WO2019085754A1

WO2019085754A1 - Application cleaning method and apparatus, and storage medium and electronic device

Info

Publication number: WO2019085754A1
Application number: PCT/CN2018/110632
Authority: WO
Inventors: 曾元清
Original assignee: Oppo广东移动通信有限公司
Priority date: 2017-10-31
Filing date: 2018-10-17
Publication date: 2019-05-09
Also published as: CN107807730A; CN107807730B

Abstract

Disclosed are an application cleaning method and apparatus, and a storage medium and an electronic device. The method in the embodiments of the present application involves: acquiring multi-dimensional features of an application to obtain a training feature set of the application; training a ridge regression model according to the training feature set to obtain a trained ridge regression model; acquiring the multi-dimensional features of the application to obtain a predictive feature set of the application; and predicting whether the application can be cleaned according to the predictive feature set and the trained ridge regression model.

Description

Application cleaning method, device, storage medium and electronic device

This application claims the priority of the Chinese Patent Application filed on Oct. 31, 2017, filed on Jan. 31, 2011, filed Jan. In this application.

Technical field

The present invention relates to the field of electronic device communication technologies, and in particular, to an application cleaning method, device, storage medium, and electronic device.

Background technique

At present, on electronic devices such as smart phones, there are usually multiple applications running simultaneously, one of which runs in the foreground and the other runs in the background. If the application running in the background is not cleaned for a long time, the available memory of the electronic device becomes smaller, and the central processing unit (CPU) usage rate is too high, causing the electronic device to run slower, jamming, and power consumption. Too fast and other issues.

Summary of the invention

The embodiment of the present application provides an application cleaning method, a device, a storage medium, and an electronic device, which can improve the running fluency of the electronic device and reduce power consumption.

In a first aspect, an application cleaning method provided by the embodiment of the present application includes:

Obtaining a multi-dimensional feature of the application to obtain a training feature set of the application;

The ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained;

Obtaining a multi-dimensional feature of the application to obtain a predicted feature set of the application;

And predicting whether the application is cleanable according to the predicted feature set and the trained ridge regression model.

In a second aspect, an application cleaning apparatus provided by the embodiment of the present application includes:

a training feature acquiring unit, configured to acquire a multi-dimensional feature of the application, and obtain a training feature set of the application;

a training unit, configured to train the ridge regression model according to the applied training feature set, and obtain a trained ridge regression model;

a prediction feature acquiring unit, configured to acquire a multi-dimensional feature of the application, to obtain a predicted feature set of the application;

And a prediction unit, configured to predict whether the application is cleanable according to the predicted feature set and the trained ridge regression model.

In a third aspect, a storage medium provided by an embodiment of the present application has a computer program stored thereon, and when the computer program is run on a computer, the computer is caused to perform an application cleaning method according to any embodiment of the present application.

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

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the present application. Other drawings can also be obtained from those skilled in the art based on these drawings without paying any creative effort.

FIG. 1 is a schematic diagram of an application scenario of an application cleaning method according to an embodiment of the present disclosure.

FIG. 2 is a schematic flowchart of an application cleaning method provided by an embodiment of the present application.

FIG. 3 is another schematic flowchart of an application cleaning method provided by an embodiment of the present application.

FIG. 4 is a schematic structural diagram of an application cleaning device according to an embodiment of the present application.

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

FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

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

Detailed ways

Referring to the drawings, the principles of the application are exemplified by being implemented in a suitable computing environment. The following description is based on the specific embodiments of the present invention as illustrated, and should not be construed as limiting the specific embodiments that are not described herein.

References to "an embodiment" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the present application. The appearances of the phrases in various places in the specification are not necessarily referring to the same embodiments, and are not exclusive or alternative embodiments that are mutually exclusive. Those skilled in the art will understand and implicitly understand that the embodiments described herein can be combined with other embodiments.

An embodiment of the present application provides an application cleaning method, including:

In some embodiments, the ridge regression model is trained according to the applied training samples to obtain a trained ridge regression model, including:

Establishing an error judgment function of the ridge regression model;

Obtaining a target ridge regression parameter of the ridge regression model according to the training feature set and the error judgment function, where the target ridge regression parameter includes a ridge parameter and a regression parameter;

A trained ridge regression model is obtained according to the target ridge regression parameter and the ridge regression model.

In some embodiments, acquiring a target ridge regression parameter of the ridge regression model according to the training feature set and the error determination function includes:

Obtaining a plurality of sets of ridge regression parameters according to the error judgment function, the ridge regression parameters including: a ridge parameter and a regression parameter;

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to the training feature set, the ridge regression parameter, and the error judgment function, and obtaining an error corresponding to each set of ridge regression parameters ;

Corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters.

In some embodiments, acquiring a plurality of sets of ridge regression parameters according to the error determination function comprises:

Obtaining a corresponding regression parameter acquisition function according to the error judgment function;

According to a plurality of preset ridge parameters and the regression parameter acquisition function, regression parameters corresponding to each preset ridge parameter are obtained, and multiple sets of ridge regression parameters are obtained.

In some embodiments, the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to the training feature set, the ridge regression parameter, and the error judgment function, including:

Dividing the training feature set into a plurality of sub-training feature sets;

Obtaining a sub-error of the sub-training set for the ridge regression model under the ridge regression parameter according to the sub-training feature set, the ridge regression parameter, and the error judging function, and obtaining a corresponding sub-training feature set Narrative error

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to a sub-error corresponding to each sub-training feature set.

In some embodiments, the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to a sub-error corresponding to each sub-training feature set, including:

Obtaining an average error of the sub-training feature set according to the sub-error corresponding to each sub-training feature set;

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to the average error.

In some embodiments, the corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters, including:

Determine the minimum error from the error corresponding to each set of ridge regression parameters;

A ridge regression parameter corresponding to the minimum error is selected from the plurality of sets of ridge regression parameters as a target ridge regression parameter.

In some embodiments, obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to the average error comprises:

The average error is directly taken as the error of the training feature set for the ridge regression model under the ridge regression parameter.

In some embodiments, predicting whether the application is cleanable based on the predicted feature set and the trained ridge regression model comprises:

Calculating a probability that the application can be cleaned according to the predicted feature set and the trained ridge regression model;

When the probability is greater than a preset threshold, it is determined that the application is cleanable.

The embodiment of the present application provides an application cleaning method, which may be a background application cleaning device provided by an embodiment of the present application, or an electronic device integrated with the application cleaning device, where the application cleaning device may adopt hardware. Or software implementation. The electronic device may be a device such as a smart phone, a tablet computer, a palmtop computer, a notebook computer, or a desktop computer.

Referring to FIG. 1 , FIG. 1 is a schematic diagram of an application scenario of an application cleaning method according to an embodiment of the present application. The application device is integrated into an electronic device as an example, and the electronic device can acquire a multi-dimensional feature of the application and obtain a training feature set of the application. According to the applied training feature set, the ridge regression model is trained to obtain the trained ridge regression model; the applied multi-dimensional features are obtained to obtain the applied prediction feature set; and the predicted feature set and the trained ridge regression model are used to predict whether the application can be used. Clean up. In addition, electronic devices can be cleaned up with cleanable applications.

Specifically, for example, as shown in FIG. 1 , in order to determine whether the application a (such as a mailbox application, a game application, and the like) running in the background can be cleaned up, the multi-dimensional feature of the application a can be collected in a historical time period (for example, the application a is The duration of the background running, the time information of the application a, etc.), the feature set of the application a is obtained, and the ridge regression model is trained according to the feature set (for example, the time length of the application a running in the background, the time information of the application a running, etc.) The ridge regression model after training; collecting the multi-dimensional features corresponding to the application according to the prediction time (such as t) (for example, the time length of the application a running in the background at time t, the time information of the application a running, etc.), and obtaining the predicted feature set of the application a; It is predicted whether the application a can be cleaned based on the predicted feature set and the trained ridge regression model. In addition, the electronic device cleans up the application a when it is predicted that the application a can be cleaned up.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of an application cleaning method according to an embodiment of the present application. The specific process of the application cleaning method provided by the embodiment of the present application may be as follows:

201. Acquire a multi-dimensional feature of the application, and obtain a training feature set of the application.

The application mentioned in the embodiment of the present application may be any application installed on the electronic device, such as an office application, a communication application, a game application, a shopping application, and the like. The application may include an application running in the foreground, that is, a foreground application, and may also include an application running in the background, that is, a background application.

In an embodiment, the application cleanup request may be received, the application to be cleaned is determined according to the application cleanup request, and then the multidimensional feature of the application is obtained, and the applied training feature set is obtained.

Specifically, the multi-dimensional feature of the application may be obtained from the feature database, wherein the multi-dimensional feature may be a multi-dimensional feature collected by the historical time, that is, a historical multi-dimensional feature. The feature database stores various features of the application at historical time.

The training feature set may include multi-dimensional features of the application, that is, multiple features of the application.

The multi-dimensional feature of the application has a dimension of a certain length, and the parameters in each dimension correspond to a feature information that represents the application, that is, the multi-dimensional feature is composed of multiple features. The plurality of features may include feature information related to the application itself, for example, the duration of the application cutting into the background; the duration of the electronic device when the application is cut into the background; the number of times the application enters the foreground; the time when the application is in the foreground; the application is in the background The time, the way the application enters the background, such as being switched by the home button (home button), being switched back by the return button, being switched in by other applications, etc.; the type of application, including the first level (common application), the second level (other applications) )Wait.

The multi-dimensional feature information may further include related feature information of the electronic device where the application is located, for example, the time when the electronic device is off, the time of the bright screen, the current power, the wireless network connection status of the electronic device, whether the electronic device is in the charging state, or the like.

Among them, the applied training samples include multi-dimensional features of the application. The multi-dimensional feature may be a plurality of features acquired at a preset frequency during a historical time period. The historical time period may be, for example, the past 7 days or 10 days; the preset frequency may be, for example, collected every 10 minutes and collected every half hour. It can be understood that the multi-dimensional feature data of the once collected application constitutes a training feature set.

In an embodiment, in order to facilitate application cleaning, the feature information that is not directly represented by the value in the multi-dimensional feature information of the application may be quantized by a specific value, for example, the feature information of the wireless network connection state of the electronic device may be used. The value 1 indicates the normal state, and the value 0 indicates the abnormal state (or vice versa); for example, for the characteristic information of whether the electronic device is in the charging state, the value 1 indicates the state of charge, and the value 0 indicates the uncharged state ( The opposite is also possible).

202. Train the ridge regression model according to the applied training feature set, and obtain the trained ridge regression model.

Among them, the ridge regression model can be a machine learning algorithm, ridge regression (Tikhonov regularization) algorithm, also known as ridge regression is a biased estimation regression method dedicated to collinear data analysis, which is essentially an improved The least squares estimation method, by abandoning the unbiasedness of the least squares method, obtains a more realistic and reliable regression method with the regression coefficient at the cost of losing part of the information and reducing the accuracy, and fits the ill-conditioned data more strongly than the least squares method. .

In the embodiment of the present application, the ridge regression model can be used to predict whether the application can be cleaned, wherein the output of the ridge regression model includes cleanable or non-cleanable. When using the ridge regression model to predict whether the application can be cleaned up, it is necessary to use the existing feature information to train the model to improve the accuracy of the prediction.

In an embodiment, the process of training the ridge regression model is a process of solving the ridge regression parameter of the ridge regression model. For example, the ridge regression parameter required by the ridge regression model may be calculated first, and then the ridge is determined based on the ridge regression parameter. The regression model is set. For example, the step "training the ridge regression model according to the applied training samples to obtain the post-training ridge regression model" may include:

Establish an error judgment function of the ridge regression model;

The target ridge regression parameter of the ridge regression model is obtained according to the training feature set and the error judgment function, and the target ridge regression parameter includes a ridge parameter and a regression parameter;

The trained ridge regression model is obtained according to the target ridge regression parameter and the ridge regression model.

Among them, the ridge regression parameter may include a ridge parameter and a regression parameter, and Ridge Regression is to add a regular term on the basis of the square error, and the balance between the variance and the deviation can be achieved by determining the value of λ: with λ As the model increases, the variance of the model decreases and the deviation increases. The ridge parameter can normalize the parameter λ, which can be the model parameter w of the ridge regression model to be solved.

In the embodiment of the present application, the error judgment function is a loss function of the ridge regression model, and is used to calculate an error between the output value and the true value of the ridge regression model on the sample.

In an embodiment, the error judgment function of the ridge regression model may include the following functions:

Where λ is the ridge parameter, ie the regularization parameter, x is the characteristic of the sample, w is the regression parameter of the ridge regression model, and n is the dimension of the feature.

In an embodiment, the error judgment function of the ridge regression model may be deformed to obtain a regression parameter acquisition function, and then the ridge regression parameter is obtained based on the regression parameter acquisition function. For example, the error judgment function can be derived to obtain a regression parameter acquisition function, and then the ridge regression parameter is obtained based on the regression parameter acquisition function and the training feature set.

For example, the error judgment function of the ridge regression model may include the following functions:

The error judgment function can be derived to obtain a function:

2X ^T (Y-XW)-2λW, where X is a matrix or vector of features x, X ^T is a transpose of X, and Y is a matrix or vector of y;

Then, by making 2X ^T (Y-XW)-2λW equal to zero, the following regression parameter calculation formula can be obtained:

among them,

The regression parameter to be solved.

After the regression parameter calculation formula is obtained, the regression parameter can be calculated based on the formula and the training feature set.

Finally, the ridge parameter λ and the corresponding regression parameters are obtained.

In an embodiment, in order to improve the prediction accuracy, multiple sets of ridge regression parameters may be calculated, and then the most suitable ridge regression parameters are selected. For example, the step "acquiring the target ridge regression parameter of the ridge regression model according to the training feature set and the error judgment function" may include:

According to the error judgment function, multiple sets of ridge regression parameters are obtained, and the ridge regression parameters include: ridge parameters and regression parameters;

According to the training feature set, the ridge regression parameter and the error judgment function, the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained, and the error corresponding to each set of ridge regression parameters is obtained;

According to the error corresponding to each group of ridge regression parameters, the corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters;

The error corresponding to the ridge regression parameter is the ridge regression model under the ridge regression parameter, and the error between the predicted value and the true value obtained by inputting the training sample set.

For example, you can get the ridge regression parameters.

Where m can be a positive integer greater than 2, which can be set according to actual needs, for example, 20, 30, 40...

Then, according to the training feature set, the ridge regression parameter

And an error judgment function to obtain regression parameters in the set

For the error Fk of the lower training model set for the ridge regression model, the errors corresponding to each set of ridge regression parameters are obtained, such as F1, F2, ..., Fk...Fm. Error F from the ridge regression parameters based on the regression parameters of each set of ridges

Select the corresponding target ridge regression parameter

In an embodiment, the error judgment function of the ridge regression model may be deformed to obtain a regression parameter acquisition function, and then the regression parameter is obtained based on the regression parameter acquisition function and the plurality of preset ridge parameters λ.

Get multiple sets of ridge regression parameters

For example, the error judgment function may be derived to obtain a regression parameter acquisition function, and then the ridge regression parameter is obtained based on the regression parameter acquisition function and the training feature set.

The error judgment function can be derived to obtain a function:

among them,

The regression parameter to be solved.

After obtaining the regression parameter calculation formula, the regression parameter can be calculated based on the formula and a plurality of preset ridge parameters λ.

For example, the initial value of the ridge parameter λ is 1, using the formula

Calculate the corresponding λ=1

Value; λ plus 1, reuse formula

Find λ=2 corresponding

Value; λ plus 1, reuse formula

Find λ=3 corresponding

Value... until the corresponding λ=m is obtained

Value, such as m=20. At this point, you can get m groups such as 20 groups of different

Value, and then get m group such as 20 groups

In an embodiment, in order to accurately and quickly acquire the error of the training feature set for the ridge regression model, the training feature set may be divided into a plurality of sub-training feature sets, and each sub-training feature set is obtained under the ridge regression parameter. The error f of the ridge regression model is then obtained based on the error of the sub-training feature set for the ridge regression model under the ridge regression parameter to obtain the error F of the entire training feature set for the ridge regression model under the ridge regression parameter.

For example, the step "acquiring the error of the training feature set to the ridge regression model under the ridge regression parameter according to the training feature set, the ridge regression parameter, and the error judgment function" may include:

According to the sub-training feature set, the ridge regression parameter and the error judgment function, the sub-errors of the sub-training set for the ridge regression model under the ridge regression parameter are obtained, and the sub-error corresponding to each sub-training feature set is obtained;

According to the sub-error corresponding to each sub-training feature set, the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained.

The number of sub-training feature set partitions can be set according to actual needs, such as 10, 20, and so on. In an embodiment, to improve the accuracy of the error acquisition, the sub-training feature set includes the same number of features, that is, the training feature set is equally divided into a plurality of sub-training feature sets.

For example, the training feature set D may be divided into M sub-training feature sets to obtain sub-training feature sets D1, D2, . . . DM; wherein M is a positive integer greater than one. Then, according to the error judgment function and the ridge regression parameter, calculate the sub-error of each sub-training feature set for the ridge regression model under the ridge regression parameter, such as D1 in the ridge regression parameter

The sub-errors f11 and D2 for the ridge regression model are in the ridge regression parameters.

Sub-errors f12, ... DM in the ridge regression model

The sub-error f1M for the ridge regression model is based on the ridge regression parameter for each sub-training feature set.

The sub-errors for the ridge regression model, ie f11, f12...f1M, obtain the training feature D in the ridge regression parameter

The error F1 for the ridge regression model.

Then, according to the error judgment function and the next set of ridge regression parameters, calculate the sub-error of each sub-training feature set under the ridge regression parameter for the next set of ridge regression models, such as D1 in the ridge regression parameter

The sub-errors f21 and D2 for the ridge regression model are in the ridge regression parameters.

Sub-errors for the ridge regression model, f22, ... DM in the ridge regression parameters

The sub-error f2M for the ridge regression model is based on the ridge regression parameter for each sub-training feature set.

The sub-errors for the ridge regression model, ie f21, f22...f2M, obtain the training feature D in the ridge regression parameter

The error F2 for the ridge regression model.

By analogy, the error of the training feature set in the m-group ridge regression parameter for the ridge regression model can be calculated, and the errors F1, F2, ... Fm are obtained.

After obtaining the sub-error corresponding to each sub-training feature set, the embodiment of the present application can obtain the error of the entire training feature set for the ridge regression model based on the sub-error, and the obtaining manner can be various. For example, in an embodiment, in order to improve the accuracy of the error, the average value of the sub-errors may be calculated, and then the error of the entire training feature set for the ridge regression model is obtained based on the average value. For example, the step “acquiring the error of the training feature set to the ridge regression model under the ridge regression parameter according to the sub-error corresponding to each sub-training feature set” may include:

The error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to the average error.

In an embodiment, the average error can be used as an error of the training feature set for the ridge regression model under the ridge regression parameter.

For example, the ridge regression parameter is

For example, in the calculation of the sub-errors f11, D2 for the ridge regression model under the ridges in the ridge regression parameters

Sub-errors f12, ... DM in the ridge regression model

For the sub-errors of the ridge regression model, ie f11, f12...f1M, the average error f'=(f11+f12+...+f1M)/M can be calculated; the f' is the training feature D in the ridge regression parameter.

The error F1 for the ridge regression model.

In an embodiment, in order to improve parameter accuracy and prediction accuracy, after obtaining the error corresponding to each set of ridge regression parameters, the ridge regression parameter corresponding to the smallest error may be selected as the target ridge regression parameter of the ridge regression model, that is, finally parameter.

For example, after obtaining the error corresponding to each group of ridge regression parameters such as F1, F2, ..., Fk...Fm, it is assumed that Fk is the smallest. At this time, the ridge regression parameter corresponding to Fk can be selected.

The target ridge regression parameter as a ridge regression model.

According to the above description, the selection process of the target ridge regression parameters, that is, the training process of the ridge regression model, will be introduced as follows: the ridge regression parameter is 20 groups and the number of sub-training feature sets is 10.

(1) The error judgment function for establishing the ridge regression is:

For the purpose of derivation, the result is:

2X ^T (Y-XW)-2λW

Let its value be 0 to find the value of w:

(2), the initial value of λ is 1, according to the step (3)

Formula calculation

value.

(3), λ plus 1, repeat (2) steps to obtain 20 different sets

value;

(4), divide the feature set into 10 equal parts, and select (3) step

A numerical value, the following error judgment formula respectively calculates the different error values of each sub-feature set of 10 equal parts for the ridge regression, and obtains 10 different error values:

Then calculate the average error value of the error value of 10 sets of sub-feature sets for the ridge regression, and use the average error value as the feature set in the selected

And the error of the regression of λ on the ridge;

(5), repeat (4) steps, respectively calculate the feature set in 20 different groups

The characteristic error for the ridge regression under the value;

(6), the 20 sets of characteristic errors obtained from (5) take the minimum value corresponding to

And λ value, the

The λ value is the ridge regression fitting to obtain the ridge regression parameters, that is, the parameters finally selected by the ridge regression model.

The ridge regression parameters corresponding to each application can be calculated by the above steps (1)-(6).

203. Obtain a multi-dimensional feature of the application, and obtain a set of predicted features of the application.

For example, the multidimensional features of the application can be collected as prediction samples based on the predicted time.

The prediction time can be set according to requirements, such as the current time.

For example, a multi-dimensional feature of an application can be acquired as a prediction sample at a predicted time point.

In the embodiment of the present application, the multi-dimensional features collected in

steps

201 and 203 are the same type of features, for example, the length of time when the application is cut into the background; the time when the application is cut into the background, the duration of the electronic device; the number of times the application enters the foreground; The time at the front desk; the way the app enters the background.

204. Predict whether the application is cleanable according to the predicted feature set and the trained ridge regression model.

For example, the probability that the application can be cleaned can be calculated based on the ridge regression model and the predicted feature set, and when the probability is greater than a certain threshold, the application can be cleaned and the like.

As can be seen from the above, the embodiment of the present application obtains the multi-dimensional features of the application, and obtains the applied training feature set; the ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained; the multi-dimensional feature of the application is obtained, and the application is obtained. The set of predicted features; predicting whether the application can be cleaned according to the predicted feature set and the trained ridge regression model; in order to clean up the cleanable application; the solution can automatically clean the application and improve the running fluency of the electronic device. Reduced power consumption and saved resources.

Further, since the feature set includes a plurality of feature information that reflects the behavior of the user using the application, the embodiment of the present application can make the cleaning of the corresponding application more personalized and intelligent.

Further, the application of the cleanup prediction based on the ridge regression model can improve the accuracy of the user behavior prediction, thereby improving the accuracy of the cleanup. In addition, in the embodiment of the present application, multiple sets of ridge regression parameters can be calculated during the training of the model, and the ridge regression parameter with the lowest error of the feature error is used as the final parameter of the ridge regression model, and the ridge regression model can be further improved. The accuracy of the forecast for application cleanup.

The cleaning method of the present application will be further described below based on the method described in the above embodiments. Referring to FIG. 3, the application cleaning method may include:

301. Obtain a multi-dimensional feature of the application, and obtain a training feature set of the application.

For example, the multi-dimensional feature of the application is obtained from the feature database, wherein the multi-dimensional feature can be a multi-dimensional feature collected by the historical time, that is, a historical multi-dimensional feature. The feature database stores various features of the application at historical time.

A specific training feature set may be as follows, including feature information of multiple dimensions (30 dimensions). It should be noted that the feature information shown below is only an example. In practice, a feature included in a training feature set is included. The number of the information may be more than the number of the information shown below, or may be less than the number of the information shown below. The specific feature information may be different from the feature information shown below, and is not specifically limited herein.

The last time the APP cuts into the background until now;

The length of the screen off time accumulated by the APP during the last cut into the background;

The number of times the APP enters the foreground in one day (by daily statistics);

The number of times the APP enters the foreground in the day (the rest day is divided by the working day and the rest day). For example, if the current forecasting time is the working day, the feature usage value is the average number of working days in the foreground for each working day.

APP time (by daily statistics) is in the foreground;

The background APP is followed by the number of times the current foreground APP is opened, regardless of the workday rest day statistics;

The background APP is closely followed by the number of times the current foreground APP is opened, and is counted as a workday rest day;

The way in which the target APP is switched is divided into a home key switch, a recent key switch, and another APP switch;

Target APP level type (common application);

Target APP secondary type (other applications);

The screen of the mobile phone is off;

Phone screen bright time;

The current screen is on and off;

Current power;

Current wifi status;

The last time the app cuts into the background until now;

The last time the APP was used in the foreground;

If there are 6 time periods in a day, 4 hours each, and the current predicted time is 8:30 in the morning, then in the third paragraph, the feature indicates that the target app is being used every day from 8:00 to 12:00. Length of time used;

The current front-end APP enters the background to the target APP and enters the foreground according to the average interval of daily statistics;

The current foreground APP goes to the background until the target APP enters the foreground and the average screen is extinguished by daily statistics;

The target APP stays in the background for the first bin of the histogram (the proportion of times corresponding to 0-5 minutes);

The target APP stays in the background for the first bin of the histogram (5-10 minutes corresponds to the proportion of times);

The target APP stays in the background for the first bin of the histogram (10-15 minutes corresponds to the proportion of times);

The target APP stays in the background for the first bin of the histogram (15-20 minutes corresponds to the proportion of times);

The target APP stays in the background for the first bin of the histogram (25-30 minutes corresponds to the proportion of times);

The target APP stays in the background for the first bin of the histogram (the proportion of the corresponding number of times after 30 minutes);

Is there currently charging?

302. Establish an error judgment function of the ridge regression model, and obtain a corresponding regression parameter acquisition function according to the error judgment function.

The error judgment function can be derived to obtain a function:

among them,

The regression parameter to be solved.

303. Acquire a plurality of regression parameters according to a plurality of preset ridge parameters and a regression parameter acquisition function, and obtain multiple sets of ridge regression parameters.

Among them, the ridge regression parameters include the ridge parameter λ and the corresponding regression parameters

Calculate the corresponding λ=1

Value; λ plus 1, reuse formula

Find λ=2 corresponding

Value; λ plus 1, reuse formula

Find λ=3 corresponding

Value... until the corresponding λ=m is obtained

Value, and then get m group such as 20 groups

304. Divide the training feature set into a plurality of sub-training feature sets.

305. Obtain a sub-error of the sub-training set for the ridge regression model under the ridge regression parameter according to the sub-training feature set, the ridge regression parameter, and the error judging function.

Sub-errors f12, ... DM in the ridge regression model

For the sub-error f1M of the ridge regression model, the sub-training feature set is obtained in the ridge regression parameter.

The sub-errors for the ridge regression model, f11, f12...f1M.

306. According to the sub-error of each sub-training set for the ridge regression model under the ridge regression parameter, obtain the error of the training feature set for the ridge regression model under the ridge regression parameter, and repeat

steps

305 and 306 to obtain the training characteristics of each set of ridge regression parameters. The error for the ridge regression model.

For example, according to the sub-error corresponding to each sub-training feature set, the average error of the sub-training feature set is obtained; and the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to the average error.

For example, the ridge regression parameter is

Sub-errors f12, ... DM in the ridge regression model

The error F1 for the ridge regression model.

Then repeat

steps

305 and 306 to calculate the error of the training feature set for the ridge regression model under each set of ridge regression parameters; such as the ridge regression parameter

Corresponding errors F1, F2, ..., Fk ... Fm, respectively.

307. Select the ridge regression parameter corresponding to the minimum error as the target ridge regression parameter of the ridge regression model.

The target ridge regression parameter as a ridge regression model.

In an embodiment, the above steps 301-307 are repeated to obtain the ridge regression parameters corresponding to each application.

308. Update the corresponding parameters in the ridge regression model according to the target ridge regression parameter, and obtain the trained ridge regression model.

For example, the value of the regression parameter w in the ridge regression model is updated.

In an embodiment, repeating the above steps 301-308 can obtain a post-training ridge regression model corresponding to each application.

309. Obtain a multi-dimensional feature of the application, and obtain a set of predicted features of the application.

In the embodiment of the present application, the multi-dimensional feature collected in the step is the same type of feature as the feature acquired in step 301, that is, the predicted feature set and the feature set included in the training feature set are the same, for example, the time length of the application is cut into the background. The time when the application is cut into the background, the duration of the electronic device; the number of times the application enters the foreground; the time the application is in the foreground; and the way the application enters the background.

310. Predict whether the application can be cleaned according to the predicted feature set and the trained ridge regression model.

In a specific example, the post-training ridge regression model of each background application can be obtained through the above steps 301-308; then, based on the post-training ridge regression model of each background application, it is predicted whether multiple applications running in the background can be cleaned up, As shown in Table 1, it is determined that the application A1 and the application A3 running in the background can be cleaned, while the state in which the application A2 is running in the background is maintained.

应用application	预测结果forecast result
应用A1Application A1	可清理Can be cleaned
应用A2Application A2	不可清理Can't clean
应用A3Application A3	可清理Can be cleaned

Table 1

The embodiment of the present application further provides an application cleaning device, including:

In some embodiments, the training unit includes:

Establishing a subunit for establishing an error judgment function of the ridge regression model;

a parameter obtaining subunit, configured to acquire a target ridge regression parameter of the ridge regression model according to the training feature set and the error judgment function, where the target ridge regression parameter includes a ridge parameter and a regression parameter;

And a training subunit, configured to obtain a trained ridge regression model according to the target ridge regression parameter and the ridge regression model.

In some embodiments, the parameter acquisition subunit is configured to:

In some embodiments, the parameter acquisition subunit is specifically configured to:

In some embodiments, the parameter acquisition subunit is specifically configured to: directly use the average error as an error of the training feature set for the ridge regression model under the ridge regression parameter.

In some embodiments, the predicting unit is configured to:

An application cleaning device is also provided in an embodiment. Please refer to FIG. 4. FIG. 4 is a schematic structural diagram of an application cleaning apparatus according to an embodiment of the present application. The application cleaning device is applied to an electronic device, and the application cleaning device includes a training feature acquisition unit 401, a training unit 402, a prediction feature acquisition unit 403, and a prediction unit 404, as follows:

The training feature acquiring unit 401 is configured to acquire a multi-dimensional feature of the application, and obtain a training feature set of the application;

The training unit 402 is configured to train the ridge regression model according to the applied training feature set to obtain a trained ridge regression model;

a prediction feature acquisition unit 403, configured to acquire a multi-dimensional feature of the application, to obtain a prediction feature set of the application;

The prediction unit 404 is configured to predict whether the application is cleanable according to the predicted feature set and the trained ridge regression model.

In an embodiment, referring to FIG. 5, the training unit 402 includes:

Establishing a sub-unit 4021 for establishing an error judgment function of the ridge regression model;

a parameter obtaining sub-unit 4022, configured to acquire a target ridge regression parameter of the ridge regression model according to the training feature set and the error determination function, where the target ridge regression parameter includes a ridge parameter and a regression parameter;

The training sub-unit 4023 is configured to obtain a trained ridge regression model according to the target ridge regression parameter and the ridge regression model.

In an embodiment, the parameter acquisition subunit 4022 can be used to:

In an embodiment, the parameter obtaining subunit 4022 may be specifically configured to:

In an embodiment, the parameter obtaining sub-unit 4022 may be specifically configured to: directly use the average error as an error of the training feature set for the ridge regression model under the ridge regression parameter.

In an embodiment, the predicting unit 404 is configured to:

When the probability is greater than a preset threshold, it is determined that the application is cleanable. The steps performed by each unit in the application cleaning device may refer to the method steps described in the foregoing method embodiments. The application cleaning device can be integrated in an electronic device such as a mobile phone, a tablet computer, or the like.

In the specific implementation, the foregoing various units may be implemented as an independent entity, and may be implemented in any combination, and may be implemented as the same entity or a plurality of entities. For the specific implementation of the foregoing units, refer to the foregoing embodiments, and details are not described herein again.

The term "module" "unit" as used herein may be taken to mean a software object that is executed on the computing system. The different components, modules, engines, and services described herein can be considered as implementation objects on the computing system. The apparatus and method described herein may be implemented in software, and may of course be implemented in hardware, all of which are within the scope of the present application.

It can be seen that, in the embodiment, the application cleaning device can obtain the multi-dimensional feature of the application by the training feature acquiring unit 401, and obtain the applied training feature set; the training unit 402 trains the ridge regression model according to the applied training feature set, and obtains the training. The ridge regression model; the multi-dimensional feature of the application is obtained by the prediction feature acquisition unit 403 to obtain the applied prediction feature set; the prediction unit 404 predicts whether the application can be cleaned according to the predicted feature set and the trained ridge regression model; The application is cleaned; the solution can automatically clean the application, improve the running fluency of the electronic device, reduce power consumption and save resources.

An embodiment of the present application further provides an electronic device. Referring to FIG. 6, the electronic device 500 includes a processor 501 and a memory 502. The processor 501 is electrically connected to the memory 502.

The processor 500 is a control center of the electronic device 500 that connects various portions of the entire electronic device using various interfaces and lines, by running or loading a computer program stored in the memory 502, and recalling data stored in the memory 502, The various functions of the electronic device 500 are performed and the data is processed to perform overall monitoring of the electronic device 500.

The memory 502 can be used to store software programs and modules, and the processor 501 executes various functional applications and data processing by running computer programs and modules stored in the memory 502. The memory 502 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, a computer program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be stored according to Data created by the use of electronic devices, etc. Moreover, memory 502 can include high speed random access memory, and can 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, memory 502 can also include a memory controller to provide processor 501 access to memory 502.

In the embodiment of the present application, the processor 501 in the electronic device 500 loads the instructions corresponding to the process of one or more computer programs into the memory 502 according to the following steps, and is stored in the memory 502 by the processor 501. The computer program in which to implement various functions, as follows:

Obtaining multi-dimensional features of the application, and obtaining a training feature set of the application;

And predicting whether the application is clearable according to the predicted feature set and the trained ridge regression model.

In some embodiments, when the ridge regression model is trained according to the applied training samples to obtain the trained ridge regression model, the processor 501 may specifically perform the following steps:

Establishing an error judgment function of the ridge regression model;

In some embodiments, when acquiring the target ridge regression parameter of the ridge regression model according to the training feature set and the error judgment function, the processor 501 may specifically perform the following steps:

In some embodiments, when acquiring a plurality of sets of ridge regression parameters according to the error determination function, the processor 501 may specifically perform the following steps:

In some embodiments, when an error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to the training feature set, the ridge regression parameter, and the error judgment function, The processor 501 can specifically perform the following steps:

In some embodiments, when obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to a sub-error corresponding to each sub-training feature set, the processor 501 may specifically perform the following steps. :

In some embodiments, when selecting a corresponding target ridge regression parameter from the plurality of sets of ridge regression parameters according to an error corresponding to each set of ridge regression parameters, the processor 501 may specifically perform the following steps:

In some embodiments, when acquiring an error of the training feature set for the ridge regression model under the ridge regression parameter according to the average error, the processor 501 may specifically perform the following steps:

In some embodiments, when predicting whether the application is cleanable according to the predicted feature set and the trained ridge regression model, the processor 501 may specifically perform the following steps:

It can be seen from the above that the electronic device in the embodiment of the present application acquires the multi-dimensional feature of the application, obtains the applied training feature set, and trains the ridge regression model according to the applied training feature set to obtain the trained ridge regression model; Feature, obtain the applied feature set; according to the predicted feature set and the trained ridge regression model, predict whether the application can be cleaned; to clean up the cleanable application; the solution can automatically clean the application and improve the electronic device Smooth operation and reduced power consumption.

Referring to FIG. 7 together, in some embodiments, the electronic device 500 may further include: a display 503, a radio frequency circuit 504, an audio circuit 505, and a power source 506. The display 503, the radio frequency circuit 504, the audio circuit 505, and the power source 506 are electrically connected to the processor 501, respectively.

The display 503 can be used to display information entered by a user or information provided to a user, as well as various graphical user interfaces, which can be composed of graphics, text, icons, video, and any combination thereof. The display 503 can include a display panel. In some embodiments, the display panel can be configured in the form of a liquid crystal display (LCD) or an organic light-emitting diode (OLED).

The radio frequency circuit 504 can be used to transmit and receive radio frequency signals to establish wireless communication with a network device or other electronic device through wireless communication, and to transmit and receive signals with a network device or other electronic device.

The audio circuit 505 can be used to provide an audio interface between a user and an electronic device through a speaker or a microphone.

The power source 506 can be used to power various components of the electronic device 500. In some embodiments, the power source 506 can be logically coupled to the processor 501 through a power management system to enable functions such as managing charging, discharging, and power management through the power management system.

Although not shown in FIG. 7, the electronic device 500 may further include a camera, a Bluetooth module, and the like, and details are not described herein again.

The 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, causes the computer to execute an application cleaning method in any of the above embodiments, such as: obtaining Applying the multi-dimensional characteristics, the applied training feature set is obtained; the ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained; the applied multi-dimensional features are obtained, and the applied predicted feature set is obtained; according to the predicted feature set And the trained ridge regression model to predict whether the application can be cleaned up.

In the embodiment of the present application, the storage medium may be a magnetic disk, an optical disk, a read only memory (ROM), or a random access memory (RAM).

In the above embodiments, the descriptions of the various embodiments are different, and the details that are not detailed in a certain embodiment can be referred to the related descriptions of other embodiments.

It should be noted that, in the application cleaning method of the embodiment of the present application, a common tester in the art can understand all or part of the process of implementing the application cleaning method of the embodiment of the present application, which can be completed by controlling the related hardware through a computer program. The computer program may be stored in a computer readable storage medium, such as in a memory of the electronic device, and executed by at least one processor in the electronic device, and may include, for example, an application cleaning method during execution. The flow of the embodiment. The storage medium may be a magnetic disk, an optical disk, a read only memory, a random access memory, or the like.

For the application cleaning device of the embodiment of the present application, each functional module may be integrated into one processing chip, or each module may exist physically separately, or two or more modules may be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. The integrated module, if implemented in the form of a software functional module and sold or used as a standalone product, may also be stored in a computer readable storage medium, such as a read only memory, a magnetic disk or an optical disk, etc. .

The application clearing method, device, storage medium and electronic device provided by the embodiments of the present application are described in detail. The principles and implementation manners of the application are described in the specific examples. The description of the above embodiments is only The method for understanding the present application and its core idea; at the same time, those skilled in the art, according to the idea of the present application, there will be changes in the specific embodiment and the scope of application, in summary, the present specification The content should not be construed as limiting the application.

Claims

An application cleaning method, which includes:

Obtaining a multi-dimensional feature of the application to obtain a training feature set of the application;

The ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained;

Obtaining a multi-dimensional feature of the application to obtain a predicted feature set of the application;

And predicting whether the application is cleanable according to the predicted feature set and the trained ridge regression model.
The application cleaning method according to claim 1, wherein the ridge regression model is trained according to the applied training samples, and the trained ridge regression model is obtained, including:

Establishing an error judgment function of the ridge regression model;

Obtaining a target ridge regression parameter of the ridge regression model according to the training feature set and the error judgment function, where the target ridge regression parameter includes a ridge parameter and a regression parameter;

A trained ridge regression model is obtained according to the target ridge regression parameter and the ridge regression model.
The application cleanup method according to claim 2, wherein the target ridge regression parameters of the ridge regression model are obtained according to the training feature set and the error judgment function, including:

Obtaining a plurality of sets of ridge regression parameters according to the error judgment function, the ridge regression parameters including: a ridge parameter and a regression parameter;

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to the training feature set, the ridge regression parameter, and the error judgment function, and obtaining an error corresponding to each set of ridge regression parameters ;

Corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters.
The application cleaning method according to claim 3, wherein the plurality of sets of ridge regression parameters are obtained according to the error judgment function, including:

Obtaining a corresponding regression parameter acquisition function according to the error judgment function;

According to a plurality of preset ridge parameters and the regression parameter acquisition function, regression parameters corresponding to each preset ridge parameter are obtained, and multiple sets of ridge regression parameters are obtained.
The application cleaning method according to claim 3, wherein the training feature set is acquired for the ridge regression under the ridge regression parameter according to the training feature set, the ridge regression parameter, and the error judgment function Model errors, including:

Dividing the training feature set into a plurality of sub-training feature sets;

Obtaining a sub-error of the sub-training set for the ridge regression model under the ridge regression parameter according to the sub-training feature set, the ridge regression parameter, and the error judging function, and obtaining a corresponding sub-training feature set Narrative error

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to a sub-error corresponding to each sub-training feature set.
The application cleaning method according to claim 5, wherein the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to a sub-error corresponding to each sub-training feature set, including:

Obtaining an average error of the sub-training feature set according to the sub-error corresponding to each sub-training feature set;

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to the average error.
The application cleaning method according to claim 3, wherein the corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters, including:

Determine the minimum error from the error corresponding to each set of ridge regression parameters;

A ridge regression parameter corresponding to the minimum error is selected from the plurality of sets of ridge regression parameters as a target ridge regression parameter.
The application cleaning method according to claim 6, wherein the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to the average error, including:

The average error is directly taken as the error of the training feature set for the ridge regression model under the ridge regression parameter.
The application cleanup method according to claim 1, wherein predicting whether the application is cleanable according to the predicted feature set and the trained ridge regression model comprises:

Calculating a probability that the application can be cleaned according to the predicted feature set and the trained ridge regression model;

When the probability is greater than a preset threshold, it is determined that the application is cleanable.
An application cleaning device, comprising:

a training feature acquiring unit, configured to acquire a multi-dimensional feature of the application, and obtain a training feature set of the application;

a training unit, configured to train the ridge regression model according to the applied training feature set, and obtain a trained ridge regression model;

a prediction feature acquiring unit, configured to acquire a multi-dimensional feature of the application, to obtain a predicted feature set of the application;

And a prediction unit, configured to predict whether the application is cleanable according to the predicted feature set and the trained ridge regression model.
The application cleaning device of claim 10, wherein the training unit comprises:

Establishing a subunit for establishing an error judgment function of the ridge regression model;

a parameter obtaining subunit, configured to acquire a target ridge regression parameter of the ridge regression model according to the training feature set and the error judgment function, where the target ridge regression parameter includes a ridge parameter and a regression parameter;

And a training subunit, configured to obtain a trained ridge regression model according to the target ridge regression parameter and the ridge regression model.
The application cleaning device according to claim 11, wherein said parameter acquisition subunit is configured to:

Obtaining a plurality of sets of ridge regression parameters according to the error judgment function, the ridge regression parameters including: a ridge parameter and a regression parameter;

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to the training feature set, the ridge regression parameter, and the error judgment function, and obtaining an error corresponding to each set of ridge regression parameters ;

Corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters.
The application cleaning device according to claim 12, wherein the parameter acquisition subunit is specifically configured to:

Obtaining a corresponding regression parameter acquisition function according to the error judgment function;

According to a plurality of preset ridge parameters and the regression parameter acquisition function, regression parameters corresponding to each preset ridge parameter are obtained, and multiple sets of ridge regression parameters are obtained.
The application cleaning device according to claim 12, wherein the parameter acquisition subunit is specifically configured to:

Dividing the training feature set into a plurality of sub-training feature sets;

Obtaining a sub-error of the sub-training set for the ridge regression model under the ridge regression parameter according to the sub-training feature set, the ridge regression parameter, and the error judging function, and obtaining a corresponding sub-training feature set Narrative error

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to a sub-error corresponding to each sub-training feature set.
The application cleaning device according to claim 14, wherein the parameter acquisition subunit is specifically configured to:

Dividing the training feature set into a plurality of sub-training feature sets;

Obtaining a sub-error of the sub-training set for the ridge regression model under the ridge regression parameter according to the sub-training feature set, the ridge regression parameter, and the error judging function, and obtaining a corresponding sub-training feature set Narrative error

Obtaining an average error of the sub-training feature set according to the sub-error corresponding to each sub-training feature set;

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to the average error.
The application cleaning device according to claim 12, wherein the parameter acquisition subunit is specifically configured to:

Obtaining a plurality of sets of ridge regression parameters according to the error judgment function, the ridge regression parameters including: a ridge parameter and a regression parameter;

Obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to the training feature set, the ridge regression parameter, and the error judgment function, and obtaining an error corresponding to each set of ridge regression parameters ;

Determine the minimum error from the error corresponding to each set of ridge regression parameters;

A ridge regression parameter corresponding to the minimum error is selected from the plurality of sets of ridge regression parameters as a target ridge regression parameter.
The application cleaning device according to claim 15, wherein the parameter acquisition subunit is specifically configured to: directly use the average error as the training feature set for the ridge regression model under the ridge regression parameter error.
The application cleaning device according to claim 10, wherein the prediction unit is configured to:

Calculating a probability that the application can be cleaned according to the predicted feature set and the trained ridge regression model;

When the probability is greater than a preset threshold, it is determined that the application is cleanable.
A storage medium having stored thereon a computer program, wherein when the computer program is run on a computer, the computer is caused to perform the application cleaning method according to any one of claims 1 to 9.
An electronic device comprising a processor and a memory, the memory having a computer program, wherein the processor is configured to execute the application cleaning method according to any one of claims 1 to 9 by calling the computer program.