CN113168596A - Behavior recommendation method and device, storage medium and electronic equipment - Google Patents
Behavior recommendation method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
A behavior recommendation method is applied to electronic equipment, can obtain use information (101) of a user using the electronic equipment, obtain work and rest behavior information (102) representing work and rest behaviors of the user, obtain behavior suggestions (103) corresponding to the user according to the obtained use information, the work and rest behavior information and a preset behavior recommendation model, show the behavior suggestions (104) to the user, and help the user to work and rest regularly, so that adverse effects on the body of the user caused by influences on normal work and rest due to use of the electronic equipment are reduced.
Description
The present application belongs to the field of computer technologies, and in particular, to a behavior recommendation method, apparatus, storage medium, and electronic device.
At present, with the wide application and development of computer technology, electronic devices such as smart phones and tablet computers appear in the lives of people. The electronic device may install applications of different application types to provide different functions to the user, for example, a video-class application may be installed to provide a video playing function, a social service-class application may be installed to provide a social function, a game-class application may be installed to provide a game function, and so on. However, due to the functions provided by the electronic device, the user may use the electronic device to affect the normal work and rest, which may adversely affect the physical health of the user.
Disclosure of Invention
The embodiment of the application provides a behavior recommendation method and device, a storage medium and electronic equipment, which can reduce adverse effects on the body of a user caused by using the electronic equipment.
In a first aspect, an embodiment of the present application provides a behavior recommendation method, which is applied to an electronic device, and includes:
acquiring use information of the electronic equipment used by a user;
acquiring work and rest behavior information representing the work and rest behaviors of a user;
acquiring a behavior suggestion corresponding to the user according to the use information, the work and rest behavior information and a preset behavior recommendation model;
presenting the behavior suggestion to the user.
In a second aspect, an embodiment of the present application provides a behavior recommendation device, which is applied to an electronic device, and includes:
the first acquisition module is used for acquiring the use information of the electronic equipment used by the user;
the second acquisition module is used for acquiring the work and rest behavior information representing the work and rest behaviors of the user;
the third obtaining module is used for obtaining a behavior suggestion corresponding to the user according to the use information, the work and rest behavior information and a preset behavior recommendation model;
and the suggestion display module is used for displaying the behavior suggestion to the user.
In a third aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, and when the computer program is executed on a computer, the computer program is enabled to execute the steps in the behavior recommendation method provided by the embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides an electronic device, which includes a memory and a processor, where the processor is configured to execute steps in the behavior recommendation method provided in the embodiment of the present application by calling a computer program stored in the memory.
In the application embodiment, the use information of the electronic equipment used by the user can be acquired, the work and rest behavior information representing the work and rest behaviors of the user can be acquired, the behavior suggestions corresponding to the user can be acquired according to the acquired use information, the work and rest behavior information and the preset behavior recommendation model, and the acquired behavior suggestions are displayed for the user, so that adverse effects on the body of the user caused by influences on normal work and rest due to the use of the electronic equipment are reduced.
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 schematic flow chart of a behavior recommendation method according to an embodiment of the present application.
Fig. 2 is a schematic diagram of obtaining behavior suggestions according to the usage information, the work and rest behavior information, and the behavior recommendation model in the embodiment of the present application.
Fig. 3 is another flowchart of a behavior recommendation method according to an embodiment of the present application.
Fig. 4 is an exemplary diagram of an electronic device acquiring work and rest behavior information in an embodiment of the application.
Fig. 5 is a diagram showing an example of behavior suggestion through a screen in the embodiment of the present application.
Fig. 6 is another exemplary diagram for presenting behavior suggestions through a screen in the embodiment of the present application.
Fig. 7 is a schematic structural diagram of an action recommendation device according to an embodiment of the present application.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 9 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 behavior recommendation method according to an embodiment of the present application. The behavior recommendation method can be applied to electronic equipment. The flow of the behavior recommendation method can comprise the following steps:
in 101, usage information of a user using an electronic device is acquired.
In the embodiment of the present application, after the electronic device is powered on, the usage information of the electronic device used by the user may be periodically acquired according to a preset information acquisition period (a suitable value may be empirically obtained by a person having ordinary skill in the art, for example, may be set to a natural day), where the usage information is used to describe how the user uses the electronic device, and includes but is not limited to one or more of information describing where the user uses the electronic device, information describing when the user uses the electronic device, and information describing how the user specifically operates the electronic device.
For example, when the electronic device reaches an information acquisition period, usage information of the electronic device used by the user in the information acquisition period is acquired.
At 102, the work and rest behavior information characterizing the work and rest behavior of the user is obtained.
In the embodiment of the application, the electronic device obtains the use information of the electronic device used by the user and also obtains the work and rest behavior information representing the work and rest behavior of the user. The rest behavior information includes one or more of information describing when the user is resting (e.g., sleeping, nap, etc.), information describing when the user is working, and information describing when the user is moving.
It should be noted that the execution sequence of 101 and 102 is not limited by the sequence number, and 102 may be executed after 101, 102 may be executed before 101, or 101 and 102 may be executed in parallel.
In 103, behavior suggestions of corresponding users are obtained according to the obtained use information, the work and rest behavior information and a preset behavior recommendation model.
It should be noted that, in the embodiment of the present application, a behavior recommendation model is stored in advance in the electronic device, and as shown in fig. 2, the behavior recommendation model takes the use information of the electronic device used by the user and the work and rest behavior information of the user as input, and takes the corresponding behavior suggestion as output. The behavior advice includes, but is not limited to, sleep advice, exercise advice, and the like.
In the embodiment of the application, after the electronic equipment acquires the use information of the electronic equipment used by a user and the work and rest behavior information representing the work and rest behaviors of the user, the acquired use information and the work and rest behavior information are input into a preset behavior recommendation model, and the behavior suggestion of the corresponding user output by the behavior recommendation model is acquired.
For example, assuming that the usage information and the rest behavior information describe "the user is always using some applications during the period of time when he or she is sleeping time", the behavior recommendation model will output a sleep recommendation "please rest early".
For another example, assuming that the usage information and the rest behavior information describe that "the user is always using some applications during the non-sleep period", the behavior recommendation model outputs a motion recommendation "please relax the motion"
In other embodiments, the behavior suggestion may also be a work suggestion, for example, assuming that the usage information and the work and rest behavior information describe that "the user is always using certain applications during the period of time when the user is working", the behavior recommendation model will output a work suggestion "please work attentively".
At 104, the obtained behavior suggestions are presented to the user.
It should be noted that, in the embodiment of the present application, a manner of how the electronic device presents the behavior suggestion to the user is not specifically limited, the behavior suggestion may be presented to the user in an audio manner, the behavior suggestion may be presented to the user in a text manner, the behavior suggestion may be presented to the user in a picture manner, and the like.
Therefore, in the embodiment of the application, the use information of the electronic equipment used by the user and the work and rest behavior information representing the work and rest behaviors of the user can be obtained, the behavior suggestions corresponding to the user are obtained according to the obtained use information, the work and rest behavior information and the preset behavior recommendation model, the obtained behavior suggestions are displayed for the user, the user is helped to work and rest regularly, and therefore adverse effects on the body of the user caused by influences on normal work and rest due to the use of the electronic equipment are reduced.
Referring to fig. 3, fig. 3 is another flow chart of the behavior recommendation method according to the embodiment of the present application. The behavior recommendation method can be applied to electronic equipment. The flow of the behavior recommendation method can comprise the following steps:
in 201, usage information of a user using an electronic device is acquired.
In the embodiment of the present application, after the electronic device is powered on, the usage information of the electronic device used by the user may be periodically acquired according to a preset information acquisition period (a suitable value may be empirically obtained by a person having ordinary skill in the art, for example, may be set to a natural day), where the usage information is used to describe how the user uses the electronic device, and includes but is not limited to one or more of information describing where the user uses the electronic device, information describing when the user uses the electronic device, and information describing how the user specifically operates the electronic device.
For example, when the electronic device reaches an information acquisition period, usage information of the electronic device used by the user in the information acquisition period is acquired.
At 202, a work-rest behavior information characterizing the work-rest behavior of the user is obtained.
In the embodiment of the application, the electronic device obtains the use information of the electronic device used by the user and also obtains the work and rest behavior information representing the work and rest behavior of the user. The rest behavior information includes one or more of information describing when the user is resting (e.g., sleeping, nap, etc.), information describing when the user is working, and information describing when the user is moving.
It should be noted that the execution sequence of 201 and 202 is not limited by the sequence number, and 202 may be executed after 201, 202 may be executed before 201, or 201 and 202 may be executed in parallel.
As an optional implementation, "acquiring the work and rest behavior information characterizing the work and rest behavior of the user" may include:
(1) acquiring sensor data acquired by a sensor in electronic equipment;
(2) and generating work and rest behavior information representing the work and rest behaviors of the user according to the acquired sensor data.
The electronic device is configured with sensors including, but not limited to, a gravity sensor, an acceleration sensor, a positioning sensor (such as a satellite positioning sensor, a base station positioning sensor, etc.), a sound sensor, a light sensor, and the like.
When the electronic device acquires the work and rest behavior information representing the work and rest behavior of the user, the electronic device can acquire sensor data acquired by a sensor configured with the electronic device, and then generate the work and rest behavior information representing the work and rest behavior of the user according to the acquired sensor data.
For example, in a time period, if the position data collected by the positioning sensor describes that the electronic device is in the "home" of the user, the sound data collected by the sound sensor describes that the electronic device is in the "quiet environment", the light data collected by the light sensor describes that the electronic device is in the "dark environment", and the corresponding sensor data collected by the gravity sensor and the acceleration sensor does not change, the electronic device determines that the user is in a sleep state in the time period, and correspondingly generates work and rest behavior information describing that the user is in the sleep state in the time period.
For another example, in a time period, if the position data collected by the positioning sensor describes that the electronic device is located outdoors, the sound data collected by the sound sensor describes that the electronic device is located in a noisy environment, the light data collected by the light sensor describes that the electronic device is located in a bright environment, and the corresponding sensor data collected by the gravity sensor and the acceleration sensor does not change frequently, the electronic device determines that the user is in a motion state in the time period, and correspondingly generates work and rest behavior information describing that the user is in the motion state in the time period.
As another optional implementation, "acquiring the work and rest behavior information characterizing the work and rest behavior of the user" may include:
(1) sending a data acquisition request to the pre-associated wearable device, wherein the data acquisition request is used for indicating the wearable device to return the acquired work and rest behavior information of the user;
(2) and receiving the work and rest behavior information returned by the wearable device.
It should be noted that, in the embodiment of the present application, the electronic device associates with a wearable device (for example, a smart band, a smart watch, a smart jewelry, etc.) of a user in advance according to a received input operation of the user. For example, bluetooth pairing may be performed based on bluetooth technology, and an association relationship with the wearable device may be established after pairing is successful.
It is easy to understand that, the wearable device is usually worn by the user, and the wearable device can collect the work and rest behavior information of the user more accurately. Referring to fig. 4, when acquiring the work and rest behavior information representing the work and rest behavior of the user, on one hand, the electronic device may generate a data acquisition request according to an agreed message format, send the generated data acquisition request to the pre-associated wearable device, and instruct the wearable device to return the collected work and rest behavior information of the user through the data acquisition request; on the other hand, after receiving a data acquisition request from the electronic device, the wearable device returns the acquired work and rest behavior information of the user to the electronic device according to the data acquisition request.
In 203, behavior suggestions corresponding to the user are obtained according to the obtained use information, the work and rest behavior information and a preset behavior recommendation model.
It should be noted that, in the embodiment of the present application, a behavior recommendation model is stored in advance in the electronic device, and as shown in fig. 2, the behavior recommendation model takes the use information of the electronic device used by the user and the work and rest behavior information of the user as input, and takes the corresponding behavior suggestion as output. The behavior advice includes, but is not limited to, sleep advice, exercise advice, and the like.
In the embodiment of the application, after the electronic equipment acquires the use information of the electronic equipment used by a user and the work and rest behavior information representing the work and rest behaviors of the user, the acquired use information and the work and rest behavior information are input into a preset behavior recommendation model, and the behavior suggestion of the corresponding user output by the behavior recommendation model is acquired.
For example, assuming that the usage information and the rest behavior information describe "the user is always using some applications during the period of time when he or she is sleeping time", the behavior recommendation model will output a sleep recommendation "please rest early".
For another example, assuming that the usage information and the rest behavior information describe that "the user is always using some applications during the non-sleep period", the behavior recommendation model outputs a motion recommendation "please relax the motion"
In other embodiments, the behavior suggestion may also be a work suggestion, for example, assuming that the usage information and the work and rest behavior information describe that "the user is always using certain applications during the period of time when the user is working", the behavior recommendation model will output a work suggestion "please work attentively".
In 204, it is determined whether the user is looking at the screen of the electronic device, if yes, the process proceeds to 205, otherwise the process proceeds to 206.
In 205, the obtained behavior suggestion is presented to the user through a screen of the electronic device.
At 206, the obtained behavior suggestions are presented to the user through an audio output module of the electronic device.
In the embodiment of the application, the electronic equipment displays the acquired behavior suggestions to the user in different modes according to whether the user looks at the screen of the electronic equipment. The electronic device first determines whether the user gazes at the screen of the electronic device, for example, the electronic device may track the gazing position of the user by using an eye tracking technique, and if the gazing position of the user is located on the screen, it may be determined that the user gazes at the screen.
And if the fact that the user watches the screen of the electronic equipment is judged, the electronic equipment displays the acquired behavior suggestions to the user through the screen of the electronic equipment in the modes of characters, pictures and the like.
And if the user is not gazed at the screen of the electronic equipment, the electronic equipment displays the acquired behavior suggestion to the user through an audio output module of the electronic equipment. The audio output module may be an audio output module (e.g., an internal speaker) built in the electronic device, or an audio output module (e.g., an external speaker, an earphone, etc.) externally connected to the electronic device.
In an embodiment, before "the obtained behavior suggestion is presented to the user through an audio output module of the electronic device", the method further includes:
(1) obtaining a distance between the electronic device and the pre-associated wearable device;
(2) judging whether the distance between the electronic equipment and the pre-associated wearable equipment is smaller than or equal to a preset distance or not;
(3) and if so, displaying the acquired behavior suggestion to the user through an audio output module of the electronic equipment.
It should be noted that a wearable device is typically worn with the user, and the location of the wearable device is the location of the user. Thus, the electronic device may obtain the distance between it and the pre-associated wearable device as the distance between it and the user. For example, the electronic device may acquire a first location where the electronic device is located by using a positioning technology, and simultaneously instruct the pre-associated wearable device to acquire a second location where the electronic device is located by using the positioning technology, and return to the electronic device, so that the electronic device may calculate a distance between the electronic device and the pre-associated wearable device according to the first location and the second location.
After the electronic equipment obtains the distance between the electronic equipment and the pre-associated wearable equipment, whether the distance between the electronic equipment and the pre-associated wearable equipment is smaller than or equal to a preset distance is further judged, if yes, the electronic equipment judges that the user can hear the behavior suggestion output by the audio output module, and if not, the electronic equipment judges that the user cannot hear the behavior suggestion output by the audio output module. The preset distance may be set by a person skilled in the art according to experience, and the value of the preset distance is not specifically limited in the embodiment of the present application.
According to the above description, it can be understood by those skilled in the art that after the electronic device determines whether the distance between the electronic device and the pre-associated wearable device is less than or equal to the preset distance, if the obtained determination result is yes, it indicates that the user can hear the behavior suggestion output by the audio output module, and at this time, the electronic device may output the behavior suggestion in an audio manner by the audio output module, and display the behavior suggestion to the user.
In an embodiment, after "determining whether the distance between the electronic device and the pre-associated wearable device is less than or equal to the preset distance", the method further includes:
and if not, sending the obtained behavior suggestion to the pre-associated wearable equipment, and indicating the pre-associated wearable equipment to show the behavior suggestion to the user.
According to the above description, it can be understood by those skilled in the art that after the electronic device determines whether the distance between the electronic device and the pre-associated wearable device is less than or equal to the preset distance, if the obtained determination result is no, it indicates that the user cannot hear the behavior suggestion output by the audio output module, and at this time, the electronic device may send the obtained behavior suggestion to the pre-associated wearable device, and instruct the pre-associated wearable device to show the behavior suggestion to the user.
It should be noted that, in the embodiment of the present application, a manner of how the wearable device presents the behavior suggestion to the user is not specifically limited, the behavior suggestion may be presented to the user in an audio manner, the behavior suggestion may be presented to the user in a text manner, the behavior suggestion may be presented to the user in a picture manner, and the like.
In one embodiment, the "presenting the obtained behavior suggestion to the user through the screen of the electronic device" may include:
generating a prompt box comprising the behavior suggestion, and displaying the generated prompt box in a screen of the electronic equipment.
For example, referring to fig. 5, when the electronic device displays the obtained behavior suggestion to the user through the screen thereof, assuming that the obtained behavior suggestion is "please take eye protection exercise to relieve asthenopia and then take a rest as soon as possible", the electronic device generates a prompt box including the behavior suggestion, and displays the prompt box including the behavior suggestion in the screen thereof.
In one embodiment, the "presenting the obtained behavior suggestion to the user through the screen of the electronic device" may include:
and adding the acquired behavior suggestion in a notification bar displayed on a screen of the electronic equipment.
For example, referring to fig. 6, when the electronic device displays the acquired behavior suggestion to the user through the screen thereof, assuming that the acquired behavior suggestion is "please take eye protection exercise to relieve asthenopia and then take a rest as soon as possible", the electronic device may add the acquired behavior suggestion to a notification bar displayed on the screen for scrolling.
In one embodiment, the "presenting the obtained behavior suggestion to the user" includes:
(1) determining a current time zone of the electronic equipment, and judging whether the current time zone of the electronic equipment is a preset time zone;
(2) and if so, directly displaying the behavior suggestion to the user.
It should be noted that the behavior suggestion in the embodiment of the present application is time-dependent, and there is a time difference between different time zones, and the time thereof is also different. In order to ensure that the behavior suggestion displayed to the user is matched with the time zone in which the user is located, in the embodiment of the present application, the electronic device first determines the current time zone in which the electronic device is located (since the electronic device is carried by the user, the current time zone in which the electronic device is located is also the current time zone in which the user is located), and then determines whether the current time zone in which the electronic device is located is a preset time zone (for example, the time zone in which the electronic device is located at the collection time of the aforementioned usage information and the work and rest behavior information may be configured), wherein if the determination result is yes, it may be determined that the time zone in which the electronic device is located is not changed, otherwise, it is determined that the time zone in which the electronic device is located is changed.
Based on the above description, if the time zone in which the electronic device is located does not change, it is indicated that the obtained behavior suggestion is matched with the current time zone in which the user is located, and the behavior suggestion can be directly shown to the user.
In one embodiment, after determining whether the current time zone of the electronic device is a preset time zone, the method further includes:
(1) if not, acquiring the time difference between the current time zone of the electronic equipment and a preset time zone;
(2) adjusting the behavior suggestions according to the acquired time difference, and displaying the adjusted behavior suggestions to a user; alternatively, the first and second electrodes may be,
(3) and determining the display time of the behavior suggestion according to the acquired time difference, and displaying the behavior suggestion when the determined display time is reached.
Based on the above description, it can be understood by those skilled in the art that if the time zone in which the electronic device is located changes, it indicates that the obtained behavior suggestion is not matched with the current time zone in which the user is located, at this time, the electronic device may obtain the time difference between the current time zone in which the electronic device is located and the preset time zone, adjust the behavior suggestion according to the time difference, obtain an adjusted behavior suggestion matched with the current time zone in which the user is located, and then show the adjusted behavior suggestion to the user.
For example, if the behavior suggestion initially acquired by the electronic device is "please rest at 22: 00", if the time difference between the current time zone of the electronic device and the preset time zone is 2 hours, for example, 2 hours later, the initially acquired behavior suggestion may be adjusted to obtain an adjusted behavior suggestion "please rest at 20: 00".
For another example, if the behavior suggestion initially acquired by the electronic device is "having a rest to the time", if the time difference between the current time zone of the electronic device and the preset time zone is 2 hours, for example, 2 hours earlier, the electronic device takes the current time as a reference, and two hours later are taken as the display time of the behavior suggestion, that is, the behavior suggestion is displayed after 2 hours later.
In an embodiment, the behavior recommendation method provided in the embodiment of the present application further includes:
and adjusting the work and rest plan pre-configured by the user according to the work and rest behavior information.
It should be noted that a work plan includes, but is not limited to, a wake up alarm, a schedule, etc.
In the embodiment of the application, the electronic device can provide corresponding behavior suggestions for the user, and can adjust a work and rest plan configured in advance by the user according to the work and rest behavior information of the user.
For example, if the electronic device recognizes that the user shifts to a later time according to the work and rest behavior information, the ringing time of the wake-up alarm clock configured by the user may be delayed.
In one embodiment, before "acquiring the use information of the electronic device used by the user", the method further includes:
and training by adopting a machine learning algorithm to obtain a behavior recommendation model.
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, a healthy behavior suggestion can be given according to the historical work and rest behavior information of the user and the use information 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, and the like in handwritten number recognition. When the recognition model is established, a learning process is established through supervised learning, scene type information is compared with an actual result of training data, and the recognition model is continuously adjusted until the scene type information of the model reaches an expected accuracy rate. 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 labeled and the recognition model is to infer some of the intrinsic 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).
In the embodiment of the application, the electronic device may obtain the usage information samples and the work and rest behavior information samples, and randomly combine the usage information samples and the work and rest behavior information samples to obtain a plurality of usage information-work and rest behavior information sample pairs. Corresponding behavioral suggestions can then be made by a professional (e.g., a health professional) based on these pairs of information samples, e.g., assuming a sample pair describes that the user is always using certain applications during what would otherwise be sleep time, the user can be suggested to have a nap, eye protection movement, reinforcement, or other small activity to relieve fatigue.
Then, a supervised learning algorithm is adopted, a plurality of using information-work and rest behavior information sample pairs are used as training input, behavior suggestions corresponding to the sample pairs are used as target output, and model training is carried out to obtain a behavior recommendation model.
In one embodiment, before "acquiring the use information of the electronic device used by the user", the method further includes:
(1) acquiring an initial behavior recommendation model trained by a server;
(2) and acquiring sign data of the user, and updating the initial behavior recommendation model according to the sign data of the user to obtain the behavior recommendation model.
The server can train a general behavior recommendation model in advance, and the general behavior recommendation model is marked as an initial behavior recommendation model. In this embodiment of the application, the electronic device may obtain, from the server, the initial behavior recommendation model trained by the server. Then, the electronic device obtains the physical sign data (such as gender, age, height, weight, and the like) of the user, and updates (colloquially, i.e., personalizes) the initial behavior recommendation model according to the physical sign data of the user, so as to obtain a behavior recommendation model matched with the user.
It should be noted that, the electronic device updates the initial behavior recommendation model and does not change the configuration of the initial behavior recommendation model, but changes the parameters of the initial behavior recommendation model, so that the updated initial behavior recommendation model can output the behavior suggestion matched with the user.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a behavior recommendation device according to an embodiment of the present application. The behavior recommending device can be applied to electronic equipment. The behavior recommending means may include: a first obtaining module 401, a second obtaining module 402, a third obtaining module 403, and a suggestion presentation module 404.
A first obtaining module 401, configured to obtain usage information of a user using an electronic device;
a second obtaining module 402, configured to obtain work and rest behavior information representing a work and rest behavior of a user;
a third obtaining module 403, configured to obtain a behavior suggestion of a corresponding user according to the obtained usage information, the obtained work and rest behavior information, and a preset behavior recommendation model;
and a suggestion display module 404, configured to display the obtained behavior suggestion to a user.
In one embodiment, the behavior advice includes sleep advice or exercise advice.
In an embodiment, prior to presenting the behavior suggestions to the user, the suggestion presentation module 404 may be configured to:
determining a current time zone of the electronic equipment, and judging whether the current time zone of the electronic equipment is a preset time zone;
and if so, directly displaying the behavior suggestion to the user.
In one embodiment, after determining whether the current time zone of the electronic device is a preset time zone, the suggestion display module 404 may be configured to:
if not, acquiring the time difference between the current time zone of the electronic equipment and a preset time zone;
adjusting the behavior suggestions according to the acquired time difference, and displaying the adjusted behavior suggestions to a user; alternatively, the first and second electrodes may be,
and determining the display time of the behavior suggestion according to the acquired time difference, and displaying the behavior suggestion when the determined display time is reached.
In one embodiment, the behavior recommendation device further comprises an adjustment module configured to:
and adjusting the work and rest plan pre-configured by the user according to the work and rest behavior information.
In one embodiment, the behavior recommendation device further comprises a first model training module configured to:
before the first obtaining module 401 obtains the use information of the electronic device used by the user, the behavior recommendation model is obtained by training with a machine learning algorithm.
In one embodiment, the behavior recommendation device further comprises a second model training module configured to:
before the first obtaining module 401 obtains the use information of the electronic device used by the user, obtaining an initial behavior recommendation model trained by the server;
and acquiring sign data of the user, and updating the initial behavior recommendation model according to the acquired sign data to obtain the behavior recommendation model.
In one embodiment, when presenting a behavior suggestion to a user, suggestion presentation module 404 may be configured to:
judging whether a user watches a screen of the electronic equipment;
if so, displaying the behavior suggestion to a user through a screen of the electronic equipment;
and if not, displaying the behavior suggestion to the user through an audio output module of the electronic equipment.
In an embodiment, when presenting the aforementioned behavior suggestions to the user through a screen of the electronic device, the suggestion presentation module 404 may be configured to:
and generating a prompt box comprising the behavior suggestion, and displaying the generated prompt box in a screen.
In an embodiment, when presenting the aforementioned behavior suggestions to the user through a screen of the electronic device, the suggestion presentation module 404 may be configured to:
and adding the behavior suggestions into a notification bar displayed on a screen.
The embodiment of the present application provides 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 behavior recommendation method provided by the embodiment 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 behavior recommendation method provided in the embodiment of the present application by calling the computer program stored in the memory.
Referring to fig. 8, fig. 8 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. 8 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:
acquiring use information of the electronic equipment used by a user;
acquiring work and rest behavior information representing the work and rest behaviors of a user;
acquiring a behavior suggestion of a corresponding user according to the acquired use information, the work and rest behavior information and a preset behavior recommendation model;
and displaying the obtained behavior suggestion to the user.
Referring to fig. 9, fig. 9 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. 8 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, among other things, to receive input numbers, character information, or user characteristic information (such as a fingerprint), and to generate keyboard, mouse, joystick, optical or trackball signal inputs, etc., 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 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:
acquiring use information of the electronic equipment used by a user;
acquiring work and rest behavior information representing the work and rest behaviors of a user;
acquiring a behavior suggestion of a corresponding user according to the acquired use information, the work and rest behavior information and a preset behavior recommendation model;
and displaying the obtained behavior suggestion to the user.
In one embodiment, the behavior advice includes sleep advice or exercise advice.
In an embodiment, before presenting the behavior suggestions to the user, the processor 602 may perform:
determining a current time zone of the electronic equipment, and judging whether the current time zone of the electronic equipment is a preset time zone;
and if so, directly displaying the behavior suggestion to the user.
In an embodiment, after determining whether the time zone in which the electronic device is currently located is a preset time zone, the processor 602 may perform:
if not, acquiring the time difference between the current time zone of the electronic equipment and a preset time zone;
adjusting the behavior suggestions according to the acquired time difference, and displaying the adjusted behavior suggestions to a user; alternatively, the first and second electrodes may be,
and determining the display time of the behavior suggestion according to the acquired time difference, and displaying the behavior suggestion when the determined display time is reached.
In an embodiment, the processor 602 may further perform:
and adjusting the work and rest plan pre-configured by the user according to the work and rest behavior information.
In an embodiment, before obtaining the usage information of the electronic device used by the user, the processor 602 may perform:
and training by adopting a machine learning algorithm to obtain the behavior recommendation model.
In an embodiment, before obtaining the usage information of the electronic device used by the user, the processor 602 may perform:
acquiring an initial behavior recommendation model trained by a server;
and acquiring sign data of the user, and updating the initial behavior recommendation model according to the acquired sign data to obtain the behavior recommendation model.
In an embodiment, when presenting the behavior suggestions to the user, the processor 602 may perform:
judging whether a user watches a screen of the electronic equipment;
if so, displaying the behavior suggestion to a user through a screen of the electronic equipment;
and if not, displaying the behavior suggestion to the user through an audio output module of the electronic equipment.
In an embodiment, when the aforementioned behavior suggestion is presented to the user through a screen of the electronic device, the processor 602 may perform:
and generating a prompt box comprising the behavior suggestion, and displaying the generated prompt box in a screen.
In an embodiment, when the aforementioned behavior suggestion is presented to the user through a screen of the electronic device, the processor 602 may perform:
and adding the behavior suggestions into a notification bar displayed on a screen.
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 behavior recommendation method, and are not described herein again.
The behavior recommendation device/electronic device provided in the embodiment of the application and the behavior recommendation method in the embodiments above belong to the same concept, any method provided in the behavior recommendation method embodiment may be run on the behavior recommendation device/electronic device, and a specific implementation process thereof is described in detail in the behavior recommendation method embodiment, and is not described herein again.
It should be noted that, for the implementation of the present application as the recommendation method, it can be understood by those skilled in the art that all or part of the process of implementing the implementation of the present application as the recommendation method can be implemented by controlling the related hardware through a computer program, and 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, the process of implementing the embodiment as the behavior recommendation 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.
For the behavior recommendation device in 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 behavior recommendation method, apparatus, storage medium, and electronic device provided in the embodiments of the present application are described in detail above, and specific examples are applied herein to explain the principles and implementations of the present application, and the descriptions of the above 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 (13)
- A behavior recommendation method is applied to electronic equipment, and comprises the following steps:acquiring use information of the electronic equipment used by a user;acquiring work and rest behavior information representing the work and rest behaviors of a user;acquiring a behavior suggestion corresponding to the user according to the use information, the work and rest behavior information and a preset behavior recommendation model;presenting the behavior suggestion to the user.
- The behavior recommendation method of claim 1, wherein the presenting the behavior suggestions to the user comprises:determining a current time zone of the electronic equipment, and judging whether the time zone is a preset time zone;and if so, directly displaying the behavior suggestion to the user.
- The behavior recommendation method according to claim 2, wherein after determining whether the time zone is a preset time zone, the method further comprises:if not, acquiring the time difference between the time zone and the preset time zone;adjusting the behavior suggestions according to the time difference, and displaying the adjusted behavior suggestions to a user;or determining the display time of the behavior suggestion according to the time difference, and displaying the behavior suggestion when the display time is reached.
- The behavior recommendation method of claim 1, wherein the behavior recommendation method further comprises:and adjusting the work and rest plan pre-configured by the user according to the work and rest behavior information.
- The behavior recommendation method according to claim 1, wherein before the obtaining of the usage information of the electronic device used by the user, further comprising:and training by adopting a machine learning algorithm to obtain the behavior recommendation model.
- The behavior recommendation method according to claim 1, wherein before the obtaining of the usage information of the electronic device used by the user, further comprising:acquiring an initial behavior recommendation model trained by a server;and acquiring sign data of a user, and updating the initial behavior recommendation model according to the sign data to obtain the behavior recommendation model.
- The behavior recommendation method of claim 1, wherein the presenting the behavior suggestion to the user comprises:judging whether the user gazes at a screen of the electronic equipment;if yes, displaying the behavior suggestion to a user through the screen;and if not, displaying the behavior suggestion to a user through an audio output module of the electronic equipment.
- The behavior recommendation method of claim 7, wherein the presenting the behavior suggestions to the user through the screen comprises:generating a prompt box comprising the behavior suggestion, and displaying the prompt box in the screen.
- The behavior recommendation method of claim 7, wherein the presenting the behavior suggestions to the user through the screen comprises:adding the behavior suggestion in a notification bar of the screen display.
- The behavior recommendation method of claim 1, wherein the behavior recommendation comprises a sleep recommendation or an exercise recommendation.
- A behavior recommendation device applied to electronic equipment comprises:the first acquisition module is used for acquiring the use information of the electronic equipment used by the user;the second acquisition module is used for acquiring the work and rest behavior information representing the work and rest behaviors of the user;the third obtaining module is used for obtaining a behavior suggestion corresponding to the user according to the use information, the work and rest behavior information and a preset behavior recommendation model;and the suggestion display module is used for displaying the behavior suggestion to the user.
- A storage medium having stored thereon a computer program, wherein the computer program, when executed on a computer, causes the computer to execute the behavior recommendation method according to any one of claims 1 to 10.
- An electronic device comprising a memory, a processor, wherein the processor is configured to perform the behavior recommendation method of any of claims 1-10 by invoking a computer program stored in the memory.
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| PCT/CN2019/075367 WO2020168454A1 (en) | 2019-02-18 | 2019-02-18 | Behavior recommendation method and apparatus, storage medium, and electronic device |
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| CN106210332A (en) * | 2016-07-21 | 2016-12-07 | 广东欧珀移动通信有限公司 | Control method and control device |
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| CN104898834A (en) * | 2015-05-18 | 2015-09-09 | 百度在线网络技术（北京）有限公司 | Behavior state adjusting method and device |
| CN106383450A (en) * | 2016-11-10 | 2017-02-08 | 北京工商大学 | Smart home user behavior analyzing system and smart home user behavior analyzing method based on big data |
| CN107609324A (en) * | 2017-10-11 | 2018-01-19 | 上海展扬通信技术有限公司 | A kind of management method and managing device of user's work and rest based on smart machine |
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- 2019-02-18 WO PCT/CN2019/075367 patent/WO2020168454A1/en active Application Filing
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| CN105662394A (en) * | 2014-11-21 | 2016-06-15 | 中国移动通信集团公司 | Method and device for prompting information to users |
| CN106210332A (en) * | 2016-07-21 | 2016-12-07 | 广东欧珀移动通信有限公司 | Control method and control device |
| CN108536099A (en) * | 2018-02-26 | 2018-09-14 | 维沃移动通信有限公司 | A kind of information processing method, device and mobile terminal |
| CN108876284A (en) * | 2018-05-14 | 2018-11-23 | 平安科技（深圳）有限公司 | A kind of user behavior prompt generation method and terminal device |
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