CN107748697B

Movatterモバイル変換

Info

Publication number: CN107748697B
Application number: CN201711050141.4A
Authority: CN
Inventors: 曾元清
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date: 2017-10-31
Filing date: 2017-10-31
Publication date: 2020-06-02
Anticipated expiration: 2037-10-31
Also published as: CN107748697A

Abstract

The embodiment of the application discloses an application closing method, an application closing device, a storage medium and electronic equipment, wherein the application closing request is received, and then the current geographic position of the electronic equipment is obtained according to the application closing request; selecting corresponding target characteristic information from the applied plurality of characteristic information according to the current geographic position; predicting whether the application can be closed or not according to the target characteristic information and the prediction model; if yes, closing the application; the scheme realizes the automatic closing of the application, improves the operation smoothness of the electronic equipment and reduces the power consumption.

Description

Application closing method and device, storage medium and electronic equipment

Technical Field

The present application relates to the field of communications technologies, and in particular, to an application shutdown method, an application shutdown device, a storage medium, and an electronic device.

Background

At present, a plurality of applications are generally run simultaneously on electronic equipment such as a smart phone, wherein one application runs in a foreground and the other applications run in a background. If the application running in the background is not cleaned for a long time, the available memory of the electronic equipment is reduced, the occupancy rate of a Central Processing Unit (CPU) is too high, and the problems of slow running speed, blockage, too high power consumption and the like of the electronic equipment are caused. Therefore, it is necessary to provide a method to solve the above problems.

Disclosure of Invention

The embodiment of the application closing method and device, the storage medium and the electronic equipment can improve the operation smoothness of the electronic equipment and reduce power consumption.

In a first aspect, an embodiment of the present application provides an application shutdown method, including:

receiving an application closing request;

acquiring the current geographic position of the electronic equipment according to the application closing request;

selecting corresponding target characteristic information from the applied plurality of characteristic information according to the current geographic position;

predicting whether the application can be closed according to the target characteristic information and a prediction model;

and if so, closing the application.

In a second aspect, an embodiment of the present application provides an application shutdown apparatus, including:

a receiving unit configured to receive an application shutdown request;

the position acquisition unit is used for acquiring the current geographic position of the electronic equipment according to the application closing request;

the characteristic selection unit is used for selecting corresponding target characteristic information from a plurality of applied characteristic information according to the current geographic position;

a prediction unit for predicting whether the application can be closed or not according to the target feature information and a prediction model;

a closing unit configured to close the application when the prediction unit predicts that the application can be closed.

In a third aspect, a storage medium is provided in this application, where a computer program is stored, and when the computer program runs on a computer, the computer is caused to execute an application shutdown method as provided in any embodiment of this application.

In a fourth aspect, an electronic device provided in an embodiment of the present application includes a processor and a memory, where the memory has a computer program, and the processor is configured to execute the application shutdown method provided in any embodiment of the present application by calling the computer program.

The method comprises the steps of receiving an application closing request, and then obtaining the current geographic position of the electronic equipment according to the application closing request; selecting corresponding target characteristic information from the applied plurality of characteristic information according to the current geographic position; predicting whether the application can be closed or not according to the target characteristic information and the prediction model; if yes, closing the application; the scheme realizes the automatic closing of the application, improves the operation smoothness of the electronic equipment and reduces the power consumption.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario of an application shutdown method according to an embodiment of the present application.

Fig. 2 is a schematic flowchart of an application shutdown method according to an embodiment of the present application.

Fig. 3 is a schematic diagram of a display interface provided in an embodiment of the present application.

Fig. 4 is another schematic flowchart of an application shutdown method according to an embodiment of the present application.

FIG. 5 is a schematic structural diagram of an application shutdown device according to an embodiment of the present application

Fig. 6 is another schematic structural diagram of an application shutdown device provided in an embodiment of the present application.

Fig. 7 is another schematic structural diagram of an application shutdown device provided in 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.

Detailed Description

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.

In the description that follows, specific embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise indicated. Accordingly, these steps and operations will be referred to, several times, as being performed by a computer, the computer performing operations involving a processing unit of the computer in electronic signals representing data in a structured form. This operation transforms the data or maintains it at locations in the computer's memory system, which may be reconfigured or otherwise altered in a manner well known to those skilled in the art. The data maintains a data structure that is a physical location of the memory that has particular characteristics defined by the data format. However, while the principles of the application have been described in language specific to above, it is not intended to be limited to the specific form set forth herein, and it will be recognized by those of ordinary skill in the art that various of the steps and operations described below may be implemented in hardware.

The term module, as used herein, may be considered a software object executing on the computing system. The various components, modules, engines, and services described herein may be viewed as objects implemented on the computing system. The apparatus and method described herein may be implemented in software, but may also be implemented in hardware, and are within the scope of the present application.

The terms "first", "second", and "third", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules listed, but rather, some embodiments may include other steps or modules not listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

An execution main body of the application closing method may be the background application closing device provided in the embodiment of the present application, or an electronic device integrated with the application closing device, where the application closing device may be implemented in a hardware or software manner. The electronic device may be a smart phone, a tablet computer, a palm computer, a notebook computer, or a desktop computer.

Referring to fig. 1, fig. 1 is a schematic view of an application scenario of an application shutdown method provided in an embodiment of the present application, taking an example that an application shutdown device is integrated in an electronic device, where the electronic device may receive an application shutdown request, and then obtain a current geographic location of the electronic device according to the application shutdown request; selecting corresponding target characteristic information from the applied plurality of characteristic information according to the current geographic position; predicting whether the application can be closed or not according to the target characteristic information and the prediction model; if yes, closing the application.

Specifically, for example, as shown in fig. 1, taking application a as an example (for example, application a may be a mailbox application, a game application, or the like), when the electronic device receives an application closing request, obtaining a current geographic location of the electronic device according to the application closing request, selecting corresponding target feature information (for example, time information of application a running in the background, time information of application a running, number of times of application a entering the background, manner of application a switching, or the like) from a plurality of feature information, that is, multidimensional feature information (for example, time information of application a running in the background, number of times of application a entering the background) of application a according to the current geographic location, and predicting whether the application can be closed according to the target feature information and a prediction model; if yes, closing the application a.

Referring to fig. 2, fig. 2 is a schematic flowchart illustrating an application shutdown method according to an embodiment of the present disclosure. The specific process of the application shutdown method provided by the embodiment of the application can be as follows:

201. an application close request is received.

The application closing request may be automatically triggered by the electronic device, for example, the electronic device triggers the application closing request at regular intervals, and at this time, the application closing request may be received.

In an embodiment, the application closing request may also be triggered by a user operating the electronic device, for example, an application closing interface may be set on the display interface, so that the user may operate the application closing interface to trigger sending the application closing request.

The presentation form of the application closing interface is various, such as an icon, a button, an input box, and the like.

For example, referring to fig. 3, a floating application close button "application clear" is set on a video display interface of the electronic device, and when the user clicks the application close button "application clear", the electronic device sends an application close request.

202. And acquiring the current geographic position of the electronic equipment according to the application closing request.

The manner of acquiring the geographic position may be a GPS positioning manner, a base station positioning manner, or the like. The actual application can be set according to requirements.

203. And selecting corresponding target characteristic information from the applied plurality of characteristic information according to the current geographic position.

The application related to the embodiment of the application can be a financial application, a shopping application, a social application and the like. Further, the application may also be a foreground application or a background application.

The applied characteristic information is applied multidimensional characteristic information which can be collected in the using process of the application.

The applied multidimensional feature has dimensions with a certain length, and the parameter on each dimension corresponds to one feature information for representing the application, namely the multidimensional feature information is composed of a plurality of features. The plurality of feature information may include application-related feature information, such as: applying the duration of the cut-in to the background; the screen-off duration of the electronic equipment is prolonged when the application is switched into the background; the number of times the application enters the foreground; the time the application is in the foreground; the mode that the application enters the background, such as being switched into by a home key, being switched into by a return key, being switched into by other applications, and the like; types of applications, including primary (common applications), secondary (other applications); the histogram information of the background stay time is applied, for example, the first bin (the number of times corresponding to 0-5 minutes) of the histogram of the background stay time is applied.

The plurality of feature information may further include related feature information of the electronic device where the application is located, for example: the screen-off time, the screen-on time and the current electric quantity of the electronic equipment, the wireless network connection state of the electronic equipment, whether the electronic equipment is in a charging state or not and the like.

For example, a plurality of characteristic information of the application may be collected according to a preset frequency in a historical time period. Historical time periods, such as the past 7 days, 10 days; the preset frequency may be, for example, one acquisition every 10 minutes, one acquisition every half hour.

In one embodiment, in order to facilitate application shutdown, feature information that is not directly represented by a numerical value in the multidimensional feature information of the application may be quantized by a specific numerical value, for example, the feature information of a wireless network connection state of an electronic device may be represented by anumerical value 1 to indicate a normal state, and may be represented by anumerical value 0 to indicate an abnormal state (or vice versa); for another example, the characteristic information of whether the electronic device is in the charging state may be represented by avalue 1, and avalue 0 to represent the non-charging state (or vice versa).

For example, in an embodiment, a feature type corresponding to the current geographic location may be obtained, and then, the feature information is selected based on the feature type. That is, the step of "selecting corresponding target feature information from the plurality of applied feature information according to the current geographic location" may include:

acquiring a feature type corresponding to the current geographic position to obtain a feature type set;

and selecting corresponding target characteristic information from the applied plurality of characteristic information according to the characteristic type set.

Wherein, the characteristic types can be set according to actual requirements and can be divided according to the attribute of the characteristic information,

for example, features can be divided into: the characteristics of the application itself and the characteristics of the electronic device in which the application is located.

For another example, the features may be further divided into: time characteristics (e.g., usage duration of an application in the foreground or background, time of an application entering the background, dwell time of an application in the background, etc.), time characteristics (e.g., number of times an application enters the background, foreground, etc.), application switching characteristics (e.g., manner in which an application is switched), etc. Further, the features may be divided into electronic bright screen features, off screen features, electrical quantity features, network features, and the like.

The manner of obtaining the feature type corresponding to the geographic location may be multiple, for example, the feature type corresponding to the geographic location may be obtained based on a type mapping relationship set, where the type mapping relationship set includes: a mapping (i.e., correspondence) of geographic location to feature type. The expression form of the mapping relation set is various, such as a table form and the like.

In an embodiment, the feature type corresponding to the geographic location may be one or more, for example, the feature type corresponding to the geographic location may include a time feature, and the like. Therefore, in the embodiment of the present application, the feature type set may include one or more feature types.

For example, in an embodiment, the feature information corresponding to the feature type may be selected from a plurality of applied feature information. For example, when the feature type includes a temporal feature, an application feature, temporal feature information, application switching feature information, and the like may be included in the plurality of feature information of the background application.

In one embodiment, in order to improve the accuracy of the application shutdown prediction, it is necessary to ensure that the number of feature types satisfies a certain condition. For example, the step "selecting corresponding target feature information from a plurality of applied feature information according to the feature type set" may include:

acquiring the number of the feature types in the feature type set;

when the number is larger than the preset number, selecting corresponding target characteristic information from the applied plurality of characteristic information according to the characteristic type set;

when the number is not more than the preset number, adding a new feature type to the feature type set; and selecting corresponding target characteristic information from the applied plurality of characteristic information according to the characteristic type set.

According to the embodiment of the application, after the feature type set is obtained, the number of the feature types can be determined, namely whether the number of the feature types is larger than a preset threshold value or not, if so, the features are rich enough, and at the moment, target feature information can be selected based on the feature type set. When the number of the feature types is not larger than the preset threshold value, the feature is not rich enough, the prediction is not accurate enough due to the fact that the feature information selected by the current feature types is used for prediction, and in order to guarantee the prediction accuracy, a new feature type can be added into the feature type set to enrich the feature types.

The preset number may be set according to actual requirements, and may be, for example, 3, 4, 5, and so on.

For example, after the current geographic location of the electronic device is obtained, the feature types corresponding to the current geographic location include: time characteristics, frequency characteristics, application switching characteristics, screen-on characteristics, screen-off characteristics. At this time, the feature type set includes five types, assuming that the preset number is 4, that is, four types of features are required to be predicted, the number 5 of the types of the features is greater than thepreset number 4, which indicates that the feature types are sufficiently abundant, and at this time, the time feature, the frequency feature, the application switching feature, the screen-on feature, and the screen-off feature may be selected from the applied feature information.

For another example, after the current geographic location of the electronic device is obtained, the feature types corresponding to the current geographic location include: time characteristics, frequency characteristics, application switching characteristics. At this time, the feature type set includes three types, assuming that the preset number is 4, that is, four features are required to be predicted, thenumber 3 of the types of the feature types is not greater than thepreset number 4, which indicates that the feature types are not rich enough, at this time, new feature types such as a bright screen feature and a dark screen feature may be added to the feature type set, and the added feature type set includes: five characteristics of time characteristic, frequency characteristic, application switching characteristic, bright screen characteristic and off screen characteristic; and then selecting time characteristics, frequency characteristics, application switching characteristics, screen lightening characteristics and screen extinguishing characteristics from the characteristic information of the application.

The new feature type determination method added may be implemented in various ways, for example, in an embodiment, to improve the accuracy of prediction, the new feature type determination method may be determined based on the training feature type of the prediction model, and the like.

In one embodiment, the step of "adding a new feature type to the set of feature types" may comprise:

obtaining a training characteristic type of a prediction model;

determining a candidate feature type to be added according to the training feature type and the feature type set, wherein the candidate feature type is different from the feature types in the feature type set;

and selecting a corresponding number of target candidate feature types from the candidate feature types according to the number difference between the preset number and the number, and adding the target candidate feature types to the feature type set.

The prediction model is a machine learning algorithm, and is used for predicting occurrence of a certain event, for example, whether an application can be shut down or not can be predicted. The predictive model may include: decision tree models, logistic regression models, bayesian models, neural network models, clustering models, and the like.

The training feature type is the feature type of the training feature adopted by the prediction model; such as time characteristics, frequency characteristics, etc.

The candidate feature type is a new feature type relative to the feature type set, that is, a feature type does not exist in the current feature type set. For example, the set of feature types includes: time characteristics, frequency characteristics, application switching characteristics; the candidate feature types may include application type features, power features, and the like.

In an embodiment, a distinguishing feature type between the training feature type and the feature type set may be obtained, where the distinguishing feature type is a candidate feature type to be added. For example, the feature type set comprises a time feature, a frequency feature and an application switching feature, and the training feature type comprises a time feature, a frequency feature, an application switching feature, an application type feature and a charging feature; the distinguishing feature types between the application type feature and the charging feature are application type features and charging features, and the distinguishing feature types can be used as candidate feature types to be added.

In an embodiment, the number of the added candidate feature types may be set according to an actual requirement, for example, in order to ensure that the total number of the feature types is greater than a preset number and improve prediction accuracy, the number of the added candidate feature types may be determined based on a difference between the preset number and the number of the feature types in the current feature type set. Wherein the number of the added candidate feature types and the difference value may be equal or 1 is added to the difference value. For example, the preset number is 5, and the current feature type set includes 3 features, at this time, it may be determined that the number of candidate feature types to be added, that is, the types are 3.

For example, the feature type set comprises three features of time feature, frequency feature and application switching feature; the training feature types comprise 7 features such as time feature, frequency feature, application switching feature, application type feature, charging feature, network feature and screen state feature; the distinguishing feature types between the two, namely the candidate feature types, include: application type feature, charging feature, network feature, screen status feature. Assuming that the number of the preset feature types is 5, at this time, the number of thefeature types 3 included in the current feature type set is smaller than the number of the preset feature types 5, at this time, adifference 2 between the two is obtained, and then 2+ 1-3 feature types are randomly selected from candidate feature types such as an application type feature, a charging feature, a network feature and a screen state feature and are added to the feature type set, such as the application type feature, the charging feature and the network feature.

In an embodiment, to improve the accuracy of the application shutdown prediction, different prediction models may be selected for prediction based on different geographic locations, for example, a corresponding prediction model may be selected based on a geographic location, then feature information may be selected based on a type of the model, and finally whether the application is shutdown may be predicted based on the selected feature information and the prediction model. For example, the step "selecting corresponding target feature information from a plurality of applied feature information according to the current geographic location" may include:

selecting a target prediction model corresponding to the current geographic position from a plurality of different prediction models;

and selecting target characteristic information corresponding to the target prediction model from the plurality of applied characteristic information.

For example, the prediction model corresponding to the current geographic location is a decision tree model, and in this case, the feature information corresponding to the decision tree may be selected from the applied plurality of feature information.

The different types of prediction models may correspond to different feature types, such as a logistic regression model corresponding to a time feature, an application type feature, and the like, a decision tree model corresponding to a network feature, a bluetooth feature, a network feature, and the like. Specifically, it may be set according to actual requirements.

For example, three prediction models, such as a decision tree model, a logistic regression model, and a bayesian model, are preset, and a prediction model corresponding to the current geographic location is selected from the three prediction models, such as the logistic regression model; at this time, feature information corresponding to the logistic regression model, such as time feature, application type feature, and the like, is selected from the plurality of feature information of the application.

204. And predicting whether the application can be closed according to the target characteristic information and the prediction model, and if so, executing thestep 205.

For example, predicting whether an application can be closed based on target feature information and a decision tree model; corresponding leaf nodes can be determined according to the target features and the decision tree model, and the output of the leaf nodes is used as a prediction output result. If the target feature is used to determine the current leaf node according to the branch condition of the decision tree (i.e. the feature value of the partition feature), the output of the leaf node is taken as the prediction result. Since the output of the leaf node includes closeable, or not closeable, it may be determined whether the application is closeable at this time based on the decision tree.

The prediction model may be a prediction model trained or learned by a large number of samples.

205. The application is closed.

In one embodiment, when it is predicted that the application cannot be closed, no processing may be performed on the application.

As can be seen from the above, in the embodiment of the present application, an application closing request is received, and then, a current geographic location of an electronic device is obtained according to the application closing request; selecting corresponding target characteristic information from the applied plurality of characteristic information according to the current geographic position; predicting whether the application can be closed or not according to the target characteristic information and the prediction model; if yes, closing the application; the scheme realizes the automatic closing of the application, improves the operation smoothness of the electronic equipment and reduces the power consumption.

Further, the characteristic information comprises a plurality of characteristic information reflecting the behavior habit of the user using the application; therefore, the closing of the corresponding application can be more personalized and intelligent.

Further, in the embodiment of the application, the feature is selected based on the current geographic location of the electronic device, and whether the application is closed is predicted based on the selected feature.

On the other hand, the scheme selects the features based on the geographic position, so that the feature selection can be associated with the geographic position, the application closing is associated with the current position of the user, the application closing is more intelligent, and the user experience is greatly improved. For example, whether the post-application associated with the current place is closed or not can be flexibly selected by adopting the scheme, so that the application associated with the current place is prevented from being closed (possibly used by a user), the application closing is more intelligent, and the user experience is greatly improved.

The cleaning method of the present application will be further described below on the basis of the method described in the above embodiment. Referring to fig. 4, the application shutdown method may include:

301. an application close request is received.

Receiving an application close request

302. And acquiring the current geographic position of the electronic equipment according to the application closing request.

For example, the current geographic location may be obtained from a geographic location database in which various geographic location data is stored based on the application shutdown request.

303. And obtaining the feature type corresponding to the current geographic position to obtain a feature type set.

The feature type corresponding to the geographic location may be one or more, and thus, the feature type set may include one or more feature types.

The feature types can be set according to actual requirements and can be divided according to attributes of feature information. For example, features can be divided into: the characteristics of the application itself and the characteristics of the electronic device in which the application is located.

For another example, the features may be further divided into: time characteristics (e.g., usage duration of an application in the foreground or background, time of an application entering the background, dwell time of an application in the background, etc.), time characteristics (e.g., number of times an application enters the background, foreground, etc.), application switching characteristics (e.g., manner in which an application is switched), etc. Further, the features may be divided into electronic device on-screen features, off-screen features, power features, network features, and the like.

304. And acquiring the number of the feature types in the feature type set.

For example, the number of feature types corresponding to the geographic location, that is, the feature number, may be counted.

305. And judging whether the number of the feature types is larger than the preset number, if so, executingstep 306, and if not, executingstep 307.

In the embodiment of the application, after the feature type set is obtained, the number of the feature types, that is, whether the number of the feature types is greater than a preset threshold value, is determined, if so, it indicates that the features are abundant enough, and at this time,step 306 may be executed to select the target feature information based on the feature type set. When the number of the feature types is not greater than the preset threshold, it indicates that the features are not rich enough, and the prediction is not accurate enough by using the feature information selected by the current feature type for prediction, and in order to ensure the prediction accuracy,step 307 may be executed to add a new feature type to the feature type set to enrich the feature types.

306. And selecting corresponding target feature information from the applied plurality of feature information according to the feature type set, and turning to step 308.

For example, the applied plurality of feature information may include the following 30-dimensional features, and it should be noted that the feature information shown below is only an example, and the number of the feature information actually included may be greater than or less than the number of the feature information shown below, and the specific feature information may be different from that shown below, and is not limited in detail here. The 30-dimensional features include:

the last time the APP switches into the background to the current time;

accumulating the screen closing time length during the period from the last time the APP switches into the background to the present time;

the number of times the APP enters the foreground in one day (counted per day);

the number of times that the APP enters the foreground in one day (the rest days are counted separately according to the working days and the rest days), for example, if the current predicted time is the working day, the feature usage value is the average usage number of the foreground in each working day counted by the working days;

the time of day (counted daily) of APP in the foreground;

the background APP is opened for times following the current foreground APP, and the times are obtained by statistics on the rest days without dividing into working days;

the background APP is opened for times following the current foreground APP, and statistics is carried out according to working days and rest days;

the switching modes of the target APP are divided into home key switching, receiver key switching and other APP switching;

target APP primary type (common application);

target APP secondary type (other applications);

the screen off time of the mobile phone screen;

the screen lightening time of the mobile phone screen;

the current screen is in a bright or dark state;

the current amount of power;

a current wifi state;

the last time that App switches into the background to the present time;

the last time the APP is used in the foreground;

if 6 time periods are divided in one day, each time period is 4 hours, the current prediction time point is 8:30 in the morning, and the current prediction time point is in the 3 rd period, the characteristic represents the time length of the target app used in the time period of 8: 00-12: 00 every day;

counting the average interval time of each day from the current foreground APP entering the background to the target APP entering the foreground;

counting average screen-off time per day from the current foreground APP entering the background to the target APP entering the foreground;

target APP in the background residence time histogram first bin (0-5 minutes corresponding times ratio);

target APP in the background residence time histogram first bin (5-10 minutes corresponding times ratio);

target APP in the first bin of the background residence time histogram (10-15 minutes corresponding times in proportion);

target APP in the first bin of the background residence time histogram (15-20 minutes corresponding times in proportion);

target APP in the first bin of the background residence time histogram (25-30 minutes corresponding times in proportion);

target APP in the first bin of the background dwell time histogram (corresponding number of times after 30 minutes is a ratio);

whether there is charging currently.

307. Adding a new feature type to the feature type set; and selecting corresponding target characteristic information from the applied plurality of characteristic information according to the characteristic type set.

For example, a training feature type of the prediction model may be obtained; determining a candidate feature type to be added according to the training feature type and the feature type set, wherein the candidate feature type is different from the feature types in the feature type set; and selecting a corresponding number of target candidate feature types from the candidate feature types according to the number difference between the preset number and the number, and adding the target candidate feature types to the feature type set.

308. And predicting whether the application can be closed according to the target characteristic information and the prediction model, if so, executing astep 309, and if not, ending the process or not processing the application.

For example, the probability that the application can be closed is obtained based on the target feature information and the logistic regression model; and when the probability is larger than the preset probability value, determining that the application can be closed, otherwise, not closing.

309. The application is closed.

An application shutdown device is also provided in an embodiment. Referring to fig. 5, fig. 5 is a schematic structural diagram of an application shutdown device according to an embodiment of the present disclosure. The application shutdown device is applied to an electronic device, and includes a receivingunit 401, alocation obtaining unit 402, afeature selecting unit 403, a predictingunit 404, and ashutdown unit 405, as follows:

a receivingunit 401, configured to receive an application closing request;

alocation obtaining unit 402, configured to obtain a current geographic location of the electronic device according to the application closing request;

afeature selecting unit 403, configured to select corresponding target feature information from multiple applied feature information according to the current geographic location;

aprediction unit 404, configured to predict whether the application can be closed according to the target feature information and a prediction model;

aclosing unit 405, configured to close the application when theprediction unit 404 predicts that the application may be closed.

In an embodiment, referring to fig. 6, thefeature extracting unit 403 may include:

atype obtaining subunit 4031, configured to obtain a feature type corresponding to the current geographic location, to obtain a feature type set;

a selectingsubunit 4032, configured to select, according to the feature type set, corresponding target feature information from the multiple pieces of applied feature information.

In an embodiment, the selectingsubunit 4032 may be used to:

acquiring the number of the feature types in the feature type set;

when the number is larger than a preset number, selecting corresponding target feature information from the applied plurality of feature information according to the feature type set;

when the number is not larger than the preset number, adding a new feature type to the feature type set; and selecting corresponding target characteristic information from the applied plurality of characteristic information according to the characteristic type set.

In an embodiment, the selectingsubunit 4032 may be used to obtain a training feature type of the prediction model; determining a candidate feature type to be added according to the training feature type and the feature type set, wherein the candidate feature type is different from the feature types in the feature type set; and selecting a corresponding number of target candidate feature types from the candidate feature types according to the number difference between the preset number and the number, and adding the target candidate feature types to the feature type set.

In an embodiment, referring to fig. 7, thefeature extracting unit 403 may include:

a model determining subunit 4033, configured to select a target prediction model corresponding to the current geographic location from multiple different prediction models;

aninformation selecting subunit 4034, configured to select target feature information corresponding to the target prediction model from the multiple applied feature information;

wherein the predictingunit 404 is configured to predict whether the application can be closed according to the target feature information and the target prediction model.

The steps performed by each unit in the application shutdown device may refer to the method steps described in the above method embodiments. The application shutdown device can be integrated in electronic equipment such as a mobile phone, a tablet computer and the like.

In a specific implementation, the above units may be implemented as independent entities, or may be combined arbitrarily to be implemented as the same or several entities, and the specific implementation of the above units may refer to the foregoing embodiments, which are not described herein again.

As can be seen from the above, in the application shutdown apparatus of this embodiment, the receivingunit 401 may receive the application shutdown request, and then thelocation obtaining unit 402 obtains the current geographic location of the electronic device according to the application shutdown request; selecting corresponding target feature information from the applied plurality of feature information by thefeature selection unit 403 according to the current geographic location; predicting, by theprediction unit 404, whether the application can be shut down based on the target feature information and the prediction model; if yes, theclosing unit 405 closes the application; the scheme realizes the automatic closing of the application, improves the operation smoothness of the electronic equipment and reduces the power consumption.

The embodiment of the application also provides the electronic equipment. Referring to fig. 8, anelectronic device 500 includes aprocessor 501 and amemory 502. Theprocessor 501 is electrically connected to thememory 502.

Theprocessor 500 is a control center of theelectronic device 500, connects various parts of the whole electronic device by using various interfaces and lines, executes various functions of theelectronic device 500 and processes data by running or loading a computer program stored in thememory 502 and calling data stored in thememory 502, thereby performing overall monitoring of theelectronic device 500.

Thememory 502 may be used to store software programs and modules, and theprocessor 501 executes various functional applications and data processing by operating the computer programs and modules stored in thememory 502. Thememory 502 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, a computer program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like. Further, thememory 502 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, thememory 502 may also include a memory controller to provide theprocessor 501 with access to thememory 502.

In this embodiment, theprocessor 501 in theelectronic device 500 loads instructions corresponding to one or more processes of the computer program into thememory 502, and theprocessor 501 runs the computer program stored in thememory 502, so as to implement various functions as follows:

receiving an application closing request;

and if so, closing the application.

In some embodiments, when selecting corresponding target feature information from the plurality of feature information of the application according to the current geographic location, theprocessor 501 may specifically perform the following steps:

In some embodiments, when selecting corresponding target feature information from the plurality of applied feature information according to the feature type set, theprocessor 501 may specifically perform the following steps:

acquiring the number of the feature types in the feature type set;

In some embodiments, when adding a new feature type to the set of feature types, theprocessor 501 may specifically perform the following steps:

obtaining a training feature type of the prediction model;

selecting target characteristic information corresponding to the target prediction model from the applied plurality of characteristic information;

when predicting whether the application can be closed according to the target feature information and the prediction model, theprocessor 501 may further specifically perform the following steps:

and predicting whether the application can be closed or not according to the target characteristic information and the target prediction model.

As can be seen from the above, the electronic device according to the embodiment of the present application may receive an application closing request, and then obtain a current geographic location of the electronic device according to the application closing request; selecting corresponding target characteristic information from the applied plurality of characteristic information according to the current geographic position; predicting whether the application can be closed or not according to the target characteristic information and the prediction model; if yes, closing the application; the scheme realizes the automatic closing of the application, improves the operation smoothness of the electronic equipment and reduces the power consumption.

Referring to fig. 9, in some embodiments, theelectronic device 500 may further include: adisplay 503,radio frequency circuitry 504,audio circuitry 505, and apower supply 506. Thedisplay 503, therf circuit 504, theaudio circuit 505, and thepower source 506 are electrically connected to theprocessor 501.

Thedisplay 503 may be used to display information entered by or provided to the user as well as various graphical user interfaces, which may be made up of graphics, text, icons, video, and any combination thereof. Thedisplay 503 may include a display panel, and in some embodiments, the display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.

Therf circuit 504 may be used for transceiving rf signals to establish wireless communication with a network device or other electronic devices via wireless communication, and for transceiving signals with the network device or other electronic devices.

Theaudio circuit 505 may be used to provide an audio interface between a user and an electronic device through a speaker, microphone.

Thepower source 506 may be used to power various components of theelectronic device 500. In some embodiments,power supply 506 may be logically coupled toprocessor 501 through a power management system, such that functions of managing charging, discharging, and power consumption are performed through the power management system.

Although not shown in fig. 9, theelectronic device 500 may further include a camera, a bluetooth module, and the like, which are not described in detail herein.

An embodiment of the present application further provides a storage medium, where the storage medium stores a computer program, and when the computer program runs on a computer, the computer is caused to execute the application shutdown method in any one of the above embodiments, for example: receiving an application closing request, and then acquiring the current geographic position of the electronic equipment according to the application closing request; selecting corresponding target characteristic information from the applied plurality of characteristic information according to the current geographic position; predicting whether the application can be closed or not according to the target characteristic information and the prediction model; if yes, closing the application; .

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

It should be noted that, for the application shutdown method in the embodiment of the present application, it can be understood by a person skilled in the art that all or part of the process of implementing the application shutdown method in the embodiment of the present application can be completed by controlling the relevant hardware through a computer program, where the computer program can be stored in a computer-readable storage medium, such as a memory of an electronic device, and executed by at least one processor in the electronic device, and during the execution process, the process of the embodiment of the application shutdown method can be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.

In the application shutdown device 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 application shutdown method, the application shutdown device, the storage medium and the electronic device provided by the embodiments of the present application are described in detail above, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiments is only used to help understand the method and the core idea 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

Translated fromChinese

1.一种应用关闭方法，其特征在于，包括：1. an application closing method, is characterized in that, comprises:

接收应用关闭请求；Receive an application shutdown request;

根据所述应用关闭请求获取电子设备的当前地理位置；Obtain the current geographic location of the electronic device according to the application closing request;

获取所述当前地理位置对应的特征类型，得到特征类型集合；Obtain the feature type corresponding to the current geographic location, and obtain a feature type set;

获取所述特征类型集合中特征类型的数量；Obtain the number of feature types in the feature type set;

当所述数量大于预设数量时，根据所述特征类型集合从所述应用的多个特征信息中选取相应的目标特征信息；When the number is greater than a preset number, selecting corresponding target feature information from a plurality of feature information of the application according to the feature type set;

当所述数量不大于预设数量时，添加新的特征类型至所述特征类型集合；When the number is not greater than the preset number, adding a new feature type to the feature type set;

根据所述特征类型集合从所述应用的多个特征信息中选取相应的目标特征信息，所述新的特征类型通过预测模型对应的训练特征类型确定，所述多个特征信息反映用户使用应用的行为习惯；Corresponding target feature information is selected from multiple feature information of the application according to the feature type set, the new feature type is determined by the training feature type corresponding to the prediction model, and the multiple feature information reflects the user's use of the application. behavioral habits;

从多个不同的预测模型中选取所述当前地理位置对应的目标预测模型；Select a target prediction model corresponding to the current geographic location from a plurality of different prediction models;

根据所述目标特征信息和所述目标预测模型预测所述应用是否可关闭；Predict whether the application can be closed according to the target feature information and the target prediction model;

若是，则对所述应用进行关闭。If so, the application is closed.

2.如权利要求1所述的应用关闭方法，其特征在于，根据所述特征类型集合从应用的多个特征信息中选取相应的目标特征信息，包括：2. The application closing method according to claim 1, wherein selecting corresponding target feature information from a plurality of feature information of the application according to the feature type set, comprising:

当所述数量不大于预设数量时，添加新的特征类型至所述特征类型集合；根据所述特征类型集合从所述应用的多个特征信息中选取相应的目标特征信息。When the number is not greater than the preset number, a new feature type is added to the feature type set; corresponding target feature information is selected from a plurality of feature information of the application according to the feature type set.

3.如权利要求2所述的应用关闭方法，其特征在于，添加新的特征类型至所述特征类型集合，包括：3. The application closing method of claim 2, wherein adding a new feature type to the feature type set comprises:

获取所述预测模型的训练特征类型；obtaining the training feature type of the prediction model;

根据所述训练特征类型以及所述特征类型集合，确定待添加的候选特征类型，所述候选特征类型区别于所述特征类型集合中的特征类型；According to the training feature type and the feature type set, determine the candidate feature type to be added, and the candidate feature type is different from the feature type in the feature type set;

根据所述预设数量与所述数量的数量差值，从候选特征类型中选取相应数量的目标候选特征类型，并添加至所述特征类型集合。According to the difference between the preset number and the number, a corresponding number of target candidate feature types are selected from the candidate feature types, and added to the feature type set.

4.一种应用关闭装置，其特征在于，包括：4. An application closing device, characterized in that, comprising:

接收单元，用于接收应用关闭请求；a receiving unit, configured to receive an application closing request;

位置获取单元，用于根据所述应用关闭请求获取电子设备的当前地理位置；a location acquiring unit, configured to acquire the current geographic location of the electronic device according to the application closing request;

类型获取子单元，用于获取所述当前地理位置对应的特征类型，得到特征类型集合；a type acquisition subunit, used to acquire the feature type corresponding to the current geographic location, and obtain a feature type set;

选取子单元，用于根据所述特征类型集合从应用的多个特征信息中选取相应的目标特征信息；A selection subunit for selecting corresponding target feature information from a plurality of applied feature information according to the feature type set;

模型确定子单元，用于获取所述特征类型集合中特征类型的数量，当所述数量大于预设数量时，根据所述特征类型集合从所述应用的多个特征信息中选取相应的目标特征信息，当所述数量不大于预设数量时，添加新的特征类型至所述特征类型集合，根据所述特征类型集合从所述应用的多个特征信息中选取相应的目标特征信息，所述新的特征类型通过预测模型对应的训练特征类型确定所述多个特征信息反映用户使用应用的行为习惯；A model determination subunit, configured to obtain the number of feature types in the feature type set, and when the number is greater than a preset number, select a corresponding target feature from a plurality of feature information of the application according to the feature type set information, when the number is not greater than the preset number, add a new feature type to the feature type set, and select corresponding target feature information from multiple feature information of the application according to the feature type set, and the The new feature type determines that the plurality of feature information reflects the behavior habits of the user using the application by using the training feature type corresponding to the prediction model;

所述预测单元，用于根据所述目标特征信息和所述目标预测模型，预测所述应用是否可关闭；the prediction unit, configured to predict whether the application can be closed according to the target feature information and the target prediction model;

关闭单元，用于在所述预测单元预测所述应用可关闭时，对所述应用进行关闭。A closing unit, configured to close the application when the prediction unit predicts that the application can be closed.

5.如权利要求4所述的应用关闭装置，其特征在于，所述选取子单元，用于：5. The application closing device according to claim 4, wherein the selection subunit is used for:

6.如权利要求5所述的应用关闭装置，其特征在于，所述选取子单元，用于：6. The application closing device according to claim 5, wherein the selection subunit is used for:

7.一种存储介质，其上存储有计算机程序，其特征在于，当所述计算机程序在计算机上运行时，使得所述计算机执行如权利要求1至3任一项所述的应用关闭方法。7. A storage medium on which a computer program is stored, characterized in that, when the computer program runs on a computer, the computer is caused to execute the application closing method according to any one of claims 1 to 3.

8.一种电子设备，包括处理器和存储器，所述存储器有计算机程序，其特征在于，所述处理器通过调用所述计算机程序，用于执行如权利要求1至3任一项所述的应用关闭方法。8. An electronic device comprising a processor and a memory, wherein the memory has a computer program, wherein the processor is used to execute the computer program according to any one of claims 1 to 3 by invoking the computer program. App close method.