CN107870811B

CN107870811B - Application cleaning method and device, storage medium and electronic equipment

Info

Publication number: CN107870811B
Application number: CN201711047124.5A
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: 2020-02-11
Anticipated expiration: 2037-10-31
Also published as: CN107870811A

Abstract

The embodiment of the application discloses an application cleaning method, an application cleaning device, a storage medium and electronic equipment, wherein a plurality of characteristic information of an application are acquired; selecting a plurality of same prediction models; selecting characteristic information corresponding to each prediction model from the plurality of characteristic information to obtain a characteristic information set of each prediction model, wherein the characteristic information sets of each prediction model are different; predicting whether the application can be cleaned or not according to the prediction models and the characteristic information sets thereof to obtain the prediction result of each prediction model; and finally determining whether the application can be cleaned or not according to the prediction result of each prediction model so as to clean the cleanable application, thereby realizing automatic cleaning of the application, improving the operation smoothness of the electronic equipment and reducing the power consumption.

Description

Application cleaning 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 cleaning method, an application cleaning apparatus, 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 cleaning 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 cleaning method, including:

acquiring a plurality of characteristic information of an application;

selecting a plurality of same prediction models;

selecting feature information corresponding to each prediction model from the feature information to obtain a feature information set of each prediction model, wherein the feature information sets of each prediction model are different;

predicting whether the application can be cleaned or not according to the prediction model and the characteristic information set of the prediction model to obtain a prediction result of each prediction model;

and finally determining whether the application can be cleaned according to the prediction result of each prediction model.

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

a feature acquisition unit configured to acquire a plurality of feature information of an application;

the model selection unit is used for selecting a plurality of same prediction models;

the characteristic selection unit is used for selecting the characteristic information corresponding to each prediction model from the plurality of characteristic information to obtain a characteristic information set of each prediction model, wherein the characteristic information sets of each prediction model are different;

the prediction unit is used for predicting whether the application can be cleaned or not according to the prediction model and the characteristic information set of the prediction model to obtain a prediction result of each prediction model;

and the determining unit is used for finally determining whether the application can be cleaned according to the prediction result of each prediction model.

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 cleaning 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 cleaning method provided in any embodiment of the present application by calling the computer program.

The method includes the steps that a plurality of pieces of characteristic information of an application are obtained; selecting a plurality of same prediction models; selecting characteristic information corresponding to each prediction model from the plurality of characteristic information to obtain a characteristic information set of each prediction model, wherein the characteristic information sets of each prediction model are different; predicting whether the application can be cleaned or not according to the prediction models and the characteristic information sets thereof to obtain the prediction result of each prediction model; and finally determining whether the application can be cleaned or not according to the prediction result of each prediction model so as to clean the cleanable application, thereby realizing automatic cleaning of the application, improving the operation smoothness of the electronic equipment and reducing 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 cleaning method according to an embodiment of the present application.

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

Fig. 3 is another schematic flow chart of an application cleaning method according to an embodiment of the present application.

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

Fig. 5 is another schematic structural diagram of an application cleaning apparatus 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 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 cleaning method may be the application cleaning device provided in the embodiment of the present application, or an electronic device integrated with the application cleaning device, where the application cleaning 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 cleaning method according to an embodiment of the present application, taking an example that an application cleaning apparatus is integrated in an electronic device, where the electronic device may obtain a plurality of feature information of an application; selecting a plurality of same prediction models; selecting characteristic information corresponding to each prediction model from the plurality of characteristic information to obtain a characteristic information set of each prediction model, wherein the characteristic information sets of each prediction model are different; predicting whether the application can be cleaned or not according to the prediction models and the characteristic information sets thereof to obtain the prediction result of each prediction model; and finally determining whether the application can be cleaned according to the prediction result of each prediction model.

Specifically, for example, as shown in fig. 1, taking as an example to determine whether an application a (such as a mailbox application, a game application, and the like) running in the background can be cleaned, multiple pieces of feature information of the application a, that is, multidimensional features (for example, a duration of the application a running in the background, time information of the application a running, and the like) may be collected; selecting a plurality of same prediction models (such as a decision tree model and the like), selecting feature information corresponding to each prediction model from the plurality of feature information to obtain a feature information set of each prediction model, wherein the feature information sets of each prediction model are different, and predicting whether application can be cleaned according to the prediction models and the feature information sets thereof to obtain a prediction result of each prediction model; and finally determining whether the application a can be cleaned or not according to the prediction result of each prediction model. In addition, when the application a is predicted to be cleanable, the electronic device cleans the application a.

Referring to fig. 2, fig. 2 is a schematic flowchart illustrating 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 application cleaning method can be as follows:

201. a plurality of characteristic information of the application is acquired.

The application mentioned in the embodiment 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. In addition, the application may 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 a numerical value 1 to indicate a normal state, and may be represented by a numerical 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 a value 1, and a value 0 to represent the non-charging state (or vice versa).

The electronic device can collect a plurality of applied feature information in each time period and store the feature information in the feature database, so that the embodiment of the application can extract the plurality of applied feature information from the feature database.

202. A plurality of identical prediction models are selected.

The prediction model is a machine learning algorithm, and is used for predicting occurrence of a certain event, for example, whether an application is cleanable 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.

In the embodiment of the present application, a plurality of the same prediction models may be selected for application cleaning prediction, for example, M prediction models are selected, where M is a positive integer greater than 1, and may be 2, 3, 4 … … 9, 10, and the like.

The embodiment of the application can preset n prediction models, and when application cleaning is needed, M prediction models can be selected from the n prediction models.

The timing sequence between

steps

201 and 202 is not limited by the sequence number, and step 202 may be performed before step 201, or may be performed simultaneously.

The number of prediction models may be set according to actual requirements, for example, in an embodiment, the number may be determined based on the feature information of the application. That is, the step of "selecting a plurality of identical prediction models" may include:

determining the target number of the needed prediction model according to the number of the characteristic information, wherein the target number is smaller than the number of the characteristic information;

and selecting a plurality of same prediction models according to the target number.

In one embodiment, the number of predictive models may be less than the number of feature information, e.g., if a total of 10 feature information are applied, then the number of predictive models may be 8.

For example, in order to improve the prediction accuracy, the number of prediction models is preferably larger, and in an embodiment, the number of prediction models may be only 1 smaller than the number of feature information. That is, the number M of prediction models is the number of features t-1.

In an embodiment, the target number of the required prediction models may also be determined based on the number of features and the number of features corresponding to each prediction model.

For example, if a total of K features are applied, where K is a positive integer greater than 1, and the number of features of each prediction model is set to be I, then I features may be selected from the K features, and the K features have a total

Seed method, i.e. number of prediction models

For example, if the application has a total of 10 features, then 9 models (each with 9-dimensional features as input) can be selected. Selecting 9-dimensional features from 10 dimensions, there being a total So 9 models can be selected.

In an embodiment, in order to improve the application cleaning prediction speed and the success rate, related storage information, such as the spatial complexity of the prediction model and the current amount of storage space of the electronic device, needs to be considered when determining the number of the prediction models. The step of determining the target number of the required prediction models according to the number of the feature information may include:

obtaining the spatial complexity of a prediction model;

and determining the target number of the needed prediction models according to the space complexity, the current storage space amount of the electronic equipment and the number of the characteristic information.

Wherein, the spatial complexity refers to the measurement of the required storage space when the algorithm is executed in the electronic equipment; generally denoted by S (n). The spatial complexity s (n) is defined as the memory consumed by the algorithm, which is also a function of the problem size n. Asymptotic spatial complexity is also often referred to simply as spatial complexity.

The current storage space amount of the electronic device is a measure of the current storage space or the remaining storage space of the electronic device, and the storage space may include a memory space and the like.

In one embodiment, the maximum selectable prediction model may be calculated based on the spatial complexity of the prediction model and the current amount of storage space, and then the maximum number and the number of features determine the number of models to be finally selected. For example, the step "determining the target number of the required prediction models according to the space complexity, the current storage space amount of the electronic device, and the number of the feature information" may include:

acquiring the maximum number of available prediction models according to the space complexity and the current storage space amount of the electronic equipment;

when the maximum number is smaller than the number of the feature information, the maximum number is set as a target number of the required prediction models.

The maximum number Mmax of prediction models is determined based on the amount of memory D/the spatial complexity S.

In the embodiment of the application, the maximum number of the prediction models can be obtained, and when the maximum number of the prediction models is smaller than the number of the features, the maximum number is indicated to be appropriate, so that the requirements of the features and the models can be met, the accuracy of the prediction result is ensured, the space requirements required by the prediction models can be met, and the success rate of prediction is improved.

For example, if the total applied feature information is 30, the spatial complexity of the prediction model is 20KB, and the current amount of storage space is 500KB, then the maximum selected number 500/20 of the prediction model may be calculated to be 25, and it is seen that the maximum selected number 25 is less than the feature number 30, then 25 prediction models may be selected for application cleaning prediction.

When the maximum number of prediction models is greater than the number of features, indicating that the current number of features does not satisfy the requirements of the prediction models, the selected number of prediction models may be re-determined, for example, in one embodiment,

when the maximum number of prediction models is greater than the number of features, the number of model selections may be determined based on a difference between a preset number of features and the number of prediction models. That is, the step "determining the target number of the required prediction models according to the space complexity, the current storage space amount of the electronic device, and the number of the feature information" may further include:

and when the maximum quantity is larger than the quantity of the characteristic information, determining the target quantity of the required prediction models according to a preset quantity difference, wherein the preset quantity difference is a quantity difference value between the quantity of the characteristic information and the quantity of the required prediction models.

The preset number difference may be set according to actual requirements, such as 1, 2, 3, 4, and so on.

For example, if there are 30 feature information applications, the spatial complexity of the prediction model is 10KB, and the current storage space amount is 500KB, then the maximum selection number 500/10 of the prediction model is calculated to be 50, and the maximum selection number 50 is larger than the feature number 30, and assuming that the difference between the predetermined feature number and the prediction model number is 1, then 30-1 to 29 prediction models can be selected for the application cleaning prediction.

203. And selecting the characteristic information corresponding to each prediction model from the plurality of characteristic information to obtain a characteristic information set of each prediction model, wherein the characteristic information sets of each prediction model are different.

The feature information sets of the prediction models are different, and some features in the feature information sets of any two prediction models are different, or all features are different. In addition, the number of pieces of feature information included in each set of feature information of the prediction model may be the same or different.

For example, the feature information set of the prediction model 1 includes feature 1, feature 2, feature 3, feature 4, and feature 5, and the feature information set of the prediction model 2 includes feature 1, feature 2, feature 3, feature 7, and feature 8.

For another example, the feature information set of the prediction model includes feature 1, feature 2, feature 3, feature 4, and feature 5, and the feature information set of the prediction model 2 includes feature 6, feature 7, feature 8, feature 9, and feature 10.

In one embodiment, after determining the number of prediction models, the feature information corresponding to each prediction model may be selected based on the number. That is, the step of "selecting target feature information corresponding to each prediction model from the plurality of feature information" may include: and selecting target characteristic information corresponding to each prediction model from the plurality of characteristic information according to the target quantity.

The feature information quantity corresponding to each prediction model may be the same as the quantity of the prediction models, and in some embodiments, the feature information quantity and the quantity of the prediction models may be different.

For example, if a total of K features are applied, where K is a positive integer greater than 1, and the number of the selected prediction models is assumed to be M, and the number of features of each prediction model is M, then M features may be selected from the K features and the total number of the features is K

The method of seed extraction.

Assuming that there are a total of 10 features for an application, then 9 models (each with 9-dimensional features as input) can be chosen. At this time, 9-dimensional features are selected from 10 dimensions, and there are a total

And (4) taking the method.

204. And predicting whether the application can be cleaned or not according to the prediction model and the characteristic information set thereof to obtain a prediction result of each prediction model.

For each prediction model, whether the application can be cleaned can be predicted according to the prediction model and the corresponding characteristic information set, so that a plurality of prediction results can be obtained. Wherein, the prediction result comprises: the application may be cleanable, or the application may not be cleanable.

For example, when the prediction model is a decision tree model, whether the application is cleanable is predicted based on each decision tree model and its feature information; specifically, a corresponding leaf node may be determined according to the features and the decision tree model, and an output of the leaf node may be 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 cleanable or uncleanable.

For example, when the number of the prediction models is M, the prediction result of each prediction model may be obtained according to each prediction model and the corresponding feature information set, that is, M prediction results may be obtained.

205. And finally determining whether the application can be cleaned according to the prediction result of each prediction model.

After the prediction result of each prediction model is obtained, whether the application is cleanable or not can be finally determined based on the prediction result of each prediction model.

For example, a first number of predicted results that can be cleared by the application and a second number of predicted results that cannot be cleared by the application may be obtained, and when the first number of predicted results is greater than the second number of predicted results, it is determined that the application is cleared, otherwise, it is determined that the application is not cleared.

For example, after obtaining M predictors, if J predictors are clear to the application and M-J predictors are not clear to the application, if J > M-J, it can be finally determined that the application is clear, otherwise it is determined that the application is not clear.

As can be seen from the above, the embodiment of the present application obtains a plurality of pieces of feature information of an application; selecting a plurality of same prediction models; selecting characteristic information corresponding to each prediction model from the plurality of characteristic information to obtain a characteristic information set of each prediction model, wherein the characteristic information sets of each prediction model are different; predicting whether the application can be cleaned or not according to the prediction models and the characteristic information sets thereof to obtain the prediction result of each prediction model; and finally determining whether the application can be cleaned or not according to the prediction result of each prediction model so as to clean the cleanable application, thereby realizing automatic cleaning of the application, improving the operation smoothness of the electronic equipment and reducing the power consumption.

Further, each sample of the sample set comprises a plurality of characteristic information reflecting behavior habits of users using the applications, so that the cleaning of the corresponding applications can be more personalized and intelligent.

Furthermore, a plurality of same prediction models are adopted, and different feature information is used for each prediction model to realize application cleaning prediction, so that the accuracy of user behavior prediction can be improved, and the accuracy of cleaning is further improved; in addition, the application cleaning is predicted in parallel by adopting a plurality of same prediction models and partial characteristic information, and compared with a mode of predicting and applying cleaning by adopting one prediction model and all characteristic information, the speed of application cleaning prediction can be improved, and the prediction time is shortened.

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. 3, the application cleaning method may include:

301. a plurality of characteristic information of the application is acquired.

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;

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.

302. And determining the target number of the needed prediction models according to the number of the characteristic information.

For example, if a total of K features are applied, where K is a positive integer greater than 1, and the number of features of each prediction model is set to be I, then I features may be selected from the K features, and the K features have a total Seed method, i.e. number of prediction models

For example, if the application has a total of 10 features, then 9 models (each with 9-dimensional features as input) can be selected. Selecting 9-dimensional features from 10 dimensions, there being a total

So 9 models can be selected.

303. And selecting a plurality of same prediction models from the prediction model database according to the target number.

The same prediction models are stored in the prediction model database, and after the number of the prediction models required to be used is determined, the corresponding number of the prediction models can be selected from the database.

For example, the target number is M, and in this case, M identical prediction models may be selected from the database.

304. And selecting target characteristic information corresponding to each prediction model from the plurality of characteristic information according to the target number to obtain a characteristic information set of each prediction model, wherein the characteristic information sets of each prediction model are different.

The method of seed extraction.

And (4) taking the method.

305. And predicting whether the application can be cleaned or not according to the prediction model and the characteristic information set thereof to obtain a prediction result of each prediction model.

Wherein, the prediction result comprises: the application may be cleanable, or the application may not be cleanable.

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 greater than the preset probability value, determining that the application can be cleaned, otherwise, not cleaning.

306. And finally determining whether the application can be cleaned according to the prediction result of each prediction model.

In a specific example, whether a plurality of applications running in the background can be cleaned can be predicted by using the method of the embodiment of the present application, and as shown in table 1, it is determined that the applications a1 and A3 running in the background can be cleaned, while the state of the application a2 running in the background is kept unchanged.

Applications of	Predicted results
		Application A1	Can be cleaned
Application A2	Can not be cleaned
		Application A3	Can be cleaned

TABLE 1

In one embodiment, an application cleaning device is also provided. Referring 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 includes a feature obtaining unit 401, a model selecting unit 402, a feature selecting unit 403, a predicting unit 404, and a determining unit 405, as follows:

a feature acquisition unit 401 configured to acquire a plurality of feature information of an application;

a model selecting unit 402, configured to select a plurality of identical prediction models;

a feature selecting unit 403, configured to select feature information corresponding to each prediction model from the multiple pieces of feature information, to obtain a feature information set of each prediction model, where the feature information sets of each prediction model are different;

a prediction unit 404, configured to predict whether the application can be cleaned according to the prediction model and the feature information set thereof, so as to obtain a prediction result of each prediction model;

a determining unit 405, configured to finally determine whether the application is cleanable according to a prediction result of each prediction model.

In an embodiment, referring to fig. 5, the model selecting unit 402 may include:

a quantity determination subunit 4021, configured to determine a target quantity of the required prediction models according to the quantity of the feature information;

a selecting subunit 4022, configured to select a plurality of same prediction models according to the target number.

In an embodiment, the subunit 4022 is selected and may be specifically configured to: and selecting target characteristic information corresponding to each prediction model from the plurality of characteristic information according to the target quantity.

In an embodiment, the number determining subunit 4021 may be specifically configured to:

obtaining the spatial complexity of the prediction model;

and determining the target quantity of the needed prediction models according to the space complexity, the current storage space quantity of the electronic equipment and the quantity of the characteristic information.

when the maximum number is smaller than the number of the feature information, taking the maximum number as the target number of the needed prediction models;

and when the maximum number is larger than the number of the characteristic information, determining the target number of the required prediction models according to a preset number difference, wherein the preset number difference is a number difference value between the number of the characteristic information and the number of the required prediction models.

The steps performed by each unit in the application cleaning device may refer to the method steps described in the above method embodiments. The application cleaning 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, the application cleaning apparatus of the present embodiment may obtain a plurality of feature information of the application by the feature obtaining unit 401; a plurality of same prediction models are selected by the model selection unit 402; the feature selection unit 403 selects feature information corresponding to each prediction model from the plurality of feature information to obtain a feature information set of each prediction model, wherein the feature information sets of each prediction model are different; predicting whether the application can be cleaned or not by the prediction unit 404 according to the prediction model and the characteristic information set thereof to obtain a prediction result of each prediction model; the determining unit 405 finally determines whether the application is cleanable according to the prediction result of each prediction model, so that the cleanable application is cleaned, automatic cleaning of the application is achieved, the operating smoothness of the electronic device is improved, and power consumption is reduced.

The embodiment of the application also provides the electronic equipment. Referring to fig. 6, an 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, connects various parts of the whole electronic device by using various interfaces and lines, executes various functions of the electronic device 500 and processes data by running or loading a computer program stored in the memory 502 and calling data stored in the memory 502, thereby performing overall monitoring of the electronic device 500.

The memory 502 may be used to store software programs and modules, and the processor 501 executes various functional applications and data processing by operating the computer programs and modules stored in the memory 502. The memory 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, the memory 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, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.

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

acquiring a plurality of characteristic information of an application;

selecting a plurality of same prediction models;

In some embodiments, when selecting a plurality of the same prediction models, the processor 501 may specifically perform the following steps:

determining the target number of the needed prediction models according to the number of the characteristic information;

In some embodiments, when selecting the target feature information corresponding to each prediction model from the plurality of feature information, the processor 501 may specifically perform the following steps:

and selecting target characteristic information corresponding to each prediction model from the plurality of characteristic information according to the target quantity.

In some embodiments, when determining the target number of the required prediction models according to the number of the feature information, the processor 501 may specifically perform the following steps:

obtaining the spatial complexity of the prediction model;

In some embodiments, when determining the target number of the required prediction models according to the space complexity, the current storage space amount of the electronic device, and the number of the feature information, the processor 501 may further specifically perform the following steps:

and when the maximum number is smaller than the number of the characteristic information, taking the maximum number as the target number of the required prediction models.

As can be seen from the above, the electronic device according to the embodiment of the present application acquires a plurality of pieces of feature information of an application; selecting a plurality of same prediction models; selecting characteristic information corresponding to each prediction model from the plurality of characteristic information to obtain a characteristic information set of each prediction model, wherein the characteristic information sets of each prediction model are different; predicting whether the application can be cleaned or not according to the prediction models and the characteristic information sets thereof to obtain the prediction result of each prediction model; and finally determining whether the application can be cleaned or not according to the prediction result of each prediction model so as to clean the cleanable application, thereby realizing automatic cleaning of the application, improving the operation smoothness of the electronic equipment and reducing the power consumption.

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

The display 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. The display 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.

The rf 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.

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

The power source 506 may be used to power various components of the electronic device 500. In some embodiments, power supply 506 may be logically coupled to processor 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. 7, the electronic 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 cleaning method in any one of the above embodiments, for example: acquiring a plurality of characteristic information of an application; selecting a plurality of same prediction models; selecting characteristic information corresponding to each prediction model from the plurality of characteristic information to obtain a characteristic information set of each prediction model, wherein the characteristic information sets of each prediction model are different; predicting whether the application can be cleaned or not according to the prediction models and the characteristic information sets thereof to obtain the prediction result of each prediction model; and finally determining whether the application can be cleaned according to the prediction result of each prediction model.

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 cleaning 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 cleaning 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 implementing the embodiment of the application cleaning method can be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.

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

The application cleaning method, the application cleaning 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

1. An application cleaning method, comprising:

acquiring a plurality of characteristic information of the application, wherein the characteristic information reflects behavior habits of a user using the application;

determining the target number of the prediction models required according to the number of the characteristic information, wherein the target number is smaller than the number of the characteristic information, and selecting the prediction models of the target number;

predicting whether the application can be cleaned or not according to the prediction model and the corresponding characteristic information set to obtain a prediction result of the target number of prediction models;

comparing the prediction results of the target number of prediction models to obtain a comparison result;

and finally determining whether the application can be cleaned or not according to the comparison result.

2. The application cleaning method of claim 1, wherein selecting a plurality of identical predictive models comprises:

3. The application cleaning method of claim 2, wherein selecting the target feature information corresponding to each predictive model from the plurality of feature information comprises:

4. The application cleaning method of claim 2, wherein determining the target number of the required predictive models according to the number of the feature information comprises:

obtaining the spatial complexity of the prediction model;

5. The application cleaning method of claim 4, wherein determining the target number of the required predictive models according to the space complexity, the current storage space amount of the electronic device and the number of the feature information comprises:

6. The application cleaning method of claim 5, wherein determining the target number of the required predictive models according to the space complexity, the current storage space amount of the electronic device and the number of the feature information, further comprises:

7. An application cleaning apparatus, comprising:

the device comprises a characteristic acquisition unit, a characteristic analysis unit and a characteristic analysis unit, wherein the characteristic acquisition unit is used for acquiring a plurality of characteristic information of the application, and the characteristic information reflects the behavior habit of a user using the application;

the model selecting unit is used for determining the target number of the prediction models according to the number of the characteristic information, selecting the prediction models with the target number, wherein the target number is smaller than the number of the characteristic information;

the prediction unit is used for predicting whether the application can be cleaned or not according to the prediction model and the corresponding characteristic information set to obtain the prediction results of the prediction models with the target quantity;

and the determining unit is used for comparing the prediction results of the target number of prediction models to obtain a comparison result, and finally determining whether the application can be cleaned according to the comparison result.

8. The application cleaning apparatus of claim 7, wherein the model selecting unit comprises:

the quantity determining subunit is used for determining the target quantity of the needed prediction model according to the quantity of the characteristic information;

and the selecting subunit is used for selecting a plurality of same prediction models according to the target quantity.

9. The application cleaning apparatus of claim 8, wherein the selecting subunit is specifically configured to: and selecting target characteristic information corresponding to each prediction model from the plurality of characteristic information according to the target quantity.

10. The application cleaning apparatus of claim 8, wherein the number determination subunit is specifically configured to:

obtaining the spatial complexity of the prediction model;

11. The application cleaning apparatus of claim 10, wherein the number determination subunit is configured to:

12. A storage medium having stored thereon a computer program, characterized in that, when the computer program is run on a computer, it causes the computer to execute the application cleaning method according to any one of claims 1 to 6.

13. An electronic device comprising a processor and a memory, said memory having a computer program, wherein said processor is adapted to perform the application cleaning method of any of claims 1 to 6 by invoking said computer program.