CN113509145A

CN113509145A - Sleep risk monitoring method, electronic device and storage medium

Info

Publication number: CN113509145A
Application number: CN202011455211.6A
Authority: CN
Inventors: 许培达; 李靖
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-04-10
Filing date: 2020-12-10
Publication date: 2021-10-19
Anticipated expiration: 2040-12-10
Also published as: CN113509145B

Abstract

The application relates to the technical field of artificial intelligence, and provides a sleep risk monitoring method, electronic equipment and a storage medium, wherein the sleep risk monitoring method comprises the following steps: acquiring physiological information of a user and current altitude information of the user; the sleep monitoring model is determined according to the current altitude information of the user, wherein the sleep monitoring model is obtained by training the preset physiological information of the preset altitude area and the corresponding risk information as training samples, so that a more reasonable sleep monitoring model matched with the physiological information of the user in the current altitude area can be determined. And then, the physiological information of the user is input into the sleep monitoring model to obtain the sleep risk information output by the sleep monitoring model, so that the sleep risk monitoring of the user in different areas can be realized, and the accuracy of the sleep risk monitoring is improved.

Description

Sleep risk monitoring method, electronic device and storage medium

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a sleep risk monitoring method, an electronic device, and a storage medium.

Background

The prior art may determine the sleep risk level of a user by monitoring physiological information of the user (e.g., blood oxygen, pulse, snoring, etc.). However, the inventor finds that environmental factors may have a certain influence on physiological information when analyzing the prior art, and if only physiological information of a user is monitored during sleep risk monitoring, accuracy of sleep risk monitoring may be affected.

Disclosure of Invention

The application provides a sleep risk monitoring method, an electronic device and a storage medium, which can improve the accuracy of sleep risk monitoring.

In a first aspect, an embodiment of the present application provides a sleep risk monitoring method, including: acquiring physiological information of a user and current altitude information of the user; determining a sleep monitoring model according to the current altitude information of the user, wherein the sleep monitoring model is obtained by training by taking preset physiological information of a preset altitude area and corresponding risk information as training samples; and inputting the user physiological information into the sleep monitoring model to obtain sleep risk information output by the sleep monitoring model.

In the embodiment, the sleep monitoring model is determined according to the current altitude information of the user by acquiring the physiological information of the user and the current altitude information of the user, and is obtained by training the sleep monitoring model by taking the preset physiological information of the preset altitude area and the corresponding sleep risk information as training samples; and inputting the physiological information of the user into the sleep monitoring model to obtain the sleep risk information output by the sleep monitoring model. As the air pressure and the concentration of oxygen in the air can change along with the change of the altitude, the physiological information of the users in different altitude areas is different, so that the sleep monitoring model is determined according to the current altitude information of the user, and a more reasonable sleep monitoring model matched with the physiological information of the user in the current altitude area can be selected, thereby realizing the sleep risk monitoring of the users in different areas, improving the accuracy of the sleep risk monitoring and enhancing the coverage of products.

In a possible implementation manner of the first aspect, the inputting the user physiological information into the sleep monitoring model to obtain sleep risk information output by the sleep monitoring model, where the user physiological information includes physiological information of a user in a first predetermined time period, includes: and inputting the physiological information of the user in a first preset time period into the sleep monitoring model to obtain the sleep risk information output by the sleep monitoring model. The sleep monitoring model is obtained by training physiological information and corresponding risk information of a user in a preset altitude area in a first preset time period. Because the physiological information of the user in the first preset time period can better reflect the sleep condition of the user, the physiological information of the user in the first preset time period is input into the sleep monitoring model, the sleep risk of the user is output, and the monitoring precision of the sleep risk is improved.

In a possible implementation manner of the first aspect, the determining a sleep monitoring model according to the altitude information where the user is currently located includes:

determining an altitude interval corresponding to the altitude information where the user is currently located; and determining a sleep monitoring model according to the altitude interval and the blood oxygen statistic value corresponding to the first preset time period. Wherein, the blood oxygen statistic value corresponding to the first predetermined time period is obtained by counting the blood oxygen information of different users in different altitude areas in the first predetermined time period. For example, the blood oxygen statistic value corresponding to the first predetermined period of time is an average value of blood oxygen of the user at the current altitude interval in the first predetermined period of time, and reflects the blood oxygen level of the user at the current altitude interval in the first predetermined period of time.

determining an altitude interval corresponding to the altitude information where the user is currently located; and determining a sleep monitoring model according to the altitude interval and the blood oxygen statistic value corresponding to a second preset time period, wherein the second preset time period is a time period outside the first preset time period. The blood oxygen statistic value corresponding to the second predetermined time period is obtained by counting blood oxygen information of different users in different altitude areas in the second predetermined time period. For example, the blood oxygen statistic value corresponding to the second predetermined period of time is an average value of blood oxygen of the user at the current altitude interval in the daytime, reflects the blood oxygen level of the user at the current altitude interval in the second predetermined period of time, determines the sleep monitoring model according to the altitude interval and the preset daytime blood oxygen statistic value, and can determine the sleep monitoring model more matched with the physiological information of the user at the current altitude area.

Furthermore, because the physiological information of the users in the same altitude area is different, and the physiological information of the users with the sleep risks in the same risk level is also different, the sleep monitoring model is determined according to the physiological information of the users and the altitude information of the users in the current altitude area, so that the sleep monitoring model matched with the physiological information of the users in the current altitude area can be determined.

Illustratively, for a scenario where the user only wears or uses the electronic device for a first predetermined period of time, the physiological information collected by the electronic device for the first predetermined period of time includes blood oxygen information of the user for the first predetermined period of time, and the determining the sleep monitoring model according to the physiological information of the user and the altitude information where the user is currently located includes: determining a blood oxygen interval corresponding to the blood oxygen information of the first preset time period, and determining an altitude interval corresponding to the altitude information where the user is currently located; and determining a sleep monitoring model according to the blood oxygen interval and the altitude interval. Since the blood oxygen information in the physiological information changes most obviously when the altitude information of the user changes, the sleep monitoring model determined by the blood oxygen information of the user in the first preset time period is more matched with the physiological information of the user.

If the first predetermined period of time is a night period of time and the second predetermined period of time is a day period of time, for a scene in which the user wears or uses the electronic device in both the first predetermined period of time and the second predetermined period of time, the physiological information of the user in the second predetermined period of time further includes the physiological information of the user in the second predetermined period of time, and the determining a sleep monitoring model according to the physiological information of the user and the altitude information of the user at which the user is currently located includes: determining a blood oxygen interval corresponding to the blood oxygen information of the second preset time period, and determining an altitude interval corresponding to the altitude information where the user is currently located; and determining a sleep monitoring model according to the blood oxygen interval and the altitude interval. Because the blood oxygen information of the users with sleep risks in the first preset time interval is unstable, and the blood oxygen information in the second preset time interval is relatively stable, the stability of data of training samples for training the sleep monitoring model can be ensured according to the sleep monitoring models divided by the blood oxygen information of different users in the second preset time interval, the accuracy of the sleep monitoring model is ensured, and then the corresponding sleep monitoring model is determined according to the blood oxygen information and the altitude information of the users, so that the accuracy of sleep risk monitoring is improved.

In a second aspect, an embodiment of the present application provides a sleep risk monitoring device, including:

the acquisition module is used for acquiring the physiological information of the user and the current altitude information of the user;

the determining module is used for determining a sleep monitoring model according to the current altitude information of the user, wherein the sleep monitoring model is obtained by training a training sample by using preset physiological information of a preset altitude area and corresponding risk information;

and the output module is used for inputting the user physiological information into the sleep monitoring model to obtain the sleep risk information output by the sleep monitoring model.

In a possible implementation manner of the second aspect, the physiological information of the user includes physiological information of the user at a first predetermined time period, and the output module is specifically configured to:

and inputting the physiological information of the user in a first preset time period into the sleep monitoring model to obtain the sleep risk information output by the sleep monitoring model.

In a possible implementation manner of the second aspect, the determining module is specifically configured to:

determining an altitude interval corresponding to the altitude information where the user is currently located;

and determining a sleep monitoring model according to the altitude interval and the blood oxygen statistic value corresponding to the first preset time period.

In a possible implementation manner of the second aspect, the blood oxygen statistic corresponding to the first predetermined period is obtained by counting blood oxygen information of different users in different altitude areas in the first predetermined period.

and determining a sleep monitoring model according to the altitude interval and the blood oxygen statistic value corresponding to a second preset time period, wherein the second preset time period is a time period outside the first preset time period.

and determining the sleep monitoring model according to the physiological information of the user and the current altitude information of the user.

In a possible implementation manner of the second aspect, the physiological information of the first predetermined period includes blood oxygen information of the user in the first predetermined period, and the determining module is specifically configured to:

determining a blood oxygen interval corresponding to the blood oxygen information of the first preset time period, and determining an altitude interval corresponding to the altitude information where the user is currently located;

and determining a sleep monitoring model according to the blood oxygen interval and the altitude interval.

In a possible implementation manner of the second aspect, the physiological information of the user further includes physiological information of the user in a second predetermined period, the physiological information of the user in the second predetermined period includes blood oxygen information of the user in the second predetermined period, and the determining module is specifically configured to:

determining a blood oxygen interval corresponding to the blood oxygen information of the second preset time period, and determining an altitude interval corresponding to the altitude information where the user is currently located;

In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the sleep risk monitoring method according to the first aspect as described above when executing the computer program.

In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the sleep risk monitoring method according to the first aspect is implemented.

In a fifth aspect, an embodiment of the present application provides a computer program product, which, when run on an electronic device, causes the electronic device to execute the sleep risk monitoring method according to the first aspect.

It is understood that the beneficial effects of the second aspect to the fifth aspect can be referred to the related description of the first aspect, and are not described herein again.

Drawings

Fig. 1 is a schematic diagram of a sleep risk monitoring method provided in an embodiment of the present application;

FIG. 2 is a schematic diagram of an electronic device provided by an embodiment of the application;

fig. 3 is a schematic flowchart of a sleep risk monitoring method according to an embodiment of the present application;

fig. 4 is a schematic flowchart of a sleep risk monitoring method in an application scenario according to an embodiment of the present application;

fig. 5 is a schematic flowchart of a sleep risk monitoring method in another application scenario according to an embodiment of the present application;

fig. 6 is a schematic flowchart of a sleep risk monitoring method in another application scenario according to an embodiment of the present application;

fig. 7 is a schematic flowchart of a sleep risk monitoring method in another application scenario according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".

Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.

In order to monitor the sleep risk information of the user, in one possible implementation, the monitored physiological information (e.g., blood oxygen, pulse, snore, etc.) of the user may be input into the sleep monitoring model, and the sleep risk information of the user is determined according to the output result of the sleep monitoring model. However, determining the sleep risk information of the user only by monitoring the physiological information of the user may affect the accuracy of the sleep risk monitoring.

Therefore, the sleep risk monitoring method provided by the application corrects the sleep monitoring model according to the current altitude information of the user to obtain the corrected sleep monitoring model, inputs the physiological information of the user into the corrected sleep monitoring model, and obtains the sleep risk information output by the corrected sleep monitoring model so as to improve the accuracy of sleep risk monitoring.

In another possible implementation manner, the sleep monitoring model may be modified according to the altitude information of the current location of the user and the physiological information of the user to obtain a modified sleep monitoring model, and then the physiological information of the user is input into the modified sleep monitoring model to obtain the sleep risk information output by the modified sleep monitoring model, so as to further improve the accuracy of sleep risk monitoring.

For example, as shown in fig. 1, the user physiological information includes pulse information, heart rate information, snore information, body motion information, and blood oxygen information, and the sleep monitoring model may be corrected according to the blood oxygen information in the user physiological information and the current altitude information of the user, so as to obtain a corrected sleep monitoring model, and then the user physiological information is input into the corrected sleep monitoring model, so as to obtain sleep risk information output by the corrected sleep monitoring model, so as to improve the accuracy of sleep risk monitoring. The sleep risk information may be a sleep risk level, the sleep risk level includes a normal level, a low level, a medium level, and a high level, and the user may determine the body state of the user according to the sleep risk level.

The following describes an exemplary sleep risk monitoring method provided by the present application.

The sleep risk monitoring method provided by the embodiment of the application is applied to electronic equipment, the electronic equipment can be wearable equipment, computers, medical equipment and the like, and the embodiment of the application does not limit the specific type of the electronic equipment.

By way of example and not limitation, when the electronic device is a wearable device, the wearable device may also be a generic term for intelligently designing daily wearing by applying wearable technology, developing wearable devices, such as watches, jewelry, and the like. A wearable device is a portable device that is worn directly on the body or integrated into the clothing or accessories of the user. The wearable device is not only a hardware device, but also realizes powerful functions through software support, data interaction and cloud interaction. The generalized wearable intelligent device has the advantages that the generalized wearable intelligent device is complete in function and large in size, can realize complete or partial functions without depending on a smart phone, such as a smart watch or smart glasses, and only is concentrated on a certain application function, and needs to be matched with other devices such as the smart phone for use, such as various smart bracelets for monitoring physical signs, smart jewelry and the like.

As shown in fig. 2, the electronic device provided by the embodiment of the present application includes an altitude/barometric pressure sensor 10, a pulse oximetry sensor 20, other biosensors 30, a data storage unit 40, a sleep risk monitoring module 50, and a display unit 60. The altitude/atmospheric pressure sensor 10 is used to collect altitude information or atmospheric pressure information at which the user is located. The pulse oximetry sensor 20 is used to collect pulse information, heart rate information, and blood oxygen information of the user. The other biosensor 30 may be a radar wave sensor, an acceleration sensor, or a sound pickup device for collecting snore information and body motion information of the user. The data storage unit 40 is used for storing altitude information or atmospheric pressure information, pulse information, heart rate information, blood oxygen information, snore information and body action information of a user, and sending the information to the sleep risk monitoring module 50, the sleep risk monitoring module 50 is used for determining the sleep risk information of the user according to the altitude information or atmospheric pressure information, pulse information, heart rate information, blood oxygen information, snore information and body action information of the user, and the display unit 60 is used for displaying the sleep risk information of the user. The sleep risk monitoring method provided by the embodiment of the present application is described below with reference to the electronic device shown in fig. 2.

Referring to fig. 3, a sleep risk monitoring method according to an embodiment of the present application includes:

s101: and acquiring the physiological information of the user and the current altitude information of the user.

Specifically, the user physiological information includes blood oxygen information, heart rate information, pulse information and the like, and the electronic device acquires the user physiological information from a pulse blood oxygen sensor and/or other biosensors. The user physiological information may be the physiological information of the user in the second predetermined time period, may also be the physiological information of the user in the first predetermined time period, and may also include both the physiological information of the user in the second predetermined time period and the physiological information of the user in the first predetermined time period. The first predetermined period of time and the second predetermined period of time are different periods of time, illustratively, the first predetermined period of time is a night time period and the second predetermined period of time is a day time period. Wherein the night time period may be a fixed time period, for example, 23: 00-6: 00, or may be a fixed time length after detecting that the user enters the sleep state, for example, 4 hours after the user enters the sleep state. Similarly, the daytime period may be a fixed period, or may be a fixed duration after the user is detected to be awake. Blood oxygenation information is the volume of oxygenated hemoglobin in blood bound by oxygen as a percentage of the total available hemoglobin volume, i.e., the concentration of blood oxygen in blood.

Altitude information at which the user is currently located is acquired from an altitude/atmospheric pressure sensor. If the altitude information of the user is obtained from the altitude sensor, the altitude information is the altitude, and if the altitude information of the user is obtained from the atmospheric pressure sensor, the electronic device calculates the altitude according to the atmospheric pressure collected by the atmospheric pressure sensor.

S102: and determining a sleep monitoring model according to the current altitude information of the user, wherein the sleep monitoring model is obtained by training by taking preset physiological information of a preset altitude area and corresponding sleep risk information as training samples.

In one possible implementation, a plurality of sleep monitoring models are trained first. Specifically, physiological information of different users in different altitude areas is collected to obtain a collected sample, wherein the physiological information of the different users may be physiological information of a second predetermined time period or physiological information of a first predetermined time period. And dividing the collected samples according to different altitude intervals to obtain a plurality of samples corresponding to the altitude intervals, and dividing each sample corresponding to the altitude interval according to the physiological information intervals to obtain a plurality of samples corresponding to the physiological information intervals. The physiological information interval may be a blood oxygen interval of the user, a heart rate interval of the user, or a pulse interval of the user. The sample corresponding to the altitude interval may be divided according to the physiological information interval of the user in the second predetermined period, or the sample corresponding to the altitude interval may be divided according to the physiological information interval of the user in the first predetermined period. For example, the range of the altitude in the collected sample is 1000-4000, the range of the blood oxygen in the physiological information of the user in the second preset time interval is 80% -90%, 3 altitude intervals of 1000-2000, 2000-3000 and 3000-4000 are set, the collected sample is divided according to the altitude intervals, and 3 samples of the sample corresponding to the altitude interval 1000-2000, the sample corresponding to the altitude interval 2000-3000 and the sample corresponding to the altitude interval 3000-4000 are obtained. Setting the blood oxygen interval to be 80% -85% and 85% -90%, dividing each sample corresponding to the altitude interval according to the blood oxygen interval, and obtaining 2 sub-samples corresponding to the blood oxygen interval of 80% -85% and the blood oxygen interval of 85% -90% for each sample, thereby dividing the collected sample into 6 sub-samples. And taking the physiological information of each user and the corresponding sleep risk information in each subsample as training samples, training the classification model by adopting a machine learning algorithm to obtain the optimal parameters of the classification model corresponding to each subsample, and generating a corresponding sleep monitoring model according to the optimal parameters, namely the sleep monitoring model corresponding to each subsample. The sleep risk information may be a sleep risk level, a sleep state, and the like.

It should be noted that, the collected samples may also be divided according to the altitude interval only to obtain sub-samples corresponding to the altitude interval, and the physiological information of each user and the corresponding sleep risk information in each sub-sample are used as training samples to train and obtain the sleep monitoring model corresponding to each sub-sample. The physiological information of the users in the training samples may be the physiological information of different users in the second predetermined period, the physiological information of different users in the first predetermined period, or the physiological information of different users in the second predetermined period and the physiological information of different users in the first predetermined period. The sleep risk information corresponding to the physiological information of the user in the training sample may all be normal, e.g., all be risk-free levels; or may be partially normal, partially abnormal, including, for example, a high risk level, a low risk level, and an no risk level.

After obtaining the plurality of sleep monitoring models, according to the altitude interval where the obtained altitude information of the user is located currently, the sleep monitoring model corresponding to the altitude interval is determined, and the sleep monitoring model corresponding to the altitude interval is also the corrected sleep monitoring model.

In a possible implementation manner, a statistical average value of physiological information of different users close to the current altitude of the user is obtained, a physiological information interval where the statistical average value of the physiological information is located is determined, and then a sleep monitoring model corresponding to the altitude interval and the physiological information interval is determined.

In another possible implementation manner, according to a physiological information interval where the acquired physiological information of the user is located, a sleep monitoring model corresponding to the physiological information interval and the altitude interval is determined.

In another possible implementation manner, after the sleep monitoring model is determined, a threshold used for judging the sleep risk information in the sleep monitoring model is determined according to the current altitude information of the user and/or the physiological information of the user, so as to improve the accuracy of subsequently judging the sleep risk information.

S103: and inputting the user physiological information into the sleep monitoring model to obtain sleep risk information output by the sleep monitoring model.

Specifically, the physiological information of the user is input into a sleep monitoring model, and the sleep monitoring model outputs corresponding sleep risk information according to a set threshold value. The output sleep risk information may be information such as a sleep risk level or a sleep state. E.g., output sleep risk level, which may be normal, high risk, low risk, no risk; or outputting a sleep risk assessment score, wherein the assessment score corresponds to the risk level; or output sleep state, which may be good, medium, or bad. In a possible implementation manner, the sleep risk information, such as risk information of obstructive sleep apnea and hypopnea syndrome, is mainly determined according to the physiological information of the user in the first predetermined period, and the physiological information of the user in the first predetermined period is input into the determined sleep monitoring model to output the sleep risk information, so that the accuracy of sleep risk monitoring can be improved.

It should be noted that the sleep monitoring model may be determined according to the physiological information of the user in the second predetermined period and the current altitude information of the user, the physiological information of the user in the first predetermined period is input into the determined sleep monitoring model, and the sleep risk information is output. Or determining the sleep monitoring model according to the physiological information of the user in the second preset time period and the current altitude information of the user, inputting the physiological information of the user in the second preset time period into the determined sleep monitoring model, and outputting the sleep risk information. Or determining a sleep monitoring model according to the physiological information of the user in the first preset time period and the current altitude information of the user, inputting the physiological information of the user in the first preset time period into the determined sleep monitoring model, and outputting the sleep risk information.

Optionally, if the sleep risk to be monitored is the risk of obstructive sleep apnea and hypopnea syndrome, besides inputting the physiological information of the user into the sleep monitoring model, the oronasal airflow information, snore information, electrocardio signal information, body action information and the like of the user are also input into the sleep monitoring model.

In the embodiment, the physiological information of the user and the current altitude information of the user are acquired; determining a sleep monitoring model according to the current altitude information of a user, wherein the sleep monitoring model is obtained by taking preset physiological information of a preset altitude area and corresponding sleep risk information as training samples for training, namely the determined sleep monitoring model is matched with the preset physiological information of the preset altitude area; therefore, the sleep monitoring model is determined according to the current altitude information of the user, and a more reasonable sleep monitoring model matched with the physiological information of the user in the current altitude area can be determined; after the sleep monitoring model is determined, the physiological information of the user is input into the determined sleep monitoring model, and the sleep risk information output by the sleep monitoring model is obtained, so that the monitoring precision of the sleep risk of the user in different altitude areas can be improved, and the coverage of the product is enhanced.

The specific process of the sleep risk monitoring method provided by the embodiment of the present application is further described below with reference to specific application scenarios.

In one application scenario, the user wears or uses the electronic device only for the first predetermined period of time, i.e., nighttime, and the physiological information collected by the electronic device does not include blood oxygen information of the user.

Referring to fig. 4, in the application scenario, the sleep risk monitoring method includes:

s201: the method comprises the steps of obtaining physiological information of a user in a first preset time period and current altitude information of the user, wherein the physiological information of the user in the first preset time period comprises pulse information and/or heart rate information, and the current altitude information of the user is the altitude of an area where the user is located.

In a possible implementation manner, the information acquired by the electronic device further includes snore information and body action information of the user in the first predetermined period, wherein the snore information and the body action information are acquired by the acceleration sensor.

S202: and determining a sleep monitoring model according to the current altitude information of the user, wherein the sleep monitoring model is obtained by training by taking preset blood oxygen information of a preset altitude area and corresponding sleep risk information as training samples.

Specifically, before the sleep monitoring model is determined, physiological information of different users in different altitude areas in a first preset time period is collected, and a collected sample is obtained. The blood oxygen information of the corresponding user in the first predetermined time period is calculated according to the physiological information of different users in different altitude areas in the first predetermined time period, for example, the blood oxygen information of the corresponding user in the first predetermined time period is calculated according to the preset corresponding relationship between the heart rate information and the blood oxygen information and the heart rate information of the user in the first predetermined time period. And dividing the collected sample into a plurality of subsamples according to the altitude interval and the blood oxygen interval of the first preset time period. And taking the physiological information of each user in each subsample in the first preset time period and the corresponding sleep risk information as training samples, and training the classification model by adopting a machine learning algorithm to obtain a sleep monitoring model corresponding to each subsample.

After the sleep monitoring model corresponding to each sub-sample is obtained, an altitude interval corresponding to the altitude information where the user is located currently is determined, and then the sleep monitoring model corresponding to the altitude interval is determined.

In a possible implementation manner, before determining the sleep monitoring model, obtaining a blood oxygen statistic value corresponding to a first predetermined time period, specifically, counting blood oxygen information of different users in different altitude areas in the first predetermined time period to obtain a mapping relation between the altitude and the blood oxygen information of the first predetermined time period, and obtaining a blood oxygen statistic value corresponding to the first predetermined time period according to the mapping relation, where the blood oxygen statistic value corresponding to the first predetermined time period may be an average value of blood oxygen of different users in an altitude area close to the altitude where the user is located in the first predetermined time period. After obtaining the blood oxygen statistic value, determining a blood oxygen interval corresponding to the blood oxygen statistic value and an altitude interval corresponding to the altitude information where the user is currently located, and determining a corresponding sleep monitoring model according to the altitude interval and the blood oxygen interval. Because blood oxygen is an important index for evaluating sleep risk information, and blood oxygen information of different users in the same area has certain difference, the blood oxygen level of the user in the current area can be reflected more according to the sleep monitoring model determined in the blood oxygen interval in which the blood oxygen statistic value is positioned, so that a more accurate sleep monitoring model suitable for the current user is obtained.

In a possible implementation manner, after the sleep monitoring model is determined, according to the altitude interval and the blood oxygen interval corresponding to the blood oxygen statistic value of the first predetermined time period, a threshold value used for judging the sleep risk information in the sleep monitoring model is determined, so that the accuracy of subsequently judging the sleep risk information is improved.

S203: and inputting physiological information of the user in a first preset time period into the sleep monitoring model to obtain sleep risk information output by the sleep monitoring model.

In the above embodiment, the sleep monitoring model is determined according to the altitude information of the user currently located and the blood oxygen statistic corresponding to the first predetermined time period, the sleep monitoring model matched with the physiological information of the user in the current altitude area can be determined, and then the sleep risk information is output according to the physiological information of the user in the first predetermined time period and the determined sleep monitoring model, so that the monitoring accuracy of the sleep risk is improved.

In another application scenario, the user wears or uses the electronic device only for the first predetermined period of time, and the blood oxygen information is included in the physiological information collected by the electronic device.

Referring to fig. 5, in the application scenario, the sleep risk monitoring method includes:

s301: the method comprises the steps of acquiring physiological information of a user in a first preset time period and altitude information of the user at present.

Specifically, the physiological information of the user at the first predetermined period of time includes blood oxygen information, heart rate information, pulse information, and the like.

In one possible implementation, the data acquired by the electronic device further includes snore information and body motion information of the user for a first predetermined period of time.

S302: the sleep monitoring method comprises the steps of determining a sleep monitoring model according to blood oxygen information of a user in a first preset time period and current altitude information of the user, wherein the sleep monitoring model is obtained by training with preset blood oxygen information of a preset altitude area and corresponding sleep risk information as training samples.

Specifically, before the sleep monitoring model is determined, physiological information of different users in different altitude areas in a first preset time period is collected, and a collected sample is obtained. And dividing the collected sample into a plurality of subsamples according to the altitude interval and the blood oxygen interval of the first preset time period. And taking the physiological information of each user in the first preset time period and the corresponding sleep risk information in each group of subsamples as training samples, and training the classification model by adopting a machine learning algorithm to obtain a sleep monitoring model corresponding to each subsample.

After the sleep monitoring model corresponding to each sub-sample is obtained, an altitude interval corresponding to the altitude information where the user is located currently is determined, a blood oxygen interval corresponding to the blood oxygen information of the user in a first preset time period is determined, then the sleep monitoring model corresponding to the altitude interval and the blood oxygen interval is determined, and the sleep monitoring model corresponding to the altitude interval and the blood oxygen interval is also a corrected sleep monitoring model.

In a possible implementation manner, after the sleep monitoring model is determined, according to the altitude interval and the blood oxygen interval corresponding to the blood oxygen information of the first predetermined time period, a threshold value used for judging the sleep risk information in the sleep monitoring model is determined, so that the accuracy of subsequently judging the sleep risk information is improved.

S303: and inputting the physiological information of the user in a first preset time period into the sleep monitoring model to obtain the sleep risk information output by the sleep monitoring model.

Due to the fact that blood oxygen information of users in different altitude areas is different, blood oxygen information of users with sleep respiration risks in the same risk level is different. In the above embodiment, the sleep monitoring model is determined according to the blood oxygen information of the user in the first predetermined period of time and the current altitude information of the user, the sleep monitoring model matched with the altitude area of the user and the blood oxygen information of the user can be determined, and then the sleep risk information is output according to the physiological information of the user in the first predetermined period of time and the determined sleep monitoring model, so that the monitoring accuracy of the sleep risk is improved.

In yet another application scenario, the user wears or uses the electronic device in both the first predetermined period and the second predetermined period, that is, the user wears or uses the electronic device in both day and night, and the physiological information acquired by the electronic device in the second predetermined period does not include the blood oxygen information of the user in the second predetermined period.

Referring to fig. 6, in the application scenario, the sleep risk monitoring method includes:

s401: the method comprises the steps of obtaining physiological information of a user and altitude information of the user at present.

The physiological information of the user comprises physiological information of the user in a first preset time period and physiological information of the user in a second preset time period, and the physiological information of the user in the first preset time period comprises blood oxygen information, heart rate information and/or pulse information; the physiological information of the user at the second predetermined time period includes pulse information and/or heart rate information.

S402: and determining a sleep monitoring model according to the current altitude information of the user, wherein the sleep monitoring model is obtained by training by taking preset blood oxygen information of a preset altitude area and corresponding sleep risk information as training samples.

Specifically, before the sleep monitoring model is determined, physiological information of different users in different altitude areas in a second preset time period and a first preset time period is collected, and a collected sample is obtained. And calculating the blood oxygen information of the corresponding user in the second preset time period according to the physiological information of different users in different altitude areas in the second preset time period. And dividing the collected sample into a plurality of subsamples according to the altitude interval and the blood oxygen interval of the second preset time period. And taking the physiological information of each user in each subsample in the first preset time period and the corresponding sleep risk information as training samples, and training the classification model by adopting a machine learning algorithm to obtain a sleep monitoring model corresponding to each subsample.

After the sleep monitoring model corresponding to each sub-sample is obtained, a corresponding altitude interval is determined according to the altitude information of the current user, and then the sleep monitoring model corresponding to the altitude interval is determined.

In a possible implementation manner, before determining the sleep monitoring model, obtaining a blood oxygen statistic value corresponding to a second predetermined time period, specifically, counting blood oxygen information of different users in different altitude areas in the second predetermined time period to obtain a mapping relation between the altitude and the blood oxygen information of the second predetermined time period, and obtaining a blood oxygen statistic value corresponding to the second predetermined time period according to the mapping relation, where the blood oxygen statistic value may be an average value of blood oxygen of different users in an altitude area close to the altitude where the user is located and the blood oxygen of the second predetermined time period. After obtaining the blood oxygen statistic value corresponding to the second preset time period, determining a blood oxygen interval corresponding to the blood oxygen statistic value corresponding to the second preset time period and an altitude interval corresponding to the altitude information where the user is currently located, and determining a corresponding sleep monitoring model according to the altitude interval and the blood oxygen interval.

In a possible implementation manner, after the sleep monitoring model is determined, according to the blood oxygen interval corresponding to the blood oxygen statistic corresponding to the altitude interval and the second predetermined time period, the threshold value used for judging the sleep risk information in the sleep monitoring model is determined, so that the accuracy of subsequently judging the sleep risk information is improved.

S403: and inputting the physiological information of the user in a first preset time period into the sleep monitoring model to obtain the sleep risk information output by the sleep monitoring model.

In the above embodiment, the sleep monitoring model is determined according to the altitude information of the current location of the user and the blood oxygen statistic corresponding to the second predetermined time period, the sleep monitoring model matched with the physiological information of the user in the current altitude area can be determined, and the sleep risk information is output according to the physiological information of the user in the first predetermined time period and the determined sleep monitoring model, so that the monitoring accuracy of the sleep risk is improved.

In yet another application scenario, the electronic device is worn or used by the user for both the first predetermined period and the second predetermined period, and the physiological information acquired by the electronic device for the second predetermined period includes blood oxygen information of the user for the second predetermined period.

Referring to fig. 7, in the application scenario, the sleep risk monitoring method includes:

s501: the method comprises the steps of obtaining physiological information of a user and altitude information of the user at present.

Specifically, the physiological information of the user comprises physiological information of the user in a second preset time period and physiological information of the user in a first preset time period, the physiological information of the user in the second preset time period comprises blood oxygen information, and the physiological information of the user in the first preset time period comprises blood oxygen information, heart rate information and/or pulse information.

S502: and determining a sleep monitoring model according to the blood oxygen information of the user in the second preset time period and the altitude information of the user, wherein the sleep monitoring model is obtained by training by taking preset blood oxygen information of a preset altitude area and corresponding sleep risk information as training samples.

Specifically, before the sleep monitoring model is determined, physiological information of different users in different altitude areas in a second preset time period and a first preset time period is collected, and a collected sample is obtained. And dividing the collected sample into a plurality of subsamples according to the altitude interval and the blood oxygen interval of the second preset time period. And taking the physiological information of each user in each subsample in the first preset time period and the corresponding sleep risk information as training samples, and training the classification model by adopting a machine learning algorithm to obtain a sleep monitoring model corresponding to each subsample.

After the sleep monitoring model corresponding to each sub-sample is obtained, an altitude interval corresponding to the altitude information where the user is located currently is determined, a blood oxygen interval corresponding to the blood oxygen information of the user in a second preset time period is determined, and then the sleep monitoring model corresponding to the altitude interval and the blood oxygen interval is determined.

In a possible implementation manner, after the sleep monitoring model is determined, according to the altitude interval and the blood oxygen interval corresponding to the blood oxygen information of the second predetermined time period, a threshold value used for judging the sleep risk information in the sleep monitoring model is determined, so that the accuracy of subsequently judging the sleep risk information is improved.

S503: and inputting the physiological information of the user in a first preset time period into the sleep monitoring model to obtain the sleep risk information output by the sleep monitoring model.

Due to the fact that blood oxygen information of users in different altitude areas is different, blood oxygen information of users with sleep risks in the same risk level is different, and blood oxygen information of the users in the second preset time period is more stable relative to blood oxygen information of the users in the first preset time period. For example, for a user with a sleep risk problem, the blood oxygen information of the user in the first predetermined period of time fluctuates greatly. In the above embodiment, the collected samples are divided according to the blood oxygen interval and the altitude interval of the user in the second predetermined time period, and the sleep monitoring model is trained, so that the training data of the sleep monitoring model is more stable, and a more accurate model can be trained. And then, a sleep monitoring model is determined according to the blood oxygen information of the user in the second preset time period and the current altitude information of the user, a sleep monitoring model matched with the altitude area of the user and the blood oxygen information of the user can be determined, and sleep risk information is output according to the physiological information of the user in the first preset time period and the determined sleep monitoring model, so that the monitoring accuracy of the sleep risk is improved.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Fig. 8 is a block diagram of an electronic device 100 according to an embodiment of the present disclosure, and as shown in fig. 8, 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, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a Subscriber Identity Module (SIM) card interface 195. The sensor module 180 may include a pressure sensor 180A, a gyroscope 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, an ambient light sensor 180L, a bone conduction sensor 180M, and the like.

It is to be understood that the illustrated structure of the embodiment of the present invention does not specifically limit the electronic device 100. In other embodiments of the present application, electronic device 100 may include more or fewer components than shown, or some components may be combined, some components may be split, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

Processor 110 may include one or more processing units, such as: the processor 110 may include an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a video codec, a Digital Signal Processor (DSP), a baseband processor, and/or a neural-Network Processing Unit (NPU), etc. The different processing units may be separate devices or may be integrated into one or more processors.

The controller can generate an operation control signal according to the instruction operation code and the timing signal to complete the control of instruction fetching and instruction execution.

A memory may also be provided in processor 110 for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory may hold instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to reuse the instruction or data, it can be called directly from the memory. Avoiding repeated accesses reduces the latency of the processor 110, thereby increasing the efficiency of the system.

In some embodiments, processor 110 may include one or more interfaces. The interface may include an integrated circuit (I2C) interface, an integrated circuit built-in audio (I2S) interface, a Pulse Code Modulation (PCM) interface, a universal asynchronous receiver/transmitter (UART) interface, a Mobile Industry Processor Interface (MIPI), a general-purpose input/output (GPIO) interface, a Subscriber Identity Module (SIM) interface, and/or a Universal Serial Bus (USB) interface, etc.

The I2C interface is a bi-directional synchronous serial bus that includes a serial data line (SDA) and a Serial Clock Line (SCL). In some embodiments, processor 110 may include multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, the charger, the flash, the camera 193, etc. through different I2C bus interfaces, respectively. For example: the processor 110 may be coupled to the touch sensor 180K via an I2C interface, such that the processor 110 and the touch sensor 180K communicate via an I2C bus interface to implement the touch functionality of the electronic device 100.

The I2S interface may be used for audio communication. In some embodiments, processor 110 may include multiple sets of I2S buses. The processor 110 may be coupled to the audio module 170 via an I2S bus to enable communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may communicate audio signals to the wireless communication module 160 via the I2S interface, enabling answering of calls via a bluetooth headset.

The PCM interface may also be used for audio communication, sampling, quantizing and encoding analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled by a PCM bus interface. In some embodiments, the audio module 170 may also transmit audio signals to the wireless communication module 160 through the PCM interface, so as to implement a function of answering a call through a bluetooth headset. Both the I2S interface and the PCM interface may be used for audio communication.

The UART interface is a universal serial data bus used for asynchronous communications. The bus may 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 with the wireless communication module 160. For example: the processor 110 communicates with a bluetooth module in the wireless communication module 160 through a UART interface to implement a bluetooth function. In some embodiments, the audio module 170 may transmit the audio signal to the wireless communication module 160 through a UART interface, so as to realize the function of playing music through a bluetooth headset.

MIPI interfaces may be used to connect processor 110 with peripheral devices such as display screen 194, camera 193, and the like. The MIPI interface includes a Camera Serial Interface (CSI), a Display Serial Interface (DSI), and the like. In some embodiments, processor 110 and camera 193 communicate through a CSI interface to implement the capture functionality of 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 may be configured by software. The GPIO interface may be configured as a control signal and may also be configured as a data signal. In some embodiments, a GPIO interface may be used to connect the processor 110 with the camera 193, the display 194, the wireless communication module 160, the audio module 170, the sensor module 180, and the like. The GPIO interface may also be configured as an I2C interface, an I2S interface, a UART interface, a MIPI interface, and the like.

The USB interface 130 is an interface conforming to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface 130 may be used to connect a charger to charge the electronic device 100, and may also be used to transmit data between the electronic device 100 and a peripheral device. And the earphone can also be used for connecting an earphone and playing audio through the earphone. The interface may also be used to connect other electronic devices, such as AR devices and the like.

It should be understood that the connection relationship between the modules according to the embodiment of the present invention is only illustrative, and is not limited to the structure of the electronic device 100. In other embodiments of the present application, the electronic device 100 may also adopt different interface connection manners or a combination of multiple interface connection manners in the above embodiments.

The charging management module 140 is configured to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from a wired charger via the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive a wireless charging input through a wireless charging coil of the electronic device 100. The charging management module 140 may also supply power to the electronic device through the power management module 141 while charging the battery 142.

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 charge management module 140, and supplies power to the processor 110, the internal memory 121, the display 194, the camera 193, the wireless communication module 160, and the like. The power management module 141 may also be used to monitor parameters such as battery capacity, battery cycle count, battery state of health (leakage, impedance), etc. In some other embodiments, the power management module 141 may also be disposed in the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may be disposed in the same device.

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

The antennas 1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the electronic device 100 may be used to cover a single or multiple communication bands. Different antennas can also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 may provide a solution including 2G/3G/4G/5G wireless communication 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), and the like. The mobile communication module 150 may receive the electromagnetic wave from the antenna 1, filter, amplify, etc. the received electromagnetic wave, and transmit the electromagnetic wave to the modem processor for demodulation. The mobile communication module 150 may also amplify the signal modulated by the modem processor, and convert the signal into electromagnetic wave through the antenna 1 to radiate the electromagnetic wave. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the same device as at least some of the modules of the processor 110.

The modem processor may include a modulator and a demodulator. The modulator is used for modulating a low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used for demodulating the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then passes the demodulated low frequency baseband signal to a baseband processor for processing. The low frequency baseband signal is processed by the baseband processor and then transferred to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.) or displays an image or video through the display screen 194. In some embodiments, the modem processor may be a stand-alone device. In other embodiments, the modem processor may be provided in the same device as the mobile communication module 150 or other functional modules, independent of the processor 110.

The wireless communication module 160 may provide a solution for wireless communication applied to the electronic device 100, including Wireless Local Area Networks (WLANs) (e.g., wireless fidelity (Wi-Fi) networks), bluetooth (bluetooth, BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), Infrared (IR), and the like. 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, performs frequency modulation and filtering processing on electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be transmitted from the processor 110, perform frequency modulation and amplification on the signal, and convert the signal into electromagnetic waves through the antenna 2 to radiate the electromagnetic waves.

In some embodiments, antenna 1 of electronic device 100 is coupled to mobile communication module 150 and antenna 2 is coupled to wireless communication module 160 so that electronic device 100 can communicate with networks and other devices through wireless communication techniques. The wireless communication technology may include global system for mobile communications (GSM), General Packet Radio Service (GPRS), code division multiple access (code division multiple access, CDMA), Wideband Code Division Multiple Access (WCDMA), time-division code division multiple access (time-division code division multiple access, TD-SCDMA), Long Term Evolution (LTE), LTE, BT, GNSS, WLAN, NFC, FM, and/or IR technologies, etc. The GNSS may include a Global Positioning System (GPS), a global navigation satellite system (GLONASS), a beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS), and/or a Satellite Based Augmentation System (SBAS).

The electronic device 100 implements display functions via the GPU, the display screen 194, and the application processor. The GPU is a microprocessor for image processing, and is connected to the display screen 194 and an application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The 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, video, and the like. The display screen 194 includes a display panel. The display panel may adopt a Liquid Crystal Display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (active-matrix organic light-emitting diode, AMOLED), a flexible light-emitting diode (FLED), a miniature, a Micro-oeld, a quantum dot light-emitting diode (QLED), and the like. In some embodiments, the electronic device 100 may include 1 or N display screens 194, with N being a positive integer greater than 1.

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

The ISP is used to process the data fed back by the camera 193. For example, when a photo is taken, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to the ISP for processing and converting into an image visible to naked eyes. The ISP can also carry out algorithm optimization on the noise, brightness and skin color of the image. The ISP can also optimize parameters such as exposure, color temperature and the like of a shooting scene. In some embodiments, the ISP may be provided in camera 193.

The camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects the optical image to the photosensitive element. The photosensitive element may be a Charge Coupled Device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The light sensing element converts the optical signal into an electrical signal, which is then passed to the ISP where it is converted into a digital image signal. And the ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into image signal in standard RGB, YUV and other formats. In some embodiments, the electronic device 100 may include 1 or N cameras 193, N being a positive integer greater than 1.

The digital signal processor is used for processing digital signals, and can process digital image signals and other digital signals. For example, when the electronic device 100 selects a frequency bin, the digital signal processor is used to perform fourier transform or the like on the frequency bin energy.

Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 may play or record video in a variety of encoding formats, such as: moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, and the like.

The NPU is a neural-network (NN) computing processor that processes input information quickly by using a biological neural network structure, for example, by using a transfer mode between neurons of a human brain, and can also learn by itself continuously. Applications such as intelligent recognition of the electronic device 100 can be realized through the NPU, for example: image recognition, face recognition, speech recognition, text understanding, and the like.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to extend the memory capability of the electronic device 100. The external memory card communicates with the processor 110 through the external memory interface 120 to implement a data storage function. For example, files such as music, video, etc. are saved in an external memory card.

The internal memory 121 may be used to store computer-executable program code, which includes instructions. The internal memory 121 may include a program storage area and a data storage area. The storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required by at least one function, and the like. The storage data area may store data (such as audio data, phone book, etc.) created during use of the electronic device 100, and the like. In addition, the internal memory 121 may include a high-speed random access memory, and may further include a nonvolatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (UFS), and the like. The processor 110 executes various functional applications of the electronic device 100 and data processing by executing instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.

The electronic device 100 may implement audio functions via the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the headphone interface 170D, and the application processor. Such as music playing, recording, etc.

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

The speaker 170A, also called a "horn", is used to convert the audio electrical signal into an acoustic signal. The electronic apparatus 100 can listen to music through the speaker 170A or listen to a handsfree call.

The receiver 170B, also called "earpiece", is used to convert the electrical audio signal into an acoustic signal. When the electronic apparatus 100 receives a call or voice information, it can receive voice by placing the receiver 170B close to the ear of the person.

The microphone 170C, also referred to as a "microphone," is used to convert sound signals into electrical signals. When making a call or transmitting voice information, the user can input a voice signal to the microphone 170C by speaking the user's mouth near 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 to achieve a noise reduction function in addition to collecting sound signals. In other embodiments, the electronic device 100 may further include three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, perform directional recording, and so on.

The headphone interface 170D is used to connect a wired headphone. The headset interface 170D may be the USB interface 130, or may be a 3.5mm open mobile electronic device platform (OMTP) standard interface, a cellular telecommunications industry association (cellular telecommunications industry association of the USA, CTIA) standard interface.

The pressure sensor 180A is used for sensing a pressure signal, and converting the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be disposed on the display screen 194. The pressure sensor 180A can be of a wide variety, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a sensor comprising at least two parallel plates having an electrically conductive material. When a force acts on the pressure sensor 180A, the capacitance between the electrodes changes. The electronic device 100 determines the strength of the pressure from the change in capacitance. When a touch operation is applied to the display screen 194, the electronic apparatus 100 detects the intensity of the touch operation according to the pressure sensor 180A. The electronic apparatus 100 may also calculate the touched position from the detection signal of the pressure sensor 180A. In some embodiments, the touch operations that are applied to the same touch position but different touch operation intensities may correspond to different operation instructions.

The gyro sensor 180B may be used to determine the motion attitude of the electronic device 100. In some embodiments, the angular velocity of electronic device 100 about three axes (i.e., the x, y, and z axes) may be determined by gyroscope sensor 180B. The gyro sensor 180B may be used for photographing anti-shake. For example, when the shutter is pressed, the gyro sensor 180B detects a shake angle of the electronic device 100, calculates a distance to be compensated for by the lens module according to the shake angle, and allows the lens to counteract the shake of the electronic device 100 through a reverse movement, thereby achieving anti-shake. The gyroscope sensor 180B may also be used for navigation, somatosensory gaming scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, electronic device 100 calculates altitude, aiding in positioning and navigation, from barometric pressure values measured by barometric pressure sensor 180C.

The magnetic sensor 180D includes a hall sensor. The electronic device 100 may detect the opening and closing of the flip holster using the magnetic sensor 180D. In some embodiments, when the electronic device 100 is a flip phone, the electronic device 100 may detect the opening and closing of the flip according to the magnetic sensor 180D. And then according to the opening and closing state of the leather sheath or the opening and closing state of the flip cover, the automatic unlocking of the flip cover is set.

The acceleration sensor 180E may detect the magnitude of acceleration of the electronic device 100 in various directions (typically three axes). The magnitude and direction of gravity can be detected when the electronic device 100 is stationary. The method can also be used for recognizing the posture of the electronic equipment, and is applied to horizontal and vertical screen switching, pedometers and other applications.

A distance sensor 180F for measuring a distance. The electronic device 100 may measure distance by radar, infrared, or laser. In some embodiments, taking a picture of a scene, electronic device 100 may utilize range sensor 180F to range for fast focus. In some embodiments, the electronic device 100 may also measure the distance and speed of an obstacle using the distance sensor 180F.

The 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 to the outside through the light emitting diode. The electronic device 100 detects infrared reflected light from nearby objects using a photodiode. 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 are no objects near the electronic device 100. The electronic device 100 can utilize the proximity light sensor 180G to detect that the user holds the electronic device 100 close to the ear for talking, so as to automatically turn off the screen to achieve the purpose of saving power. The proximity light sensor 180G may also be used in a holster mode, a pocket mode automatically unlocks and locks the screen.

The ambient light sensor 180L is used to sense the ambient light level. Electronic device 100 may adaptively adjust the brightness of display screen 194 based on the perceived ambient light level. The ambient light sensor 180L may also be used to automatically adjust the white balance when taking a picture. The ambient light sensor 180L may 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 a fingerprint. The electronic device 100 can utilize the collected fingerprint characteristics to unlock the fingerprint, access the application lock, photograph the fingerprint, answer an incoming call with the fingerprint, and so on.

The temperature sensor 180J is used to detect temperature. In some embodiments, electronic device 100 implements a temperature processing strategy using the temperature detected by temperature sensor 180J. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold, the electronic device 100 performs a reduction in performance of a processor located near the temperature sensor 180J, so as to reduce power consumption and implement thermal protection. In other embodiments, the electronic device 100 heats the battery 142 when the temperature is below another threshold to avoid the low temperature causing the electronic device 100 to shut down abnormally. In other embodiments, when the temperature is lower than a further threshold, the electronic device 100 performs boosting on the output voltage of the battery 142 to avoid abnormal shutdown due to low temperature.

The touch sensor 180K is also called a "touch device". The touch sensor 180K may be disposed on the display screen 194, and 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 applied thereto or nearby. The touch sensor can communicate the detected touch operation to the application processor to determine the touch event type. Visual output associated with the touch operation may be provided through the display screen 194. In other embodiments, the touch sensor 180K may be disposed on a surface of the electronic device 100, different from the position of the display screen 194.

The bone conduction sensor 180M may acquire a vibration signal. In some embodiments, the bone conduction sensor 180M may acquire a vibration signal of the human vocal part vibrating the bone mass. The bone conduction sensor 180M may also contact the human pulse to receive the blood pressure pulsation signal. In some embodiments, the bone conduction sensor 180M may also be disposed in a headset, integrated into a bone conduction headset. The audio module 170 may analyze a voice signal based on the vibration signal of the bone mass vibrated by the sound part acquired by the bone conduction sensor 180M, so as to implement a voice function.

The keys 190 include a power-on key, a volume key, and the like. The keys 190 may be mechanical keys. Or may be touch keys. The electronic apparatus 100 may receive a key input, and generate a key signal input related to user setting and function control of the electronic apparatus 100.

The motor 191 may generate a vibration cue. The motor 191 may be used for incoming call vibration cues, as well as for touch vibration feedback. For example, touch operations applied to different applications (e.g., photographing, audio playing, etc.) may correspond to different vibration feedback effects. The motor 191 may also respond to different vibration feedback effects for touch operations applied to different areas of the display screen 194. Different application scenes (such as time reminding, receiving information, alarm clock, game and the like) can also correspond to different vibration feedback effects. The touch vibration feedback effect may also support customization.

Indicator 192 may be an indicator light that may be used to indicate a state of charge, a change in charge, or a message, missed call, notification, etc.

The SIM card interface 195 is used to connect a SIM card. The SIM card can be brought into and out of contact with the electronic apparatus 100 by being inserted into the SIM card interface 195 or being pulled out of the SIM card interface 195. The electronic device 100 may support 1 or N SIM card interfaces, N being a positive integer greater than 1. The SIM card interface 195 may support a Nano SIM card, a Micro SIM card, a SIM card, etc. The same SIM card interface 195 can be inserted with multiple cards at the same time. The types of the plurality of cards may be the same or different. The SIM card interface 195 may also be compatible with different types of SIM cards. The SIM card interface 195 may also be compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to implement functions such as communication and data communication. In some embodiments, the electronic device 100 employs esims, namely: an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/electronic device, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims

1. A sleep risk monitoring method, comprising:

acquiring physiological information of a user and current altitude information of the user;

determining a sleep monitoring model according to the current altitude information of the user, wherein the sleep monitoring model is obtained by training by taking preset physiological information of a preset altitude area and corresponding sleep risk information as training samples;

and inputting the user physiological information into the sleep monitoring model to obtain sleep risk information output by the sleep monitoring model.

2. The sleep risk monitoring method as claimed in claim 1, wherein the user physiological information includes physiological information of a user in a first predetermined period, the inputting the user physiological information into the sleep monitoring model to obtain the sleep risk information output by the sleep monitoring model includes:

3. The sleep risk monitoring method as claimed in claim 2, wherein the determining a sleep monitoring model according to the altitude information where the user is currently located comprises:

4. The sleep risk monitoring method as claimed in claim 3, wherein the blood oxygen statistic corresponding to the first predetermined period is obtained by counting blood oxygen information of different users in different altitude areas in the first predetermined period.

5. The sleep risk monitoring method as claimed in claim 2, wherein the determining a sleep monitoring model according to the altitude information where the user is currently located comprises:

6. The sleep risk monitoring method as claimed in claim 2, wherein the determining a sleep monitoring model according to the altitude information where the user is currently located comprises:

and determining a sleep monitoring model according to the physiological information of the user and the current altitude information of the user.

7. The sleep risk monitoring method as claimed in claim 6, wherein the physiological information of the first predetermined period of time includes blood oxygen information of the user in the first predetermined period of time, and the determining the sleep monitoring model according to the physiological information of the user and the altitude information where the user is currently located includes:

8. The sleep risk monitoring method as claimed in claim 6, wherein the user physiological information further includes physiological information of the user at a second predetermined time period, the physiological information of the user at the second predetermined time period includes blood oxygen information of the user at the second predetermined time period, the determining a sleep monitoring model according to the user physiological information and altitude information where the user is currently located includes:

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the sleep risk monitoring method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a sleep risk monitoring method according to any one of claims 1 to 8.