CN117796760A - A sleep quality prediction method, electronic device and system - Google Patents

Abstract

The present application provides a sleep quality prediction method, electronic device and system. The method includes: the electronic device acquires user data in response to a user operation to start sleep prediction; the user data includes at least one of physical energy data, energy data and environmental data. ; The electronic device obtains the user's sleep aid suggestions and the first sleep quality based on the user data. The first sleep quality is the sleep quality when the user adopts the sleep aid suggestions; the electronic device displays at least one of the first sleep quality and sleep improvement. As well as sleep aid suggestions, sleep improvement is based on the first sleep quality. Implementing the embodiments of this application can predict sleep quality based on user data, meet the user's sleep needs, and improve user experience.

Description

A sleep quality prediction method, electronic device and system

Technical field

Embodiments of the present application relate to terminal technology, and in particular, to a sleep quality prediction method, electronic device, and system.

Background technique

People in modern life have a demand for healthy living that exceeds previous eras. This demand has given rise to the birth of wearable devices and home monitoring devices that use the Internet of Things and artificial intelligence technology to measure physiological characteristics such as heart rate and blood oxygen. More and more wearable devices make it possible to monitor normalized physiological characteristics in real time, which allows people to gain knowledge of health status from individual data changes down to the minute level. Taking the insomnia problem that plagues modern people as an example, current sleep monitoring equipment has entered the stage of stable commercialization for its monitoring functions of various stages of human sleep state.

How to meet users' sleep needs and improve user experience is the current and future research direction.

Summary of the invention

This application provides a sleep quality prediction method, electronic device and system. In the sleep quality prediction method, sleep quality can be predicted based on user data to meet the user's sleep needs and improve the user experience.

In a first aspect, embodiments of the present application provide a method for predicting sleep quality, which is applied to electronic devices. The method includes:

The electronic device acquires user data in response to a user operation to start sleep prediction; the user data includes at least one of the user's physical energy data, energy data, and environmental data;

Based on the user data, the electronic device obtains the user's sleep aid suggestions and the first sleep quality. The first sleep quality is the sleep quality when the user adopts the sleep aid suggestions;

The electronic device displays at least one of the first sleep quality and the sleep improvement condition and the sleep aid suggestion, where the sleep improvement condition is the improvement of the sleep quality when the user adopts the sleep aid suggestion.

In the embodiments of the present application, the electronic device can automatically obtain user data in response to user operations without user input; further, based on the user data, the user's sleep aid suggestions and the sleep quality when the user adopts the sleep aid suggestions (i.e., the first sleep) are obtained. quality); finally, display at least one of the first sleep quality and sleep improvement status as well as sleep aid suggestions, and the sleep improvement status is the improvement of sleep quality when the user adopts the sleep aid suggestions. This method does not require user input and can automatically display at least one of the first sleep quality and sleep improvement conditions as well as sleep aid suggestions. The display content is conducive to satisfying the user's awareness of personal health status and can satisfy the user's desire to receive sleep aid suggestions. And the sleep aid suggestions meet the needs of improving sleep conditions and improve user experience.

In combination with the first aspect, in a possible implementation method, physical data is relevant data that characterizes the user's physical energy consumption, for example, physical data may include at least one of heart rate, exercise duration, number of steps, calories, and activity hours; energy data is relevant data that characterizes the user's energy status, for example, energy data may include at least one of blood pressure, blood oxygen, pressure, caffeine intake, and alcohol intake; environmental data is relevant data that characterizes the user's environment, for example, environmental data may include at least one of ambient light and noise decibels.

In combination with the first aspect, in a possible implementation, the physical data is used to determine the physical exertion factor, the energy data is used to determine the energy exertion factor, and the environmental data is used to determine the sleep environment impact factor. For example, the user data includes data for multiple time periods, and the data for each time period includes at least one of the physical data, energy data, and environmental data within the time period; the physical data for each time period is used to determine the physical exertion factor for the time period, the energy data for each time period is used to determine the energy exertion factor for the time period, and the environmental data for each time period is used to determine the sleep environment impact factor for the time period.

Combined with the first aspect, in a possible implementation manner, the method also includes:

The electronic device obtains the second sleep quality based on the user data, and the second sleep quality is the sleep quality when the user does not adopt the sleep aid suggestions;

Electronic devices display second sleep quality.

In the embodiment of the present application, the electronic device can automatically obtain user data in response to user operations without user input; further, based on the user data, the second sleep quality is obtained, and the second sleep quality is the sleep quality when the user does not adopt the sleep aid suggestions. ;Furthermore, the second sleep quality is displayed. This method does not require user input and can automatically display the second sleep quality. The display content is conducive to satisfying the user's understanding of personal health status, can meet the user's need to predict the current sleep quality when falling asleep, and improve the user experience.

Combined with the first aspect, in a possible implementation, sleep quality includes at least one of sleep duration and deep sleep ratio within a preset duration, and sleep improvement includes improvement of sleep duration and improvement of deep sleep ratio. At least one of; the method also includes:

The electronic device obtains at least one of an improvement in sleep duration and an improvement in sleep depth ratio based on the first sleep quality and the second sleep quality; the second sleep quality is the sleep quality when the user does not adopt the sleep-aiding suggestions.

In a possible implementation, sleep quality may include sleep duration within a preset duration and deep sleep ratio. At least one of the light sleep ratio and the rapid eye movement ratio, and the sleep improvement may include at least one of the improvement of sleep duration, the improvement of the deep ratio, the improvement of the light sleep ratio, and the rapid eye movement ratio.

In the embodiment of the present application, the electronic device displays at least one of the improvement of sleep duration, the improvement of depth ratio, the improvement of light sleep ratio and the rapid eye movement ratio, which is conducive to the user's intuitive cognitive sleep assistance suggestions. The improvement effect can improve the user experience.

Combined with the first aspect, in a possible implementation, the user data includes feature values of multiple features, and the electronic device obtains the user's sleep aid suggestions and first sleep quality based on the user data, including:

The electronic device determines the target feature from the plurality of features based on the feature values of the plurality of features;

The electronic device determines sleep aid suggestions based on the target feature; the sleep aid suggestions based on the target feature are used to update feature values of the target feature.

In the embodiment of the present application, the electronic device can determine the target feature based on the feature values of multiple features; the sleep-aiding suggestion is obtained for the target feature. The method can make targeted sleep-aiding suggestions for the features that the user needs to adjust, and the method can improve the effectiveness of the sleep-aiding suggestions.

Combined with the first aspect, in a possible implementation manner, the electronic device determines the target feature from the multiple features based on the feature values of the multiple features, including:

The electronic device compares the feature value of each feature among the multiple features with the preset value corresponding to each feature to obtain the degree of deviation of each feature;

The electronic device determines the first N features with large deviations as target features, where N is a positive integer.

In the embodiment of the present application, the electronic device may determine the target feature based on the degree of deviation between the feature value of each feature among the multiple features and the preset value corresponding to each feature. This method can determine the characteristics that have a greater impact on the user's sleep, thereby making targeted sleep aid suggestions. This method can improve the effectiveness of the sleep aid suggestions and effectively improve the user's sleep quality.

Combined with the first aspect, in a possible implementation, the user data includes characteristic data of the Mth period and characteristic data of other periods; M is a positive integer, and the electronic device obtains the user's sleep aid suggestions and the user's sleep aid suggestions based on the user data. 1. Sleep quality, including:

Based on the sleep aid suggestions for the target feature, the electronic device updates the feature data of the target feature in the feature data of the Mth time period, and obtains the updated feature data of the Mth time period;

The electronic device obtains the updated sleep impact factor of the M-th period based on the updated characteristic data of the M-th period;

The electronic device takes the updated sleep influencing factor of the Mth period and the sleep influencing factors of other periods as input, and obtains the first sleep quality through the target model;

The sleep influencing factors of other periods are obtained based on the characteristic data of other periods; the target model is trained using the sample sleep influencing factors as input and the sleep quality corresponding to the sample sleep influencing factors as labels.

In the embodiment of the present application, the electronic device can predict the sleep quality after adopting the sleep-aiding suggestion based on the updated feature data. In this method, a sleep-aiding suggestion for the target feature is adopted, and the sleep-aiding suggestion can effectively improve the user's sleep quality, so that a significantly improved sleep quality can be obtained; this method can enable the user to intuitively see the improvement effect of the sleep-aiding suggestion, thereby improving the user experience.

In conjunction with the first aspect, in a possible implementation, the user data includes characteristic data of the Mth period and characteristic data of other periods, where the Mth period is the current period, that is, the electronic device responds to starting sleep. The predicted period of time when the user operates; other periods are several periods between the day when the user wakes up and the current period.

Combined with the first aspect, in a possible implementation, the user data includes characteristic data of multiple periods, and the electronic device obtains the second sleep quality based on the user data, including:

The electronic device obtains a sleep influencing factor for each time period based on the characteristic data of each time period in the plurality of time periods;

The electronic device obtains the second sleep quality based on the sleep influencing factors and target models of each period;

The target model is trained by taking the sample sleep influencing factors as input and the sleep quality corresponding to the sample sleep influencing factors as labels.

In the embodiment of the present application, the user data includes characteristic data of multiple periods; the electronic device obtains the sleep impact factor of each period based on the characteristic data of each period in the multiple periods; based on the sleep influence factor and target model of each period You can get second quality sleep. This method considers activity correlation between various periods and can improve the accuracy of predicting second sleep quality.

Combined with the first aspect, in a possible implementation manner, the sleep influencing factors within the preset period include at least one of a physical consumption factor, an energy consumption factor and a sleep environment influencing factor;

The physical exertion factor is determined based on at least one of the heart rate, exercise duration, steps, calories, and activity hours in a preset period; the energy consumption factor is determined based on at least one of the blood pressure, blood oxygen, stress, caffeine intake, and alcohol intake in a preset period; and the sleep environment impact factor is determined based on at least one of the ambient light and noise decibels in a preset period. In the embodiment of the present application, the user data has a large amount of information, which can effectively improve the prediction accuracy.

Combined with the first aspect, in a possible implementation manner, the method also includes:

The electronic device obtains a pre-trained model, where the pre-trained model is obtained based on historical user data of the group to which the user belongs;

The electronic device trains the pre-training model based on the user's historical user data to obtain the target model.

In the embodiment of this application, the target model is first trained based on the historical user data of the user's group, and then trained based on the user's historical user data. In this method, first training based on the historical user data of the user's group can ensure that the prediction results do not deviate from the group; then using the group data as the benchmark and training with personal data can make the target model more personalized. This method can improve the target The accuracy of the model's output.

In a possible implementation, the pre-trained model is trained by the server, and the target model is trained by the electronic device based on the pre-trained model. This method can effectively control the calculation amount of the server and the electronic device, and avoid the situation where the calculation amount is too large and affects the speed when the training is all performed by the server or the electronic device; when the electronic device trains the pre-training model based on the user's historical user data, it can also Protect the privacy and security of users; train the pre-trained model on the server and provide the pre-trained model to other users in the group, which can avoid repeated calculations and waste of computing resources. This method can reduce the overall calculation amount, improve calculation speed, and protect user privacy.

Combined with the first aspect, in a possible implementation manner, the electronic device responds to a user operation to start sleep prediction, and obtains user data, including:

The electronic device responds to the user operation of starting sleep prediction and sends an acquisition request to the server;

The electronic device receives user data sent by the server, and the user data includes data obtained by the server from the device worn by the user.

In this embodiment of the present application, the server may obtain user data from at least one device worn by the user, and the at least one device may include a sports watch, smart glasses, etc.; and then send the user data to the electronic device. In this method, the electronic device can obtain user data from other devices of the user. The user data has a large amount of data, which can improve the accuracy of prediction; no user input is required, which can improve the user experience.

In a second aspect, embodiments of the present application provide an electronic device, including one or more functional modules. The one or more functional modules can be used to perform any of the above aspects or any possible implementation of any aspect. sleep quality prediction method.

In a third aspect, the present application provides a computer storage medium, including computer instructions, which, when executed on an electronic device, enables a communication device to execute a sleep quality prediction method in any of the above aspects or in any possible implementation of any of the aspects.

In a fourth aspect, the present application provides a computer program product. When the computer program product is run on a computer, it causes the computer to execute the sleep quality prediction method in any of the above aspects or any possible implementation of any aspect.

In a fifth aspect, the present application provides a chip, comprising: a processor and an interface, wherein the processor and the interface cooperate with each other so that the chip executes the sleep quality prediction method in any of the above aspects or any possible implementation of any of the aspects.

It can be understood that the electronic device provided in the second aspect, the computer-readable storage medium provided in the third aspect, the computer program product provided in the fourth aspect, and the chip provided in the fifth aspect are all used to execute the tasks provided by the embodiments of the present application. method. Therefore, the beneficial effects it can achieve can be referred to the beneficial effects in the corresponding methods, and will not be described again here.

Description of drawings

Figure 1 is a framework diagram of a prediction system provided by an embodiment of the present application;

Figure 2 is a schematic diagram of the hardware structure of an electronic device 100 provided by an embodiment of the present application;

Figure 3 is a software structure block diagram of the electronic device 100 provided by the embodiment of the present application;

Figure 4 is a schematic diagram of the overall flow of a sleep quality prediction method provided by this application;

FIG5 is a flow chart of a sleep quality prediction method provided by the present application;

Figure 6 is a schematic diagram of constructing a sleep influencing factor for a period provided by an embodiment of the present application;

Figure 7 is a schematic diagram of a training process provided by the embodiment of the present application;

Figure 8 is a schematic diagram of a prediction process provided by an embodiment of the present application;

Figure 9A is a schematic diagram of determining target characteristics provided by an embodiment of the present application;

Figure 9B is another schematic diagram for determining target features provided by an embodiment of the present application;

Figure 10 is a schematic diagram of a feature provided by an embodiment of the present application and a course corresponding to the feature;

Figure 11 is a schematic diagram of a target user's characteristic data of the day provided by the embodiment of the present application;

Figure 12A is a schematic diagram of updated characteristic data of the day provided by the embodiment of the present application;

Figure 12B is a schematic diagram of another updated characteristic data of the day provided by the embodiment of the present application;

Figure 13 is a schematic diagram for determining sleep quality after adopting suggestions provided by an embodiment of the present application;

Figure 14 is a schematic diagram of updating sleep impact factors provided by an embodiment of the present application;

Figures 15A-15D are user interfaces implemented on the electronic device provided by the embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, "/" means or, for example, A/B can mean A or B; "and/or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and/or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

Hereinafter, the terms “first” and “second” are used for descriptive purposes only and shall not be understood as implying or implying relative importance or implicitly specifying the quantity of indicated technical features. Therefore, the features defined as “first” and “second” may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, “plurality” The meaning is two or more.

The term "user interface (UI)" in the following embodiments of this application is a media interface for interaction and information exchange between an application or operating system and a user. It realizes the difference between the internal form of information and the form acceptable to the user. conversion between. The user interface is source code written in specific computer languages such as Java and extensible markup language (XML). The interface source code is parsed and rendered on the electronic device, and finally presented as content that the user can recognize. The commonly used form of user interface is graphical user interface (GUI), which refers to a user interface related to computer operations that is displayed graphically. It can be text, icons, buttons, menus, tabs, text boxes, dialog boxes, status bars, navigation bars, widgets and other visual interface elements displayed on the display screen of an electronic device.

In order to introduce the sleep quality prediction method provided by the embodiment of the present application more clearly and in detail, the prediction system provided by the embodiment of the present application is first introduced below.

Please refer to Figure 1. Figure 1 is a framework diagram of a prediction system provided by an embodiment of the present application.

As shown in Fig. 1, the prediction system may include a server 10 and a terminal device 20. The server 10 may be a server deployed in the cloud; the terminal device 20 may be an electronic device such as a mobile phone or a watch (Fig. 1 exemplarily shows that the terminal device 20 is a mobile phone).

In some embodiments, the terminal device 20 may send an acquisition request to the server 10, the acquisition request including the identification of the target user; the server 10 may respond to the acquisition request, send the target user's sleep impact data and the pre-training model to the terminal device 20 ; The terminal device 20 can predict the target user's current sleep quality based on the pre-trained model and the target user's sleep impact data, and can also determine sleep aid suggestions for the target user and the sleep quality after adopting the sleep aid suggestions. The specific implementation of how the terminal device 20 predicts the current sleep quality of the target user, or determines the sleep aid suggestions for the target user and the sleep quality after adopting the sleep aid suggestions can be found in the relevant description below and will not be elaborated here. Among them, the current sleep quality is the sleep quality predicted by the user at the current moment when the sleep aid suggestions are not adopted.

Among them, the server 100 in the embodiment of the present application can be implemented as an independent server or a server cluster composed of multiple servers.

The terminal device 20 may include but is not limited to a mobile phone, a wearable device (such as a watch, etc.), a tablet, a monitor, a television, a desktop computer, a laptop computer, a handheld computer, a notebook computer, a super mobile personal computer, a netbook, or an augmented reality device. , virtual reality equipment, artificial intelligence equipment, vehicle equipment, smart home equipment and other electronic equipment.

In one implementation, the terminal device 20 may include multiple electronic devices; the multiple electronic devices may be electronic devices used by different users, and the multiple electronic devices may respectively send user data of their users to the server 10 , where the user The data can include sleep impact data and personal information, among other things. Furthermore, the server 10 can save the user's user data sent by different electronic devices. For example, the user wears a watch on a daily basis, and the watch is the terminal device 20; after obtaining the user's consent, the watch can send user data such as the user's personal information, exercise status, and sleep quality to the server 10; the server 10 can save the user data.

It can be understood that the prediction system architecture in Figure 1 is only an exemplary implementation in the embodiment of the present application. The prediction system architecture in the embodiment of the present application includes but is not limited to the above prediction system architecture.

The following takes the electronic device 100 as an example to introduce the terminal device 20 in FIG. 1 .

FIG. 2 shows a schematic diagram of the hardware structure of the electronic device 100 .

The following uses the electronic device 100 as an example to describe the embodiment in detail. It should be understood that electronic device 100 may have more or fewer components than shown in the figures, may combine two or more components, or may have a different component configuration. The various components shown in the figures may be implemented in hardware, software, or a combination of hardware and software including one or more signal processing and/or application specific integrated circuits.

The electronic device 100 may include: a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2. Mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone interface 170D, sensor module 180, button 190, motor 191, indicator 192, camera 193, display screen 194 and Subscriber identification module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyro sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and ambient light. Sensor 180L, bone conduction sensor 180M, etc.

It can be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the electronic device 100 . In other embodiments of the present application, the electronic device 100 may include more or fewer components than shown in the figures, or some components may be combined, some components may be separated, or some components may be arranged differently. The components illustrated may be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units. For example, the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processing unit (GPU), an image signal processor ( image signal processor (ISP), controller, memory, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU), etc. . Among them, different processing units can be independent devices or integrated in one or more processors.

The controller may be the nerve center and command center of the electronic device 100 . The controller can generate operation control signals based on the instruction operation code and timing signals to complete the control of fetching and executing instructions.

The processor 110 may also be provided with a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory may store instructions or data that the processor 110 has just used or cyclically used. If the processor 110 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.

In some embodiments, processor 110 may include one or more interfaces. The interface may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuitsound, I2S) interface, a pulse code modulation (PCM) interface, and a universal asynchronous receiver (universal asynchronous receiver) /transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (subscriber identity module, SIM) interface, and/or Universal serial bus (USB) interface, etc.

The I2C interface is a bidirectional synchronous serial bus, including a serial data line (SDA) and a serial clock line (derail clock line, SCL). In some embodiments, processor 110 may include multiple sets of I2C buses. The processor 110 can separately couple the touch sensor 180K, charger, flash, camera 193, etc. through different I2C bus interfaces. For example, the processor 110 can be coupled to the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to implement the touch function of the electronic device 100 .

The I2S interface can be used for audio communication. In some embodiments, the processor 110 can include multiple I2S buses. The processor 110 can be coupled to the audio module 170 via the I2S bus to achieve communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 can transmit an audio signal to the wireless communication module 160 via the I2S interface to achieve the function of answering a call through a Bluetooth headset.

The PCM interface can also be used for audio communications to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 can also transmit audio signals to the wireless communication module 160 through the PCM interface to implement the function of answering calls through a Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

The UART interface is a universal serial data bus used for asynchronous communication. The bus can be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, a UART interface is generally used to connect the processor 110 and the wireless communication module 160 . For example, the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to implement the Bluetooth function. In some embodiments, the audio module 170 can transmit audio signals to the wireless communication module 160 through the UART interface to implement the function of playing music through a Bluetooth headset.

The MIPI interface can be used to connect the processor 110 with peripheral devices such as the display screen 194 and the camera 193 . MIPI interfaces include camera serial interface (CSI), display serial interface (displayserial interface, DSI), etc. In some embodiments, the processor 110 and the camera 193 communicate through the CSI interface to implement the shooting function of the electronic device 100 . The processor 110 and the display screen 194 communicate through the DSI interface to implement the display function of the electronic device 100 .

The GPIO interface can be configured through software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface can be used to connect the processor 110 with the camera 193, display screen 194, wireless communication module 160, audio module 170, sensor module 180, etc. The GPIO interface can also be configured as an I2C interface, I2S interface, UART interface, MIPI interface, etc.

The SIM card interface can be used to communicate with the SIM card interface 195 to implement the function of transmitting data to the SIM card or reading data in the SIM card.

The USB interface 130 is an interface that complies with USB standard specifications, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, etc. The USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transmit data between the electronic device 100 and peripheral devices. It can also be used to connect headphones to play audio through them. This interface can also be used to connect other electronic devices, such as AR devices, etc.

It can be understood that the interface connection relationships between the modules illustrated in the embodiments of the present application are only schematic illustrations and do not constitute a structural limitation of the electronic device 100 . In other embodiments of the present application, the electronic device 100 may also adopt different interface connection methods in the above embodiments, or a combination of multiple interface connection methods.

The charging management module 140 is used to receive charging input from a charger, where the charger can be a wireless charger or a wired charger.

The power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charging management module 140 to power the processor 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the wireless communication module 160.

The wireless communication function of the electronic device 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor.

Antenna 1 and Antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 100 may be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example: Antenna 1 can be reused as a diversity antenna for a wireless LAN. In other embodiments, antennas may be used in conjunction with tuning switches.

The mobile communication module 150 can provide solutions for wireless communications including 2G/3G/4G/5G, etc., applied to the electronic device 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set in the same device as at least some of the modules of the processor 110.

A modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be sent into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs sound signals through audio devices (not limited to speaker 170A, receiver 170B, etc.), or displays images or videos through display screen 194. In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be independent of the processor 110 and may be provided in the same device as the mobile communication module 150 or other functional modules.

The wireless communication module 160 can provide applications on the electronic device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) network), Bluetooth (bluetooth, BT), and global navigation satellite system. (global navigation satellite system, GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (infrared, IR) and other wireless communication solutions. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2 , frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110 . The wireless communication module 160 can also receive the signal to be sent from the processor 110, frequency modulate it, amplify it, and convert it into electromagnetic waves through the antenna 2 for radiation.

In some embodiments, the antenna 1 of the electronic device 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the electronic device 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and/or IR technology, etc. The GNSS may include global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), quasi-zenith satellite system (QZSS) and/or satellite based augmentation system (SBAS).

The electronic device 100 implements display functions through a GPU, a display screen 194, an application processor, and the like. The GPU is an image processing microprocessor and is connected to the display screen 194 and the application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. Processor 110 may include one or more GPUs that execute program instructions to generate or alter display information.

The display screen 194 is used to display images, videos, etc. Display 194 includes a display panel. The display panel can use a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active matrix organic light emitting diode or an active matrix organic light emitting diode (active-matrix organic light emitting diode). , AMOLED), flexible light-emitting diodes (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diodes (QLED), etc. In some embodiments, the electronic device 100 may include 1 or N display screens 194, where N is a positive integer greater than 1.

The electronic device 100 can implement the shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.

The ISP is used to process the data fed back by the camera 193. For example, when taking a photo, the shutter is opened, the light is transmitted to the camera sensor through the lens, the optical signal is converted into an electrical signal, and the camera sensor passes the electrical signal to the ISP for processing, and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on image noise, brightness, and skin color. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then passes the electrical signal to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV or other format. In some embodiments, the electronic device 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.

The digital signal processor is used to process digital signals, and can process not only digital image signals but also other digital signals. For example, when the electronic device 100 is selecting a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.

Video codecs are used to compress or decompress digital videos. The electronic device 100 may support one or more video codecs. Thus, the electronic device 100 may play or record videos in a variety of coding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

NPU is a neural network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transmission mode between neurons in the human brain, it can quickly process input information and can also continuously self-learn. Through NPU, applications such as intelligent cognition of electronic device 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100 . The external memory card communicates with the processor 110 through the external memory interface 120 to implement the data storage function. Such as saving music, videos, etc. files in external memory card.

Internal memory 121 may be used to store computer executable program code, which includes instructions. The processor 110 executes instructions stored in the internal memory 121 to execute various functional applications and data processing of the electronic device 100 . The internal memory 121 may include a program storage area and a data storage area. Among them, the stored program area can store the operating system, at least one application required for the function (such as face recognition function, fingerprint recognition function, mobile payment function, etc.). The storage data area can store data created during the use of the electronic device 100 (such as face information template data, fingerprint information templates, etc.). In addition, the internal memory 121 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, universal flash storage (UFS), etc.

The electronic device 100 can implement audio functions such as music playing and recording through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the headphone jack 170D, and the application processor.

The audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. Audio module 170 may also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be provided in the processor 110 , or some functional modules of the audio module 170 may be provided in the processor 110 .

The speaker 170A, also called a "speaker", is used to convert an audio electrical signal into a sound signal. The electronic device 100 can listen to music or listen to a hands-free call through the speaker 170A.

Receiver 170B, also called "earpiece", is used to convert audio electrical signals into sound signals. When the electronic device 100 answers a call or a voice message, the voice can be heard by bringing the receiver 170B close to the human ear.

Microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak close to the microphone 170C with the human mouth and input the sound signal to the microphone 170C. The electronic device 100 may be provided with at least one microphone 170C. In other embodiments, the electronic device 100 may be provided with two microphones 170C, which in addition to collecting sound signals, may also implement a noise reduction function. In other embodiments, the electronic device 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and implement directional recording functions, etc.

The headphone interface 170D is used to connect wired headphones. The headphone interface 170D may be a USB interface 130, or may be a 3.5 mm open mobile terminal platform (OMTP) standard interface or a Cellular Telecommunications Industry Association of the USA (CTIA) standard interface.

The pressure sensor 180A is used to sense pressure signals and can convert the pressure signals into electrical signals. In some embodiments, pressure sensor 180A may be disposed on display screen 194 . There are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, capacitive pressure sensors, etc. A capacitive pressure sensor may include at least two parallel plates of conductive material. When a force is applied to pressure sensor 180A, the capacitance between the electrodes changes. The electronic device 100 determines the intensity of the pressure based on the change in capacitance. When a touch operation is performed on the display screen 194, the electronic device 100 detects the intensity of the touch operation according to the pressure sensor 180A. The electronic device 100 may also calculate the touched position based on the detection signal of the pressure sensor 180A. In some embodiments, touch operations acting on the same touch location but with different touch operation intensities may correspond to different operation instructions. For example: when a touch operation with a touch operation intensity smaller than the first pressure threshold is applied to the short message application icon, an instruction to view the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold is applied to the short message application icon, an instruction to create a new short message is executed.

The gyro sensor 180B may be used to determine the motion posture of the electronic device 100 . In some embodiments, the angular velocity of electronic device 100 about three axes (ie, x, y, and z axes) may be determined by gyro sensor 180B. The gyro sensor 180B can be used for image stabilization. For example, when the shutter is pressed, the gyro sensor 180B detects the angle at which the electronic device 100 shakes, calculates the distance that the lens module needs to compensate based on the angle, and allows the lens to offset the shake of the electronic device 100 through reverse movement to achieve anti-shake. The gyro sensor 180B can also be used for navigation and somatosensory gaming scenarios.

Air pressure sensor 180C is used to measure air pressure. In some embodiments, the electronic device 100 calculates the altitude through the air pressure value measured by the air pressure sensor 180C to assist positioning and navigation.

Magnetic sensor 180D includes a Hall sensor. The electronic device 100 may utilize the magnetic sensor 180D to detect opening and closing of the flip holster. In some embodiments, when the electronic device 100 is a flip machine, the electronic device 100 may detect the opening and closing of the flip according to the magnetic sensor 180D. Then, based on the detected opening and closing status of the leather case or the opening and closing status of the flip cover, features such as automatic unlocking of the flip cover are set.

The acceleration sensor 180E can detect the acceleration of the electronic device 100 in various directions (generally three axes). When the electronic device 100 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of electronic devices and be used in horizontal and vertical screen switching, pedometer and other applications.

Distance sensor 180F for measuring distance. Electronic device 100 can measure distance via infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 may utilize the distance sensor 180F to measure distance to achieve fast focusing.

Proximity light sensor 180G may include, for example, a light emitting diode (LED) and a light detector, such as a photodiode. The light emitting diode may be an infrared light emitting diode. The electronic device 100 emits infrared light outwardly through the light emitting diode. Electronic device 100 uses photodiodes to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 100 . When insufficient reflected light is detected, the electronic device 100 may determine that there is no object near the electronic device 100 . The electronic device 100 can use the proximity light sensor 180G to detect when the user holds the electronic device 100 close to the ear for talking, so as to automatically turn off the screen to save power. The proximity light sensor 180G can also be used in holster mode, and pocket mode automatically unlocks and locks the screen.

The ambient light sensor 180L is used to sense the brightness of the ambient light. The electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived ambient light brightness. The ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures. The ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in a pocket to prevent accidental touches.

The fingerprint sensor 180H is used to collect fingerprints. The electronic device 100 can use the collected fingerprint characteristics to implement fingerprint unlocking, access application locks, fingerprint photography, fingerprint call answering, etc.

The temperature sensor 180J is used to detect temperature. In some embodiments, the electronic device 100 uses the temperature detected by the temperature sensor 180J to execute a temperature processing strategy. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the electronic device 100 reduces the performance of a processor located near the temperature sensor 180J to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is lower than another threshold, the electronic device 100 heats the battery 142 to avoid abnormal shutdown of the electronic device 100 due to low temperature. In other embodiments, when the temperature is lower than another threshold, the electronic device 100 boosts the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.

Touch sensor 180K, also called "touch panel". The touch sensor 180K can be disposed on the display screen 194. The touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is used to detect a touch operation on or near the touch sensor 180K. The touch sensor can pass the detected touch operation to the application processor to determine the touch event type. Visual output related to the touch operation may be provided through display screen 194 . In other embodiments, the touch sensor 180K may also be disposed on the surface of the electronic device 100 at a location different from that of the display screen 194 .

The buttons 190 include a power button, a volume button, etc. Key 190 may be a mechanical key. It can also be a touch button. The electronic device 100 may receive key inputs and generate key signal inputs related to user settings and function control of the electronic device 100 .

The motor 191 can generate vibration prompts. The motor 191 can be used for vibration prompts for incoming calls and can also be used for touch vibration feedback. For example, touch operations for different applications (such as taking pictures, audio playback, etc.) can correspond to different vibration feedback effects. The motor 191 can also respond to different vibration feedback effects for touch operations in different areas of the display screen 194 . Different application scenarios (such as time reminders, receiving information, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also be customized.

The indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, or may be used to synthesize requests, missed calls, notifications, etc.

The SIM card interface 195 is used to connect a SIM card. The SIM card can be connected to or separated from the electronic device 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195 . The electronic device 100 may support 1 or N SIM card interfaces, where N is a positive integer greater than 1. SIM card interface 195 can support Nano SIM card, Micro SIM card, SIM card, etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the plurality of cards may be the same or different. The SIM card interface 195 is also compatible with different types of SIM cards. The SIM card interface 195 is also compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to implement functions such as calls and data communications.

In this embodiment of the present application, the electronic device 100 can execute the sleep quality prediction method through the processor 110 .

FIG. 3 is a software structure block diagram of the electronic device 100 provided by the embodiment of the present application.

The layered architecture divides the software into several layers, and each layer has clear roles and division of labor. The layers communicate through software interfaces. In some embodiments, the Android system is divided into four layers, from top to bottom: application layer, application framework layer, Android runtime (Android runtime) and system libraries, and kernel layer.

The application layer can include a series of application packages.

As shown in FIG. 3 , the application package may include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, and sports health.

In some embodiments, the user can communicate with other devices (such as the server 10 above) through the sports health of the electronic device 100, for example, send acquisition requests to other devices or obtain sleep impact data sent by other devices.

The application framework layer provides an application programming interface (API) and programming framework for applications in the application layer. The application framework layer includes some predefined functions.

As shown in Figure 3, the application framework layer can include a display manager, a sensor manager, a cross-device connection manager, an event manager, an activity manager, a window manager, and a content provider. , view system, resource manager, notification manager, etc.

The display manager is used for system display management and is responsible for the management of all display-related transactions, including creation, destruction, orientation switching, size and status changes, etc. Generally speaking, there will only be one default display module on a single device, which is the main display module.

The sensor manager is responsible for managing the status of sensors, managing applications to monitor sensor events, and reporting events to applications in real time.

Cross-device connection manager is used to establish communication connections with other devices.

The event manager is used for the event management service of the system. It is responsible for receiving the events uploaded by the underlying layer and distributing them to each window, and completing the reception and distribution of events.

The task manager is used for the management of task (Activity) components, including startup management, life cycle management, task direction management, etc.

The window manager is used to manage window programs. The window manager can obtain the display screen size, determine whether there is a status bar, lock the screen, capture the screen, etc. The window manager is also responsible for window display management, including window display mode, display size, display coordinate position, display level, etc.

For the specific execution process of each of the above embodiments, please refer to the relevant content of the human-machine dialogue method below.

Content providers are used to store and retrieve data and make this data accessible to applications. Said data can include videos, images, audio, calls made and received, browsing history and bookmarks, phone books, etc.

The view system includes visual controls, such as controls that display text, controls that display pictures, etc. A view system can be used to build applications. The display interface can be composed of one or more views. For example, a display interface including a text message notification icon may include a view for displaying text and a view for displaying pictures.

The resource manager provides various resources to applications, such as localized strings, icons, pictures, layout files, video files, etc.

The notification manager allows applications to display notification information in the status bar, which can be used to convey notification-type messages and can automatically disappear after a short stay without user interaction. For example, the notification manager is used to notify download completion, message reminders, etc. The notification manager can also be notifications that appear in the status bar at the top of the system in the form of charts or scroll bar text, such as notifications for applications running in the background, or notifications that appear on the screen in the form of conversation windows. For example, text information is prompted in the status bar, a beep sounds, the electronic device vibrates, the indicator light flashes, etc.

Android Runtime includes core libraries and virtual machines. The Android runtime is responsible for the scheduling and management of the Android system.

The core library contains two parts: one is the functional functions that need to be called by the Java language, and the other is the core library of Android.

The application layer and application framework layer run in virtual machines. The virtual machine executes the java files of the application layer and application framework layer as binary files. The virtual machine is used to perform object life cycle management, stack management, thread management, security and exception management, and garbage collection and other functions.

The system library (also called data management layer) can include multiple functional modules. For example: surface manager (surface manager), media libraries (Media Libraries), 3D graphics processing libraries (for example: OpenGL ES), 2D graphics engines (for example: SGL) and event data, etc.

The surface manager is used to manage the display subsystem and provides the fusion of 2D and 3D layers for multiple applications.

The media library supports playback and recording of a variety of commonly used audio and video formats, as well as static image files, etc. The media library can support a variety of audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.

The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, composition, and layer processing.

2D Graphics Engine is a drawing engine for 2D drawing.

The kernel layer is the layer between hardware and software. The kernel layer contains at least display driver, camera driver, audio driver, and sensor driver.

Next, the overall process of the sleep quality prediction method is introduced as an example based on the software framework of the prediction system.

Please refer to Figure 4, which is a schematic diagram of the overall process of a sleep quality prediction method provided by the present application. The server in Figure 4 can be the server 10 in the prediction system shown in Figure 1; the electronic device in Figure 4 can be the terminal device 20 in the prediction system shown in Figure 1.

Among them, the target user is the user of the electronic device, the server includes the target user's personal database and the personal databases of other users, and the training parameter database in Figure 4 is the personal database of other users.

As shown in Figure 4, the software framework may include a data access layer, an algorithm logic layer, and a display layer. The data access layer may include a training parameter database, a target user's personal database, and a data acquisition module, wherein the training parameter database and the target user's personal database are located on a server, and the data acquisition module is located in an electronic device; the algorithm logic layer and the display layer are both located in the electronic device.

Among them, the data acquisition module is used to obtain the user data of the target user from the personal database of the target user. The user data may include the user's personal information and the user's sleep impact data, where the personal information includes age, gender, regional sunshine duration data, etc.; the sleep impact data may include historical sleep data and characteristic data of the day, where the historical sleep data may It includes daily characteristic data and sleep quality for several days before the current day. The characteristic data for that day is the characteristic data of the target user on the day when the user data is obtained. The characteristic data can include data of one or more characteristics, and the characteristics can be heart rate, exercise duration, etc.; sleep Quality can include sleep duration, deep sleep ratio, light sleep ratio, rapid eye movement ratio, etc.

The data acquisition module is also used to obtain historical sleep data of the target user's group from the training parameter database based on the target user's personal information. Among them, the information gap between the personal information of the users in the target user's group and the target user's personal information is within a preset range; the target user's group can also be called the target group.

The feature preprocessing module is used to obtain training data based on the historical sleep data of the target user and the historical sleep data of the target group. The training data may include group training data and individual training data of the target user.

The feature preprocessing module is also used to obtain the target user's sleep influencing factors for the day based on the target user's feature data for the day.

The model training module is used to train the initial sleep quality prediction model based on the training data to obtain the target model.

The sleep quality prediction module is used to obtain the current prediction results based on the target model and the sleep influencing factors of the target user on that day. The prediction results may include sleep duration, deep sleep ratio, light sleep ratio and rapid eye movement ratio.

The presentation layer is used to display the current prediction results.

The improved service recommendation module is used to determine sleep-aiding suggestions based on the weights in the model parameters of the target model and the target user's characteristic data of the day; based on the sleep-aiding suggestions, the target user's characteristic data of the day is updated.

The sleep quality prediction module obtains the prediction results after adopting the sleep aid suggestions based on the updated characteristic data of the day and the target model. The display layer is used to display the sleep aid suggestions and the predicted results after adopting the sleep aid suggestions.

The following takes a mobile phone as an example of an electronic device to introduce in detail the sleep quality prediction method provided in the embodiment of the present application.

Please refer to Figure 5, which is a schematic flow chart of a sleep quality prediction method provided by this application.

S501: In response to a user operation for predicting sleep quality, the electronic device sends an acquisition request to the server, where the acquisition request includes the user identification of the target user.

In some embodiments, the electronic device is installed with a sports and health application, and the application interface may include a control for predicting sleep quality; the electronic device may display the application interface, and the electronic device may respond when detecting a user operation on the control. In response to this user operation, a fetch request is sent to the server. The acquisition request may include the user ID of the target user, and the user ID may be the target user's sports and health account or Huawei account, etc.

For example, the electronic device may display a user interface as shown in FIG. 15B; when detecting a user operation on the sleep quality prediction control 620, the electronic device sends an acquisition request to the server.

S502: The server responds to the acquisition request and acquires the target user's personal information and the target user's sleep impact data based on the user identification.

In one implementation, the server includes a personal database of the target user. Then, the server can obtain the personal information of the target user and the sleep impact data of the target user from the personal database of the target user based on the user identifier.

Among them, personal information can include the sunshine duration of the month, gender, and age group in the city where the user is located; sleep impact data includes historical sleep data and characteristic data of the day, where historical sleep data can include characteristic data and sleep quality of each day in the past.

S503: The server determines the target group to which the target user belongs based on the user's personal information.

Among them, the target group may include at least two users; the personal information of the users in the target group all meets the preset requirements, for example, the gender of the users in the target group is the preset gender, the age is within the preset age group, and the area where the user is located is sunny. The duration is the default duration.

In some embodiments, the server can divide the users into different groups based on the sunshine duration in the city where the users are located, gender, and age group, wherein the gender of the same group is the same; the daily sunshine duration in the city where the users in the same group are located is at the same level, for example, the server can count the sunshine duration in different cities that month, from the minimum value to the maximum value, and divide them into the same level every ten hours; the users in the same group are of the same age group, for example, the age group is divided into ten years as the difference, and the age ending with zero is used as the starting point of the age group.

It should be noted that there can be other methods for dividing groups, and the method for dividing groups is not limited here.

S504: The server obtains the historical sleep data of the target group.

In some embodiments, after determining the target group to which the target user belongs, the server may obtain the historical sleep data of each user in the target group from the storage. For example, the server includes a training parameter database, and the training parameter database includes historical sleep data of the target group, then the server can obtain the historical sleep data of the target group from the training parameter database.

The above historical sleep data may include the characteristic data and sleep quality of every day in the past, that is, the historical sleep data of the target group includes the characteristic data and sleep quality of each user in the target group every day in the past. Among them, the characteristic data may include data of one or more characteristics, and the characteristics may be heart rate, exercise duration, etc.; sleep quality may include sleep duration, deep sleep ratio, light sleep ratio, rapid eye movement ratio, etc.

For example, if the target group includes N users, the historical sleep data of the target group includes the historical sleep data of each of the N users. The historical sleep data of a user is the daily characteristic data and daily sleep quality of the user in the past M days, where M is a positive integer. For example, M is 30, and user A is a user in the target group. The historical sleep data of user A may include the daily characteristic data and daily sleep quality of user A for nearly 30 days before today. For example, if today is September 5, 2022, the electronic device may obtain the daily characteristic data and daily sleep quality of user A from August 6, 2022 to September 4, 2022.

S505: The server trains the initial sleep quality prediction model based on the historical sleep data of the target group to obtain a pre-trained model.

Among them, the historical sleep data of the target group includes the historical sleep data of each user in the target group for each past day; the model parameters of the initial sleep quality prediction model include the initial weight corresponding to each feature, and the model parameters are continuously updated during the training process. The weight corresponding to each feature. Among them, the initial sleep quality prediction model can be a recurrent neural network, a long short-term memory neural network (Long short-term memory, LSTM), or other neural networks, which are not limited here.

In some embodiments, the server can use the historical sleep data of each user in the target group in the past day as a piece of historical sleep data. For example, the historical sleep data of the target group includes the daily characteristic data of each of the N users in the past M days and Daily sleep quality, that is, there are N×M pieces of historical sleep data; furthermore, the server can target each piece of historical sleep data based on the feature data in the historical sleep data and the features in the model parameters of the model after the last training. The weight constructs the sleep influence factor; finally, the sleep influence factor and the sleep quality in the historical sleep data are used as a group training data to train the model after the last training, where the sleep influence factor is the input data and the sleep quality is Output target. It should be noted that the model after the last training is the model trained on the previous piece of historical sleep data; the feature weight used in the first training of the initial sleep quality prediction model is the initial sleep quality prediction model. The initial weights of the model parameters in .

For example, the server can generate a sleep impact factor corresponding to the first piece of historical sleep data based on the feature data in the first piece of historical sleep data and the initial weight corresponding to each feature, and combine the sleep impact factor and the initial weight in the piece of historical sleep data. Sleep quality is used as the first training data; use the first training data to train the initial sleep quality prediction model to obtain the model after the first training; next, based on the model parameters of the model after the first training The weight corresponding to each feature and the feature data in the second piece of historical sleep data are used to generate the sleep influence factor corresponding to the second piece of historical sleep data, and the sleep influence factor and the sleep quality in the piece of historical sleep data are used as the second piece of historical sleep data. Training data; use the second piece of training data to train the initial sleep quality prediction model to obtain the model after the second training, and so on, until the preset conditions are met and the training is terminated to obtain the pre-trained model.

Among them, the preset condition can be the minimum loss, the number of iterations reaching a preset number, or other preset conditions, which are not limited in the embodiments of the present application.

Among them, the sleep influencing factors in a piece of training data can include sleep influencing factors for each period of the day; the sleep influencing factors can include one or more factors, and the factors can be physical consumption factors (Strength Consume Factor, SCF), energy consumption factors ( Energy Consume Factor (ECF), SleepEnvironment Impact Factor (SIEF), etc. Each time period of a day can be divided based on the time period starting from the user's sleep end time of the day and ending with the user's sleep time of the day. For example, the user's sleep end time of a certain day is 9 am, and the user falls asleep on that day. If the time is ten o'clock in the evening, the electronic device can divide nine o'clock in the morning to ten o'clock in the evening into several time periods. The length of the time period is not limited here, for example, it can be one hour.

The following takes user A's characteristic data in period t as an example to introduce a possible implementation method of generating sleep influence factors.

Please refer to FIG. 6 , which is a schematic diagram of constructing a sleep influencing factor for a period provided by an embodiment of the present application. Figure 6 exemplarily shows 12 features of user A in period t. Feature 1 is heart rate, feature 2 is exercise duration, feature 3 is step count, feature 4 is calories, feature 5 is activity hours, and feature 6 is blood pressure. , feature 7 is blood oxygen, feature 8 is pressure, feature 9 is caffeine intake, feature 10 is alcohol intake, feature 11 is ambient lighting, and feature 12 is noise decibels. The feature data of user A in period t can be composed of the feature values of 12 features, as shown in Figure 6. Among them, the feature values of each feature are x ₁ , x ₂ , x ₃ , x ₄ , x ₅ , respectively. x ₆ , x ₇ , x ₈ , x ₉ , x ₁₀ , x ₁₁ and x ₁₂ . Each feature has a corresponding weight in the sleep quality prediction model, which are w ₁ , w ₂ , w ₃ , w ₄ , w ₅ , w ₆ , w ₇ , w ₈ , w ₉ , w ₁₀ , w ₁₁ and w ₁₂ .

As shown in Figure 6, the electronic device can multiply the data of the feature corresponding to the physical exertion factor and the weight of the feature and sum them up to obtain the physical exertion factor. The electronic device can multiply the data of the feature corresponding to the energy consumption factor and the weight of the feature and sum them up to obtain the energy consumption factor. The electronic device can multiply the data of the characteristics corresponding to the sleep environment impact factor and the weights of the features and sum them up to obtain the sleep environment impact factor/> Then, the electronic device can finally obtain user A's sleep impact factor y _t =[SCFt, ECFt, SIEFt] in period t.

In one implementation, if period t is the period after sunset, the sleep impact factor of user A in period t can be the above y _t , that is, including physical consumption factor, energy consumption factor, and sleep environment influencing factor; if period t is the period after sunset, then user A's sleep influencing factors in period t may only include physical consumption factors and energy consumption factors.

The following uses a piece of training data from user A as an example to introduce a training process. Assume that this piece of training data is the training data corresponding to the i-th day of user A. This piece of training data includes the sleep impact factor of each period in the t periods on the i-th day and the sleep quality O _t ' of the i-th day, where, the i-th Day is one of M days, i is a positive integer; t periods include the jth period, j is a positive integer, and t is a positive integer. It should be noted that the latent vector m _j is the output of the hidden layer of the target model in the j-th period, and the latent vector m _j is used to represent the impact of the feature data in the j-th period on user A’s sleep on the i-th day.

Please refer to Figure 7, which is a schematic diagram of a training process provided in an embodiment of the present application.

As shown in Figure 7, the electronic device can input the sleep influence factor y ₁ of the first period after user A wakes up on the i-th day into the sleep quality prediction model LSTM trained on the previous training data, and the obtained output result is sleep quality o ₁ , you can also get the latent vector m ₁ , which is the output of the hidden layer of the target model in the first period. The latent vector m ₁ is used to represent the impact of the feature data in the first period on user A’s sleep on the i-th day _. ;Furthermore, the electronic device inputs o1 and m1 into the LSTM, and the output result is the sleep quality o ₂ , and the hidden vector m ₂ can also be obtained; and so on, until the last period (that is, the t-th period), m _{t- 1Input} LSTM, and the output result obtained is sleep quality o _t . Among them, the sleep influencing factors before sunset can be composed of physical consumption factors and energy consumption factors, and the sleep influencing factors after sunset can be composed of physical consumption factors, energy consumption factors, and sleep environment influencing factors.

Furthermore, the electronic device can obtain the loss based on the sleep quality o _t and the sleep quality o _t ' on the i-th day, and adjust the weight corresponding to each feature in the LSTM based on the loss. Among them, the loss can be obtained based on the loss function cross-entropy function, or can be obtained based on other loss functions, which is not limited here.

It should be noted that the electronic device can determine the model with the smallest loss as the target model; the electronic device can also obtain the target model after performing multiple rounds of iterative training. The embodiment of the present application does not limit the number of iterations.

S506: The server sends the pre-trained model and the target user's sleep impact data to the electronic device, where the target user's sleep impact data includes the target user's historical sleep data and the characteristic data of the day.

The historical sleep data of the target user may include the characteristic data of each day and the sleep quality of each day in the past m days of the target user, wherein m is a positive integer. It should be noted that the above n and the above m may be equal or unequal.

Among them, the characteristic data of the target user on the day is the characteristic data of the target user on the day when the electronic device starts to predict the sleep quality. For example, in step S501, if the electronic device responds to the user operation of predicting sleep quality at 21 o'clock on the day, then the target user's characteristic data of the day is the target user's characteristic data of the day up to 21 o'clock.

The current day may refer to the time period from when the target user wakes up on the day when the electronic device starts predicting sleep quality to when the prediction starts. Assuming that the time period is divided into multiple time periods, the target user's characteristic data of the day may include characteristic data of multiple time periods, wherein the characteristic data of one time period may include characteristic values of multiple characteristics.

S507: The electronic device trains the pre-training model based on the target user's historical sleep data to obtain the target model.

In some embodiments, the server can use the target user's historical sleep data of the past day as a piece of historical sleep data. For example, the target user's historical sleep data includes his daily characteristic data and daily sleep quality in the past M days, that is, there are M pieces of historical sleep data. Historical sleep data; then, for each piece of historical sleep data, a sleep impact factor is constructed based on the feature data in the historical sleep data and the feature weights in the model parameters of the pre-trained model after the last training; finally, the sleep impact factor is The factor and the sleep quality in the historical sleep data are used as an individual training data to train the model after the last training, in which the sleep influencing factor is the input data and the sleep quality is the output target. It should be noted that the pre-trained model after the last training is the model trained on the previous piece of historical sleep data; the feature weights used in the first training of the pre-trained model are the models of the pre-trained model. Initial weights in parameters.

For the specific implementation of constructing the sleep influencing factors and training process, please refer to the relevant content in step S505, which will not be described again here.

S508: The electronic device determines the target user's sleep influencing factors for each period of the day based on the target user's feature data of the day and the feature weight of the target model.

In some embodiments, the electronic device can divide multiple periods using the end time of the target user's sleep that day as the starting point and the time when the prediction starts (such as when the electronic device detects a user operation for predicting sleep quality in step S101) as the end point; and then, , based on the characteristic data of each period in the target user's characteristic data of the day and the weight of each feature in the model parameters of the target model, the user's sleep influence factors for each period of the day are constructed. The characteristic data may include data of multiple characteristics, such as heart rate, exercise duration, etc.; the sleep influencing factors may include at least one of physical consumption factors, energy consumption factors, and sleep environment influencing factors.

In one implementation, all time periods of the day can be divided into a time period before sunset and a time period after sunset; the electronic device can take the wake-up time of the day as the starting point, calculate [SCF _t , ECF _t ] every hour before sunset as the sleep impact factor of each time period before sunset; and calculate [SCF _t , ECF _t , SIEF _t ] every hour after sunset as the sleep impact factor of each time period after sunset.

It should be noted that the electronic device calculates the sleep influencing factors for each period of the target day. Please refer to the calculation process corresponding to Figure 6 above. That is, the weight in Figure 6 is the weight of the target model, and the characteristic data of user A is The sleep influencing factors corresponding to the target user can be obtained from the target user's data for the day, which will not be described again here.

S509: The electronic device obtains the current sleep quality based on the sleep influencing factors and the target model of the target user in each period of the day.

Wherein, the current sleep quality is the predicted sleep quality of the current period; the current period is the period when the electronic device detects the user operation that predicts the sleep quality. For example, the current period may be the period during which the electronic device responds to the user operation for the predicted sleep quality control in step S501.

In some embodiments, the electronic device inputs the target user's sleep influencing factors for each period of the day into the target model in chronological order from morning to evening. In addition to the first period, the data entered into the target model for other periods also includes the above. The latent vector obtained after inputting the sleep influencing factor of a period into the target model, and the last period is the current period; the current sleep quality can be obtained after inputting the sleep influencing factor of the current period and the implicit vector obtained from the previous period of the current period. There is no limit to the length of each period here, and the length of each period can be equal or unequal.

Please refer to Figure 8, which is a schematic diagram of a prediction process provided by an embodiment of the present application. As shown in Figure 8, assuming that the current time period is the kth time period, the time period after the target user wakes up that day to the current time period is the first time period, the second time period, and the kth time period in chronological order, the sleep influence factor before sunset is composed of, and the sleep influence factor after sunset is composed of; the electronic device inputs the sleep influence factor _Y1 of the target user in the first time period after waking up that day into the target model to obtain the sleep quality _O1 and the latent vector _M1 ; then, the electronic device inputs the sleep influence factor _Y2 and the latent vector _M1 of the second time period after waking up that day into the target model to obtain the sleep quality _O2 and the latent vector _M2 ; then, the sleep influence factor _Y3 and the latent vector _M2 of the second time period after waking up that day will be input into the target model to obtain the sleep quality _O3 and the latent vector _M3 ; until the sleep influence factor _Yk and the latent vector Mk _-1 of the current time period are input into the target model to obtain the sleep quality _Ok , and the sleep quality _Ok is the predicted current sleep quality of the target user.

S510: The electronic device obtains sleep aid suggestions based on the target user's characteristic data and target model of the day.

In some embodiments, the model parameters of the target model include the weight of at least one feature; the electronic device can determine the target feature based on the user's feature data of the day and the weight of the at least one feature; and then determine the recommendation for the target feature as Sleep aid suggestions.

In one implementation, the electronic device may determine the feature with the largest weight and the smallest feature value on that day as the target feature. Among them, the characteristic value of the day can be the accumulation of the characteristic values of each period of the day; it can also be the average value of the characteristic values of each period of the day. It should be noted that each period of the day may refer to each period from when the user wakes up to the current period.

Please refer to FIG. 9A. FIG. 9A is a schematic diagram of determining target characteristics provided by an embodiment of the present application. As shown in Figure 9A, taking the 12 features shown in the figure as an example; the weights of these 12 features in the model parameters of the target model are respectively w ₁ , w ₂ , and w ₁₂ ; each of the target users The sum of the day's data for a feature is the sum of the 12 data from X ₁ , X ₂ to X ₁₂ , that is, the sum of the target user's heart rate for the day is X ₁ , and the sum of the target user's exercise duration for the day is X ₂ .

In another implementation, the electronic device may determine the degree of deviation of each feature based on the preset feature value and the feature value of the target user on the day; and take the first several features with larger deviation degrees as the target features. When the deviation degrees are the same, the electronic device may give priority to the features with larger weights. The degree of deviation may be obtained by normalizing the deviation value between the preset feature value and the feature value of the target user on the day.

Please refer to Figure 9B, which is another schematic diagram of determining the target feature provided by an embodiment of the present application. As shown in Figure 9B, the electronic device can obtain the best feature data when the model trained by the group training data outputs the best sleep quality, and the best feature data includes the sum of the feature values of each feature; the feature value of each feature of the target user on the day is compared with the sum of the feature values of each feature in the best feature data to obtain the deviation value of each feature; then, the deviation value of each feature is normalized to obtain the deviation ratio of each feature, and the deviation ratio is used to indicate the degree of deviation between the feature value of the feature and the feature value in the best feature data; finally, the first several features with larger deviation ratios are used as target features.

As shown in Figure 9A, i is an integer from 1 to 12; assume that the weight with the largest value among w ₁ to w ₁₂ is w _i ; the sum of the data from X ₁ to X ₁₂ that determines the smallest characteristic value on the day is X _i ; then Feature i is the target feature, and the courses corresponding to feature i are suggestions for feature i.

It should be noted that the embodiments of this application do not limit the course content corresponding to the target features. The following is an example of the courses corresponding to some features.

Please refer to Figure 10, which is a schematic diagram of a feature provided by an embodiment of the present application and a course corresponding to the feature. As shown in Figure 10, the course corresponding to calories (feature 4) can be fitness course 1; the course corresponding to pressure (feature 8) and noise decibel (feature 12) can be sleeping music; the course corresponding to pressure (feature 8) can be It is breathing training and meditation; the course corresponding to heart rate (feature 1) can be fitness course 2. Among them, the course corresponding to the feature can have parameters corresponding to the course, which are used to update the data of the feature; for example, fitness course 1 can consume x’ calories.

S511: The electronic device updates the target user's characteristic data for the day based on the sleep aid suggestion to obtain updated characteristic data for the day.

The target user's feature data for the day includes at least one target feature; and the updated feature data for the day includes updated data of at least one target feature.

In some embodiments, the sleep aid suggestions are suggestions corresponding to the target feature; the electronic device can update the data of the target feature based on the suggestions corresponding to the target feature to obtain updated data of the target feature; the updated feature data of the day includes updated The data of the target features after the update and the data of the features that are not updated.

The following takes the current day's characteristic data shown in Figure 11 as an example to introduce two exemplary methods for updating the current day's characteristic data.

Please refer to Figure 11. Figure 11 is a schematic diagram of a target user's characteristic data of the day provided by an embodiment of the present application. As shown in Figure 11, the target user's characteristic data of the day includes characteristic data of other periods before the current period and characteristic data of the current period; among them, the characteristic data of the current period includes the characteristic values of the non-updated characteristics and the characteristic values of the target characteristics. There is no limit on the number of non-updating features and target features here.

In one implementation, the electronic device can update the characteristic data of the current period in the characteristic data of the day based on the sleep aid suggestion, and obtain the updated characteristic data of the day. The characteristic data of other periods of the day except the current period remain unchanged. Among them, the electronic device can update only the feature value of the target feature in the feature data of the current period (ie, the bold part in Figure 11) based on the sleep aid suggestion for the target feature, to obtain the updated target in Figure 12A The characteristic value of the feature (i.e., the bold font part in Figure 12A); the feature value of other features (i.e., the feature that is not updated) in the current period remains unchanged; the data shown in Figure 12A is the updated feature data of the day.

For example, the feature data of the current period includes the feature value x ₁ of feature 1, the feature value x ₂ of feature 2, and the feature value x ₃ of feature 3. Feature 1 is the target feature, and feature 2 and feature 3 are non-updated features. Feature 1 is calories, and the sleep aid suggestion corresponding to feature 1 is fitness course 1. This fitness course can consume x' calories, then the electronic device can update the feature value of feature 1 to x ₁ '=x ₁ -x', and the updated The eigenvalue x ₁ ' of feature 1 is the data of the above-mentioned updated target feature. The eigenvalues of feature 2 and feature 3 that have not been updated are the feature data of the above-mentioned non-updated features. The updated feature data of the current period includes updated The updated feature 1 eigenvalues and the unupdated feature 2 and feature 3 eigenvalues.

In another implementation, the electronic device can predict the characteristic data of the next period in the current period based on the sleep aid suggestions; add the characteristic data of the next period to the target user's characteristic data of the day to obtain the updated characteristic data of the day. . For example, the electronic device can predict the feature value of the target feature in the next period (ie, the feature value of the target feature in Figure 11) based on the sleep aid suggestion for the target feature and the feature value of the target feature in Figure 11 (ie, the bold part in Figure 11). The bold part in 12B); the characteristic values of other features (i.e., non-updated features) except the target feature in the next period are the same as the characteristic values of the non-updated features in the current period, and the characteristic data of the next period (i.e. The new part marked in Figure 11); add the characteristic data of the next period to the target user's characteristic data of the day to obtain the updated characteristic data of the day.

The following is an example of an implementation method for updating feature data of a target feature.

In one implementation, the sleep aid suggestion is a course corresponding to the target feature, and the parameters corresponding to the course are used to update the data of the feature; then the electronic device updates the feature data of the target feature based on the parameters corresponding to the course. For example, fitness class 1 can consume x' calories. Then add x' calories corresponding to fitness class 1 to the calorie feature data of the current period to obtain the updated feature data of the day. In the updated feature data of the day The characteristic value of calories in the current period has changed, and the characteristic values of other features in the updated characteristic data of the day have not changed.

S512: The electronic device obtains improvement results based on the updated feature data and target model of the day.

Among them, the updated characteristic data of the day includes new data of at least one target characteristic; the improvement effect may include the sleep quality after adopting the sleep aid suggestions and the improvement of sleep quality, where the sleep quality may include sleep duration, deep sleep ratio, light sleep ratio, and sleep quality improvement. Sleep ratio and rapid eye movement, etc., the improvement of sleep quality can include the increase in sleep duration and the increase in deep sleep duration.

In some embodiments, the updated characteristic data of the current day includes non-updated characteristic data of other periods and updated characteristic data of the current period (as shown in Figure 12A); the electronic device is based on the updated characteristic data of the current period, Calculate the updated sleep impact factor for the current period; based on the sleep impact factors for other periods that have not been updated and the updated sleep impact factor for the current period, predict the sleep quality after adopting the recommendations through the target model. The detailed process of predicting the sleep quality after adopting the advice through the target model can be found in the related implementation of step S509.

In other embodiments, the updated characteristic data of the day includes the original characteristic data of the day and the predicted characteristic data of the next time period (as shown in FIG. 12B ); the electronic device calculates the sleep impact factor of the next time period based on the characteristic data of the next time period; based on the sleep impact factor corresponding to the original characteristic data of the day and the sleep impact factor of the next time period, the target model is used to predict the sleep quality after adopting the suggestion. The detailed process of predicting the sleep quality after adopting the suggestion through the target model can be referred to the relevant implementation of step S509.

Please refer to Figure 13. Figure 13 is a schematic diagram for determining sleep quality after adopting recommendations provided by an embodiment of the present application. As shown in Figure 13, assuming that feature i is the target feature and the sleep aid recommendation is the course corresponding to feature i, the electronic device can update the data of feature i based on the course corresponding to feature i; update the target user's data based on the updated data of feature i Sleep influencing factors: input the updated sleep influencing factors into the target model to obtain the sleep quality after adopting the recommendations. Among them, the process of obtaining the sleep quality after adopting the recommendations based on the updated sleep influencing factors and target model can be found in the above-mentioned process of predicting the current sleep quality, which will not be discussed here.

The following is an exemplary introduction to the implementation of updating the sleep impact factor.

In one implementation, the data of all features in the updated feature data of the current day have been updated, and then the electronic device can update each factor in the sleep influencing factors based on the updated data of all features.

In another implementation, the updated feature data of the day may include new data of the target feature and data of the un-updated feature; then the updated sleep influencing factors may include updated factors and un-updated factors.

For example, multiple factors may include physical consumption factors, energy consumption factors, sleep environment influencing factors, etc.; each factor corresponds to a different feature, and the electronic device can only update the factor corresponding to the target feature to obtain the updated sleep influencing factors. For example, the sleep influencing factors include physical exertion factors, energy consumption factors, and sleep environment influencing factors. The target feature is the feature corresponding to the physical exertion factor. Then the electronic device can update the physical exertion factor based on the updated feature data to obtain the updated Sleep influencing factors. The updated sleep influencing factors include the unupdated energy consumption factor, the sleep environment influencing factor and the updated physical exertion factor.

Taking Figure 6 as an example, if the target feature is one or more features from features 1 to features 5, the electronic device updates the physical exertion factor based on the updated feature data of the day; if the target feature is one or more features from features 6 to features 10, the electronic device updates the energy exertion factor based on the updated feature data of the day; if the target feature is one or more features from features 11 to features 12, the electronic device updates the sleeping environment impact factor based on the updated feature data of the day.

Please refer to Figure 14, which is a schematic diagram of updating the sleep impact factor provided by an embodiment of the present application. Figure 14 exemplarily shows the sleep impact factor before updating, and the sleep impact factor before updating includes the sleep impact factors of k time periods; the new data of the target feature is used to update the sleep impact factor of the last time period (i.e., Y _k ), and the sleep impact factors of other time periods such as Y ₁ and Y ₂ have not changed; assuming that the feature corresponding to the physical exertion factor is the target feature, and other features are not updated, then the updated factor in the updated sleep impact factor is the physical exertion factor, the physical exertion factor before updating is SCF _k , and the physical exertion factor after updating is SCF _k' ; the factors that have not been updated are the energy consumption factor EFC _k and the sleeping environment impact factor SIEF _k . The details of the sleep impact factor before updating can be found in the relevant introduction of Figure 8, which will not be repeated here.

S513: The electronic device displays the above-mentioned current sleep quality, the above-mentioned sleep aid suggestions and the above-mentioned improvement effect.

In some embodiments, the electronic device may respond to the user operation of starting sleep prediction, perform the above-mentioned steps S501 to S510 to obtain the current sleep quality and the sleep-aid suggestions, and then display the current sleep quality and the above-mentioned sleep-aid suggestions; and then respond to the prediction The user operates the sleep quality after adopting the sleep aid suggestion and performs the above-mentioned step S511 and step S512 to obtain an improvement effect, and then displays the above-mentioned improvement effect.

For example, in response to the user operation of the sleep quality prediction control 620 displayed on the user interface 62 shown in FIG. 15B , the electronic device performs the above steps S501 to S507 to obtain the current sleep quality, and then displays the current sleep quality through the user interface 63 shown in FIG. 15C The current sleep quality and the above-mentioned sleep aid suggestions, for example, the display window 630 is used to display the current sleep quality; furthermore, in response to the user operation on the view control 634 displayed on the user interface 63, the above-mentioned steps S508 and S509 are performed to obtain an improvement effect, and further, The above improvement effect is displayed through the user interface 64 as shown in Figure 15D.

In other embodiments, the electronic device may perform the above steps S501 to S509 in response to the user operation of starting sleep prediction to obtain the current sleep quality, and then display the current sleep quality; and then in response to the user operation of providing sleep aid suggestions, perform the above steps. In step S510, the above-mentioned sleep aid suggestions are obtained, and then the sleep aid suggestions are displayed; and then in response to the user operation of improving the effect, the above-mentioned steps S511 and step S512 are executed to obtain the improvement effect, and then the above-mentioned improvement effect is displayed.

In some embodiments, the electronic device may display the current sleep quality, the sleep aid suggestions, and the improvement effect after performing the above steps S501 to S512 in response to the user operation of starting sleep prediction.

The following exemplarily shows a possible user interface of the electronic device in the embodiment of the present application. Figures 15A-15D are user interfaces implemented on the electronic device.

FIG15A shows an exemplary user interface 61 for displaying installed applications on an electronic device. The user interface 61 displays: a status bar, an icon 610 of a sports and health application, an icon of a gallery application, and icons of other applications, etc. The status bar may include: one or more signal strength indicators of a mobile communication signal (also referred to as a cellular signal), one or more signal strength indicators of a Wi-Fi signal, a battery status indicator, a time indicator, etc.

As shown in Figure 15A, the user can click the icon 610 of the sports and health application. Correspondingly, the electronic device can display the user interface 62 as shown in Figure 15B in response to the user operation. It should be understood that the user interface 62 is an application interface of an exemplary sports and health application and should not be limited to the embodiments of the present application.

As shown in FIG. 15B , the user interface 62 displays a sleep quality prediction control 620 ; in response to the user's operation of the sleep quality prediction control 620 displayed on the user interface 62 shown in FIG. 15B , the electronic device may display a user interface as shown in FIG. 15C Interface 63.

The user interface 63 includes a display window 630, a breathing training control 631, a fitness course control 632, sleep music 633 and a viewing control 634. Among them, the display window 630 is used to display the predicted sleep quality, such as sleep duration, deep sleep ratio, light sleep ratio, rapid eye movement, etc.; the breathing training control 631 is used to display the course content corresponding to the breathing training; the fitness course control 632 is used to Display the course content corresponding to the fitness course; the sleeping music 633 is used to display the course content corresponding to the sleeping music; and the viewing control 634 is used to display the sleep quality after adopting the suggestions.

As shown in Figure 15C, the user can click the view control 634. Correspondingly, the electronic device can display the user interface 64 as shown in Figure 15D in response to the user operation on the view control 634. The user interface 64 can display the sleep quality after adopting the recommendations, such as sleep duration, deep sleep ratio, light sleep ratio, rapid eye movement, etc.; as well as the increased value of sleep duration and the increased value of deep sleep duration.

In other embodiments, the target model may be generated by the server. For example, in response to a user operation for predicting sleep quality, the electronic device sends an acquisition request to the server; in response to the acquisition request, the server trains the initial sleep quality prediction model based on the historical sleep data of the target group and the historical sleep data of the target user, and obtains the target model; further. The server sends the target model and the target user's characteristic data of the day to the electronic device; finally, the electronic device can perform steps S508 to S513.

In some embodiments, the above-mentioned current sleep quality, the above-mentioned sleep aid suggestions and the above-mentioned improvement effects may all be generated by the server. For example, in response to a user operation for predicting sleep quality, the electronic device sends an acquisition request to the server; in response to the acquisition request, the server trains the initial sleep quality prediction model based on the historical sleep data of the target group and the historical sleep data of the target user, and obtains the target model; furthermore, the server obtains the above-mentioned current sleep quality, the above-mentioned sleep-aid suggestions and the above-mentioned improvement effect based on the target model and the target user's characteristic data of the day; furthermore, the server can send the above-mentioned current sleep quality, the above-mentioned sleep-aid suggestions and the above-mentioned improvements to the electronic device Effect; Finally, the electronic device can perform step S513.

Embodiments of the present application also provide an electronic device. The electronic device includes one or more processors and one or more memories; wherein the one or more memories are coupled to the one or more processors, and the one or more memories are used to For storing computer program code, the computer program code includes computer instructions. When one or more processors execute the computer instructions, the electronic device causes the electronic device to perform the method described in the above embodiments.

The embodiments of the present application also provide a computer program product including instructions. When the computer program product is executed on an electronic device, the electronic device executes the method described in the above embodiments.

Embodiments of the present application also provide a computer-readable storage medium, which includes instructions. When the instructions are run on an electronic device, the electronic device causes the electronic device to execute the method described in the above embodiment.

It can be understood that the various embodiments of the present application can be combined arbitrarily to achieve different technical effects.

In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in this application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk), etc.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments are implemented. This process can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, , may include the processes of the above method embodiments. The aforementioned storage media include: ROM, random access memory (RAM), magnetic disks, optical disks and other media that can store program codes.

In short, the above description is only an embodiment of the technical solution of this application, and is not intended to limit the protection scope of this application. Any modification, equivalent replacement, improvement, etc. made according to the disclosure of this application shall be included in the protection scope of this application.

Claims (12)

1. A sleep quality prediction method, characterized in that it is applied to electronic devices, and the method includes: The electronic device acquires user data in response to a user operation to start sleep prediction; the user data includes at least one of physical energy data, energy data, and environmental data; The electronic device obtains the user's sleep aid suggestions and first sleep quality based on the user data, where the first sleep quality is the sleep quality when the user adopts the sleep aid suggestions; The electronic device displays at least one of the first sleep quality and sleep improvement situation and the sleep assistance suggestion, and the sleep improvement situation is the improvement of sleep quality when the user adopts the sleep assistance suggestion. 2. The method according to claim 1, characterized in that, the method further comprises: The electronic device obtains a second sleep quality based on the user data, where the second sleep quality is the sleep quality when the user does not adopt the sleep aid suggestion; The electronic device displays the second sleep quality. 3. The method according to claim 1 or 2, wherein the sleep quality includes at least one of sleep duration within a preset duration and deep sleep ratio, and the sleep improvement includes an improvement in sleep duration. and at least one of improvement of depth ratio; the method further includes: The electronic device obtains at least one of the improvement in the sleep duration and the improvement in the sleep depth ratio based on the first sleep quality and the second sleep quality; the second sleep quality is the sleep quality when the user does not adopt the sleep aid suggestion. 4. The method according to any one of claims 1 to 3, characterized in that the user data includes feature values of a plurality of features, and the electronic device obtains the user's sleep aid suggestions based on the user data and First sleep quality, including: The electronic device determines a target feature from the plurality of features based on feature values of the plurality of features; The electronic device determines a sleep aid suggestion for the target feature; the sleep aid suggestion for the target feature is used to update the feature value of the target feature. 5. The method of claim 4, wherein the electronic device determines a target feature from the plurality of features based on feature values of the multiple features, including: The electronic device compares the characteristic value of each feature in the plurality of features with the preset value corresponding to each feature to obtain the degree of deviation of each feature; The electronic device determines the first N features with large deviations as the target features, where N is a positive integer. 6. The method according to claim 4 or 5, characterized in that the user data includes characteristic data of the Mth time period and characteristic data of other time periods, the characteristic data includes characteristic values of multiple characteristics, and M is a positive integer; the electronic device obtains the user's sleep-aiding advice and the first sleep quality based on the user data, comprising: The electronic device updates the feature value of the target feature in the feature data of the Mth time period based on the sleep aid suggestion for the target feature, and obtains the updated feature data of the Mth time period; The electronic device obtains the updated sleep impact factor of the Mth period based on the updated characteristic data of the Mth period; The electronic device uses the updated sleep impact factor of the Mth time period and the sleep impact factors of other time periods as input, and obtains the first sleep quality through the target model; The sleep impact factors of other time periods are obtained based on the characteristic data of other time periods; the target model is trained using the sample sleep impact factors as input and the sleep quality corresponding to the sample sleep impact factors as labels. 7. The method according to any one of claims 2 to 6, wherein the user data includes characteristic data of multiple time periods, and the electronic device obtains the second sleep quality based on the user data, including : The electronic device obtains the sleep impact factor of each time period based on the characteristic data of each time period in the multiple time periods; The electronic device obtains the second sleep quality based on the sleep influencing factor and the target model of each time period; The target model is trained using the sample sleep influencing factors as input and the sleep quality corresponding to the sample sleep influencing factors as labels. 8. The method according to claim 6 or 7, characterized in that the sleep influencing factors within the preset period include at least one of a physical consumption factor, an energy consumption factor and a sleeping environment influencing factor; The physical exertion factor is determined based on at least one of the heart rate, exercise duration, number of steps, calories and activity hours in the preset time period; the energy exertion factor is determined based on at least one of the blood pressure, blood oxygen, stress, caffeine intake and alcohol intake in the preset time period; the sleep environment impact factor is determined based on at least one of the ambient light and noise decibels in the preset time period. 9. The method according to any one of claims 6-8, characterized in that the method further includes: The electronic device acquires a pre-training model, where the pre-training model is obtained based on historical user data of the group to which the user belongs; The electronic device trains the pre-trained model based on the historical user data of the user to obtain the target model. 10. The method according to any one of claims 1 to 8, characterized in that the electronic device acquires user data in response to a user operation of starting sleep prediction, comprising: The electronic device sends a request to the server in response to a user operation of starting sleep prediction; The electronic device receives the user data sent by the server, where the user data includes data acquired by the server from a device worn by the user. 11. An electronic device, characterized in that the electronic device comprises one or more processors and one or more memories; wherein the one or more memories are coupled to the one or more processors, the one or more memories are used to store computer program codes, and the computer program codes include computer instructions, and when the one or more processors execute the computer instructions, the electronic device executes the method as described in any one of claims 1-10. 12. A computer-readable storage medium comprising instructions, characterized in that, when the instructions are run on an electronic device, the electronic device is caused to perform the method according to any one of claims 1-10.