WO2020168444A1

WO2020168444A1 - Sleep prediction method and apparatus, storage medium, and electronic device

Info

Publication number: WO2020168444A1
Application number: PCT/CN2019/075342
Authority: WO
Inventors: 戴堃; 吴建文; 陆天洋; 帅朝春; 张寅祥
Original assignee: 深圳市欢太科技有限公司; Oppo广东移动通信有限公司
Priority date: 2019-02-18
Filing date: 2019-02-18
Publication date: 2020-08-27
Also published as: CN112997148A

Abstract

A sleep prediction method: when a preset sleep prediction condition is currently met and the current day is a non-working day of a user, an electronic device can acquire a pre-trained corresponding non-working day sleep prediction model and acquire behaviour data of the user on the current day and historical non-working days, and finally use the acquired behaviour data and the sleep prediction model to perform sleep prediction for the user, thereby increasing the accuracy of sleep prediction for the user.

Description

Sleep prediction method, device, storage medium and electronic equipment

Technical field

This application belongs to the field of computer technology, and in particular relates to a sleep prediction method, device, storage medium, and electronic equipment.

Background technique

At present, electronic devices such as tablet computers and mobile phones are configured to perform system updates while the user is sleeping, and other operations that affect the user's use or take a long time to avoid affecting the user's use. For this reason, the related technology achieves the foregoing purpose by predicting the user's sleep, such as predicting the user's sleep interval, etc. However, the accuracy of the related technology for predicting the user's sleep is low.

Summary of the invention

The embodiments of the present application provide a sleep prediction method, device, storage medium, and electronic device, which can enable the electronic device to accurately predict the user's sleep.

In the first aspect, an embodiment of the present application provides a sleep prediction method applied to an electronic device, including:

If the preset sleep prediction conditions are currently met, identify whether the day is a non-working day of the user;

If yes, obtain the pre-trained sleep prediction model corresponding to the non-working day;

Acquiring behavior data of the user on the current day and historical non-working days, where the historical non-working days are the same type of non-working days before the current day;

Perform sleep prediction on the user according to the behavior data and the sleep prediction model to obtain a prediction result.

In the second aspect, an embodiment of the present application provides a sleep prediction device applied to an electronic device, including:

The date identification module is used to identify whether the current day is a non-working day of the user when the preset sleep prediction conditions are currently met;

The model acquisition module is used to acquire the pre-trained sleep prediction model corresponding to the non-working day when the recognition result of the recognition module is yes;

A data acquisition module for acquiring behavioral data of the user on the current day and historical non-working days, where the historical non-working days are the same type of non-working days before the current day;

The sleep prediction module is configured to perform sleep prediction on the user according to the behavior data and the sleep prediction model to obtain a prediction result.

In a third aspect, an embodiment of the present application provides a storage medium on which a computer program is stored. When the computer program is executed on a computer, the computer is caused to execute the sleep prediction method provided in the embodiment of the present application. step.

In a fourth aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, and the processor is configured to execute the following by calling a computer program stored in the memory:

In the application embodiment, the electronic device can identify whether the current day is a non-working day of the user when the preset sleep prediction conditions are currently met, and if it is, it further obtains the pre-trained corresponding non-working day sleep prediction model, and obtains the user’s current The behavior data of the current day and historical non-working days are finally used to predict the user's sleep using the acquired behavior data and the sleep prediction model corresponding to the non-working day, which can improve the accuracy of sleep prediction for the user.

Description of the drawings

The following detailed description of specific implementations of the present application in conjunction with the accompanying drawings will make the technical solutions of the present application and its beneficial effects obvious.

FIG. 1 is a schematic flowchart of a sleep prediction method provided by an embodiment of the present application.

Fig. 2 is a schematic diagram of obtaining a sleep prediction model used for sleep prediction of a user in an embodiment of the present application.

FIG. 3 is another schematic flow chart of the sleep prediction method provided by an embodiment of the present application.

Fig. 4 is a schematic diagram of a preset operation configuration interface provided in an embodiment of the present application.

Fig. 5 is a schematic structural diagram of a sleep prediction device provided by an embodiment of the present application.

Fig. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

FIG. 7 is another schematic structural diagram of an electronic device provided by an embodiment of the present application.

detailed description

Please refer to the drawings, in which the same component symbols represent the same components, and the principle of the present application is implemented in an appropriate computing environment for illustration. The following description is based on the exemplified specific embodiments of the application, which should not be regarded as limiting other specific embodiments of the application that are not described in detail herein.

Please refer to FIG. 1, which is a schematic flowchart of a sleep prediction method provided by an embodiment of the present application. The sleep prediction method can be applied to electronic devices. The process of the sleep prediction method may include:

In 101, if the preset sleep prediction condition is currently met, it is identified whether the current day is a non-working day of the user.

It should be noted that there is no specific limitation on the setting of the sleep prediction conditions in the embodiments of the present application, and can be set by those of ordinary skill in the art according to actual needs. For example, you can set the sleep prediction condition that the ambient light brightness of the current environment of the electronic device is lower than the preset brightness, so that the electronic device can detect the ambient light brightness of its environment in real time (for example, through the set ambient light sensor The ambient light brightness of the environment is detected), and when the ambient light brightness of the environment is lower than the preset brightness, it is determined that the sleep prediction condition is currently met. For another example, the sleep prediction condition can be set as the system time of the electronic device reaches the preset time, and so on.

In the embodiment of the present application, the electronic device triggers the sleep prediction of the user when it determines that the preset sleep prediction condition is currently met. First, the electronic device recognizes whether the current day is a non-working day of the user. For example, the electronic device can determine whether the current day is a weekend or a holiday, and if yes, determine that the current day is a non-working day of the user, otherwise, determine that the current day is a working day of the user.

In 102, if yes, obtain a pre-trained sleep prediction model corresponding to a non-working day.

It should be noted that the embodiment of the present application stores a plurality of pre-trained sleep prediction model sets in the electronic device, which includes at least a sleep prediction model corresponding to non-working days (or in other words, the remaining sleep prediction models for users on non-working days) The sleep prediction model of) and the sleep prediction model corresponding to the working day (or a sleep prediction model suitable for predicting the user’s sleep on the working day). In this way, when the electronic device recognizes that the current day is a non-working day of the user, it obtains the pre-trained sleep prediction model corresponding to the non-working day.

For example, referring to Figure 2, the electronic device stores two sleep prediction models, namely the sleep prediction model A corresponding to non-working days and the sleep prediction model B corresponding to working days. In this way, the electronic device is determined to be the user’s non-working day At time of day, the A sleep prediction model is used for subsequent sleep prediction of the user, and when it is determined that the current day is the user's working day, the B sleep prediction model is obtained for the subsequent sleep prediction of the user.

It should be noted that the sleep prediction model is obtained through machine learning algorithm training in advance, and the machine learning algorithm can realize various functions through continuous feature learning, for example, it can predict the user's sleep. Among them, machine learning algorithms may include: decision tree models, logistic regression models, Bayes models, neural network models, clustering models, and so on.

The algorithm types of machine learning algorithms can be divided according to various situations. For example, machine learning algorithms can be divided into supervised learning algorithms, non-supervised learning algorithms, semi-supervised learning algorithms, reinforcement learning algorithms, etc. based on learning methods.

Under supervised learning, the input data is called "training data", and each set of training data has a clear identification or result, such as "spam" and "non-spam" in the anti-spam system, and recognition of handwritten digits. "1, 2, 3, 4" etc. When building a model, supervised learning establishes a learning process that compares the scene type information with the actual results of the "training data", and continuously adjusts the model until the model's scene type information reaches an expected accuracy rate. Common application scenarios for supervised learning are classification problems and regression problems. Common algorithms include Logistic Regression and Back Propagation Neural Network.

In unsupervised learning, the data is not specifically identified, the model is to infer some internal structure of the data. Common application scenarios include the learning of association rules and clustering. Common algorithms include Apriori algorithm and k-Means algorithm.

Semi-supervised learning algorithm. In this learning mode, the input data is partially identified. This learning model can be used for type recognition, but the model first needs to learn the internal structure of the data in order to organize the data reasonably for prediction. Application scenarios include classification and regression. Algorithms include some extensions to commonly used supervised learning algorithms. These algorithms first try to model unidentified data, and then predict the identified data on this basis. Graph inference algorithm (Graph Inference) or Laplacian SVM (Laplacian SVM), etc.

Reinforcement learning algorithm. In this learning mode, the input data is used as feedback to the model. Unlike the supervised model, the input data is only used as a way to check whether the model is right or wrong. Under reinforcement learning, the input data is directly fed back to the model. The model must be adjusted for this immediately. Common application scenarios include dynamic systems and robot control. Common algorithms include Q-Learning and Temporal difference learning.

In addition, machine learning algorithms can also be divided into:

Regression algorithm, common regression algorithms include: Ordinary Least Square, Logistic Regression, Stepwise Regression, Multivariate Adaptive Regression Splines and Local Scatter Smoothing Estimate (Locally Estimated Scatterplot Smoothing).

Example-based algorithms include k-Nearest Neighbor (KNN), Learning Vector Quantization (LVQ), and Self-Organizing Map (SOM).

Regularization methods, common algorithms include: Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net (Elastic Net).

Decision tree algorithm, common algorithms include: Classification and Regression Tree (CART), ID3 (Iterative Dichotomiser 3), C4.5, Chi-squared Automatic Interaction Detection (CHAID), Decision Stump, Random Forest (Random Forest), Multiple Adaptive Regression Spline (MARS) and Gradient Boosting Machine (GBM).

Bayesian method algorithms include: Naive Bayes algorithm, Averaged One-Dependence Estimators (AODE), and Bayesian Belief Network (BBN).

In 103, the user's behavior data on the current day and historical non-working days are acquired, and the historical non-working days are the same type of non-working days before the current day.

It should be noted that in this embodiment of the application, the sleep prediction model corresponding to non-working days performs sleep prediction based on the user’s behavior data on multiple non-working days of the same type. Therefore, the electronic device obtains the pre-trained corresponding non-working days. After the sleep prediction model of the working day, the user's behavior data on that day and at least one historical non-working day is further obtained. Among them, the historical non-working day is the same type of non-working day before the current day, and the behavior data includes but not limited to the user's exercise behavior data (such as calories burned, number of steps taken, etc.), rest behavior data, entertainment behavior data, and so on.

For example, if the current day is a weekend, it is a non-working day of the user, and the electronic device obtains the user's behavior data on that day and the user's behavior data on multiple weekends before the current day.

In 104, a sleep prediction is performed on the user according to the acquired behavior data and the sleep prediction model, and the prediction result is obtained.

Among them, after the electronic device obtains the pre-trained sleep prediction model corresponding to the non-working day, and obtains the user's behavior data on the current day and historical non-working days, it can perform the sleep prediction model and behavior data on the user according to the obtained sleep prediction model and behavior data. Sleep prediction, get the prediction result.

It should be noted that the sleep prediction of the user includes but is not limited to the time when the user enters sleep, the time when the user ends sleep, and the sleep interval composed of the time when the user enters sleep and the time when sleep ends. For example, according to the obtained sleep prediction model and behavior data, the user's sleep prediction is performed, and the user's sleep interval is obtained from 23:30 on the same day to 06:60 the next day, or the user's sleep interval is obtained from 23:30 on the previous day to 06:60 on the same day. 30.

It can be seen from the above that, in the embodiment of the present application, the electronic device can recognize whether the current day is a non-working day of the user when the preset sleep prediction conditions are currently met, and if so, further obtain the pre-trained corresponding non-working day sleep prediction model , And obtain the user's behavior data on the current day and historical non-working days, and finally use the acquired behavior data and the sleep prediction model corresponding to the non-working day to predict the user's sleep, which can improve the accuracy of the user's sleep prediction.

Please refer to FIG. 3, which is a schematic diagram of another flow of the sleep prediction method provided by an embodiment of the application. The sleep prediction method can be applied to electronic devices. The process of the sleep prediction method may include:

In 201, if the preset sleep prediction condition is currently met, the electronic device obtains the first use information of the user using the electronic device on the day.

In 202, the electronic device recognizes whether the current day is a non-working day of the user according to the first usage information.

As a first optional implementation manner, the sleep prediction condition is configured as:

The duration of the screen off state reaches the first preset duration.

For example, the electronic device can start a timer for timing when entering the screen-off state, and use the timing duration of the timer to characterize the duration of the electronic device in the screen-off state, where the electronic device’s timer duration reaches the first preset Stop the timer when the duration or exit the screen-off state, and reset the timer. In this way, the electronic device determines that the sleep prediction condition is currently met when the timer duration reaches the first preset duration, that is, when the duration of the off-screen state reaches the first preset duration.

As a second optional implementation manner, the sleep prediction condition is configured as:

The duration of the static state reaches the second preset duration.

For example, an electronic device can start a timer to count when it enters a stationary state (for example, the electronic device can detect whether there is acceleration in any direction according to the built-in three-axis acceleration sensor, and if it does not exist, it is determined to be in a stationary state). The timing duration of the timer represents the duration of the electronic device being in a static state, where the electronic device stops counting the timer and resets the timer when the timing duration of the timer reaches the second preset duration or exits the static state. In this way, when the timing duration of the timer reaches the second preset duration, that is, when the duration of the stationary state reaches the second preset duration, the electronic device determines that the sleep prediction condition is currently met.

As a third optional implementation manner, the sleep prediction condition is configured as:

When the duration of being in the off-screen state reaches the third preset duration, it is in a static state.

For example, the electronic device can start a timer for timing when entering the screen-off state, and use the timer duration to characterize the duration of the electronic device in the screen-off state, where the electronic device's timer duration reaches the third preset Stop the timer when the duration or exit the screen-off state, and reset the timer. In this way, when the timer duration reaches the third preset duration, the electronic device judges whether it is currently in a static state through the three-axis acceleration sensor, and if yes, it determines that the sleep prediction condition is currently met.

As a fourth optional implementation manner, the sleep prediction condition is configured as:

When the duration of the static state reaches the fourth preset duration, the screen is turned off.

For example, the electronic device can start a timer for timing when entering a static state, and the duration of the timer is used to characterize the duration of the electronic device in the static state, where the timer duration of the electronic device reaches the fourth preset duration or Stop the timer counting when exiting the static state and reset the timer. In this way, when the time duration of the timer reaches the fourth preset duration, the electronic device determines whether the screen is currently in the off state, and if yes, determines that the sleep prediction condition is currently met.

It should be noted that the values of the first preset duration, the second preset duration, the third preset duration, and the fourth preset duration above may be the same or different. Specifically, a person of ordinary skill in the art can select appropriate values based on experience. . For example, the first preset duration, the second preset duration, the third preset duration, and the fourth preset duration may all be set to 30 minutes.

In the embodiment of the present application, the electronic device triggers the sleep prediction of the user when it determines that the preset sleep prediction condition is currently met. First, the electronic device recognizes whether the current day is a non-working day of the user. To this end, the electronic device obtains the use information of the user using the electronic device on the current day, records it as the first use information, and identifies whether the current day is a non-working day of the user based on the first use information. It should be noted that the first use information includes, but is not limited to, information used to describe when the user uses the electronic device, where to use the electronic device, and how to use the electronic device, such as how to use the electronic device. It can provide information about which applications the user has run using the electronic device, which phone calls have been made using the electronic device, and the power consumption rate of the electronic device.

In 203, if yes, the electronic device obtains a pre-trained sleep prediction model corresponding to a non-working day.

It should be noted that the embodiment of the present application stores a plurality of pre-trained sleep prediction model sets in the electronic device, which includes at least a sleep prediction model corresponding to non-working days (or in other words, the remaining sleep prediction models for users on non-working days) The sleep prediction model of) and the sleep prediction model corresponding to the working day (or a sleep prediction model suitable for predicting the user’s sleep on the working day).

In 204, the electronic device obtains the user's behavior data on the current day and historical non-working days, and the historical non-working days are the same type of non-working days before the current day.

In 205, the electronic device performs sleep prediction on the user according to the acquired behavior data and the sleep prediction model to obtain the predicted sleep interval.

Among them, after the electronic device obtains the pre-trained sleep prediction model corresponding to the non-working day, and obtains the user's behavior data on the current day and historical non-working days, it can perform the sleep prediction model and behavior data on the user according to the obtained sleep prediction model and behavior data. Sleep prediction, get the predicted sleep interval. For example, according to the obtained sleep prediction model and behavior data to predict the user's sleep, the predicted sleep interval is from 23:30 on the same day to 06:30 the next day, or the predicted sleep interval is from 23:30 on the previous day to 06:30 on the next day: 30.

In 206, the electronic device determines whether the user is currently in a sleep state according to the predicted sleep interval.

For example, the predicted sleep interval indicates that the user's sleep interval is from 23:30 on the current day to 06:30 on the next day. If the current time of the electronic device is 23:25 on the current day, it is determined that the user is not currently in a sleep state. If the current time of the electronic device is If it is 23:45 of the day, it is determined that the user is currently asleep.

In 207, if yes, the electronic device performs a preset operation, where the preset operation includes at least one of a system update operation, an application update operation, and a power consumption control operation.

In the embodiment of the present application, when the electronic device determines that the user is currently in the sleep state, it performs a pre-configured preset operation that is executed when the user is in the sleep state. Wherein, the preset operation includes but is not limited to at least one of a system update operation, an application update operation, and a power consumption control operation, which may be manually configured by the user or may be configured by the electronic device by default.

For example, the electronic device can configure the system update operation as a preset operation, so as to perform the system update operation when the user is in sleep state, and update the system to the latest version; the electronic device can also configure the application update operation as a preset operation, The user performs application update operations when the user is asleep, updates the installed applications to the latest version, etc.; electronic devices can configure the power consumption control operation as a preset operation, thereby applying the preset for reduction when the user is asleep Power consumption control strategy for power consumption, reducing the power consumption of electronic devices and so on.

For another example, please refer to Figure 4. The electronic device provides a preset operation configuration interface. As shown in Figure 4, the preset operation configuration interface includes the prompt message "Please select the operation performed during sleep", operation selection box, drop-down button, drop-down Menu, OK button, and Cancel button. Among them, the drop-down menu is called out according to the user's click operation on the drop-down button. The drop-down menu provides various operations that the electronic device can perform during the user's sleep interval, as shown in the system update in Figure 4 Operation, application update operations, etc., the user can select the operation performed by the electronic device during sleep according to actual needs, and after selecting the operation that needs to be performed by the electronic device during sleep, click the OK button to instruct the electronic device to select the user The operation as the aforementioned preset operation. Or, if the user finds that there is no need for the operation performed by the electronic device during sleep, he can click the cancel button to instruct the electronic device to perform the preset operation of the default configuration.

In one embodiment, when identifying whether the current day is a non-working day of the user according to the first usage information, the following can be performed:

(1) The electronic device obtains the pre-stored second use information of the user using the electronic device on his workday;

(2) The electronic device judges whether the first usage information matches the second usage information, if yes, it is judged that the current day is the user's working day, otherwise it is judged that the current day is the user's non-working day.

In the embodiments of the present application, it is considered that there is a certain regularity when users use electronic devices on weekdays, such as using electronic devices during a fixed period of time, while users have irregularities when using electronic devices on non-working days. Therefore, Pre-stored in the electronic device the usage information of the electronic device used on weekdays is recorded as the second usage information. Among them, whether the usage information is the usage information of the electronic device used by the user during the working day can be calibrated by the user according to the actual situation.

In this way, when the electronic device recognizes whether the current day is a non-working day of the user according to the first usage information, it can obtain the pre-stored second usage information of the user using the electronic device on its working day, and determine whether the first usage information matches the second usage information. Use information matching, where if the first use information matches the second use information, the electronic device determines that the current day is the user's working day, otherwise it determines that the current day is the user's non-working day.

In an embodiment, when determining whether the first usage information matches the second usage information, the following can be performed:

(1) The similarity between the first use information and the second use information acquired by the electronic device;

(2) The electronic device judges whether the acquired similarity reaches the preset similarity, and if yes, it judges that the first usage information matches the second usage information, otherwise it does not match.

In the embodiment of the present application, the electronic device can determine whether the first usage information and the second usage information match according to the similarity between the two. In this way, when the electronic device determines whether the first usage information matches the second usage information, The similarity between the first use information and the second use information can be obtained, and it is determined whether the obtained similarity reaches the preset similarity. If yes, it is determined that the first use information and the second use information match, otherwise the first use information is determined The information does not match the second usage information. It should be noted that there is no specific limitation on the value of the preset similarity in the embodiments of the present application, and a person of ordinary skill in the art can select an appropriate value according to experience needs.

Wherein, when the electronic device obtains the similarity between the first usage information and the second usage information, it uses the encoder neural network to respectively encode the first usage information and the second usage information to obtain the first word vector corresponding to the first usage information Set, and obtain a second word vector set corresponding to the second usage information. Among them, the embodiment of the application does not limit the specific model and topology of the encoder neural network. For example, a single-layer recurrent neural network can be used for training to obtain an encoder neural network, or a multi-layer recurrent neural network can be used for training. The encoder neural network can also be trained using a convolutional neural network, or its variants, or neural networks of other network structures to obtain an encoder neural network.

After acquiring the first word vector set corresponding to the first use information and the second word vector set corresponding to the second use information, the electronic device calculates the characteristic distance between the first word vector set and the second word vector set, The calculated feature distance is used as the similarity between the first use information and the second use information.

Among them, a person of ordinary skill in the art can select any characteristic distance (such as Euclidean distance, Manhattan distance, Chebyshev distance, cosine distance, etc.) to measure the distance between two word vector sets according to actual needs. The implementation of this application The example does not make specific restrictions on this.

The electronic device recognizes whether the current day is a non-working day of the user according to the first usage information and the pre-trained working day recognition model.

In the embodiment of the present application, a workday recognition model for workday recognition can be trained in advance, and the workday recognition model can be configured locally in the electronic device. In this way, when the electronic device recognizes whether the current day is a non-working day of the user according to the first usage information, it can recognize whether the current day is a non-working day of the user according to the first usage information and the pre-trained working day recognition model.

For example, an unsupervised learning method can be used to train the user's usage information of electronic devices on all natural days within a year to obtain a usage information classifier that can classify the input usage information, and use the usage information classifier as the work Day recognition model. In this way, as long as the first use information is input to the use information classifier to classify the use information, it can be determined whether the day is a non-working day of the user according to the classification result output by the use information classifier, and if the output of the information classifier is used If the first usage information indicates that the first usage information is the usage information of a working day, it can be determined that the current day is the user's working day. If the output of the usage information classifier indicates that the first usage information is the usage information of a non-working day, it can be determined that the current day is the user's non-working day. Working day.

In one embodiment, before performing the preset operation, you can perform:

(1) The electronic device obtains the user-configured schedule;

(2) The electronic device obtains the sleep interval planned by the user according to the obtained schedule and determines whether it is currently within the planned sleep interval;

(3) If yes, the electronic device performs a preset operation.

In the embodiment of the present application, when the electronic device determines that the user is in a sleep state, obtains the schedule configured by the user, and further obtains the sleep interval planned by the user according to the obtained schedule, and determines whether it is currently within the planned sleep interval. If it is, it means that the predicted result is consistent with the schedule configured by the user, and the preset operation is performed at this time.

For example, if the electronic device obtains the user-configured schedule for sleeping at 10:30, starting at 6:30 and doing something at 7:00, then according to the schedule, the electronic device can obtain the user's scheduled sleep interval as 10:30 -6:30.

In one embodiment, after identifying whether the current day is a non-working day of the user according to the first usage information, the method further includes:

(1) If not, the electronic device obtains the pre-trained sleep prediction model corresponding to the working day;

(2) The electronic device obtains the user's behavior data on the current day and the historical working day, and the historical working day is the working day before the current day;

(3) The electronic device predicts the user's sleep based on the acquired behavior data and the sleep prediction model, and obtains the prediction result;

(4) The electronic device judges whether the user is currently asleep according to the obtained prediction result;

(5) If yes, the electronic device performs a preset operation.

Wherein, when the electronic device recognizes that the current day is the user's working day, the pre-trained sleep prediction model corresponding to the working day is acquired for subsequent sleep prediction of the user.

It should be noted that in the embodiments of the present application, the sleep prediction model corresponding to the working day performs sleep prediction based on the user's behavior data on multiple working days. Therefore, the electronic device obtains the pre-trained sleep prediction model corresponding to the working day Then, further obtain the user's behavior data on the current day and at least one historical working day. Among them, the historical working day is the working day before the current day.

After the electronic device obtains the pre-trained sleep prediction model corresponding to the working day, and obtains the user's behavior data on the current day and the historical working day, it can perform sleep prediction on the user according to the obtained sleep prediction model and behavior data to obtain forecast result.

After obtaining the prediction result of the sleep prediction for the user, the electronic device further determines whether the user is currently in a sleep state according to the obtained prediction result. For example, the prediction result obtained by the electronic device for the user’s sleep prediction indicates that the user’s sleep interval is from 23:30 on the current day to 06:30 on the next day. If the current time of the electronic device is 23:25 on the current day, it is determined that the user is not currently sleeping If the current time of the electronic device is 23:45 of the current day, it is determined that the user is currently sleeping.

When determining that the user is currently in the sleep state, the electronic device executes a pre-configured preset operation that is executed when the user is in the sleep state. Wherein, the preset operation includes at least one of a system update operation, an application update operation, and a power consumption control operation, which may be manually configured by the user or may be configured by the electronic device by default.

Please refer to FIG. 5, which is a schematic structural diagram of a sleep prediction device provided by an embodiment of the application. The sleep prediction device can be applied to electronic equipment. The sleep prediction device may include: a date recognition module 401, a model acquisition module 402, a data acquisition module 403, and a sleep prediction module 404.

The date identification module 401 is used to identify whether the current day is a non-working day of the user when the preset sleep prediction conditions are currently met;

The model obtaining module 402 is configured to obtain a pre-trained sleep prediction model corresponding to a non-working day when the recognition result of the date recognition module 401 is yes;

The data acquisition module 403 is used to acquire user behavior data on the current day and historical non-working days. The historical non-working days are the same type of non-working days before the current day;

The sleep prediction module 404 is configured to perform sleep prediction on the user according to the acquired behavior data and the sleep prediction model to obtain the prediction result.

In one embodiment, the behavior data includes sports behavior data, rest behavior data, and entertainment behavior data.

In an embodiment, when identifying whether the current day is a non-working day of the user, the date identification module 401 may be used to:

Obtain the first use information of the electronic device used by the user on the day;

According to the first usage information, identify whether the day is a non-working day of the user.

In an embodiment, when identifying whether the current day is a non-working day of the user according to the first usage information, the date identification module 401 may be used to:

Obtain the pre-stored second usage information of the user's use of the electronic device during his working day;

It is judged whether the first usage information matches the second usage information, if yes, it is judged that the current day is the user's working day, otherwise it is judged that the current day is the user's non-working day.

In an embodiment, when determining whether the first usage information matches the second usage information, the date identification module 401 may be used to:

Acquiring the similarity between the first usage information and the second usage information;

It is determined whether the acquired similarity reaches the preset similarity, and if yes, it is determined that the first usage information matches the second usage information, otherwise it does not match.

In one embodiment, when identifying whether the current day is a non-working day of the user according to the first usage information, the date identification module 401 may be used to:

According to the first usage information and the pre-trained working day recognition model, it is recognized whether the current day is a non-working day of the user.

In one embodiment, the sleep prediction conditions include:

The duration of the screen off state reaches the first preset duration;

Or, the duration of the static state reaches the second preset duration;

Or, stay in a static state when the duration of the screen off state reaches the third preset duration;

Or, the screen is turned off when the duration of the static state reaches the fourth preset duration.

In one embodiment, the prediction result includes the predicted sleep interval, and the sleep prediction device further includes an operation execution module for:

Judging whether the user is currently sleeping according to the predicted sleep interval;

If so, perform a preset operation, where the preset operation includes at least one of a system update operation, an application update operation, and a power consumption control operation.

In an embodiment, before executing the preset operation, the operation execution module may be used to:

Get the work schedule configured by the user;

Obtain the sleep interval planned by the user according to the obtained schedule, and determine whether it is currently within the planned sleep interval;

If yes, perform the preset operation.

In an embodiment, the preset operation includes at least one of a system update operation, an application update operation, and a power consumption control operation.

In one embodiment, after the date recognition module 401 recognizes whether the day is a non-working day of the user, if the recognition result of the date recognition module 401 is no, the model acquisition module 402 is also used to obtain the pre-trained sleep prediction corresponding to the working day model;

The data acquisition module 403 is also used to acquire user behavior data on the current day and historical working days, and the historical working day is the working day before the current day;

The sleep prediction module 404 is also used to predict the user's sleep according to the acquired behavior data and the sleep prediction model, and obtain the prediction result.

The embodiment of the present application provides a computer-readable storage medium with a computer program stored thereon, and when the stored computer program is executed on a computer, the computer executes the steps in the sleep prediction method provided in the embodiment of the present application.

An embodiment of the present application further provides an electronic device including a memory and a processor, and the processor executes the steps in the sleep prediction method provided in the embodiment of the present application by calling a computer program stored in the memory.

Please refer to FIG. 6, which is a schematic structural diagram of an electronic device provided by an embodiment of the application. The electronic device may include a memory 601 and a processor 602. A person of ordinary skill in the art can understand that the structure of the electronic device shown in FIG. 6 does not constitute a limitation on the electronic device, and may include more or less components than those shown in the figure, or a combination of certain components, or different component arrangements. .

The memory 601 can be used to store application programs and data. The application program stored in the memory 601 contains executable code. Application programs can be composed of various functional modules. The processor 602 executes various functional applications and data processing by running application programs stored in the memory 601.

The processor 602 is the control center of the electronic device. It uses various interfaces and lines to connect the various parts of the entire electronic device, and executes the electronic device by running or executing the application program stored in the memory 601 and calling the data stored in the memory 601 The various functions and processing data of the electronic device can be used to monitor the electronic equipment as a whole.

In the embodiment of the present application, the processor 602 in the electronic device will load the executable code corresponding to the process of one or more audio processing programs into the memory 601 according to the following instructions, and the processor 602 will run and store the executable code The application program in the memory 601 thus executes:

Obtain user behavior data on the current day and historical non-working days. The historical non-working days are the same types of non-working days before the current day;

Perform sleep prediction on the user according to the acquired behavior data and the sleep prediction model, and obtain the prediction result.

Please refer to FIG. 7. FIG. 7 is another schematic structural diagram of the electronic device provided by an embodiment of the application. The difference from the electronic device shown in FIG. 6 is that the electronic device further includes components such as an input unit 603 and an output unit 604.

The input unit 603 can be used to receive input numbers, character information or user characteristic information (such as fingerprints), and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

The output unit 604 may be used to output information input by the user or information provided to the user, such as a speaker.

In an embodiment, when identifying whether the current day is a non-working day of the user, the processor 602 may execute:

In an embodiment, when identifying whether the current day is a non-working day of the user according to the first usage information, the processor 602 may execute:

In an implementation manner, when determining whether the first usage information matches the second usage information, the processor 602 may execute:

In one embodiment, the sleep prediction conditions include:

The duration of the screen off state reaches the first preset duration;

Or, the duration of the static state reaches the second preset duration;

In one embodiment, the prediction result includes the predicted sleep interval. After performing sleep prediction on the user according to the acquired behavior data and the sleep prediction model, and after obtaining the prediction result, the processor 602 may execute:

In an embodiment, before performing a preset operation, the processor 602 may perform:

Get the work schedule configured by the user;

If yes, perform the preset operation.

In an embodiment, after identifying whether the current day is a non-working day of the user, the processor 602 may execute:

If not, obtain the pre-trained sleep prediction model corresponding to the working day;

Obtain the user's behavior data on the current day and the historical working day. The historical working day is the working day before the current day;

In the foregoing embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in an embodiment, please refer to the detailed description of the sleep prediction method above, which will not be repeated here.

The sleep prediction device/electronic device provided by the embodiment of the application belongs to the same concept as the sleep prediction method in the above embodiment. Any method provided in the sleep prediction method embodiment can be run on the sleep prediction device/electronic device. For the implementation process, refer to the embodiment of the sleep prediction method, which will not be repeated here.

It should be noted that for the sleep prediction method of the embodiment of the present application, those of ordinary skill in the art can understand that all or part of the process of implementing the sleep prediction method of the embodiment of the present application can be completed by controlling the relevant hardware through a computer program. The program may be stored in a computer readable storage medium, such as stored in a memory, and executed by at least one processor, and the execution process may include a process such as an embodiment of the sleep prediction method. Among them, the storage medium may be a magnetic disk, an optical disc, a read only memory (ROM, Read Only Memory), a random access memory (RAM, Random Access Memory), etc.

For the sleep prediction device of the embodiment of the present application, its functional modules may be integrated in one processing chip, or each module may exist alone physically, or two or more modules may be integrated in one module. The above-mentioned integrated modules can be implemented in the form of hardware or software functional modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium, such as a read-only memory, a magnetic disk, or an optical disk.

The above describes in detail a sleep prediction method, device, storage medium, and electronic equipment provided by the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementations of the present application. The description of the above embodiments is only It is used to help understand the method and core idea of this application; at the same time, for those skilled in the art, according to the idea of this application, there will be changes in the specific implementation and scope of application. In summary, the content of this specification does not It should be understood as a limitation of this application.

Claims

A sleep prediction method applied to electronic equipment, including:

If the preset sleep prediction conditions are currently met, identify whether the day is a non-working day of the user;

If yes, obtain the pre-trained sleep prediction model corresponding to the non-working day;

Acquiring behavior data of the user on the current day and historical non-working days, where the historical non-working days are the same type of non-working days before the current day;

Perform sleep prediction on the user according to the behavior data and the sleep prediction model to obtain a prediction result.
The sleep prediction method according to claim 1, wherein said identifying whether the current day is a non-working day of the user comprises:

Acquiring the first use information of the electronic device used by the user on that day;

Acquiring the pre-stored second usage information of the user's use of the electronic device on his workday;

It is judged whether the first usage information matches the second usage information, if yes, it is judged that the current day is a working day of the user, otherwise it is judged that the current day is a non-working day of the user.
The sleep prediction method according to claim 2, wherein said determining whether said first usage information matches said second usage information comprises:

Acquiring the similarity between the first usage information and the second usage information;

It is determined whether the similarity reaches a preset similarity, and if yes, it is determined that the first usage information matches the second usage information, otherwise it does not match.
The sleep prediction method according to claim 1, wherein the sleep prediction condition comprises:

The duration of the screen off state reaches the first preset duration;

Or, the duration of the static state reaches the second preset duration;

Or, stay in a static state when the duration of the screen off state reaches the third preset duration;

Or, the screen is turned off when the duration of the static state reaches the fourth preset duration.
The sleep prediction method according to claim 1, wherein the prediction result includes a predicted sleep interval, and the performing sleep prediction based on the behavior data and the sleep prediction model, and after obtaining the prediction result, further includes:

Judging whether the user is currently in a sleep state according to the predicted sleep interval;

If yes, perform a preset operation, where the preset operation includes at least one of a system update operation, an application update operation, and a power consumption control operation.
The sleep prediction method according to claim 7, wherein before the performing the preset operation, further comprising:

Obtaining the schedule configured by the user;

Acquire the sleep interval planned by the user according to the schedule, and determine whether it is currently within the planned sleep interval;

If yes, execute the preset operation.
The sleep prediction method according to claim 1, wherein the behavior data includes exercise behavior data, rest behavior data, and entertainment behavior data.
A sleep prediction device applied to electronic equipment, including:

The date identification module is used to identify whether the current day is a non-working day of the user when the preset sleep prediction conditions are currently met;

The model acquisition module is used to acquire the pre-trained sleep prediction model corresponding to the non-working day when the recognition result of the recognition module is yes;

A data acquisition module for acquiring behavioral data of the user on the current day and historical non-working days, where the historical non-working days are the same type of non-working days before the current day;

The sleep prediction module is configured to perform sleep prediction on the user according to the behavior data and the sleep prediction model to obtain a prediction result.
A storage medium having a computer program stored thereon, wherein when the computer program is executed on a computer, the computer is caused to execute the sleep prediction method according to any one of claims 1 to 7.
An electronic device, comprising a memory and a processor, wherein the processor is configured to execute the sleep prediction method according to any one of claims 1 to 7 by calling a computer program stored in the memory.