WO2022161067A1

WO2022161067A1 - Pre-sleep state detection method and device

Info

Publication number: WO2022161067A1
Application number: PCT/CN2021/141615
Authority: WO
Inventors: 邱兆鑫
Original assignee: 华为技术有限公司
Priority date: 2021-01-28
Filing date: 2021-12-27
Publication date: 2022-08-04
Also published as: CN114795159A

Abstract

A pre-sleep state detection method and device (201, 203). The method comprises: obtaining a first signal of a user according to a set first time interval (106) and extracting a first feature parameter from the first signal (108); inputting the first feature parameter into a pre-sleep state evaluation model to generate a pre-sleep state score (112); and forming a pre-sleep state curve (114). The pre-sleep state of the user can be accurately monitored, such that the user can grasp a complete state before and after sleep, and thus the sleep quality of the user is further improved.

Description

A bedtime state detection method and device

technical field

The present application relates to the technical field of sports health, and in particular to a method and device for detecting a bedtime state.

Background technique

With the rapid development of modern society and the increasingly intense pace of work and life, more and more people living under high pressure have problems with sleep disorders. The related technology can detect the user's on-off time and sleep stage during the sleep process, and calculate the user's sleep score by combining the on-off time and sleep stage, but it cannot monitor the user's state before going to bed, resulting in the user's inability to grasp sleep. The complete state before and after, so that it cannot further improve the quality of sleep.

SUMMARY OF THE INVENTION

In view of this, the present application provides a bedtime state detection method and device, which can accurately monitor a user's bedtime state, so that the user can grasp the complete state before and after sleep, thereby further improving their own sleep quality.

On the one hand, an embodiment of the present application provides a method for detecting a bedtime state, including:

receiving a user input of a first query operation, where the first query operation includes an operation of querying the state curve before going to bed;

In response to the first query operation, a bedtime state curve is displayed.

In the embodiment of the present application, the user inputs the first query operation to query the state curve before going to sleep, which is convenient for the user to check the state curve before going to sleep at any time, and can grasp the state before going to sleep.

In a possible implementation, it also includes:

receiving a first setting operation input by the user, where the first setting operation includes an operation of setting the detection time;

In response to the first setting operation, the detection time is set.

In the embodiment of the present application, the user inputs the first setting operation to set the detection time, so that the electronic device can perform detection according to the detection time expected by the user, so that the generated bedtime state curve is more in line with the user's expectation.

In a possible implementation, it also includes:

receiving a second setting operation input by the user, where the second setting operation includes an operation of setting the working mode;

In response to the second setting operation, the operating mode is set.

In the embodiment of the present application, the user inputs the second setting operation to set the working mode, so that the electronic device can work according to the working mode expected by the user, and the user experience is improved.

In a possible implementation, the method further includes:

Obtain the first signal of the user according to the first time interval;

extracting the first characteristic parameter from the first signal;

According to the pre-sleep state evaluation model and the first characteristic parameter, the pre-sleep state score is generated;

Based on the bedtime state score, a bedtime state curve is generated.

In the embodiment of the present application, the bedtime state curve is generated according to the first signal of the user, and the generated bedtime state curve can accurately monitor the bedtime state of the user for a period of time before bedtime, so that the user can grasp the complete state before bedtime. This will further improve your sleep quality.

In a possible implementation manner, the first signal includes one of an acceleration signal, a heart rate signal and an electroencephalogram signal or any combination thereof.

In the embodiment of the present application, using multiple signals as the first signal can further improve the accuracy of the generated bedtime state score.

In a possible implementation manner, before acquiring the first signal of the user according to the first time interval, the method includes:

get the current time;

If the current time is less than the set detection time, re-acquire the current time after a period of time;

If the current time is greater than or equal to the set detection time, acquire the first signal of the user according to the first time interval.

In the embodiment of the present application, the electronic device compares the current time with the detection time expected by the user, and obtains the user's first signal according to the detection time expected by the user, so that the generated bedtime state curve is more in line with the user's expectation.

In a possible implementation manner, after extracting the first characteristic parameter from the first signal, the method further includes:

According to the first characteristic parameter, determine the first sleep state of the user;

If the judgment result is that the first sleep state is suspected of falling asleep or falling out of sleep, generating a bedtime state score according to the bedtime state evaluation model and the first characteristic parameter, and generating a bedtime state curve according to the bedtime state score;

If the determination result is that the first sleep state is the sleep state, the sleep time is recorded, and the first signal of the user is acquired according to the second time interval.

In the embodiment of the present application, if the user does not fall asleep, the pre-sleep state score is continued to be generated and the pre-sleep state curve is generated according to the pre-sleep state score, so as to ensure the integrity of the user's pre-sleep state score within a period of time before going to sleep, thereby So that the user can grasp the complete state before sleep.

In a possible implementation, the method further includes:

Get the second sleep state of the user;

If the second sleep state is the suspected sleep state, acquiring the first signal of the user according to the first time interval;

If the second sleep state is a sleep-out state, determine whether the pre-sleep state curve satisfies the reminder condition; wherein, the reminder condition includes that the pre-sleep state score corresponding to the consecutive first specified number of time points gradually increases;

If the judgment result is that the state curve before bed meets the reminder condition, the user is reminded in a first reminder mode; the first reminder mode includes displaying a breathing light and/or a reminder message.

In the embodiment of the present application, if the user is suspected of falling asleep, the first signal is continued to be obtained to ensure the integrity of the user's pre-sleep state score within a period of time before going to sleep; If it increases gradually, the user is reminded to sleep by displaying a breathing light and/or a reminder message, which is a milder reminder, thereby improving the user's own sleep quality.

In a possible implementation, the method further includes:

Get the second sleep state of the user;

If the second sleep state is the state of falling asleep, then determine whether the pre-sleep state curve satisfies the reminder condition; wherein, the reminder condition includes that the pre-sleep state scores corresponding to the consecutive first specified number of time points are all greater than the score threshold;

In a possible implementation, the method further includes:

According to the first signal, determine whether the user is in a driving state;

If the judgment result is that the user is in a driving state, the user is reminded in a second reminder mode; the second reminder mode includes playing the first type of music;

If the judgment result is that the user is in a non-driving state, the user is reminded in a third reminder mode; the third reminder mode includes playing the second type of music.

In the embodiment of the present application, if the user is in a driving state, the first type of music that is relatively intense is played to refresh the user and improve the safety of the user during driving; Enhance the user's drowsiness, prompt the user to go to sleep as soon as possible, thereby improving the user's own sleep quality.

In a possible implementation, it also includes:

receiving a second query operation input by the user, where the second query operation includes an operation of querying the sleep quality score;

In response to the second query operation, the sleep quality score is displayed.

In this embodiment of the present application, the user inputs the second query operation to query the sleep quality score, which is convenient for the user to grasp the sleep quality during sleep.

In a possible implementation, the method further includes:

record sleep time;

Obtain the first signal of the user according to the second time interval;

generating a third sleep state of the user according to the first signal;

If the third sleep state is a sleep-out state, a sleep quality score is generated.

In the embodiment of the present application, whether the user wakes up from sleep is monitored during the user's sleep process, and if the user wakes up from sleep, a sleep quality score is generated in time for the user to check the sleep quality score.

In a second aspect, an embodiment of the present application provides an electronic device, the device comprising:

a display screen; one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, the one or more computer programs including instructions that, when executed by the device, cause The device executes the first aspect or the instructions of the method in any possible implementation of the first aspect.

In a third aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium is used for program code executed by a device, and the program code includes the first aspect or any of the first aspect. Instructions for a method in a possible implementation.

In a fourth aspect, the embodiments of the present application provide a computer program product containing instructions, when the computer program product is run on a computer or any at least one processor, the computer is made to execute the first aspect or any one of the first aspect Instructions for methods in possible implementations.

In the technical solution provided by the embodiment of the present application, the first signal of the user is obtained according to the set first time interval, and the first characteristic parameter is extracted from the first signal; through the generated bedtime state evaluation model, according to the first characteristic parameter, The pre-sleep state score is generated, which can accurately monitor the user's pre-sleep state, so that the user can grasp the complete state before and after sleep, thereby further improving their sleep quality.

Description of drawings

1 is a schematic ranking diagram of the reasons for affecting sleep in a kind of "2019 Chinese Sleep White Paper" provided by the embodiment of the present application;

2 is a schematic diagram of the distribution of the age groups of staying up late every day in a kind of "2019 Chinese Sleep White Paper" provided by the embodiment of the application;

3a is a schematic diagram of a sleepiness change curve provided by an embodiment of the application;

Fig. 3b is a schematic diagram of another drowsiness change curve provided by an embodiment of the present application;

4 is a schematic diagram of a bedtime state detection system provided by an embodiment of the present application;

5 is a schematic diagram of an external structure of a wearable device provided by an embodiment of the present application;

6 is a schematic diagram of the internal structure of a wearable device provided by an embodiment of the present application;

7 is a schematic diagram of a homepage of a terminal device provided by an embodiment of the present application;

8 is a schematic diagram of a sports health interface provided by an embodiment of the present application;

9 is a schematic diagram of a sleep function module interface provided by an embodiment of the present application;

10 is a schematic diagram of a bedtime detection function interface provided by an embodiment of the present application;

FIG. 11 is a schematic diagram of yet another bedtime detection function interface provided by an embodiment of the present application;

12 is a schematic diagram of a function setting interface provided by an embodiment of the present application;

13a is a schematic diagram of a user-selected reminder mode interface provided by an embodiment of the present application;

Figure 13b is a schematic diagram of yet another bedtime state curve provided by an embodiment of the present application;

13c is a schematic diagram of a reminder user interface provided by an embodiment of the present application;

14a is a schematic diagram of a user-selected reminder mode interface provided by an embodiment of the present application;

Figure 14b is a schematic diagram of yet another bedtime state curve provided by an embodiment of the application;

14c is a schematic diagram of another reminder user interface provided by an embodiment of the present application;

15a is a schematic diagram of a user-selected promotion mode interface provided by an embodiment of the present application;

15b is a schematic diagram of another reminder user interface provided by an embodiment of the present application;

15c is a schematic diagram of another reminder user interface provided by an embodiment of the present application;

16a is a schematic diagram of a sleep function module interface provided by an embodiment of the application;

16b is a schematic diagram of a sleep scoring interface provided by an embodiment of the application;

17 is a schematic diagram of a homepage of a wearable device provided by an embodiment of the present application;

FIG. 18 is a schematic structural diagram of a processor of an electronic device according to an embodiment of the present application;

19 is an algorithm flow chart of a method for detecting a bedtime state provided by an embodiment of the present application;

20 is a flowchart of a method for detecting a bedtime state provided by an embodiment of the present application;

FIG. 21 is a flowchart of constructing a bedtime state assessment model provided by an embodiment of the present application;

22a to 22e are schematic diagrams of a generation state curve provided by an embodiment of the present application;

23 is a flowchart of updating and training a bedtime state evaluation model provided by an embodiment of the present application;

FIG. 24 is a schematic diagram of a multi-user recording mode provided by an embodiment of the present application.

Detailed ways

In order to better understand the technical solutions of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

It should be clear that the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the embodiments of this application and the appended claims, the singular forms "a," "the," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise.

It should be understood that the term "and/or" used in this article is only an association relationship to describe related objects, indicating that there can be three kinds of relationships, for example, A and/or B, which can mean that A exists alone, and A and B exist at the same time. B, there are three cases of B alone. In addition, the character "condition." in this article generally indicates that the related objects before and after are an "or" relationship.

With the continuous improvement of material living standards in modern society, people pay more and more attention to their own health, and sleep is the easiest and most important way for the human body to recover and maintain health. Good sleep is the foundation of mental and physical health, and sleep disturbances or lack of sleep can negatively impact people's physical health, such as cardiovascular disease, over- or over-slow metabolism, mental illness, immune dysfunction.

The key to sleep is falling asleep, and difficulty falling asleep will directly lead to poor sleep quality. There are many reasons for difficulty falling asleep, such as psychological factors such as fast pace of life, stress and anxiety; unhealthy factors such as strenuous activity before bed, excessive overtime, drinking tea, coffee, excessive food intake, and excessive use of electronic products. Habits before bed. Fig. 1 is a schematic diagram of the ranking of the reasons for affecting sleep in the "2019 Chinese Sleep White Paper" provided by the embodiment of the present application. As shown in Fig. 1, among the reasons affecting sleep, the reason that accounts for the largest proportion is psychological pressure, which accounts for The ratio is 26%; the second reason affecting sleep is heavy work and study tasks, accounting for 21%; the third reason affecting sleep is personal sleep habits, accounting for 21% 19%. Fig. 2 is a schematic diagram of the distribution of the age groups of staying up late every day in a "2019 Chinese Sleep White Paper" provided by an embodiment of the application, as shown in Fig. 2, the horizontal axis of the distribution is the people of each age group, and the vertical axis is the ratio . Before 70 means people born before 1970, the proportion of staying up late before 70 is 2%; after 70 means people born after 1970 and before 1980, the proportion of post-70 staying up late is 7%; after 80 means after 1980 And for those born before 1990, the proportion of post-80s staying up late was 19%; for those born after 1990 and before 2000, the proportion of post-90s staying up late was 43%; for post-00s, those born after 2000, The rate of staying up late after 00 was 27%. It can be seen from Figure 1 and Figure 2 that psychological stress is the primary reason that affects the sleep of Chinese people. Among the people who stay up late every day, the post-90s have the highest proportion.

Users who have difficulty falling asleep usually cannot grasp their own state before going to bed. For example: the user is tired, but the user's brain is still in an excited state because he has just finished other work, so even if the user is tired, he still has difficulty falling asleep; or, The user feels tired, but there are still unfinished items to do, so he does not go to sleep. When he can sleep, his brain is in a state of excitement and cannot fall asleep; or, the user has the habit of staying up late, even if he feels tired, he still stays up unconsciously, causing the user to stay up late. Difficulty falling asleep. Fig. 3a is a schematic diagram of a drowsiness change curve provided by an embodiment of the present application. As shown in Fig. 3a, the horizontal axis of the schematic diagram of the curve is time, and the vertical axis is drowsiness. The schematic diagram of the curve includes two curves, namely a "fake" sleepiness curve and a real sleepiness curve. In the "fake" sleepiness curve, the user's sleepiness increases with time. In the real drowsiness curve, the user's drowsiness increases with time, but the increase of the user's drowsiness before the user lies on the bed is greater than the increase of the user's drowsiness after the user lies on the bed. The sleepiness before lying in bed is low, that is: the user has difficulty falling asleep; as shown by the dotted line in the real sleepiness curve, assuming that the user is not lying on the bed at this time, the user's sleepiness will increase with time, that is: the user Trying to sleep too early can make it difficult to fall asleep. Combining the two curves of the "fake" sleepiness curve and the real sleepiness curve, at each moment, the user in the "fake" sleepiness curve is more sleepy than the user in the real sleepiness curve, that is, the user feels tired or sleepy, but True drowsiness is low, so falling asleep is not smooth. Fig. 3b is a schematic diagram of yet another drowsiness change curve provided by an embodiment of the present application. As shown in Fig. 3b, the horizontal axis of the schematic diagram of the curve is time, and the vertical axis is drowsiness. From this curve, it can be seen that the user reaches the extremely sleepy point before lying on the bed. At this time, the user's sleepiness is the highest, but the user still has to-do items unfinished and delays sleeping. When the user is lying on the bed, the user's brain In the excited state, the user's sleepiness has dropped a lot compared to the extremely sleepy point, and the sleepiness is low, which makes it difficult for the user to fall asleep.

In the related art, the drowsiness depth of the user can be identified, and corresponding sleep assistance content is played for the user according to the drowsiness depth. Specifically, the user wears a wearable device, the wearable device collects the bioelectrical signal of the user, and extracts drowsiness identification information according to the bioelectrical signal; the wearable device inputs the drowsiness identification information into a pre-trained drowsiness depth detection model for identification, Generate the user's current sleepiness depth and send the current sleepiness depth to the server; the server matches the sleep aid content according to the current sleepiness depth, and sends the sleep aid content to the wearable device, so that the wearable device plays the sleep aid content to assist the user's sleep . The related technology can match and play the corresponding sleep assistance content according to the user's drowsiness depth, which improves the scientificity of the played sleep assistance content and enhances the sleep assistance effect. monitoring and improvement, lack of tracking and management of the user's bedtime state.

In the related art, the sleep stages of the user can also be detected and a sleep quality score can be calculated. Specifically, the user wears a wearable device, and the wearable device collects the user's heart rate variability signal and triaxial acceleration data; the wearable device divides the heart rate variability signal and triaxial acceleration data according to a specified time length, and extracts each time length. multiple characteristic parameters of the heart rate variability signal and triaxial acceleration data; the wearable device inputs multiple characteristic parameters within a time length into the pre-trained sleep stage prediction model to generate sleep stages within a time length; Multiple sleep stages during sleep time, and sleep quality scores are calculated based on sleep time and multiple sleep stages. The related technology can process the user's heart rate variability signal and triaxial acceleration data to generate a sleep quality score, which provides an effective reference for users to manage their own sleep. Improve, lack of tracking and management of the user's bedtime state.

In view of the above problems, the present application provides a bedtime state detection method, which can accurately monitor a user's bedtime state, so that the user can grasp the complete state before and after sleep, thereby further improving their own sleep quality.

FIG. 4 is a schematic diagram of a bedtime state detection system provided by an embodiment of the present application. As shown in FIG. 4 , the system includes at least one terminal device 201 and at least one wearable device 203 , and both the terminal device 201 and the wearable device 203 are for electronic equipment. Wherein, the terminal device 201 includes but is not limited to a mobile phone, a tablet computer, a speaker, and a personal computer; the wearable device 203 includes but is not limited to a smart watch, a smart bracelet, a head-mounted display, augmented reality (AR), Virtual reality (virtual reality, referred to as: VR) equipment. A bedtime state detection method provided by an embodiment of the present application can be applied to a bedtime state detection system. The terminal device 201 can perform wireless communication with the wearable device 203 through a wireless communication technology, wherein the wireless communication technology includes: wireless local area networks (WLAN for short) (such as wireless fidelity (Wi-Fi for short)) network), bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC) ), infrared technology (infrared, referred to as: IR) and so on.

FIG. 5 is a schematic diagram of an external structure of a wearable device according to an embodiment of the present application. As shown in FIG. 5 , the wearable device 203 includes a main microphone 150 , a secondary microphone 160 , a power button 210 , a receiver 140 and a display screen 170 .

The secondary microphone 160 is located at the top of the wearable device 203, and the main microphone 150 is located at the bottom of the wearable device. For the aesthetic effect of the wearable device 203, the secondary microphone 160 and the primary microphone 150 can be symmetrically arranged on the wearable device in the shape of a small circular hole. 203 top and bottom. The wearable device 203 is provided with two microphones, and uses the principle of dual microphone noise reduction to maintain stable calls. Among them, the main microphone 150 is used to collect the sound of the call, and the secondary microphone 160 is used to collect the noise around the call environment, and the sound of the call and the surrounding noise are processed in opposite directions, so as to achieve the purpose of noise reduction.

As shown in FIG. 5, the power button 210 is located on the side of the wearable device 203. As an optional solution, the power button 210 is arranged on the side of the wearable device 203 in the form of a raised button, which is convenient for the user to operate while holding it. It also does not need to occupy the front area of the display screen 170, which can further increase the screen ratio. The power button 210 can be used to control the wearable device 203, including the functions of keeping the screen on, brightening the screen, and turning on or off. The specific functions can be set according to user requirements. For example, when the wearable device 203 is in the on state, the user presses the power button 210 for a long time to turn the wearable device 203 into the off state; when the wearable device 203 is in the off-screen state, the user briefly presses the power button 210 to turn the wearable device 203 on. Screen.

The receiver 140, also called "earpiece", is located above the wearable device 203, and is used to convert audio electrical signals into sound signals. When the wearable device 203 answers a call or a voice message, the sound can be answered by bringing the receiver 140 close to the user's ear.

The display screen 170 is located on the front of the wearable device 203, and is used for displaying images or videos, and for receiving touch instructions input by the user. The touch instructions include single click, double click, pressing or sliding. A curved screen can also be a flat screen with no radian on the side. The display screen 170 includes a display panel, and the display panel includes a liquid crystal display 170 (liquid crystal display, LCD for short), an organic light-emitting diode (OLED for short), an active matrix organic light emitting diode or an active matrix Active-matrix organic light emitting diode (AMOLED), flexible light-emitting diode (FLED), Mini LED, Micro LED, Micro OLED (Micro-OLED) or quantum dot light emitting diodes (quantum dot light emitting diodes, referred to as: QLED). As an optional solution, the display screen 170 includes a touch display screen. The display screen 170 is also used for displaying the pre-sleep state curve for the user to view; the display screen 170 is also used for displaying the sleep quality score and providing a feedback result button for the user.

In this embodiment of the present application, from the perspective of external structure, the difference between the terminal device 201 and the wearable device 203 is only the size of the display area of the display screen, that is, the size of the display screen of the terminal device 201 is larger than that of the wearable device 203 . Other structures included in the terminal device 201 are the same as those included in the wearable device 203 , and details are not described herein again.

FIG. 6 is a schematic diagram of the internal structure of a wearable device provided by an embodiment of the application. As shown in FIG. 6 , the wearable device 203 includes a memory 100, a processor 110, a communication module, a receiver 140, a main microphone 150, and a secondary microphone. 160 , a display screen 170 , a sensor module 180 and an interaction module 190 . The sensor module 180 includes one or any combination of an acceleration (ACC) sensor 180a, a photoplethysmography sensor 180b, a brain wave sensor 180c and a humidity sensor 180d, and the communication module includes a mobile communication module 120a and/or a wireless communication module 120b. The memory 100, the processor 110 and the interaction module 190 can communicate with each other through an internal connection path to transmit control and/or data signals. The memory 100 is used to store computer programs, and the processor 110 is used to call and run the computer from the memory 100. program.

Memory 100 may be read-only memory (ROM), other types of static storage devices that can store static information and instructions, random access memory (RAM), or other types of information and instructions that can be stored It can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, CD-ROM storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or can be used to carry or store desired program code in the form of instructions or data structures and Any other medium that can be accessed by a computer, etc.

The processor 110 may include one or more processing units, for example, the processor 110 may include an application processor (application processor, referred to as: AP), a modem processor, a graphics processor (graphics processing unit, referred to as: GPU), Image signal processor (ISP), controller, memory, video codec, digital signal processor (DSP), baseband processor, neural-network processing unit (abbreviation: NPU) or any combination thereof. Wherein, different processing units may be independent devices, or may be integrated in one or more processors.

In this embodiment of the present application, the above-mentioned processor 110 and the memory 100 may be combined into a processing device, which is more commonly an independent component. The processor 110 is configured to execute program codes stored in the memory 100 to implement the above functions. During specific implementation, the memory 100 may also be integrated in the processor 110 , or be independent of the processor 110 .

In some embodiments, the processor 110 may include one or more interfaces. The interface may include an integrated circuit (inter-integrated circuit, referred to as: I2C) interface, integrated circuit built-in audio (inter-integrated circuit sound, referred to as: I2S) interface, pulse code modulation (pulse code modulation, referred to as: PCM) interface, universal asynchronous Transmitter (universal asynchronous receiver/transmitter, referred to as: UART) interface, mobile industry processor interface (mobile industry processor interface, referred to as: MIPI), general-purpose input/output (general-purpose input/output, referred to as: GPIO) interface, user Identity module (subscriber identity module, referred to as: SIM) interface, universal serial bus (universal serial bus, referred to as: USB) interface one or any combination thereof.

It can be understood that the interface connection relationship between the modules illustrated in the embodiments of the present application is only a schematic illustration, and does not constitute a structural limitation on the system architecture of the wearable device. In other embodiments, the wearable device may also adopt different interface connection manners in the foregoing embodiments, or a combination of multiple interface connection manners.

The mobile communication module 120a can provide a wireless communication solution including 2G/3G/4G/5G etc. applied on the wearable device 203 . The mobile communication module 120a may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA for short) and the like. Further, the wearable device 203 may further include the first antenna 130 . The mobile communication module 120a can receive the electromagnetic wave by the first antenna 130, filter, amplify and other processing on the received electromagnetic wave, and transmit it to the modulation and demodulation processor for demodulation. The mobile communication module 120a can also amplify the signal modulated by the modulation and demodulation processor, and then turn it into an electromagnetic wave for radiation through the first antenna 130 . In some embodiments, at least part of the functional modules of the mobile communication module 120a may be provided in the processor 110 . In some embodiments, at least some of the functional modules of the mobile communication module 120a may be provided in the same device as at least some of the modules of the processor 110.

The wireless communication module 120b can provide wireless local area networks (wireless local area networks, referred to as: WLAN) (such as wireless fidelity (wireless fidelity, referred to as: Wi-Fi) networks), Bluetooth (bluetooth, referred to as: BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), Infrared (Infrared) IR) and other wireless communication solutions. The wireless communication module 120b may be one or more devices integrating at least one communication processing module. Further, the wearable device 203 may further include the second antenna 131 . The wireless communication module 120b receives electromagnetic waves via the second antenna 131 , frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110 . The wireless communication module 120b can also receive the signal to be sent from the processor 110, frequency-modulate the signal, amplify the signal, and then convert it into an electromagnetic wave for radiation through the second antenna 131.

In this embodiment of the present application, the first antenna 130 is coupled with the mobile communication module 120a, and the second antenna 131 is coupled with the wireless communication module 120b, so that the wearable device 203 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include a global system for mobile communications (GSM for short), a general packet radio service (GPRS for short), a code division multiple access (code division multiple access), Abbreviation: CDMA), wideband code division multiple access (WCDMA), time division code division multiple access (time-division code division multiple access, abbreviation: TDSCDMA), long term evolution (long term evolution, abbreviation: LTE), BT, GNSS, WLAN, NFC, FM, and/or IR technologies, etc. The GNSS may include global positioning system (global positioning system, referred to as: GPS), global navigation satellite system (global navigation satellite system, referred to as: GLONASS), Beidou satellite navigation system (bei dou navigation satellite system, referred to as: BDS) , quasi-zenith satellite system (quasi-zenith satellite system, referred to as: QZSS) and/or satellite based augmentation systems (satellite based augmentation systems, referred to as: SBAS).

The receiver 140 is connected to the processor 110, and the receiver 140 is used for converting audio electrical signals into sound signals.

The main microphone 150 is connected to the processor 110, and the main microphone 150 is used for collecting the sound signal of the call and converting the sound signal into an electrical signal. When the user needs to make a call, send a voice signal, or trigger the wearable device 203 to perform a certain function through a voice assistant, the user can approach the main microphone and make a sound for the main microphone 150 to collect the sound signal.

The sub-microphone 160 is connected to the processor 110, and the sub-microphone 160 is used for collecting the noise around the call environment. The wearable device 203 includes two microphones, the main microphone 150 and the secondary microphone 160, so that it can not only collect sound signals, but also realize a noise reduction function. In other embodiments, the wearable device may further be provided with three or four microphones, so as to realize the functions of collecting sound signals, reducing noise, identifying sound sources, and realizing directional recording.

The display screen 170 is connected to the processor 110, and the display screen 170 is used for receiving the touch command input by the user, and sending the touch command to the processor 110, so that the processor 110 can call the relevant interface according to the touch command and send the relevant interface Sent to the display screen 170, the display screen 170 displays the relevant interface; the display screen 170 is also used to display the pre-sleep state curve for the user to view; the display screen 170 is also used to display the sleep quality score and provide the user with a feedback result button.

The sensor module 180 is connected to the processor 110 , and the sensor module 180 is used for collecting state information of each sensor for the processor 110 to process. The sensor module 180 includes an acceleration (Accelerometer, ACC for short) sensor 180a, a photoplethysmograph (PPG for short) sensor 180b, a brain wave sensor 180c and a humidity sensor 180d. The acceleration sensor 180a is used to detect the first acceleration around the x-axis, the second acceleration around the y-axis, and the third acceleration around the z-axis of the wearable device 203 in the three-dimensional space. The PPG sensor 180b is used to detect the user's heart rate signal and send the heart rate signal to the processor 110 . The brain wave sensor 180 c is used to detect the user's brain electrical signals and send the brain electrical signals to the processor 110 . The humidity sensor 180d is used to detect the humidity of the surrounding environment of the wearable device 203 and send the humidity to the processor 110 .

The interaction module 190 is connected to the processor 110, and the interaction module 190 is used for receiving a user's long-press operation or a short-press operation on the power key; the interaction module 190 is also used for receiving a feedback result input by the user.

It can be understood that the structure diagram shown in FIG. 6 does not constitute a specific limitation on the structure of the wearable device 203 . In other embodiments, the structure of the wearable device 203 may include more or less components than shown, or combine some components, or separate some components, or different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

In the embodiment of the present application, from the perspective of the internal system architecture, the difference between the terminal device 201 and the wearable device 203 lies in the size of the display area of the display screen, the type of the sensor, and the computing capability of the processor, that is, the display of the terminal device 201 The area is larger than the display area of the wearable device 203; the type of the sensor of the terminal device 201 can be different from the type of the sensor of the wearable device 203, for example: the wearable device 203 has the PPG sensor 108b, the terminal device 201 does not have the PPG sensor; the terminal device The computing power of the processor of 201 is greater than the computing power of the processor of the wearable device 203 . Other internal structures included in the terminal device 201 are the same as those included in the wearable device 203 , and details are not described herein again.

Taking the user's operation on the terminal device 201 as an example, the user can turn on the terminal device 201 by turning on the power button, so that the display screen of the terminal device 201 displays the home page of the terminal device 201. A schematic diagram of the homepage of the terminal device 201 is shown in FIG. 7 , the homepage includes a status bar 300 , a menu bar 400 and a function bar 500 . Status bar 300 includes operator, current time, current geographic location and local weather, network status, signal status, and power level. As shown in Figure 7, the operator is China Mobile; the current time is 08:08 on Monday, December 21; the current geographical location is Beijing, the weather in Beijing is cloudy and the temperature is 6 degrees Celsius; the network status is wifi network; the signal status is A full-scale signal indicates that the current signal is strong; the black part of the power supply can indicate the remaining power of the terminal device 201 . An application can be installed in the terminal device. In the process of using the terminal device 201, the user will use various applications based on their different needs. For example, the terminal device 201 is installed with an application with a function of detecting the state before going to bed, that is, a sports health application . As shown in FIG. 7 , the menu bar 400 includes icons of at least one application program, and the name of the corresponding application program is under the icon of each application program, such as: gallery, task card store, Weibo, sports health, WeChat, card package , Settings, Camera, Phone, SMS and Contacts. The positions of the icon of the application program and the name of the corresponding application program may be adjusted according to the user's preference, which is not limited in this embodiment of the present application. The function bar 500 includes a back key, a home key, and a menu key. The return key is used to return to the previous level, the home key is used to return to the home page, and the menu key is used to display multiple background applications.

It should be noted that the schematic diagram of the homepage of the terminal device 201 shown in FIG. 7 is an exemplary display of the embodiment of the present application, and the schematic diagram of the homepage of the terminal device 201 may also be in other styles, which are not limited in this embodiment of the present application.

As shown in FIG. 7 , the user can click the icon of the sports health application in the menu bar 400 , so that the display screen of the user's terminal device 201 displays the sports health interface. Fig. 8 is a schematic diagram of a sports health interface provided by an embodiment of the present application. As shown in Fig. 8 , the interface includes a classification column 410 and a plurality of functional modules 420, and the classification column 410 includes health, sports, equipment and mine. For example: when the user clicks on health, as shown in FIG. 8 , the display screen of the terminal device 201 displays the interface of exercise health; when the user clicks on exercise, the display screen of the terminal device 201 displays the detailed exercise record of the user; when the user clicks on the device , the display screen of the terminal device 201 displays the device paired with the terminal device 201, and the user can perform pairing and removal operations on the device; when the user clicks on me, the display screen of the terminal device 201 can display personal information about the user , the user can update the personal information. The function module 420 includes a sleep function module 421 , a step count function module 422 and a weight function module 423 . The sleep function module 421 is used to record and display the latest sleep date and sleep duration, for example: the sleep duration on December 21 is 7.5 hours; the step count function module 422 is used to record and display the current date and the current number of steps, for example: The current date is December 21, and the current number of steps is 1053 steps; the weight function module 423 is used to record and display the latest weight record date and weight, for example: the last weight record date is December 20, and the weight is 49.5 Kilogram.

As shown in FIG. 8 , the user may click on the sleep function module 421, so that the display screen of the user's terminal device 201 displays the interface of the sleep function module. FIG. 9 is a schematic diagram of an interface of a sleep function module provided by an embodiment of the present application. As shown in FIG. 9 , the interface includes enabling a bedtime detection module 431 and a sleep scoring module 432 . Turning on the bedtime detection module 431 is used to start the bedtime detection function; the sleep scoring module 432 is used to score the user's sleep quality, and display the score on the display screen. The user can click to open the bedtime detection module 431, so that the display screen of the user's terminal device 201 displays an interface of the bedtime detection function. FIG. 10 is a schematic diagram of a bedtime detection function interface provided by an embodiment of the application. As shown in FIG. 10 , the interface includes a sleep state curve, and the horizontal axis of the bedtime state curve is a time point, and the time point is from 22:00 Start, it indicates that the terminal device 201 starts to perform bedtime detection and generate bedtime state score from 22:00; the vertical axis is bedtime state score; the current time is 08:08, so the current time has not reached 22:00, and the bedtime state score has not yet started. Pre-bed detection and generation of bedtime state scores. FIG. 11 is a schematic diagram of another pre-sleep detection function interface provided by an embodiment of the application. As shown in FIG. 11 , the interface includes a sleep state curve, the horizontal axis of the pre-sleep state curve is the time point, and the vertical axis is the time before going to bed Status score, the current time is 23:08, indicating that the terminal device 201 starts to perform bedtime detection and generate bedtime status scores, one time point corresponds to a bedtime status score, and the bedtime status scores corresponding to each time point are connected to generate Bedtime state curve. The pre-sleep state score indicates the degree to which the user is currently suitable for sleep, and the higher the pre-sleep state score, the more suitable the user is to go to sleep.

As shown in FIG. 11 , the bedtime detection function interface further includes a function setting button 433 , and the user can click the function setting button 433 to set the work mode and detection time of bedtime detection. FIG. 12 is a schematic diagram of a function setting interface provided by an embodiment of the present application. As shown in FIG. 12 , the user can input a desired detection time through the first setting operation. For example, the user inputs the desired detection time as 22:00, Then the terminal device 201 will start to perform bedtime detection at 22:00 every day. Optionally, the terminal device 201 can also perform bedtime detection according to a specified time interval, so as to realize the real-time bedtime detection of the user; optionally, the terminal device 201 can also perform bedtime detection in response to the user's activation operation of the bedtime detection module. For example, before going to bed, the user clicks the icon of the sports health application, the sleep function module 421 and the bedtime detection module 431 in sequence on the terminal device 201 to activate the bedtime detection function, and the user starts to respond to the activation operation. Check before going to bed. It should be noted that the above description is only an exemplary operation for enabling the terminal device 201 to start bedtime detection, and other methods may also be used to enable the terminal device to start bedtime detection, which is not limited in this embodiment of the present application.

The user can also select a desired working mode through the second setting operation, the working modes include a do not disturb mode, a reminder mode and a promotion mode, and the user can select one of the above three working modes. If the user selects the do not disturb mode, the processor only generates a state curve before going to bed, and does not remind the user, so as not to disturb the user. If the user selects the reminder mode, the processor determines that the user is in the state of falling asleep and the current user state meets the reminder condition, and reminds the user through the set first reminder mode to remind the user to sleep. The current user state includes a bedtime state curve, and the bedtime state curve includes a time point and a bedtime state score corresponding to each time point. The reminder condition can be set so that the bedtime state score corresponding to the first specified number of consecutive time points gradually increases high. The first specified number can be set according to the actual situation. As an optional solution, the first specified number is 3. Optionally, the reminder condition may also be set such that the pre-sleep state scores corresponding to the second consecutive second specified number of time points are all greater than the set score threshold. The second specified number can be set according to the actual situation. As an optional solution, the second specified number is 3. The scoring threshold can be set according to the actual situation. As an optional solution, the scoring threshold is 80 points. The first reminder method includes displaying a breathing light and/or generating and displaying a reminder message.

The following uses a specific example to illustrate the reminder mode:

Fig. 13a is a schematic diagram of an interface for a user to select a reminder mode provided by an embodiment of the application. As shown in Fig. 13a, the user selects the reminder mode on the bedtime detection function interface and the desired detection time is 22:00, and the terminal device 201 is at 22:00. At 22:00, bedtime testing begins. The reminder condition is set to gradually increase the bedtime state score corresponding to three consecutive time points, and the first reminder mode is set to display the breathing light. Fig. 13b is a schematic diagram of another bedtime state curve provided by an embodiment of the present application. As shown in Fig. 13b, the bedtime state score corresponding to 22:25 is 57 points; the bedtime state score corresponding to 22:30 is 75 points ; 22:35 corresponds to a bedtime state score of 80 points; 22:40 corresponds to a bedtime state score of 85 points, namely: 22:30, 22:35 and 22:40, three consecutive time points corresponding to sleep The former state score gradually increases, indicating that the state curve before going to bed satisfies the reminder condition. Figure 13c is a schematic diagram of a reminder user interface provided by the embodiment of the application. When the state curve before going to sleep satisfies the reminder condition, the terminal device 201 displays the breathing light 434, to remind the user to sleep.

Let's use another specific example to illustrate the reminder mode:

Fig. 14a is a schematic diagram of a user selecting a reminder mode interface provided by an embodiment of the application. As shown in Fig. 14a, the user has selected the reminder mode on the bedtime detection function interface and the desired detection time is 22:00, and the terminal device 201 is at 22:00. At 22:00, bedtime testing begins. When the reminder condition is set to three consecutive time points, the pre-sleep state scores are all greater than the set score threshold, the score threshold is set to 80 points, and the first reminder mode is set to generate and display a reminder message. FIG. 14b is a schematic diagram of another bedtime state curve provided by an embodiment of the present application. As shown in FIG. 14b , the horizontal axis of the bedtime state curve is the time point, and the vertical axis is the bedtime state score. It can be seen from Figure 14b that the score of the bedtime state corresponding to 22:35 is 82 points; the bedtime state score corresponding to 22:40 is 85 points; the bedtime state score corresponding to 22:45 is 90 points, that is: 3 consecutive The pre-sleep state scores corresponding to each time point are all greater than the set score threshold, indicating that the pre-sleep state curve satisfies the reminder condition. Figure 14c is a schematic diagram of another reminder user interface provided by the embodiment of the application. condition, the terminal device 201 generates and displays a reminder message to remind the user to sleep, for example, the reminder message is: exercise health reminds you that it is time to sleep.

It should be noted that the reminder condition may also be set to other conditions, and the first reminder mode may also be set to other forms. The embodiments of the present application are only illustratively described here, and are not limited thereto.

Fig. 15a is a schematic diagram of a user-selected promotion mode interface provided by an embodiment of the application. As shown in Fig. 15a, if the user selects the promotion mode, the user needs to input a third setting operation, that is, input a desired promotion time, for example: promotion The time is set to 23:00. If the user is in a driving state and the current time is greater than the promotion time, the user is reminded by the set second reminder mode; if the user is in a non-driving state and the current time is greater than the promotion time, the user is reminded through the set third reminder mode. For example: the second reminder method includes playing the first type of music, which is refreshing music to avoid danger caused by excessive drowsiness during driving; the third reminder method includes playing the second type of music, and the second type of music is White noise music to enhance user drowsiness.

The following is a concrete example to illustrate the promotion mode:

Taking the terminal device 201 as an example, as shown in FIG. 15a, the user selects the promotion mode and the desired promotion time is 23:00. The current time is 23:08, which is greater than the promotion time. If the user is in the driving state, the terminal device 201 can play rock music, and the wearable device vibrates at the same time, so as to prevent the user from being too drowsy and dangerous during driving. Fig. 15b is this application. Another schematic diagram of a reminder user interface provided by an embodiment, the interface includes a music player, and the music player is playing rock music. In order to save the power consumption of the terminal device 201, after the display screen of the terminal device 201 keeps displaying the music player for a duration greater than or equal to the specified duration, the display screen is turned off. If the user is in a non-driving state, the terminal device 201 can play white noise to enhance the user's drowsiness. FIG. 15c is a schematic diagram of another reminding user interface provided by the embodiment of the present application. As shown in FIG. 15c, the interface includes a music player , the music player is playing white noise. In order to save the power consumption of the wearable device, after the display screen of the terminal device 201 keeps displaying the music player for a duration greater than or equal to the specified duration, the display screen is turned off. The specified duration can be set according to user requirements, for example, the specified duration is 30 seconds. It is worth noting that the promotion mode can be selected on the terminal device, and the first type of music or the second type of music can be played on the wearable device and/or the terminal device; And/or the first type of music or the second type of music is played on the terminal device.

It should be noted that the second reminder mode and the third reminder mode may also be set to other forms, and the embodiments of the present application are only illustratively described here, and the specific forms of the reminder mode are not limited.

The user can also check his own sleep quality score by entering a second query operation. Fig. 16a is a schematic diagram of a sleep function module interface provided by an embodiment of the present application. As shown in Fig. 16a, the interface includes enabling the bedtime detection module 431. And the sleep scoring module 432, the user can click on the sleep scoring module 432, so that the display screen of the terminal device 201 displays the interface of sleep scoring. Fig. 16b is a schematic diagram of a sleep scoring interface provided by an embodiment of the present application. As shown in Fig. 16b, the interface includes a date, a sleep duration, and a sleep quality score. for 7.5 hours. Users can also click the double triangle to the left of the date to view the previous day's sleep quality score and sleep duration.

As an optional solution, the user can also query the bedtime state curve through the wearable device 203. Figure 17 is a schematic diagram of a homepage of a wearable device provided by an embodiment of the present application. As shown in Figure 17, the homepage includes the current time , current geographic location, local weather and icons of multiple applications, as shown in Figure 17, the current time is 08:08 on Monday, December 21; the current geographic location is Beijing, the weather in Beijing is cloudy and the temperature is 6 degrees Celsius, Icons for multiple applications include: Gallery, Weibo, Sports Health, WeChat, SMS, and Contacts. As shown in FIG. 17 , the user can click the icon of the sports health application, so that the display screen of the user's wearable device 203 displays the sports health interface.

In the embodiment of the present application, the schematic diagram of the sports health interface of the wearable device 203 is the same as the schematic diagram of the sports interface of the terminal device 201, and the difference is only in the size of the display area of the display screen, that is, the display area of the terminal device 201 is larger, and the wearable The display area of device 203 is small. It is not repeated here.

It is worth noting that the user can also turn on the sleep function module through the wearable device 203 to check the sleep date and sleep duration, turn on the step count function module to check the current number of steps, turn on the weight function module to check the weight; the user can also use the sleep function module to check the weight. Open the bedtime detection module to view the bedtime state curve, set the detection time, and select the work mode of bedtime detection; you can also open the sleep score module to view the sleep quality score. The specific interface and operation process can be referred to FIG. 7 to FIG. 16b, which will not be repeated here.

FIG. 18 is a schematic structural diagram of a processor of an electronic device provided by an embodiment of the present application. As shown in FIG. 18 , the processor 110 of the electronic device includes a falling asleep module 111 , a pre-sleep evaluation module 113 , and a sleep staging and evaluation module 115 . Wherein, the falling asleep module 111 is respectively connected with the pre-sleep evaluation module 113 and the sleep staging and evaluation module 115 . The falling asleep module 111 can receive the first signal sent by the sensor module 180; extract the first characteristic parameter from the first signal; Sleeping state and suspected falling asleep state. The falling asleep state is a state in which the user is sleeping, the falling asleep state is a state in which the user is awake, and the suspected falling asleep state is a state in which the user may be sleeping. If the first sleeping state of the user is a suspected falling asleep state, it indicates that it is not currently determined that the user is in a sleeping or awake state, and more first signals need to be collected for further judgment.

If the first sleep state is the sleep state, the first sleep state is sent to the sleep staging and evaluation module 115, and the sleep staging and evaluation module 115 can receive the user's third sleep state sent by the sleep and fall module 111. The third sleep state includes falling asleep state, falling asleep and suspected falling asleep. If the third sleep state is the falling asleep state or the suspected falling asleep state, continue to perform the step of receiving the third sleep state of the user sent by the falling asleep module 111; if the third sleeping state is the falling asleep state, the sleep staging and evaluation module 115 generates a sleep quality score.

If the first sleep state is the state of falling asleep, the pre-sleep evaluation module 113 may receive the first signal sent by the sensor module 180; extract the first characteristic parameter from the first signal, and use the pre-sleep state evaluation model according to the first characteristic parameter , to generate a bedtime state score; according to the bedtime state score, generate a bedtime state curve, and send the generated bedtime state curve to the display screen 170 for the display screen 170 to display the bedtime state curve; continue to receive the falling asleep module 111 The second sleep state sent, the second sleep state includes falling asleep state, falling asleep state and suspected falling asleep state, if the second sleeping state includes falling asleep state, turn off the pre-sleep evaluation module 113, and open the sleep staging and evaluation module 115; The second sleeping state includes the suspected falling asleep state, indicating that it is not currently determined that the user is in a sleeping or awake state, and more first signals need to be collected for further judgment, then the falling asleep module 111 continues to receive the first signal sent by the sensor module 180; The first characteristic parameter is extracted from the first signal; the second sleep state of the user is generated according to the first characteristic parameter; if the second sleep state includes the falling asleep state, continue to execute the pre-sleep state evaluation model, and according to the first characteristic parameter , steps to generate a bedtime state score, and generate a bedtime state curve.

If the first sleeping state is a suspected falling asleep state, it indicates that it is not currently determined that the user is in a sleeping or awake state, and more first signals need to be collected for further judgment, the falling asleep module 111 continues to receive the first signal sent by the sensor module 180. signal; extracting a first characteristic parameter from the first signal; generating a second sleep state of the user according to the first characteristic parameter.

The following uses a specific embodiment, taking a wearable device as an example, to illustrate the workflow of bedtime state detection exemplarily. In this embodiment of the present application, each step is performed by the wearable device. FIG. 19 is an algorithm flowchart of a method for detecting a bedtime state provided by an embodiment of the application. As shown in FIG. 19 , the wearable device obtains the current time. If the current time is less than the set detection time, the current time time; if the current time is greater than or equal to the set detection time, obtain the first signal of the user according to the first time interval (step 11). Start the sleep module 111 of the processor to detect the first sleep state (step 12), if the sleep module 111 detects that the user's first sleep state is the sleep state, open the sleep stage and evaluation module 115 (step 13), and the sleep stage And the evaluation module 115 receives the third sleep state sent by the falling asleep module 111, and judges whether the third sleep state is the falling asleep state (step 14), if not, continue to perform step 14; if so, close the sleep stage and the evaluation module 115 (step 14). 15), save the first signal and the sleep quality score generated by the sleep stage evaluation module 115 (step 16). If it is detected that the first sleep state of the user is suspected of falling asleep or falling out of sleep, the pre-sleep evaluation module 113 is activated (step 17 ). The state score dynamically generates a bedtime state curve (step 18); the bedtime evaluation module 113 obtains the second sleep state sent by the falling asleep module 111, and judges whether the second sleep state is the sleep state (step 19), and if so, execute step 13; If not, judge whether the second sleep state is a suspected sleep state (step 20), if yes, go to step 18; if not, identify the working mode (step 21), if the working mode is the do not disturb mode, continue to execute step 18; The mode is the reminder mode, and it is judged whether the state curve before going to bed meets the set reminder condition (22), if so, remind the user (step 23), and continue to execute step 18; if not, continue to execute step 18; if the working mode is the promotion mode , judge whether the promotion time is not empty and whether the current time is greater than the promotion time (step 24), if so, judge whether the user is in a driving state (step 25), if so, play the first type of music to refresh the user (step 26), continue to perform steps 18; If no, play the second type of music to enhance sleepiness for the user (step 27), and continue to step 18.

The above steps 11 to 22 can also be executed by a terminal device, and the execution process is the same as that of the wearable device, and details are not repeated here. It can be understood that, according to the difference in display area and computing capability of the wearable device and the terminal device, some steps in the above steps 11 to 22 may be performed by the wearable device, and some steps may be performed by the terminal device. The execution order of the above steps can also be changed according to the operating conditions of the equipment and user settings. For example, step 21 identifies the working mode and can be executed before step 19 or step 20. If it is the reminder mode, there is no need to obtain the second sleep state, and the bedtime state is directly determined. Whether the curve meets the setting reminder conditions.

FIG. 20 is a flowchart of a method for detecting a bedtime state provided by an embodiment of the present application. As shown in FIG. 20 , the method includes:

Step 102: Obtain the current time.

Exemplarily, as shown in FIG. 9, the user clicks the bedtime detection module 431, and the electronic device executes step 102 to obtain the current time of the electronic device. For example, the current time shown in FIG. 10 is 8:08, and the current time shown in FIG. The current time is 23:08.

Step 104 , determine whether the current time is greater than or equal to the set detection time, if yes, go to step 106 ; if not, go to step 102 .

In this embodiment of the present application, the set detection time is the time to start tracking the first signal, and the user can set it according to his actual needs. Exemplarily, as shown in FIG. 12 , the detection time input by the user through the first setting operation is 22:00. Specifically, the interaction module of the electronic device receives a first setting operation input by the user, where the first setting operation includes an operation of setting the detection time; the processor of the electronic device sets the detection time in response to the first setting operation.

In this embodiment of the present application, the first setting operation corresponds to one or any combination of the multiple operations shown in FIG. 7 to FIG. 12 .

In the embodiment of the present application, if the processor of the wearable device determines that the current time is greater than or equal to the detection time, it indicates that the acquisition of the user's first signal can begin, and step 106 is continued; if it is determined that the current time is less than the detection time, it indicates that it has not arrived Obtain the time of the user's first signal, and after a period of time, continue to perform step 102 . For example, if the current time is 22:30 and the detection time is 22:00, the processor of the wearable device determines that the current time 22:30 is greater than the detection time 22:00, indicating that the user's first signal can be obtained, and the steps are continued. 106. For example, if the current time is 21:00 and the detection time is 22:00, the processor of the wearable device determines that the current time 21:00 is less than the detection time 22:00, indicating that it is not time to obtain the user's first signal. After 10 minutes, the current time is re-acquired, that is, step 102 is continued.

Step 106: Acquire the first signal of the user according to the set first time interval.

In this embodiment of the present application, the first time interval may be set according to actual conditions. As an option, the first time interval is 500 milliseconds (ms). As another alternative, the first time interval is 1 second (s).

In this embodiment of the present application, the first signal includes one of an acceleration signal, a heart rate signal, and an electroencephalogram signal, or any combination thereof. Optionally, the first signal may further include a humidity signal and/or a noise signal, and the humidity signal and/or the noise signal are used as auxiliary signals, which can further improve the accuracy of the subsequently generated bedtime state score.

In the embodiment of the present application, the user wears the wearable device, and the acceleration sensor of the wearable device will detect the acceleration signal of the wearable device, and send the acceleration signal to the processor, so that the processor can obtain the acceleration signal, and the acceleration signal includes electronic The device has a first acceleration around the x-axis, a second acceleration around the y-axis, and a third acceleration around the z-axis in three-dimensional space.

In the embodiment of the present application, the PPG sensor can detect the heart rate signal of the user. Specifically, the PPG sensor includes a light emitting diode (LED), and the LED sends a light signal to the skin tissue, and the skin tissue reflects the light signal and reflects it back to the PPG sensor. Reflected light signal; the PPG sensor converts the reflected light signal into an electrical signal, and then converts the electrical signal into a digital signal through analog-to-digital (A/D) conversion, which is the heart rate signal; the PPG sensor sends the heart rate signal to the processing so that the processor can obtain the heart rate signal.

In the embodiment of the present application, the brain wave sensor can detect the user's brain electrical signal, and send the brain electrical signal to the processor, so that the processor can obtain the brain electrical signal, and the processor preprocesses the brain electrical signal, After the signal is extracted, the amplitude and frequency of the EEG signal can be obtained. The EEG frequency can reflect the user's brain activity. The higher the EEG frequency, the more active the user's brain activity is; the lower the EEG frequency, the quieter the user's brain activity.

In the embodiment of the present application, the humidity sensor can detect the humidity signal, and send the humidity signal to the processor, so that the processor obtains the humidity signal and converts it into a humidity value, and the humidity signal includes the humidity of the surrounding environment of the wearable device. Specifically, the humidity sensor has a humidity sensor, and a film made of a humidity-sensitive material is covered on the substrate of the humidity sensor. When the water vapor in the air is adsorbed on the humidity-sensitive film, the resistance value of the humidity sensor will change. Changed, the humidity signal includes the resistance value of the humidity sensor.

In the embodiment of the present application, the secondary microphone may collect the noise signal and send the noise signal to the processor. First, the processor obtains the noise signal, and the noise signal includes the noise of the surrounding environment of the wearable device.

Step 108: Extract the first characteristic parameter from the first signal.

In this embodiment of the present application, the processor of the electronic device extracts the first characteristic parameter from the first signal. The electronic device is a wearable device or a terminal device.

In this embodiment of the present application, when the first signal includes an acceleration signal, the first characteristic parameter includes a first acceleration around the x-axis, a second acceleration around the y-axis, and a third acceleration around the z-axis of the electronic device in three-dimensional space. Specifically, the processor extracts the first acceleration, the second acceleration and the third acceleration from the acceleration signal.

Further, when the first signal includes the acceleration signal, the first characteristic parameter further includes the movement time interval. Specifically, the memory stores a plurality of historical acceleration signals and the time corresponding to each historical acceleration signal; the processor queries the memory for at least one motion acceleration signal that satisfies the motion quantity condition and the time corresponding to each motion acceleration signal, and the motion quantity condition Including that the first acceleration is greater than the set first acceleration threshold and/or the second acceleration is greater than the set second acceleration threshold and/or the third acceleration is greater than the set third acceleration threshold, wherein the first acceleration threshold, the second acceleration threshold and The third acceleration threshold can be set according to the actual situation; the processor queries the motion time closest to the current time from the time corresponding to each motion acceleration signal; the processor calculates the motion time interval according to the motion time and the current time.

In this embodiment of the present application, when the first signal includes a heart rate signal, the first characteristic parameter includes heart rate variability. Specifically, the processor extracts the heart rate variability from the heart rate signal by specifying an analysis method. The specified analysis method includes a time domain analysis method, a frequency domain analysis method or a nonlinear analysis method.

In this embodiment of the present application, when the first signal includes an EEG signal, the first characteristic parameter includes an EEG waveform. Specifically, the EEG signal includes an EEG frequency, and the processor extracts the EEG frequency from the EEG signal, and generates an EEG waveform according to the EEG frequency. EEG waveforms include delta waves, theta waves, alpha waves and beta waves. The EEG frequency of the delta wave is 1-3 Hz and the amplitude is 20-200 μV. Under extreme fatigue and lethargy or anesthesia, this band can be recorded in the temporal lobe and parietal lobe; the EEG frequency of theta wave is 4 to 7 Hz, and the amplitude is 5 to 20 μV. Theta waves are prone to appear; the EEG frequencies of alpha waves are 8-13Hz, and the amplitude is 20-100μV. People are most likely to appear alpha waves when the brain is quiet and eyes are closed; the EEG frequencies of beta waves are 14-30Hz, and the amplitude is 100 ~ 150μV, when people are nervous, emotional or excited, β waves are likely to appear.

In this embodiment of the present application, when the first signal includes a humidity signal, the first characteristic parameter includes humidity. Specifically, the humidity signal includes the resistance value of the humidity sensing element, and the processor generates humidity corresponding to the resistance value according to the resistance value, where the humidity is the humidity of the surrounding environment of the electronic device.

In this embodiment of the present application, when the first signal includes a noise signal, the first characteristic parameter includes noise. Specifically, the processor extracts the noise from the noise signal, and the noise is the noise of the surrounding environment of the electronic device.

Step 110: Determine the first sleep state of the user according to the first characteristic parameter. If the first sleep state is a sleep state, go to step 116; if the first sleep state is a suspected sleep state or a sleep out state, go to step 112.

As an optional solution, the processor inputs the first characteristic parameter into a support vector machine (SVM) model, and outputs the first sleep state of the user. The first sleep state of the user includes a sleep-in state, a suspected sleep-in state, or a sleep-out state. The falling asleep state is a state in which the user is sleeping, the falling asleep state is a state in which the user is awake, and the suspected falling asleep state is a state in which the user may be sleeping. For example: the first characteristic parameter includes exercise time interval, heart rate variability and brain wave pattern. If the exercise time interval is less than the first threshold, the heart rate variability is less than the second threshold, and the EEG waveform is β wave, it can be determined that the user's The first sleep state is the state of falling asleep; if the exercise time interval is greater than or equal to the first threshold and less than the third threshold, the heart rate variability is greater than or equal to the second threshold and less than the fourth threshold, and the EEG waveform is alpha wave, it can be It is determined that the user's first sleep state is a suspected sleep state; if the exercise time interval is greater than or equal to the third threshold, the heart rate variability is greater than or equal to the fourth threshold, and the brain wave pattern is delta waves, the user's first sleep state can be determined. The state is sleep state.

It should be noted that the sleep state of the user may also be generated in other manners, which is only illustratively described here, and the embodiment of the present application does not limit the manner of generating the sleep state of the user.

Step 112 , generating a pre-sleep state score according to the first characteristic parameter through the pre-sleep state evaluation model.

In this embodiment of the present application, the processor inputs the first characteristic parameter into the bedtime state evaluation model to generate a bedtime state score. Among them, the pre-sleep state score indicates the degree to which the user is currently suitable for sleep, and the higher the pre-sleep state score, the more suitable the user is to sleep.

In the embodiment of the present application, in order to reduce the power consumption of the device, a pre-sleep state score is output according to a specified time interval, which can better reflect the user's pre-sleep state. As an option, specify a time interval of 5 minutes.

The construction process of the pre-sleep state evaluation model is described below. FIG. 21 is a flowchart of constructing a pre-sleep state evaluation model provided by the embodiment of the application. As shown in FIG. 21 , the construction process includes:

Step 202: Acquire an initial first signal.

In this embodiment of the present application, multiple subjects may be recruited, the first signal of each subject may be collected, and the collected first signal may be determined as the initial first signal. Among them, the recruited subjects need to be widely distributed in terms of gender, age and occupation, which can improve the accuracy of the bedtime state assessment model.

In this embodiment of the present application, the initial first signal includes one of an acceleration signal, a heart rate signal, and an electroencephalogram signal, or any combination thereof. Optionally, the initial first signal may further include a humidity signal and/or a noise signal, and the humidity signal and/or the noise signal are used as auxiliary signals, which can further improve the accuracy of the constructed pre-sleep state evaluation model.

Step 204: Extract the initial first characteristic parameter from the initial first signal.

In this embodiment of the present application, when the first signal includes an acceleration signal, the first characteristic parameter includes the first acceleration of the electronic device around the x-axis, the second acceleration around the y-axis, and the third acceleration around the z-axis in the three-dimensional space, For the specific extraction process, please refer to step 108, which will not be repeated here.

In this embodiment of the present application, when the initial first signal includes a heart rate signal, the initial first characteristic parameter includes heart rate variability. For a specific extraction process, please refer to step 108, which will not be repeated here.

In this embodiment of the present application, when the first signal includes an EEG signal, the first characteristic parameter includes an EEG waveform. For a specific extraction process, please refer to step 108, which will not be repeated here.

In this embodiment of the present application, when the first signal includes a humidity signal, the first characteristic parameter includes humidity. For a specific extraction process, please refer to step 108, which will not be repeated here.

In this embodiment of the present application, when the first signal includes a noise signal, the first characteristic parameter includes noise. For a specific extraction process, please refer to step 108, which will not be repeated here.

Step 206: Obtain an initial sleep quality score.

In the embodiment of the present application, the initial first characteristic parameter is divided into a training set and a test set, the training set is input into the random forest classification generator for training, and a sleep quality evaluation model is generated; the test set is input into the sleep quality evaluation model, and the initial sleep quality is output. Quality rating.

Step 208 , generating a pre-sleep label value according to the initial sleep quality score.

In the embodiment of the present application, the initial sleep quality score is corrected to generate a pre-sleep label value. For example: display the initial sleep quality score to the user through the display screen, and receive the feedback result input by the user by clicking the set feedback result button. The feedback result includes high, accurate or low. If the feedback result input by the user is high, Then the initial sleep quality score is subtracted from the set first threshold to generate the pre-sleep label value. As an optional solution, the first threshold is 10; if the feedback result input by the user is accurate, then the initial sleep quality score can accurately represent The sleep quality of the user, the initial sleep quality score is determined as the bedtime label value; if the feedback result input by the user is low, the initial sleep quality score is subtracted from the set second threshold to generate the bedtime label value, which is used as a bedtime label value. An optional solution, the second threshold is 10. Optionally, the sleep quality score may be on a percentile scale.

It should be noted that other methods may also be used for the correction of the initial sleep quality score, which are exemplarily described in the embodiments of the present application, and the correction method of the initial sleep quality score is not limited.

In this embodiment of the present application, the execution order between steps 202 to 204 and steps 206 to 208 is not limited, that is, steps 202 to 204 may be executed first, and then steps 206 to 208 may be executed; or steps may be executed first Step 206 to step 208, and then perform step 202 to step 204.

Step 210: Input the initial first feature parameter and the pre-sleep label value into the machine learning algorithm for training, and generate a pre-sleep state evaluation model.

In the embodiments of the present application, the machine learning algorithm includes a decision tree algorithm, a least squares algorithm, or a linear regression algorithm.

Step 114 , generating a bedtime state curve according to the bedtime state score.

In the embodiment of the present application, each time the processor outputs a bedtime state score according to a specified time interval, the generated bedtime state score is connected with the bedtime state score output at the previous time interval to generate a bedtime state curve, and the bedtime state curve is generated. The pre-sleep state curve is sent to the display; the display shows the pre-sleep state curve for the user to view at any time. Taking the specified time interval as 10 minutes as an example, FIGS. 22a to 22e are schematic diagrams of generating a state curve provided by an embodiment of the present application. As shown in FIG. 22a, at 22:00, the processor outputs a state score 70 before going to bed. score, record the bedtime state score; at 22:10, the processor outputs a bedtime state score of 65, records the bedtime state score, and connects with the bedtime state score corresponding to 70 at 22:00, and connects The bedtime state curve after the line is shown in Figure 22b; at 22:20, the processor outputs a bedtime state score of 73 points, records the bedtime state score, and compares it with the bedtime state score of 65 points corresponding to 22:10. Connected, the bedtime state curve after the connection is shown in Figure 22c; at 22:30, the processor outputs a bedtime state score of 75 points, records the bedtime state score, and records the bedtime state score corresponding to the bedtime state at 22:20 The score of 73 points is connected, and the bedtime state curve after the connection is shown in Figure 22d; at 22:40, the processor outputs a bedtime state score of 80 points, records the bedtime state score, and corresponds to 22:30 The bedtime state score of 75 points is connected, and the bedtime state curve after the connection is shown in Figure 22e. The processor dynamically generates the bedtime state curve according to the output bedtime state score, which can not only reduce the power consumption of the device, but also better reflect the user's bedtime state.

It is worth noting that the specified time interval can be set according to the power consumption of the device and the reflection effect of the user's bedtime state. The processor may acquire the first signal according to a relatively small first time interval and generate a bedtime state score according to the first signal, and if a bedtime state score is output every time a bedtime state score is generated, the generated bedtime state curve The pre-sleep state scores corresponding to the time point of , are relatively dense, although it can accurately reflect the user's pre-sleep state, it will also lead to high power consumption of the device; if the pre-sleep state score is output according to the specified time interval, that is: If the corresponding bedtime state score is output at the time point, the generated bedtime state curve can not only better reflect the bedtime state of the user, but also reduce the power consumption of the device in the process of generating the bedtime state curve. As an option, specify a time interval of 5 minutes or 10 minutes.

As an optional solution, the interaction module of the electronic device receives a first query operation input by the user, and the first query operation includes an operation of querying the bedtime state curve; the processor of the electronic device responds to the first query operation, The curve is sent to the display, which shows the bedtime state curve. Electronic devices include wearable devices or terminal devices.

In this embodiment of the present application, the first query operation may correspond to one or any combination of the multiple operations shown in FIG. 7 to FIG. 10 .

In the embodiment of the present application, the bedtime state curve includes a time point and a bedtime state score corresponding to each time point. Fig. 13b is a schematic diagram of a bedtime state curve provided by an embodiment of the present application. As shown in Fig. 13b, the horizontal axis of the bedtime state curve is a time point, and the vertical axis is a bedtime state score. As can be seen from Figure 13b, the score of the bedtime state corresponding to 22:00 is 65 points; the bedtime state score corresponding to 22:05 is 50 points; the bedtime state score corresponding to 22:10 is 55 points; the bedtime state score corresponding to 22:15 is 55 points; 22:20 corresponds to 60 points; 22:25 corresponds to 57 points; 22:30 corresponds to 75 points; 22:30 corresponds to 75 points. 35 corresponds to a bedtime state score of 80 points; 22:40 corresponds to a bedtime state score of 85 points.

In the embodiment of the present application, the user can view the state curve before going to bed through the display screen of the electronic device, so as to provide a personalized reference for the user's management of his own work and rest. The user can decide when to sleep according to the bedtime state curve, or review the bedtime state of the previous night after waking up the next day, so as to adjust the sleep time and improve the user's sleep quality.

Step 116: Acquire the second sleep state of the user. If the sleep state includes the falling asleep state, go to step 118; if the sleep state includes the suspected falling asleep state, go to step 106;

As an optional solution, the sleeping module of the processor inputs the first characteristic parameter into the support vector machine (SVM) model, and outputs the second sleep state of the user, so that the pre-sleep evaluation module or the sleep staging and evaluation module of the processor The second sleep state of the user can be acquired. The second sleep state includes a sleep-in state, a sleep-out state, or a suspected sleep-in state. The falling asleep state is a state in which the user is sleeping, the falling asleep state is a state in which the user is awake, and the suspected falling asleep state is a state in which the user may be sleeping.

Step 118, record the time to fall asleep.

If the sleep state of the user generated in step 110 or step 116 is the sleep state, the current time of the electronic device is recorded as the time to fall asleep, and step 120 is further performed to obtain the user's sleep state at a second time interval. first signal.

Step 120: Acquire the first signal of the user according to the second time interval.

In this embodiment of the present application, the second time interval may be set according to actual conditions. For example, if it is necessary to further improve the accuracy of the current sleep state generated by the subsequent steps, as an optional solution, the second time interval can be set to 1 minute; if it is necessary to reduce the power consumption of the electronic device, as an optional solution, The second time interval can be set to 5 minutes.

As an optional solution, the first signal includes one of an acceleration signal, a heart rate signal and an electroencephalogram signal or any combination thereof. Optionally, the first signal may further include a humidity signal and/or a noise signal, and the humidity signal and/or the noise signal are used as auxiliary signals, which can further improve the accuracy of the current sleep state generated by the subsequent steps.

Step 122: Generate a third sleep state of the user according to the first signal.

In this embodiment of the present application, the processor extracts the first characteristic parameter from the first signal. Specifically, when the first signal includes a heart rate signal, the first characteristic parameter includes heart rate variability; when the first signal includes an EEG signal , the first characteristic parameter includes an EEG waveform. For the specific extraction process, please refer to step 108; when the first signal includes a humidity signal, the first characteristic parameter includes humidity; when the first signal includes a noise signal, the first characteristic parameter includes noise. . For the specific extraction process, please refer to step 108, which will not be repeated here.

As an optional solution, the processor inputs the first characteristic parameter into a support vector machine (SVM) model, and outputs the third sleep state of the user. The third sleep state includes a sleep-in state, a suspected sleep-in state, or a sleep-out state.

It should be noted that the third sleep state of the user may also be generated in other manners, which is only illustratively described here, and the embodiment of the present application does not limit the manner of generating the third sleep state of the user.

Step 124 , determine whether the third sleep state includes the falling asleep state, if yes, go to step 126 ; if not, go to step 120 .

In the embodiment of the present application, if it is determined that the third sleep state includes the falling asleep state, it indicates that the user has woken up, and the step 126 is continued; if it is determined that the third sleeping state does not include the falling asleep state, it indicates that the user is still sleeping, and the process continues Step 120 is performed.

Step 126: Generate a sleep quality score, and the process ends.

In this embodiment of the present application, step 126 specifically includes:

Step 1262: Record the sleep time and calculate the sleep time.

In the embodiment of the present application, the time to fall asleep is subtracted from the time to fall asleep to calculate the sleep duration.

Step 1264: Divide the first signal of the entire sleep duration according to the specified duration, and generate each duration and the corresponding first signal.

Step 1266: Extract the first characteristic parameter from the first signal of each time length.

Step 1268: Input the first characteristic parameter into the trained sleep stage prediction model to generate sleep stages within each time length.

Step 1270: Calculate a sleep quality score according to the sleep stage and sleep duration in each time length.

As an optional solution, a sleep quality score is generated by calculating the sleep stage and sleep duration in each time length through the set sleep quality formula. Among them, sleep stages include the first stage, the second stage, the third stage and the fourth stage. In the first phase, the brain waves are dominated by theta waves, without spindle waves or K complex waves. It is a transitional stage from fully awake to sleep. The response to external stimuli is weakened, mental activity enters a floating state, and thinking and reality are disconnected. In the second stage, the brain waves are mainly spindle waves and K complex waves, and the delta wave is less than 20%; in the third stage, the brain wave delta wave accounts for 20% to 50%; in the fourth stage, the brain wave delta wave accounts for 50%. above. For example: Calculate the proportion of the fourth period according to the sleep stages in each time length, that is: the proportion of the fourth period to the total sleep period; set the first weight for the fourth period proportion, and set the second weight for the sleep duration ; Multiply the first weight by the ratio of the fourth period to generate the first multiplication result; multiply the second weight by the sleep duration to generate the second multiplication result; add the first multiplication result and the second multiplication result, Generate a sleep quality score.

It should be noted that the sleep quality score may also be generated in other manners, which is only illustratively described here, and the embodiment of the present application does not limit the manner of generating the sleep quality score.

In the embodiment of the present application, the interaction module of the electronic device receives the second query operation input by the user, and the second query operation includes the operation of querying the sleep quality score; the processor of the electronic device sends the sleep quality score to the second query operation in response to the second query operation. Display, the display shows the sleep quality score.

In this embodiment of the present application, the second query operation corresponds to one or any combination of the multiple operations shown in FIG. 16a to FIG. 16b.

In the embodiment of the present application, the sleep quality score indicates the sleep quality of the user, and the higher the sleep quality score, the better the sleep quality of the user.

It should be noted that the sleep quality score may also be calculated in other manners, which is only illustratively described here, and the embodiment of the present application does not limit the manner of calculating the sleep quality score.

Further, the sleep quality score is stored in the memory.

Further, since different users have different personal characteristics, work and rest habits, and physiological laws, the pre-sleep state evaluation model is updated and trained, thereby improving the reliability of the pre-sleep state evaluation model. As an optional solution, FIG. 23 is a flowchart of updating and training a bedtime state evaluation model provided by an embodiment of the present application. As shown in FIG. 23 , the training process includes: the processor sends the sleep quality score to the Display screen; the display screen displays the sleep quality score and provides the user with a feedback result button; the interaction unit receives the feedback result input by the user, and sends the feedback result to the processor; the processor corrects the label value before going to bed according to the feedback result, and learns through online learning The algorithm updates the bedtime state assessment model online.

As an option, the feedback results include high, accurate, or low.

In the embodiment of the present application, when the user uses the electronic device, the pre-sleep state evaluation model is updated and trained, so that the pre-sleep state evaluation model can learn the user's personalized physiological laws, and can provide the user with more and more accurate sleep patterns. pre-status score.

Step 128: Determine whether the current user status satisfies the set reminder condition, if yes, go to Step 130; if not, go to Step 106.

In the embodiment of the present application, the current user state includes a bedtime state curve, the bedtime state curve includes a time point and a bedtime state score corresponding to each time point, and the reminder condition includes a first specified number of consecutive time points corresponding to bedtime The status score gradually increased. The first specified number can be set according to the actual situation. As an optional solution, the first specified number is 3. As shown in Figure 13b, 22:25 corresponds to a bedtime state score of 57 points; 22:30 corresponds to a bedtime state score of 75 points; 22:35 corresponds to a bedtime state score of 80 points; 22:40 corresponds to a bedtime state score of 80 points The bedtime state score is 85 points, namely: 22:30, 22:35 and 22:40. The bedtime state score corresponding to three consecutive time points gradually increases, indicating that the bedtime state curve meets the reminder condition, and the steps are continued. 128.

As an optional solution, the current user state includes a bedtime state curve, the bedtime state curve includes a time point and a bedtime state score corresponding to each time point, and the reminder condition includes a second consecutive specified number of time points corresponding to sleep The previous state scores are all greater than the set score threshold. The second specified number can be set according to the actual situation. As an optional solution, the second specified number is 3. The scoring threshold can be set according to the actual situation. As an optional solution, the scoring threshold is 80 points. For example, as shown in Figure 14b, the horizontal axis of the bedtime state curve is the time point, and the vertical axis is the bedtime state score. As can be seen from Figure 14b, the score of the bedtime state corresponding to 22:35 is 82 points; the bedtime state score corresponding to 22:40 is 85 points; the bedtime state score corresponding to 22:45 is 90 points, that is, three consecutive The pre-sleep state scores corresponding to each time point are all greater than the set score threshold, indicating that the pre-sleep state curve meets the reminder condition, and step 128 is continued.

Optionally, the current user state includes the user's driving state, and the reminder condition includes that the user is in the driving state and the acquired current time is greater than the set promotion time. If the user is in the driving state and the current time is greater than the promotion time, it indicates that the current user state satisfies the reminder condition, and proceeds to step 130; if the user is in the non-driving state and the current time is greater than the promotion time, it indicates that the current user state meets the reminder condition, and proceeds to step 130 ; If the current time is less than or equal to the promotion time, it indicates that the current user state does not meet the reminder condition, and step 106 is continued.

In the embodiment of the present application, the promotion time is the time for judging the driving state, and the user can set it according to the actual situation. As an option, the promotion time is set to 23:00. Specifically, the interaction module of the electronic device receives a third setting operation input by the user, where the third setting operation includes an operation of setting the promotion time; the processor of the electronic device sets the promotion time in response to the third setting operation.

In the embodiment of the present application, the third setting operation corresponds to FIG. 15a.

In the embodiment of the present application, judging whether the user is in a driving state specifically includes the processor judging whether there is a connection channel between the wireless communication module and the vehicle-mounted device or whether the motion trajectory of the electronic device is an arc-shaped structure. There is a connection channel between the devices or the movement trajectory of the electronic device is an arc-shaped structure, indicating that the user is in a driving state; if it is determined that there is no connection channel between the wireless communication module and the in-vehicle device and the movement trajectory of the electronic device is not an arc-shaped structure , indicating that the user is not driving. Wherein, the acceleration sensor in the electronic device can determine whether the motion trajectory of the electronic device is based on the variation law of the first acceleration around the x-axis, the second acceleration around the y-axis, and the third acceleration around the z-axis of the electronic device in three-dimensional space. For the arc-shaped structure, if it is determined that one of the first acceleration, the second acceleration and the third acceleration or any combination thereof is not zero within the first time period, it indicates that the motion trajectory of the electronic device is an arc-shaped structure. As an optional solution, by judging whether the device name of the device connected to the wireless communication module includes the device name of the in-vehicle device, it is judged whether there is a connection channel between the wireless communication module and the in-vehicle device. The device name includes the device name of the in-vehicle device, indicating that there is a connection channel between the wireless communication module and the in-vehicle device; if the device name of the device connected to the wireless communication module does not include the device name of the in-vehicle device, it indicates that there is no connection between the wireless communication module and the in-vehicle device. A connection channel exists. For example: the device name of the in-vehicle device is car kit, if the device name of the device connected to the wireless communication module includes car kit, it indicates that there is a connection channel between the wireless communication module and the in-vehicle device; if the device name of the device connected to the wireless communication module does not include car kit, indicating that there is no connection channel between the wireless communication module and the in-vehicle device.

In order to further improve the accuracy of identifying the user's driving state, as an optional solution, judging whether the user is in the driving state specifically includes the processor judging whether there is a connection channel between the wireless communication module and the in-vehicle device and whether the motion trajectory of the electronic device is Arc-shaped structure, if it is determined that there is a connection channel between the wireless communication module and the in-vehicle device and the movement trajectory of the electronic device is an arc-shaped structure, it indicates that the user is in a driving state; if it is determined that there is no connection between the wireless communication module and the in-vehicle device. The movement trajectory of the connection channel or electronic device is not an arc-like structure, indicating that the user is not driving.

In the embodiment of the present application, if the processor determines that the current user status satisfies the set reminder conditions, it indicates that the user can be reminded, and proceeds to step 130; if it determines that the current user status does not meet the set reminder conditions, it indicates that the user cannot be reminded. Reminder, continue to step 106.

It should be noted that the reminder condition may also be set to other content, which is only illustratively described here, and the specific content of the reminder condition is not limited in this embodiment of the present application.

Step 130 , remind the user, and proceed to step 106 .

In the embodiment of the present application, the working mode of the electronic device includes a reminder mode, and the electronic device reminds the user through the set first reminder mode to remind the user to sleep. For example, the first reminder method includes displaying a breathing light and/or generating and displaying a reminder message. Specifically, if the user's sleep state includes a sleep-out state and the pre-sleep state score corresponding to the first specified number of consecutive time points in the pre-sleep state curve gradually increases, the user is reminded by the first reminder; or, if The sleep state of the user includes the state of falling asleep, and the pre-sleep state scores corresponding to the first specified number of consecutive time points in the pre-sleep state curve are all greater than the set score threshold, and the user is reminded by the first reminder method.

Optionally, the working mode of the electronic device further includes a do not disturb mode, that is, the processor only generates a bedtime state curve according to the bedtime state, and does not remind the user to avoid disturbing the user.

Optionally, the working mode of the wearable device includes a facilitation mode, the user can set the facilitation mode to be turned on or off according to his own needs, and the facilitation mode is turned off by default. When the user sets to turn on the promotion mode, if the processor determines that the user is in a driving state and the current time is greater than the promotion time, it will remind the user through the set second reminder mode; if it is determined that the user is in a non-driving state and the current time is greater than the promotion time , and remind the user through the set third reminder method.

In this embodiment of the present application, the electronic device receives a second setting operation input by the user, the second setting operation includes an operation of setting the working mode, and the electronic device sets the working mode in response to the second setting operation.

In this embodiment of the present application, the second setting operation corresponds to one or any combination of the multiple operations shown in FIG. 7 to FIG. 15 .

In this embodiment of the present application, both the second reminder mode and the third reminder mode may be set according to actual conditions. For example, the second reminder mode includes playing the first type of music; the third reminder mode includes playing the second type of music. Specifically, if the processor determines that the user is in a driving state, it acquires the pre-stored first type of music from the memory and plays it. The first type of music is refreshing music, so as to prevent the user from being in danger due to excessive sleepiness during driving; When it is found that the user is in a non-driving state, the pre-stored second type of music is acquired from the memory and played, and the second type of music is white noise music to enhance the user's drowsiness.

It should be noted that the first reminder mode, the second reminder mode and the third reminder mode may also be set to other forms, and the embodiments of the present application are only illustratively described here, and the specific forms of the reminder modes are not limited.

Further, the electronic device in this embodiment of the present application supports a multi-user recording mode, that is, different users using the electronic device can save their corresponding bedtime state evaluation models for each user package. FIG. 24 is a schematic diagram of a multi-user recording mode provided by an embodiment of the present application. As shown in FIG. 24 , the bedtime state evaluation model is first constructed, and the construction steps refer to steps 202 to 210, which will not be repeated here; when user 1 uses the electronic device for the first time, the bedtime state evaluation model is initialized, that is, the factory The constructed pre-sleep state evaluation model is used as the pre-sleep state evaluation model of user 1; through the pre-sleep state evaluation model, the pre-sleep state curve and sleep quality score of user 1 are generated according to the first signal of user 1; according to the feedback results of user 1 Correct the label value before going to bed, and update the bedtime state evaluation model online through the online learning algorithm. The bedtime state evaluation model is constructed according to the physiological laws of user 1, and can accurately evaluate the bedtime state of user 1. As shown in FIG. 24 , the process of using the electronic device for User 2 and User 3 is the same as that of User 1, which will not be repeated here. The bedtime state evaluation model generated by User 2 is constructed according to the physiological law of User 2 and can accurately evaluate the user. 2's bedtime state; the bedtime state evaluation model generated by user 3 is constructed based on user 3's physiological laws, and can accurately assess user 3's bedtime state.

In the solution of the embodiment of the present application, the first signal of the user is obtained according to the set first time interval, and the first characteristic parameter is extracted from the first signal; the sleep state evaluation model is generated, and the sleep condition is generated according to the first characteristic parameter. The pre-state score can accurately monitor the user's pre-sleep state, so that the user can grasp the complete state before and after sleep, thereby further improving their sleep quality.

An embodiment of the present application further provides an electronic device, where the electronic device may be a terminal device or a circuit device built in the terminal device. The device may be used to perform the functions/steps in the above method embodiments.

The present application also provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, when the instructions are executed on the computer, the computer is made to perform the bedtime state detection shown in the above-mentioned FIG. 19 and FIG. 20 . steps in the method.

The present application also provides a computer program product containing instructions, when the computer program product runs on a computer or any at least one processor, the computer causes the computer to perform the bedtime state detection shown in the above-mentioned FIGS. 19 and 20 . steps in the method.

In the above embodiments, the involved processor 110 may include, for example, a central processing unit (CPU), a microprocessor, a microcontroller or a digital signal processor, and may also include a GPU, an NPU, and an ISP. Necessary hardware accelerators or logic processing hardware circuits may also be included, such as application-specific integrated circuits (ASICs), or one or more integrated circuits used to control the execution of the programs of the technical solution of the present application. In addition, the processor may have the functionality to operate one or more software programs, which may be stored in the memory.

The memory can be read-only memory (ROM), other types of static storage devices that can store static information and instructions, random access memory (RAM), or other types of storage devices that can store information and instructions The dynamic storage device can also be electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), compact disc read-only memory (CD-ROM) or other optical disk storage, optical disk storage ( including compact discs, laser discs, compact discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or may also be capable of carrying or storing desired program code in the form of instructions or data structures and capable of Any other medium accessed by a computer, etc.

In the embodiments of the present application, "at least one" refers to one or more, and "multiple" refers to two or more. "And/or", which describes the association relationship of the associated objects, means that there can be three kinds of relationships, for example, A and/or B, which can indicate the existence of A alone, the existence of A and B at the same time, and the existence of B alone. where A and B can be singular or plural. The character "/" generally indicates that the associated objects are an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c may be single or multiple.

Those of ordinary skill in the art can realize that the units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution, and the computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes .

The above are only specific embodiments of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims

A bedtime state detection method, applied to an electronic device, the electronic device comprising a display screen, characterized in that it includes:

receiving a first query operation input by the user, where the first query operation includes an operation of querying the state curve before going to bed;

In response to the first query operation, the bedtime state curve is displayed.
The method of claim 1, further comprising:

receiving a first setting operation input by a user, where the first setting operation includes an operation of setting a detection time;

The detection time is set in response to the first setting operation.
The method of claim 1, further comprising:

receiving a second setting operation input by a user, the second setting operation including an operation of setting a working mode;

In response to the second setting operation, the operating mode is set.
The method according to claim 1, wherein the method further comprises:

Obtain the first signal of the user according to the first time interval;

extracting a first characteristic parameter from the first signal;

generating a bedtime state score according to the bedtime state assessment model and the first characteristic parameter;

According to the bedtime state score, a bedtime state curve is generated.
The method according to claim 4, wherein the first signal comprises one of an acceleration signal, a heart rate signal and an electroencephalogram signal or any combination thereof.
The method according to claim 4, wherein before the acquiring the first signal of the user according to the first time interval, the method comprises:

get the current time;

If the current time is less than the set detection time, re-acquire the current time after a period of time;

If the current time is greater than or equal to the set detection time, the first signal of the user is acquired according to the first time interval.
The method according to claim 6, wherein after extracting the first characteristic parameter from the first signal, the method further comprises:

According to the first characteristic parameter, determine the first sleep state of the user;

If the judgment result is that the first sleep state is suspected of falling asleep or falling out of sleep, a sleep state score is generated according to the sleep state evaluation model and the first characteristic parameter, and a sleep state score is generated according to the sleep state score. pre-state curve;

If the determination result is that the first sleep state is the sleep state, the sleep time is recorded, and the first signal of the user is acquired according to a second time interval.
The method according to claim 4, wherein the method further comprises:

obtaining the second sleep state of the user;

If the second sleep state is a suspected sleep state, acquiring the first signal of the user according to the first time interval;

If the second sleep state is a sleep-out state, then determine whether the pre-sleep state curve satisfies a reminder condition; wherein the reminder condition includes that the pre-sleep state score corresponding to the first specified number of consecutive time points gradually increases. rise;

If the judgment result is that the bedtime state curve meets the reminder condition, the user is reminded in a first reminder mode; wherein, the first reminder mode includes displaying a breathing light and/or a reminder message.
The method according to claim 4, wherein the method further comprises:

obtaining the second sleep state of the user;

If the second sleep state is a suspected sleep state, acquiring the first signal of the user according to the first time interval;

If the second sleep state is the state of falling asleep, then determine whether the pre-sleep state curve satisfies the reminder condition; wherein, the reminder condition includes that the pre-sleep state scores corresponding to the first specified number of consecutive time points are all equal to each other. greater than the scoring threshold;

If the judgment result is that the bedtime state curve meets the reminder condition, the user is reminded in a first reminder mode; wherein, the first reminder mode includes displaying a breathing light and/or a reminder message.
The method according to claim 4, wherein the method further comprises:

According to the first signal, determine whether the user is in a driving state;

If the judgment result is that the user is in a driving state, the user is reminded in a second reminder mode; wherein, the second reminder mode includes playing the first type of music;

If the judgment result is that the user is in a non-driving state, the user is reminded in a third reminder mode; wherein the third reminder mode includes playing the second type of music.
The method of claim 1, further comprising:

receiving a second query operation input by the user, where the second query operation includes an operation of querying the sleep quality score;

The sleep quality score is displayed in response to the second query operation.
The method according to claim 1, wherein the method further comprises:

record sleep time;

Obtain the first signal of the user according to the second time interval;

generating a third sleep state of the user according to the first signal;

If the third sleep state is a sleep-out state, a sleep quality score is generated.
An electronic device, comprising:

a display screen; one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, the one or more computer programs comprising instructions for When the instruction is executed by the device, the device is caused to execute the bedtime state detection method according to any one of claims 1 to 12.
A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein when the program runs, a device where the computer-readable storage medium is located is controlled to execute any one of claims 1 to 12 A described method for detecting a bedtime state.
A computer program product comprising instructions, characterized in that, when the computer program product is run on a computer or any one of at least one processor, the computer is made to perform the execution of any one of claims 1 to 12. sleep state detection method.