CN113170018A - Sleep prediction method, device, storage medium and electronic equipment - Google Patents
Sleep prediction method, device, storage medium and electronic equipment Download PDFInfo
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Abstract
A sleep prediction method enables electronic equipment to acquire data required by sleep prediction of a user and a pre-trained sleep prediction model when the electronic equipment currently meets preset sleep prediction conditions, so that sleep prediction is performed on the user according to the acquired data required by the sleep prediction of the user and the sleep prediction model to obtain a prediction result, and the accuracy of the sleep prediction of the user can be improved.
Description
The present application belongs to the field of computer technologies, and in particular, to a sleep prediction method, an apparatus, a storage medium, and an electronic device.
At present, electronic devices such as tablet computers and mobile phones are configured, so that system updating and other operations which affect the use of users or take a long time can be performed when the users sleep, and therefore the influence on the use of the users is avoided. For this reason, the related art achieves the foregoing object by performing sleep prediction on a user, such as predicting a sleep interval of the user, and the like, but the accuracy of performing sleep prediction on the user in the related art is low.
Disclosure of Invention
The embodiment of the application provides a sleep prediction method, a sleep prediction device, a storage medium and electronic equipment, which can enable the electronic equipment to accurately predict the sleep of a user.
In a first aspect, an embodiment of the present application provides a sleep prediction method applied to an electronic device, including:
judging whether the current sleep prediction condition is met;
if yes, acquiring screen turning-on and turning-off data of the electronic equipment, and acquiring work and rest behavior data and work and rest plan data of a user;
acquiring a pre-trained sleep prediction model;
and carrying out sleep prediction on the user according to the screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model to obtain a prediction result.
In a second aspect, an embodiment of the present application provides a sleep prediction apparatus, which is applied to an electronic device, and includes:
the condition judgment module is used for judging whether the preset sleep prediction condition is met or not at present;
the data acquisition module is used for acquiring the screen turning-on and turning-off data of the electronic equipment and acquiring the work and rest behavior data and the work and rest plan data of the user when the judgment result of the condition judgment module is yes;
the model acquisition module is used for selecting a target sleep prediction model corresponding to the current use scene from a sleep prediction model set;
and the sleep prediction module is used for predicting the sleep of the user according to the screen lightening data, the work and rest behavior data, the work and rest plan 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 having a computer program stored thereon, where the computer program is executed on a computer, so as to make the computer execute the steps in the sleep prediction method provided by the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides an electronic device, including a memory, and a processor, where the processor is configured to, by calling a computer program stored in the memory, execute:
judging whether the current sleep prediction condition is met;
if yes, acquiring screen turning-on and turning-off data of the electronic equipment, and acquiring work and rest behavior data and work and rest plan data of a user;
acquiring a pre-trained sleep prediction model;
and carrying out sleep prediction on the user according to the screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model to obtain a prediction result.
In the embodiment of the application, when the electronic equipment currently meets the preset sleep prediction condition, the electronic equipment can acquire the screen turning-on and turning-off data of the electronic equipment, the work and rest behavior data and the work and rest plan data of a user, and in addition, a pre-trained sleep prediction model is also acquired, so that the sleep prediction is performed on the user according to the acquired screen turning-on and turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model, a prediction result is obtained, and the accuracy of the sleep prediction on the user can be improved.
The technical solutions and advantages of the present application will become apparent from the following detailed description of specific embodiments of the present application when taken in conjunction with the accompanying drawings.
Fig. 1 is a flowchart of a sleep prediction method according to an embodiment of the present application.
Fig. 2 is another flowchart of a sleep prediction method according to an embodiment of the present application.
Fig. 3 is a schematic diagram of an electronic device acquiring a sleep prediction model in an embodiment of the present application.
Fig. 4 is a schematic diagram of an operation configuration interface provided in an embodiment of the present application.
Fig. 5 is a schematic diagram of performing sleep prediction according to the acquired screen turning-off data, work and rest behavior data, work and rest plan data, and a sleep prediction model in the embodiment of the present application.
Fig. 6 is a schematic structural diagram of a sleep prediction apparatus according to an embodiment of the present application.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 8 is another schematic structural diagram of an electronic device according to an embodiment of the present application.
Referring to the drawings, wherein like reference numbers refer to like elements, the principles of the present application are illustrated as being implemented in a suitable computing environment. The following description is based on illustrated embodiments of the application and should not be taken as limiting the application with respect to other embodiments that are not detailed herein.
Referring to fig. 1, fig. 1 is a flow chart of a sleep prediction method according to an embodiment of the present application. The sleep prediction method can be applied to electronic equipment. The flow of the sleep prediction method may include:
in 101, it is determined whether a preset sleep prediction condition is currently satisfied.
It should be noted that, in the embodiment of the present application, there is no particular limitation on the setting of the sleep prediction condition, and the setting may be performed by a person having ordinary skill in the art according to actual needs. For example, the sleep prediction condition may be set such that the ambient brightness of the environment where the electronic device is currently located is lower than the preset brightness, so that the electronic device may detect the ambient brightness of the environment where the electronic device is located in real time, for example, detect the ambient brightness of the environment where the electronic device is located through the set ambient light sensor, and determine that the sleep prediction condition is currently satisfied when the ambient brightness of the environment where the electronic device is located is lower than the preset brightness.
In 102, if yes, screen turning-on and turning-off data of the electronic equipment are obtained, and work and rest behavior data and work and rest plan data of the user are obtained.
In the embodiment of the application, when the electronic equipment judges that the preset sleep prediction condition is met currently, the sleep prediction of a user is triggered. The method comprises the steps that firstly, data required for sleep prediction of a user are obtained by electronic equipment, wherein the data required for sleep prediction of the user at least comprise screen turning-on and turning-off data of the electronic equipment, and work and rest behavior data and work and rest plan data of the user.
In 103, a pre-trained sleep prediction model is obtained.
In the embodiment of the application, a sleep prediction model for predicting the sleep of the user is also trained in advance, wherein the sleep prediction model can be stored locally in the electronic device or stored in a remote server. In this way, after acquiring data required for sleep prediction of the user, the electronic device further acquires a sleep prediction model for sleep prediction of the user from a local location, or acquires a sleep prediction model for sleep prediction of the user from a remote server.
It should be noted that the sleep prediction model is obtained by training a machine learning algorithm in advance, and the machine learning algorithm can implement various functions through continuous feature learning, for example, sleep prediction can be performed on a user according to work and rest behavior data of the user, a work and rest plan and screen turning-on and off data of the electronic device. Wherein the machine learning algorithm may include: decision tree models, logistic regression models, bayesian models, neural network models, clustering models, and the like.
The algorithm type of the machine learning algorithm may be divided according to various situations, for example, the machine learning algorithm may be divided into: supervised learning algorithms, unsupervised learning algorithms, semi-supervised learning algorithms, reinforcement learning algorithms, and the like.
Under supervised learning, input data is called as "training data", and each set of training data has a definite identification or result, such as "spam" and "non-spam" in a spam prevention system, and "1, 2, 3, 4" in handwritten number recognition, and the like. Common application scenarios for supervised learning are classification problems and regression problems. Common algorithms are Logistic Regression (Logistic Regression) and Back Propagation Neural Network (Back Propagation Neural Network).
In unsupervised learning, data is not specifically identified and the model is to infer some of the internal structure of the data. Common application scenarios include learning and clustering of association rules. Common algorithms include Apriori algorithm and k-Means algorithm, among others.
Semi-supervised learning algorithms, in which input data is partially identified, can be used for type recognition, but the model first needs to learn the intrinsic structure of the data in order to reasonably organize the data for prediction. The application scenarios include classification and regression, and the algorithms include some extensions to common supervised learning algorithms that first attempt to model the unidentified data and then predict the identified data based thereon. Such as Graph theory Inference algorithm (Graph Inference) or Laplacian support vector machine (Laplacian SVM).
Reinforcement learning algorithms, in which input data is used as feedback to the model, unlike supervised models, which simply serve as a way to check for model alignment errors, are used in reinforcement learning, in which input data is fed back directly to the model, and the model must be adjusted immediately for this. Common application scenarios include dynamic systems and robot control. Common algorithms include Q-Learning and time difference Learning (Temporal difference Learning).
Further, the machine learning algorithm can also be divided into based on similarities according to the function and form of the algorithm:
regression algorithms, common ones include: least squares (ideal Least Square), Logistic Regression (Logistic Regression), Stepwise Regression (Stepwise Regression), Multivariate Adaptive Regression Splines (Multivariate Adaptive Regression Splines) and local variance Smoothing estimation (local approximated scattered Smoothing).
Example-based algorithms include k-Nearest Neighbor (KNN), Learning Vector Quantization (LVQ), and Self-Organizing Map algorithm (SOM).
A common algorithm of the regularization method includes: ridge Regression, Last Absolute Shringkgage and Selection Operator (LASSO), and Elastic networks (Elastic Net).
Decision tree algorithms, common ones include: classification And Regression Trees (CART), ID3(Iterative Dichotomiser 3), C4.5, Chi-squared automated Interaction Detection (CHAID), Decision Stump, Random Forest (Random Forest), Multivariate Adaptive Regression Spline (MARS), And Gradient Boosting Machine (GBM).
The Bayesian method algorithm comprises the following steps: naive Bayes algorithm, average single-Dependence estimation (AODE), and Bayesian Belief Network (BBN).
And in 104, performing sleep prediction on the user according to the acquired screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model to obtain a prediction result.
In the embodiment of the application, after the electronic device acquires the screen turning-off data of the electronic device, the work and rest behavior data and the work and rest plan data of the user, and acquires the sleep prediction model, the sleep prediction of the user can be performed according to the screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model, so as to obtain a prediction result, wherein when the sleep prediction is performed, the weight occupied by the screen turning-off data is greater than the weight occupied by the work and rest behavior data and the work and rest plan data.
It should be noted that the sleep prediction for the user includes, but is not limited to, a time when the user enters sleep, a time when the user ends sleep, a sleep interval composed of the time when the user enters sleep and the time when the user ends sleep, and the like. For example, the sleep prediction is performed on the user according to the sleep prediction model, the time when the user enters the sleep is 23:30 of the day, the time when the user receives the sleep is 06:60 of the next day, and the corresponding sleep interval of the user is 23:30 of the day to 06:60 of the next day.
As can be seen from the above, the electronic device in the embodiment of the application may obtain the screen turning-on and turning-off data of the electronic device, obtain the work and rest behavior data and the work and rest plan data of the user when the electronic device currently meets the preset sleep prediction condition, and further obtain a pre-trained sleep prediction model, so that the sleep prediction is performed on the user according to the obtained screen turning-on and turning-off data, the obtained work and rest behavior data, the obtained work and rest plan data, and the obtained sleep prediction model, so as to obtain a prediction result, and the accuracy of the sleep prediction performed on the user can be improved.
Referring to fig. 2, fig. 2 is another flowchart illustrating a sleep prediction method according to an embodiment of the present disclosure. The sleep prediction method can be applied to electronic equipment. The flow of the sleep prediction method may include:
in 201, the electronic device determines whether a preset sleep prediction condition is currently satisfied.
It should be noted that, in the embodiment of the present application, the setting of the sleep recognition condition is not particularly limited, and may be set by a person having ordinary skill in the art according to actual needs.
For example, as an alternative implementation, the sleep recognition condition may be configured to:
the duration of the static state reaches the preset duration.
The electronic device may start a timer to time while entering a static state (for example, the electronic device may detect whether there is acceleration in any direction according to a built-in triaxial acceleration sensor, and determine that the electronic device is in the static state if the acceleration does not exist), and use a time duration of the timer to represent a duration of the electronic device being in the static state, wherein the electronic device stops the timer to time when the time duration of the timer reaches a preset time duration or exits from the static state, and resets the timer. Therefore, when the timing duration of the timer reaches the preset duration, namely the duration of the timer in a static state reaches the preset duration, the electronic equipment judges that the sleep recognition condition is currently met.
As another alternative, the sleep recognition condition may be configured to:
and reaching the preset sleep prediction time.
The value of the sleep prediction time is not specifically limited in the embodiment of the present application, and can be configured by a person of ordinary skill in the art according to actual needs, for example, the value can be fixedly set to be 21 for each natural day: 00 such that the electronic device determines that the sleep recognition condition is satisfied after 21:00 of each natural day is reached; for another example, the sleep prediction time may be set to a time 30 minutes before the last predicted time when the user goes to sleep.
As yet another alternative embodiment, the sleep recognition condition may be configured to:
the ambient brightness of the environment is lower than or equal to the preset brightness.
In this embodiment, a value of the preset brightness is not specifically limited, and may be configured by a person skilled in the art according to actual needs, for example, the preset brightness may be configured to be 300 nits, so that the electronic device may detect the ambient brightness of the environment in real time through the ambient light sensor configured to the electronic device, and when the ambient brightness of the environment is detected to be less than or equal to 300 nits, it is determined that the sleep recognition condition is satisfied.
In 202, if yes, the electronic device acquires the screen turning-on and turning-off data of the electronic device, and acquires the work and rest behavior data and the work and rest plan data of the user.
In the embodiment of the application, when the electronic equipment judges that the preset sleep prediction condition is met currently, the sleep prediction of a user is triggered. The method comprises the steps that firstly, data required for sleep prediction of a user are obtained by electronic equipment, wherein the data required for sleep prediction of the user at least comprise screen turning-on and turning-off data of the electronic equipment, and work and rest behavior data and work and rest plan data of the user.
The screen lightening and extinguishing data include, but are not limited to, the screen lightening duration and the screen extinguishing duration obtained by describing the switching time of the electronic equipment from the screen lightening state to the screen lightening state, the switching time from the screen lightening state to the screen extinguishing state, and the switching time from the screen extinguishing state to the screen lightening state and the switching time from the screen lightening state to the screen extinguishing state.
The rest behavior data includes, but is not limited to, data describing when and how the user is resting (e.g., sleeping, nap, etc.), data describing when and how the user is exercising, and the like.
The work plan data includes, but is not limited to, data describing when the user plans to do something (such as a matter schedule), data describing when the user plans to take a break, and data describing when the user ends a break, among others
In 203, the electronic device determines a current usage scenario of the electronic device.
At 204, the electronic device obtains a sleep prediction model corresponding to a current usage scenario from a plurality of pre-trained sleep prediction models, wherein different ones of the plurality of sleep prediction models correspond to different usage scenarios.
It should be noted that in the embodiment of the present application, a plurality of sleep prediction models are trained in advance, and different sleep prediction models are suitable for performing sleep prediction on a user in different usage scenarios, where the usage scenarios are used to describe scenarios where the user uses an electronic device, and include, but are not limited to, a home vacation scenario, a trip scenario, a business trip scenario, a daily work scenario, and the like.
In this way, in order to more accurately predict the sleep of the user, after acquiring data required for predicting the sleep of the user, the electronic device further determines the current usage scenario of the electronic device, so that a sleep prediction model corresponding to the current usage scenario is acquired from a plurality of pre-trained sleep prediction models and is used for predicting the sleep of the user in the following process.
It should be noted that the plurality of pre-trained sleep prediction models may be all stored locally in the electronic device, may be all stored in a remote server, and may be partially stored locally in the electronic device and partially stored in the remote server.
For example, referring to fig. 3, four pre-trained sleep prediction models, namely an a sleep prediction model suitable for sleep prediction in a home-vacation scene, a B sleep prediction model suitable for sleep prediction in an outbound travel scene, a C sleep prediction model suitable for sleep prediction in a business trip scene, and a D sleep prediction model suitable for sleep prediction in a daily work scene, are stored in a local memory of the electronic device. If the electronic equipment determines that the current use scene is a home vacation scene, acquiring an A sleep prediction model for predicting the sleep of the user; if the electronic equipment determines that the current use scene is an out-travel scene, acquiring a B sleep prediction model for predicting the sleep of the user; if the electronic equipment determines that the current use scene is a work business trip scene, acquiring a C sleep prediction model for performing sleep prediction on the user; and if the current use scene of the electronic equipment is determined to be a daily work scene, acquiring a D sleep prediction model for predicting the sleep of the user.
In 205, the electronic device performs sleep prediction on the user according to the acquired screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model, so as to obtain a prediction result.
In the embodiment of the application, after the electronic device acquires the screen turning-off data of the electronic device, the work and rest behavior data and the work and rest plan data of the user, and acquires the sleep prediction model, the sleep prediction of the user can be performed according to the screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model, so as to obtain a prediction result, wherein when the sleep prediction is performed, the weight occupied by the screen turning-off data is greater than the weight occupied by the work and rest behavior data and the work and rest plan data. It should be noted that the sleep prediction for the user includes, but is not limited to, a time when the user enters sleep, a time when the user ends sleep, a sleep interval composed of the time when the user enters sleep and the time when the user ends sleep, and the like. For example, the sleep prediction is performed on the user according to the sleep prediction model, the time when the user enters the sleep is 23:30 of the day, the time when the user receives the sleep is 06:60 of the next day, and the corresponding sleep interval of the user is 23:30 of the day to 06:60 of the next day.
In an embodiment, the prediction result is a sleep interval of the user, and after performing sleep prediction on the user according to the acquired bright and dark screen data, the work and rest behavior data, the work and rest plan data, and the sleep prediction model to obtain the prediction result, the following steps may be performed:
and if the sleep interval is reached, the electronic equipment executes preset operation.
In the embodiment of the application, after the sleep interval of the user is predicted, if the predicted sleep interval is reached, the electronic device executes preset operation which is configured in advance and executed when the user is in a sleep state. 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, and may be configured manually by a user or by default by the electronic device.
For example, the electronic device may configure the system update operation as a preset operation, so that when the predicted sleep interval is reached, the system update operation is executed to update the system to the latest version; the electronic equipment can also configure the application updating operation as a preset operation, so that the application updating operation is executed when the predicted sleep interval is reached, and the installed application program is updated to the latest version; the electronic device may further configure "apply a preset power consumption control policy for reducing power consumption" as a preset operation, so as to apply the preset power consumption control policy for reducing power consumption when the predicted sleep interval is reached, reduce power consumption of the electronic device, and the like.
For another example, referring to fig. 4, the electronic device is provided with a preset operation configuration interface, as shown in fig. 4, the preset operation configuration interface includes a prompt message "please select an operation performed during a sleep period", an operation selection box, a pull-down button, a pull-down menu, a determination button, and a cancel button, where the pull-down menu is called according to a click operation of a user on the pull-down button, the pull-down menu is provided with a plurality of operations that the electronic device can perform in a user sleep interval, such as a system update operation, an application update operation, and the like shown in fig. 4, the user can select an operation that the electronic device performs in the user sleep interval according to actual needs, and after selecting an operation that the electronic device needs to perform in the user sleep interval, click the determination button, and instruct the electronic device to use the operation selected by the user as the preset operation. Or, if the user finds that there is no operation that the electronic device needs to execute in the user sleep interval, the user may click a cancel button to instruct the electronic device to execute a preset operation of the default configuration.
In one embodiment, when obtaining the work and rest behavior data and the work and rest plan data of the user, the following steps may be performed:
(1) the electronic equipment acquires motion sensor data, recorded by motion application, of a corresponding user, and generates work and rest behavior data of the user according to the acquired motion sensor data;
(2) the electronic equipment acquires the event arrangement data configured by the user in the memorandum application, acquires the alarm clock data configured by the user in the alarm clock application, and generates work and rest plan data of the user according to the acquired event arrangement data and the alarm clock data.
In this embodiment of the application, when acquiring the work and rest behavior data of the user, the electronic device may acquire motion sensor data (a motion sensor includes, but is not limited to, a three-axis acceleration sensor, a gyroscope, a geomagnetic sensor, and the like) of the corresponding user recorded by a motion application, so as to generate the work and rest behavior data of the user according to the acquired motion sensor data, for example, the electronic device generates the work and rest behavior data of the user according to the acquired motion sensor data, that "the user walks for ten thousand steps at 20:30-21:00, at 21: 30 minutes of rest 00-21:00 ".
When the electronic device obtains the work and rest plan data of the user, the electronic device may obtain the event arrangement data configured by the user in the memo application and obtain the alarm clock data configured by the user in the alarm clock application, so as to generate the work and rest plan data of the user according to the obtained event arrangement data and the alarm clock data, for example, if the event arrangement data configured by the user in the memo application is "visit XX client on the next day 08: 00", and the alarm clock data configured by the user in the alarm clock application is "wake-up alarm clock next day 06: 30", the electronic device may generate the work and rest plan data of the user according to the obtained event arrangement data and the alarm clock data as "user plan to get up on the next day 06:30, and visit XX client on the next day 08: 00"
In an embodiment, when performing sleep prediction on a user according to the acquired screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model to obtain a prediction result, the following steps may be performed:
(1) the electronic equipment preprocesses the acquired screen turning-on and turning-off data, the acquired work and rest behavior data and the acquired work and rest plan data;
(2) and the electronic equipment inputs the preprocessed screen lighting and extinguishing data, the work and rest behavior data and the work and rest plan data into a sleep prediction model to obtain a prediction result output by the sleep prediction model for sleep prediction of the user.
Referring to fig. 5, when the electronic device performs sleep prediction on the user according to the screen brightening and blanking data, the work and rest behavior data, the work and rest plan data, and the sleep prediction model to obtain a prediction result, the electronic device may first preprocess the obtained screen brightening and blanking data, the obtained work and rest behavior data, and the obtained work and rest plan data, then input the preprocessed screen brightening and blanking data, the obtained work and rest behavior data, and the preprocessed work and rest plan data into the sleep prediction model, perform sleep prediction on the user according to the input screen brightening and blanking data, the input work and rest behavior data, and the input work and rest plan data, and output the prediction result.
It should be noted that, when the electronic device preprocesses the acquired screen turning-off data, the operation and rest behavior data, and the operation and rest plan data, the electronic device may perform data cleaning, data integration, data transformation, and data reduction on the acquired screen turning-off data, the acquired operation and rest behavior data, and the acquired rest plan data.
Among them, the data cleansing process is a process of rechecking and verifying data, and aims to delete duplicate information, correct existing errors, and provide data consistency.
The data integration processing is to integrate the data of a single dimension into a higher and more abstract dimension, and the integrated data can be more accurate, richer and more targeted.
In the data transformation process, certain conditions are required to be met when data are subjected to statistical analysis, for example, test errors are required to have independence, unbiasedness, variance homogeneity and normality when variance analysis is performed, but in actual analysis, the independence and the unbiasedness are easily met, the variance homogeneity can be met in most cases, and the normality cannot be met sometimes. In this case, the data can be subjected to appropriate conversion, such as square root conversion, logarithmic conversion, square root arcsine conversion, etc., so that the data can satisfy the requirement of analysis of variance. Such data conversion, which is performed therein, is called data transformation.
Data reduction means to reduce the data volume to the maximum extent on the premise of keeping the original appearance of the data as much as possible (the necessary premise for completing the task is to understand the content of the mining task and the familiar data). There are two main approaches to data reduction: attribute selection and data sampling, for attributes and records in the original dataset, respectively.
In an embodiment, when determining a current usage scenario of the electronic device, the following may be performed:
(1) the method comprises the steps that the electronic equipment obtains current state information of the electronic equipment, wherein the current state information comprises information describing the current use state, the position state and the environment state of the electronic equipment;
(2) the electronic equipment determines a use scene with the state information matched with the current state information from a plurality of use scenes according to the prestored state information of the plurality of use scenes as the current use scene of the electronic equipment.
It should be noted that the current state information includes, but is not limited to, related information for describing a current usage state, a location state, an environment state, and the like of the electronic device. For example, the electronic device generates state information describing its use state from the gravity sensor data and the acceleration sensor data, generates state information describing its position state from the positioning sensor data, generates state information describing its environment state from the sound sensor and the light sensor, and the like.
In the embodiment of the application, the electronic device locally pre-stores state information of a plurality of different usage scenarios (or describes a plurality of different usage scenarios by using a plurality of different state information), such as state information of a home vacation scenario, state information of a trip scenario, state information of a business trip scenario, and state information of a daily work scenario. In this way, when the electronic device determines the current usage scenario, according to the pre-stored state information of the multiple usage scenarios, the electronic device determines the usage scenario of which the state information matches the current state information from the multiple usage scenarios as the current usage scenario of the electronic device.
The electronic equipment can judge whether the two pieces of state information are matched according to the similarity between the two pieces of state information, and therefore when the electronic equipment determines the use scene with the matched state information and the current state information, the electronic equipment can respectively acquire the similarity between the state information of each use scene and the current state information of the use scene, and determines the use scene with the similarity reaching the preset similarity as the use scene with the matched state information and the current state information of the use scene.
It should be noted that, in the embodiment of the present application, the value of the preset similarity is not specifically limited, and a person skilled in the art may take a suitable value according to actual needs.
For example, it is assumed that the electronic device prestores state information of a home vacation scene, state information of an outgoing travel scene, state information of a business trip scene, and state information of a daily work scene, and the preset similarity is configured to be 85%. If the similarity between the state information of the home vacation scene and the current state information of the home vacation scene acquired by the electronic equipment is 40%, the similarity between the state information of the travel scene and the current state information of the travel scene is 45%, the similarity between the state information of the work business scene and the current state information of the work business scene is 70%, and the similarity between the state information of the daily work scene and the current state information of the daily work scene is 86%, it can be seen that the similarity between the state information of the daily work scene and the current state information of the electronic equipment reaches the preset similarity (85%), and the electronic equipment determines the daily work scene as a use scene matched with the state information and the current state information of the electronic equipment.
When the electronic equipment acquires the similarity between the state information of each use scene and the current state information thereof, the electronic equipment performs feature extraction on any one of the pre-stored state information of a plurality of use scenes, acquires a word vector set of the state information of each use scene, and records the word vector set of the state information of each use scene as a first word vector set. In addition, the electronic device also performs feature extraction on the current state information of the electronic device, and obtains a word vector set of the current state information of the electronic device, and the word vector set is recorded as a second word vector set.
After the electronic equipment acquires the first word vector set of the state information of each use scene and the second word vector set of the current state information of the electronic equipment, the distances between the first word vector sets and the second word vector sets are respectively calculated, and the calculated distances are used as the similarity between the state information of each use scene and the current state information of the use scene.
Any characteristic distance (such as euclidean distance, manhattan distance, chebyshev distance, cosine distance, and the like) can be selected by one of ordinary skill in the art according to actual needs to measure the distance between the two word vector sets.
For example, the cosine distances of the first word vector set and the second word vector set may be obtained, specifically referring to the following formula:
wherein e represents the cosine distance between the first word vector set and the second word vector set, f represents the first word vector set, N represents the dimensionality of the first word vector set and the second word vector set (the dimensionality of the two word vector sets is the same), f represents the dimensionality of the first word vector set and the second word vector set, andirepresenting the word vector of the ith dimension in the first word vector set (the state information of a usage scenario includes state information of multiple dimensions, such as usage state information, position state information, environment state information, etc.), giAnd representing the word vector of the ith dimension in the second word vector set.
When the electronic device obtains the word vector set of the current state information of the electronic device and obtains the second word vector set, the electronic device can perform word segmentation operation on the current state information of the electronic device and then input the word vector into the encoder neural network, the word vector corresponding to the current state information is output after being processed by the encoder neural network, and correspondingly, the electronic device takes the word vector set of the current state information output by the encoder neural network as the second word vector set.
It should be noted that, in the embodiments of the present application, specific models and topology structures of the encoder neural network are not limited, for example, a single-layer recurrent neural network may be used for training to obtain the encoder neural network, a multi-layer recurrent neural network may also be used for training to obtain the encoder neural network, and a convolutional neural network, or a variant thereof, or a neural network with other network structures may also be used for training to obtain the encoder neural network.
Correspondingly, when the electronic device respectively obtains the word vector sets of the state information of each usage scenario to obtain a plurality of first word vector sets, the electronic device may respectively input the state information of each usage scenario into the encoder neural network, and use the word vector sets of the state information of each usage scenario output by the encoder neural network as the first word vector sets.
In addition, a usage scenario recognition model for usage scenario recognition may be trained in advance and configured locally in the electronic device. In this way, when the electronic device determines the current usage scenario according to the current state information of the electronic device, the current state information of the electronic device may be input to the configured usage scenario identification model, and the usage scenario identification model identifies the usage scenario corresponding to the current state information and outputs the usage scenario. Correspondingly, the electronic device takes the use scene corresponding to the current state information output by the use scene recognition model as the current use scene.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a sleep prediction apparatus according to an embodiment of the present application. The sleep prediction apparatus can be applied to an electronic device. The sleep prediction apparatus may include: a condition determination module 401, a data acquisition module 402, a model acquisition module 403, and a sleep prediction module 404.
A condition determining module 401, configured to determine whether a preset sleep prediction condition is currently met;
a data obtaining module 402, configured to, when the determination result of the condition determining module 401 is yes, obtain screen turning-on and screen turning-off data of the electronic device, and obtain work and rest behavior data and work and rest plan data of the user;
a model obtaining module 403, configured to obtain a pre-trained sleep prediction model;
and the sleep prediction module 404 is configured to perform sleep prediction on the user according to the acquired screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model, so as to obtain a prediction result.
In one embodiment, in obtaining the work activity data and the work plan data of the user, the data obtaining module 402 may be configured to:
acquiring motion sensor data of a corresponding user recorded by a motion application, and generating work and rest behavior data of the user according to the acquired motion sensor data;
acquiring event arrangement data configured by a user in a memo application, acquiring alarm clock data configured by the user in an alarm clock application, and generating work and rest plan data of the user according to the acquired event arrangement data and the alarm clock data.
In an embodiment, when performing sleep prediction on a user according to the acquired screen turning-off data, the work and rest behavior data, the work and rest plan data, and the sleep prediction model to obtain a prediction result, the sleep prediction module 404 may be configured to:
preprocessing the acquired screen lighting and extinguishing data, the work and rest behavior data and the work and rest plan data;
and inputting the preprocessed bright and dark screen data, the work and rest behavior data and the work and rest plan data into a sleep prediction model to obtain a prediction result output by the sleep prediction model for sleep prediction of the user.
In one embodiment, in preprocessing the acquired screen highlighting data, the work and rest behavior data, and the work and rest plan data, the sleep prediction module 404 may be configured to:
and carrying out data cleaning processing, data integration processing, data transformation processing and data reduction processing on the acquired bright and dark screen data, the work and rest behavior data and the work and rest plan data.
In one embodiment, the sleep prediction condition includes:
the duration in the static state reaches the preset duration;
or, a preset sleep prediction time is reached.
In one embodiment, when obtaining a pre-trained sleep prediction model, the model obtaining module 403 may be configured to:
determining a current usage scenario of the electronic device;
the method comprises the steps of obtaining a sleep prediction model corresponding to a current use scene from a plurality of pre-trained sleep prediction models, wherein different sleep prediction models in the plurality of sleep prediction models correspond to different use scenes.
In an embodiment, when determining a current usage scenario of the electronic device, the model obtaining module 403 may be configured to:
acquiring current state information of the electronic equipment, wherein the current state information comprises information describing the current use state, the position state and the environment state of the electronic equipment;
and determining a use scene with the state information matched with the current state information from the plurality of use scenes according to the pre-stored state information of the plurality of use scenes as the current use scene of the electronic equipment.
In an embodiment, when determining, according to the pre-stored state information of a plurality of usage scenarios, a usage scenario in which the state information matches the current state information from the plurality of usage scenarios, the model obtaining module 403 may be configured to:
and respectively acquiring the similarity between the state information of each use scene and the current state information of the use scene, and determining the use scene with the similarity reaching the preset similarity as the use scene matched with the state information and the current state information of the use scene.
In one embodiment, in obtaining the similarity between the status information of each usage scenario and the current status information thereof, the model obtaining module 403 may be configured to:
respectively acquiring word vector sets of the state information of each use scene to obtain a plurality of first word vector sets;
acquiring a word vector set of the current state information to obtain a second word vector set;
respectively calculating the distance between each first word vector set and each second word vector set;
and taking each calculated distance as the similarity between the state information of each use scene and the current state information.
In an embodiment, when obtaining a word vector set of state information of each usage scenario to obtain a plurality of first word vector sets, the model obtaining module 403 may be configured to:
and respectively inputting the state information of each use scene into the encoder neural network, and taking the word vector set of the state information of each use scene output by the encoder neural network as a first word vector set.
In an embodiment, when obtaining the word vector set of the current state information to obtain a second word vector set, the model obtaining module 403 may be configured to:
inputting the current state information into a neural network of an encoder;
and taking the word vector set of the current state information output by the encoder neural network as a second word vector set.
In an embodiment, after obtaining the current state information of the electronic device, the model obtaining module 403 may be further configured to:
and identifying a use scene corresponding to the current state information as a current use scene of the electronic equipment according to the current state information and a pre-trained use scene identification model.
In an embodiment, the prediction result includes a sleep interval of the user, and the sleep prediction apparatus further includes an operation execution module configured to:
after the sleep prediction module 404 performs sleep prediction on the user according to the acquired screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model, and a sleep interval of the user is obtained, if the sleep interval is reached, a preset operation is executed.
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.
Embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, and when the stored computer program is executed on a computer, the computer is caused to execute the steps in the sleep prediction method provided by the embodiments of the present application.
The embodiment of the present application further provides an electronic device, which includes 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 the computer program stored in the memory.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. The electronic device may include a memory 601 and a processor 602. Those of ordinary skill in the art will appreciate that the electronic device configuration shown in fig. 7 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The memory 601 may be used to store applications and data. The memory 601 stores applications containing executable code. The application programs may constitute 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 a control center of the electronic device, connects various parts of the whole electronic device by using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing an application program stored in the memory 601 and calling the data stored in the memory 601, thereby performing overall monitoring of the electronic device.
In the embodiment of the present application, the processor 602 in the electronic device loads 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 runs the application program stored in the memory 601, so as to perform the following steps:
judging whether the current sleep prediction condition is met;
if yes, acquiring screen turning-on and turning-off data of the electronic equipment, and acquiring work and rest behavior data and work and rest plan data of a user;
acquiring a pre-trained sleep prediction model;
and performing sleep prediction on the user according to the acquired bright screen data, the acquired work and rest behavior data, the acquired work and rest plan data and the acquired sleep prediction model to obtain a prediction result.
Referring to fig. 8, fig. 8 is another schematic structural diagram of the electronic device according to the embodiment of the present disclosure, and the difference from the electronic device shown in fig. 7 is that the electronic device further includes components such as an input unit 603 and an output unit 604.
The input unit 603 may be used to receive input numbers, character information, or user characteristic information (such as a fingerprint), and generate a keyboard, a mouse, a joystick, an optical or trackball signal input, etc., related to user settings and function control, among others.
The output unit 604 may be used to output information input by the user or information provided to the user, such as a speaker or the like.
In the embodiment of the present application, the processor 602 in the electronic device loads 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 runs the application program stored in the memory 601, so as to perform the following steps:
judging whether the current sleep prediction condition is met;
if yes, acquiring screen turning-on and turning-off data of the electronic equipment, and acquiring work and rest behavior data and work and rest plan data of a user;
acquiring a pre-trained sleep prediction model;
and performing sleep prediction on the user according to the acquired bright screen data, the acquired work and rest behavior data, the acquired work and rest plan data and the acquired sleep prediction model to obtain a prediction result.
In one embodiment, in obtaining the work activity data and the work plan data of the user, the processor 602 may perform:
acquiring motion sensor data of a corresponding user recorded by a motion application, and generating work and rest behavior data of the user according to the acquired motion sensor data;
acquiring event arrangement data configured by a user in a memo application, acquiring alarm clock data configured by the user in an alarm clock application, and generating work and rest plan data of the user according to the acquired event arrangement data and the alarm clock data.
In an embodiment, when performing sleep prediction on a user according to the acquired screen turning-off data, the work and rest behavior data, the work and rest plan data, and the sleep prediction model to obtain a prediction result, the processor 602 may perform:
preprocessing the acquired screen lighting and extinguishing data, the work and rest behavior data and the work and rest plan data;
and inputting the preprocessed bright and dark screen data, the work and rest behavior data and the work and rest plan data into a sleep prediction model to obtain a prediction result output by the sleep prediction model for sleep prediction of the user.
In one embodiment, in preprocessing the obtained screen highlighting data, rest behavior data and rest plan data, the processor 602 may perform:
and carrying out data cleaning processing, data integration processing, data transformation processing and data reduction processing on the acquired bright and dark screen data, the work and rest behavior data and the work and rest plan data.
In one embodiment, the sleep prediction condition includes:
the duration in the static state reaches the preset duration;
or, a preset sleep prediction time is reached.
In one embodiment, in obtaining a pre-trained sleep prediction model, processor 602 may perform:
determining a current usage scenario of the electronic device;
the method comprises the steps of obtaining a sleep prediction model corresponding to a current use scene from a plurality of pre-trained sleep prediction models, wherein different sleep prediction models in the plurality of sleep prediction models correspond to different use scenes.
In an embodiment, in determining a current usage scenario of the electronic device, the processor 602 may perform:
acquiring current state information of the electronic equipment, wherein the current state information comprises information describing the current use state, the position state and the environment state of the electronic equipment;
and determining a use scene with the state information matched with the current state information from the plurality of use scenes according to the pre-stored state information of the plurality of use scenes as the current use scene of the electronic equipment.
In an embodiment, when determining a usage scenario with state information matching current state information from a plurality of usage scenarios according to pre-stored state information of the plurality of usage scenarios, the processor 602 may perform:
and respectively acquiring the similarity between the state information of each use scene and the current state information of the use scene, and determining the use scene with the similarity reaching the preset similarity as the use scene matched with the state information and the current state information of the use scene.
In one embodiment, in obtaining the similarity between the status information of each usage scenario and the current status information thereof, the processor 602 may perform:
respectively acquiring word vector sets of the state information of each use scene to obtain a plurality of first word vector sets;
acquiring a word vector set of the current state information to obtain a second word vector set;
respectively calculating the distance between each first word vector set and each second word vector set;
and taking each calculated distance as the similarity between the state information of each use scene and the current state information.
In one embodiment, when obtaining a word vector set of state information of each usage scenario, resulting in a plurality of first word vector sets, the processor 602 may perform:
and respectively inputting the state information of each use scene into the encoder neural network, and taking the word vector set of the state information of each use scene output by the encoder neural network as a first word vector set.
In an embodiment, when obtaining the word vector set of the current state information to obtain the second word vector set, the processor 602 may perform:
inputting the current state information into a neural network of an encoder;
and taking the word vector set of the current state information output by the encoder neural network as a second word vector set.
In an embodiment, after obtaining the current state information of the electronic device, the processor 602 may perform:
and identifying a use scene corresponding to the current state information as a current use scene of the electronic equipment according to the current state information and a pre-trained use scene identification model.
In an embodiment, the prediction result includes a sleep interval of the user, and after performing sleep prediction on the user according to the acquired bright and dark screen data, the work and rest behavior data, the work and rest plan data, and the sleep prediction model to obtain the sleep interval of the user, the processor 602 may perform:
and if the sleep interval is reached, executing a 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 the above embodiments, the descriptions of the embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to the above detailed description of the sleep prediction method, and are not described herein again.
The sleep prediction apparatus/electronic device provided in the embodiment of the present application and the sleep prediction method in the foregoing embodiments belong to the same concept, and any method provided in the sleep prediction method embodiment may be run on the sleep prediction apparatus/electronic device, and a specific implementation process thereof is described in detail in the sleep prediction method embodiment, and is not described herein again.
It should be noted that, for the sleep prediction method of the embodiment of the present application, it can be understood by those skilled in the art that all or part of the process for 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 computer program can be stored in a computer readable storage medium, such as a memory, and executed by at least one processor, and during the execution process, the process of the embodiment of the sleep prediction method can be included. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
In the sleep prediction apparatus according to the embodiment of the present application, each functional module may be integrated into one processing chip, or each module may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium, such as a read-only memory, a magnetic or optical disk, or the like.
The foregoing describes in detail a sleep prediction method, apparatus, storage medium, and electronic device provided in the embodiments of the present application, and specific examples are applied in the present application to explain the principles and implementations of the present application, and the descriptions of the foregoing embodiments are only used to help understand the method and core ideas of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (12)
- A sleep prediction method is applied to electronic equipment, and comprises the following steps:judging whether the current sleep prediction condition is met;if yes, acquiring screen turning-on and turning-off data of the electronic equipment, and acquiring work and rest behavior data and work and rest plan data of a user;acquiring a pre-trained sleep prediction model;and carrying out sleep prediction on the user according to the screen turning-off data, the work and rest behavior data, the work and rest plan data and the sleep prediction model to obtain a prediction result.
- The sleep prediction method as claimed in claim 1, wherein the acquiring of the work and rest behavior data and the work and rest plan data of the user comprises:acquiring motion sensor data, corresponding to the user, recorded by a motion application, and generating the work and rest behavior data according to the motion sensor data;and acquiring event arrangement data configured by the user in a memorandum application, acquiring alarm clock data configured by the user in an alarm clock application, and generating the work and rest plan data according to the event arrangement data and the alarm clock data.
- The sleep prediction method according to claim 1, wherein the predicting sleep of the user according to the screen highlighting data, the work and rest behavior data, the work and rest plan data and the sleep prediction model to obtain a prediction result comprises:preprocessing the screen turning-off data, the work and rest behavior data and the work and rest plan data;inputting the preprocessed screen brightening and extinguishing data, the preprocessed work and rest behavior data and the preprocessed work and rest plan data into the sleep prediction model to obtain a prediction result output by the sleep prediction model for performing sleep prediction on the user.
- The sleep prediction method of claim 3, wherein the pre-processing of the turn-off data, the work activity data, and the work plan data comprises:and carrying out data cleaning processing, data integration processing, data transformation processing and data reduction processing on the bright and dark screen data, the work and rest behavior data and the work and rest plan data.
- The sleep prediction method as claimed in claim 1, wherein the sleep prediction condition includes:the duration in the static state reaches the preset duration;or, a preset sleep prediction time is reached.
- The sleep prediction method as set forth in claim 1, wherein the obtaining a pre-trained sleep prediction model includes:determining a current usage scenario of the electronic device;and acquiring a sleep prediction model corresponding to the current use scene from a plurality of pre-trained sleep prediction models, wherein different sleep prediction models in the plurality of sleep prediction models correspond to different use scenes.
- The sleep prediction method as claimed in claim 6, wherein the determining a current usage scenario of the electronic device comprises:acquiring current state information of the electronic equipment, wherein the current state information comprises information describing the current use state, the position state and the environment state of the electronic equipment;and determining a use scene with state information matched with the current state information from the plurality of use scenes according to the pre-stored state information of the plurality of use scenes, wherein the use scene is used as the current use scene of the electronic equipment.
- The sleep prediction method according to claim 1, wherein the prediction result includes a sleep interval of a user, and the predicting the sleep of the user according to the screen highlighting data, the work and rest behavior data, the work and rest plan data, and the sleep prediction model further includes:and if the sleep interval is reached, executing a preset operation.
- The sleep prediction method as claimed in claim 8, wherein the preset operation includes at least one of a system update operation, an application update operation, and a power consumption control operation.
- A sleep prediction device applied to an electronic device comprises:the condition judgment module is used for judging whether the preset sleep prediction condition is met or not at present;the data acquisition module is used for acquiring the screen turning-on and turning-off data of the electronic equipment and acquiring the work and rest behavior data and the work and rest plan data of the user when the judgment result of the condition judgment module is yes;the model acquisition module is used for selecting a target sleep prediction model corresponding to the current use scene from a sleep prediction model set;and the sleep prediction module is used for predicting the sleep of the user according to the screen lightening data, the work and rest behavior data, the work and rest plan data and the sleep prediction model to obtain a prediction result.
- A storage medium having stored thereon a computer program, wherein the computer program, when executed on a computer, causes the computer to perform a sleep prediction method as claimed in any one of claims 1 to 9.
- An electronic device comprising a memory, a processor, wherein the processor is configured to perform the sleep prediction method of any one of claims 1 to 9 by invoking a computer program stored in the memory.
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