Region of Interest) division, spatial conversion, and the like on the target video data.

[0111] To determine the blood pressure, a corresponding relationship between a pulse wave feature parameter and blood pressure information needs to be established in advance. Then, after the rPPG signal is extracted, the pulse wave feature parameter can be extracted from the rPPG signal using the rPPG feature parameter method, and the blood pressure information corresponding to the pulse wave feature parameter in the extracted rPPG signal can be determined based on the pre-established corresponding relationship.

[0112] To determine the heart rate, various methods can be used. For example, after the rPPG signal is extracted, the rPPG signal can be band-pass filtered, and then the filtered rPPG signal can be subjected to FFT (Fast Fourier Transform) to transform the rPPG signal to the frequency domain to obtain the frequency spectrum of the rPPG signal. Then, the frequency point with the highest energy in the frequency spectrum is found, and the heart rate value can be obtained by multiplying the frequency by 60. For another example, the frequency spectrum feature of the rPPG signal can be extracted, and then the extracted frequency spectrum feature can be input into a pre-trained heart rate information determination model to obtain the heart rate value output by the heart rate information determination model.

[0113] Steps 231 and 232 are for the analysis of the cardiovascular index. Similarly, it can be understood that the analysis of the cardiovascular index and the analysis of the skin state can be performed simultaneously, or can be performed alternately or sequentially, which is not limited here. Similarly, when the human body state is analyzed, the order of the analysis of the skin state, the analysis of the cardiovascular index, and the analysis of the human body state is also not limited.

[0114] In some embodiments, the image acquisition device is fixed on the display of the smart TV, facilitating image acquisition.

[0115] Specifically, for example, in some embodiments, the image acquisition device is fixed on the top side of the screen of the smart TV and is located at the middle position, so as to have a better image acquisition angle, more accurate and comprehensive acquisition of image and / or video data. For another example, in some embodiments, the image acquisition device can also be provided in multiple, so as to have a wider acquisition range and improve the monitoring effect.

[0116] In some embodiments, the monitoring device further comprises a wearable device, and the overnight health indicators of the user acquired by the wearable device include one or more of heart rate, pulse and blood pressure.

[0117] Based on this, the method provided by the application further comprises:

[0118] establishing a communication connection with the wearable device, receiving an instruction of matching a specified user, and matching the wearable device with the specified user in the database; wherein in the instruction of matching the specified user, the specified user is an archived user in the database.

[0119] It can be understood that after establishing the communication connection with the wearable device and matching the specified user, it can be judged that the data monitored by the wearable device belongs to the specified user in the database, so as to give a targeted monitoring result, and when the monitoring result is abnormal, accurate reminding can be performed.

[0120] It can be understood that after matching with the specified user in the database, the wearable device and the specified user always maintain a matching relationship. Therefore, after establishing the communication connection with the wearable device again, the overnight health indicators acquired by the wearable device can be directly matched with the matched user, and then a targeted monitoring result can be given, and when the monitoring result is abnormal, accurate reminding can be performed.

[0121] Based on this, the step of analyzing the overnight health indicators of the user to determine the overnight health status of the user further comprises:

[0122] Step 241, analyzing the heart rate, pulse and / or blood pressure of the user to determine whether the heart rate, pulse and / or blood pressure of the user exceeds a preset threshold; wherein the overnight health status of the user includes the state of the heart rate, pulse and / or blood pressure of the user.

[0123] It can be understood that the preset threshold can be a range. When the preset threshold is a range, the index exceeding the preset threshold means that the index is higher than the maximum value of the preset threshold or lower than the minimum value of the preset threshold.

[0124] It can be understood that step 241 is an analysis of the heart rate, pulse and / or blood pressure based on the wearable device, that is, an analysis of the user's sleep deprivation health indicators based on the wearable device. The aforementioned analysis of the skin state, the analysis of the human body posture and the analysis of the cardiovascular indicators are all analyses of the user's sleep deprivation health indicators based on the image acquisition device. It can be understood that the analysis of the user's sleep deprivation health indicators based on the wearable device and the analysis of the user's sleep deprivation health indicators based on the image acquisition device are not limited in execution sequence.

[0125] In some embodiments, in the step of matching the wearable device with the specified user in the database, the wearable device is identified by an electronic device serial code. The electronic device serial code is a code used by the user to distinguish different electronic devices. Even if the wearable devices are of the same model, their electronic device serial codes are different, so different wearable devices can be distinguished by the electronic device serial codes. Therefore, by identifying the wearable device by the electronic device serial code, the wearable device can be more accurately matched with the user in the database.

[0126] In some embodiments, the method provided by the present application further comprises receiving data of the user's sleep deprivation health indicators outside the sleep deprivation time period. The specific way of receiving data can be through the data obtained by the monitoring device during the day, or can be from the input of the user, which is not limited in particular.

[0127] Referring to Figure 6 , after the step of receiving the user's sleep deprivation health indicators obtained by the monitoring device, the method further comprises:

[0128] Step 500, analyzing the difference between the user's sleep deprivation health indicators in the sleep deprivation time period and the non-sleep deprivation time period;

[0129] Step 600, if the difference is greater than a preset threshold, a reminder is issued.

[0130] It can be understood that the greater the difference between the user's sleep deprivation health indicators in the sleep deprivation time period and the non-sleep deprivation time period, the greater the impact of sleep deprivation on the sleep deprivation health indicators. Therefore, when the difference is greater than the preset threshold, a reminder is issued, which can make the user more clearly realize the impact of sleep deprivation on health, and thus remind the user to adjust the work and rest schedule faster and reduce the damage of continued sleep deprivation to the body.

[0131] It can be understood that step 500 and step 600 are used to analyze the impact of sleep deprivation on the sleep deprivation health indicators, and step 200 and step 300 are used to analyze whether the sleep deprivation health indicators are abnormal. The execution sequence of the two is not limited here.

[0132] In some embodiments, referring to Figure 7 , the method provided by the present application further comprises:

[0133] Step 701, generating a health status report of staying up according to a preset time period;

[0134] Step 702, sending the health status report of staying up to a display of the smart TV and / or a specified peripheral terminal device.

[0135] It can be understood that the peripheral terminal device refers to a terminal device that can be communicatively connected with the smart TV.

[0136] It can be understood that the health status report of staying up is used to record changes of the health indicators of staying up in the preset time period, so as to facilitate the user to understand the influence of staying up on the body.

[0137] Optionally, in some embodiments, the health status report of staying up can further include improvement suggestions. For example, adjustment suggestions of work and rest time, diet suggestions, exercise suggestions, etc. are provided according to the change of the health indicators of staying up.

[0138] In some embodiments, referring to Figure 8 The method provided by the application further includes:

[0139] Step 801, recording the time of establishing a new user profile when the new user profile is established;

[0140] Step 802, deleting the profile of the user in the database if the monitoring time of the user exceeds a preset monitoring period and the health status of the user of staying up remains normal at least in a preset observation time period since the time of establishing the profile of the user.

[0141] The deletion of the profile of the user in the database reduces the occupation of the database space. In addition, the profile of the user in the database is deleted after the preset monitoring period, that is, the profile of the user can be saved for at least the preset monitoring period, which is convenient for reference.

[0142] It can be understood that the health status of the user of staying up remains normal at least in the preset observation time period, and the preset observation time period is the last period in the whole monitoring of the user.

[0143] For example, if the preset observation period is set as one month and the preset monitoring period is set as two years, the user 1 is filed on January 1, 2025, and the user 1 has a normal sleep health status from December 1, 2026 to December 31, 2026, that is, no abnormality, and the user 1 is deleted from the database on January 1, 2027. The user 2 is filed on January 1, 2025, and the user 2 has an abnormal sleep health status from December 15, 2026 to December 20, 2026, and the user 2 has a normal sleep health status from December 21, 2026. The sleep health status of the user 2 is normal for less than one month from December 21, 2026 to December 31, 2026, so the user 2 is still in the monitoring state even if the monitoring period of two years is met on January 1, 2027, and the user 2 is kept in the database until the user 2 has a normal sleep health status for one month.

[0144] In some embodiments, if the sleep health data of the user is abnormal, the step of issuing a reminder includes generating a pop-up window on the display of the smart television.

[0145] It can be understood that in some other embodiments, the way of issuing a reminder is not limited to a pop-up window, but can also be a sound or other notification mode sent to a terminal device such as a mobile phone.

[0146] It can be understood that the way of issuing a reminder can be one or more, which is not limited here.

[0147] In some embodiments, the preset sleep time period can be adjusted. Thus, the user can adjust the preset sleep time period according to his own physical condition and work and rest condition, so as to more effectively monitor, that is, to provide more effective monitoring results and reduce monitoring cost.

[0148] Optionally, in some embodiments, the preset sleep time period can be adjusted within a preset adjustment range to avoid user misoperation and poor monitoring effect. For example, the preset adjustment range is 21:00-24:00 and 0:00-06:00. The user can set the preset sleep time period as 22:00-24:00 and 0:00-05:00 according to needs.

[0149] In some embodiments, a user profile can also be established based on a wearable device. The specific steps are as follows:

[0150] A communication connection with the wearable device is established, a command for establishing a new user profile is received, and a new user profile is established in the profile library.

[0151] It can be understood that, based on the wearable device, the user profile is established, and the user's all-night work health indicators obtained by the monitoring device can be received. The all-night work health indicators obtained by the wearable device can be received.

[0152] Of course, based on this, in some embodiments, the monitoring device further comprises an image acquisition device; the all-night work health indicators of the user obtained by the image acquisition device include a face image, a body posture image and / or a face video of the user.

[0153] The method further comprises:

[0154] If the all-night work health status of the user is abnormal, face recognition is performed on the face image of the user and matched with the face image of the user in the profile library.

[0155] If the matching is unsuccessful and the matching instruction of the specified user is received, the face image of the user is matched with the specified user.

[0156] It can be understood that, in the present application, the wearable device refers to an electronic device that can be directly worn on the body or integrated into clothes, and can monitor, analyze or interact in real time through sensors, wireless communication and intelligent algorithms. They usually have data acquisition functions (such as heart rate, pulse, blood pressure), and can establish communication connections with other electronic devices, and are widely used in health management, sports tracking, medical monitoring and daily life optimization fields, such as smart watches, sports bands, smart glasses, etc.

[0157] Referring to Figure 9 , some embodiments of the present application provide a smart television 900, comprising:

[0158] a display module 910;

[0159] a receiving module 920, configured to receive all-night work health indicators of a user obtained by a monitoring device during a preset all-night work time period and when the smart television is in an open state;

[0160] an analysis module 930, configured to analyze the all-night work health indicators of the user to determine an all-night work health status of the user;

[0161] a reminder pushing module 940, configured to push a reminder information to the display module and / or a specified peripheral terminal device when the all-night work health status of the user is abnormal.

[0162] The various modules in the smart television can be implemented by software, hardware, and combinations thereof, in whole or in part. The various modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the smart television in software form, so as to be called and executed by the processor to perform the operations corresponding to the various modules. It should be noted that the division of the modules is illustrative, and is merely a logical function division. In actual implementation, another division manner can be used.

[0163] Based on the foregoing embodiment description of the target data collection method, in another embodiment provided in the present disclosure, a smart television is provided, and an internal structure diagram of the smart television can be as shown in Figure 10 The smart television includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the smart television is configured to provide computing and control capabilities. The memory of the smart television includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for running of the operating system and the computer program in the non-volatile storage medium. The database of the smart television is configured to store data. The input / output interface of the smart television is configured to exchange information between the processor and external devices. The communication interface of the smart television is configured to communicate with external terminals through network connection. The computer program is executed by the processor to implement a target data collection method.

[0164] Those skilled in the art can understand that Figure 10 The structure shown in the foregoing embodiment description of the smart television is merely a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the smart television to which the scheme of the present application is applied. Specifically, the smart television can include more or fewer components than those shown in the diagram, or combine certain components, or have a different component arrangement.

[0165] Based on the foregoing embodiment description of the all-night health monitoring method, in another embodiment provided in the present disclosure, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps in the foregoing method embodiments.

[0166] Based on the foregoing embodiment description of the all-night health monitoring method, in another embodiment provided in the present disclosure, a computer program product is provided, and the computer program product includes a computer program. The computer program is executed by a processor to implement the steps in the foregoing method embodiments.

[0167] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards.

[0168] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiments of each method. Any reference to memory, database or other medium used in the embodiments provided by the present 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, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetic memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0169] It should be noted that the terms "first", "second", and the like, in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of these terms herein is to be construed as interchangeable in meaning to provide cover for devices and methods which employ the claims herein in different sequential or chronological order. The embodiments described in the following examples are not meant to be limiting of the application. Rather, they are presented as exemplary examples to provide a sufficient enabling disclosure of the application. The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element. Where the term "example" is used preceding the discussion of any example, the term "comprising" is intended to be interpreted to encompass the examples.

[0170] As used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. It should be understood that the term "comprises", "comprising", or "includes" or "including" when used in this specification, specifies the presence of stated features, integers, steps, operations, elements, components, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof. Also, in the description of the specification, the terms "and / or" includes any and all combinations of one or more of the associated listed items.

[0171] In the description of the present application, the description of the terms "some embodiments", "other embodiments", "certain embodiments", etc. means that the particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the present application. The appearance of the above terms in various places in the specification are not necessarily all referring to the same embodiment or example.

[0172] It should be appreciated that each of the above-described methods of the present specification is described in progressive form, and the same / similar parts of each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments. The relevant parts can be referred to the description of other method embodiments.

[0173] It should be understood that the above examples are exemplary only, and are not intended to limit the scope of the claims. Various modifications and changes can be made thereto without departing from the scope thereof. It is intended to embrace all such modifications and changes as fall within the scope of the appended claims. It is also possible to arbitrarily combine the technical features of the above examples, and to make various modifications and changes thereto, and such combinations and modifications and changes are also within the scope of the application. Therefore, the above examples are merely illustrative, and are not intended to limit the scope of the application.

Claims

1. A method for monitoring health of staying up late, used for smart TV, characterized in that: The method comprises the steps of: During the preset staying-up-late time period and when the smart TV is turned on, the user's staying-up-late health index obtained by the monitoring device is received; Analyze the user's staying up late health index to determine the user's staying up late health status; If the user's health status of staying up late is abnormal, a reminder is issued.

2. The method according to claim 1, characterized in that The monitoring device includes an image acquisition device; the user's staying-up-late health index acquired by the image acquisition device includes a facial image of the user; The step of analyzing the user's staying up late health index and determining the user's staying up late health status includes: Extracting skin indicators for staying up late based on the user's facial image; the skin indicators for staying up late include at least one of skin color, skin texture, dark circles, eye bags, iris bloodshot density, and facial symmetry; Analyze the skin indicators for staying up late to determine the skin health status of the user who stays up late; wherein, the skin health status of the user who stays up late includes the skin health status of the user who stays up late.

3. The method according to claim 2, characterized in that After analyzing the user's skin indicators for staying up late and determining the user's skin health status for staying up late, if the user's health status for staying up late is abnormal, the method further comprises the steps of: Performing facial recognition on the user's facial image and matching it with facial images of users in the archive; If the match is unsuccessful, a new user profile is created based on the facial image, and the user's health status of staying up late is stored in the user profile; If the match is successful, the user's staying up late health status is stored in the user profile.

4. The method according to claim 2 or 3, characterized in that The user's staying-up-late health index acquired by the image acquisition device also includes the user's body posture image; The step of analyzing the user's staying up late health index and determining the user's staying up late health status further includes: Extracting a body posture index for staying up late based on the user's body posture image; the body posture index for staying up late includes a spinal condition; Analyze the staying-up-late body posture indicators to determine the user's staying-up-late body health status; wherein, the user's staying-up-late health status includes the user's staying-up-late body posture health status.

5. The method according to claim 2 or 3, characterized in that The user's staying-up-late health indicators acquired by the image acquisition device include a facial video of the user; The step of analyzing the user's staying up late health index and determining the user's staying up late health status further includes: Extracting photoplethysmography information from the user's facial video to obtain target photoplethysmography information of the user and determining a cardiovascular indicator of the user due to staying up late; the cardiovascular indicator for staying up late includes at least one of blood pressure and heart rate; Analyze the cardiovascular indicators of staying up late to determine the cardiovascular health status of the user who stays up late; wherein, the user's health status of staying up late includes the user's cardiovascular health status of staying up late.

6. The method according to claim 2 or 3, characterized in that The image acquisition device is fixedly arranged on the display of the smart TV.

7. The method according to claim 3, characterized in that The monitoring device further includes a wearable device, and the wearable device obtains the user's health indicators of staying up late, including one or more of heart rate, pulse and blood pressure; The method further comprises: Establishing a communication connection with the wearable device, receiving an instruction for matching a designated user, and matching the wearable device with the designated user in the database; wherein, in the instruction for matching a designated user, the designated user is a user archived in the database; The step of analyzing the user's staying up late health index and determining the user's staying up late health status further includes: Analyze the user's heart rate, pulse and / or blood pressure to determine whether the user's heart rate, pulse and / or blood pressure exceeds a preset threshold; wherein the user's health status of staying up late includes the status of the user's heart rate, pulse and / or blood pressure.

8. The method according to claim 7, characterized in that In the step of matching the wearable device with a designated user in a database: the wearable device is identified by the electronic device serial code.

9. The method according to claim 3, characterized in that The method further comprises: Receiving data on the user's staying-up-late health indicator outside the staying-up-late time period; After the step of receiving the user's staying-up-late health index acquired by the monitoring device, the method further includes: Analyze the difference between the user's health indicators of staying up late during the staying-up-late period and the non-stay-up-late period; If the difference is greater than the preset threshold, an alert will be issued.

10. The method according to claim 1, characterized in that The monitoring device includes a wearable device, and the user's staying-up-late health indicators obtained by the wearable device include one or more of heart rate, pulse, and blood pressure; The method further comprises: Establishing a communication connection with the wearable device, receiving an instruction to create a new user profile, and creating a new user profile in a database; The step of analyzing the user's staying up late health index and determining the user's staying up late health status includes: Analyze the user's heart rate, pulse and / or blood pressure to determine whether the user's heart rate, pulse and / or blood pressure exceeds a preset threshold; wherein the user's health status of staying up late includes the status of the user's heart rate, pulse and / or blood pressure.

11. The method according to claim 10, characterized in that The monitoring device includes an image acquisition device; the user's staying-up-late health indicators acquired by the image acquisition device include the user's face image, body image and / or face video; The method further comprises: If the user's health status of staying up late is abnormal, perform face recognition on the user's face image and match it with the face images of users in the archive; If the matching is unsuccessful and an instruction to match with a designated user is received, the facial image of the user is matched with the designated user.

12. The method according to any one of claims 3, 7 to 11, characterized in that: The method further comprises: Generate a health status report for staying up late based on the preset time period; The staying up late health status report is sent to the display of the smart TV and / or a designated peripheral terminal device.

13. The method according to claim 3 or 10, characterized in that The method further comprises: When creating a new user profile, record the user's profile creation time; Starting from the time when the user's profile is created, if the monitoring time of the user exceeds the preset monitoring period and the user's health status of staying up late remains normal for at least the preset observation period, the user's profile in the database will be deleted.

14. The method according to claim 1, wherein If the user's health data of staying up late is abnormal, the step of issuing a reminder includes generating a pop-up window on the display of the smart TV.

15. The method according to claim 1, wherein The preset staying-up-late time period is adjustable.

16. A smart TV, characterized in that: include: Display module; A receiving module, configured to receive the user's staying-up-late health index obtained by a monitoring device during a preset staying-up-late time period and when the smart TV is turned on; An analysis module, configured to analyze the user's health indicators for staying up late and determine the user's health status for staying up late; The reminder push module is used to push reminder information to the display module and / or a designated peripheral terminal device when the user's health status of staying up late is abnormal.

17. A smart TV, comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 15.

18. 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 the method according to any one of claims 1 to 15 are implemented.

19. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 15 are implemented.

Images