CN107807730A

CN107807730A - Using method for cleaning, device, storage medium and electronic equipment

Info

Publication number: CN107807730A
Application number: CN201711050187.6A
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: 2018-03-16
Anticipated expiration: 2037-10-31
Also published as: CN107807730B; WO2019085754A1

Abstract

The embodiment of the present application discloses one kind and applies method for cleaning, device, storage medium and electronic equipment, wherein, the embodiment of the present application obtains the multidimensional characteristic of application, the training characteristics set being applied；Ridge regression model is trained according to the training characteristics set of application, the ridge regression model after being trained；Obtain the multidimensional characteristic of application, the predicted characteristics set being applied；According to the ridge regression model after predicted characteristics set and training, whether prediction application can clear up；So that pair application that can be cleared up is cleared up；The program can realize the automatic cleaning of application, improve the operation fluency of electronic equipment, reduce power consumption.

Description

Using method for cleaning, device, storage medium and electronic equipment

Technical field

The application is related to communication technical field, and in particular to one kind is set using method for cleaning, device, storage medium and electronics It is standby.

Background technology

At present, on the electronic equipment such as smart mobile phone, it will usually there are multiple applications while run, wherein, one is applied preceding Platform is run, and other application is in running background.If not clearing up the application of running background for a long time, can cause electronic equipment can Diminished with internal memory, central processing unit (central processing unit, CPU) occupancy it is too high, cause electronic equipment to occur The problems such as speed of service is slack-off, interim card, and power consumption is too fast.Solved the above problems therefore, it is necessary to provide a kind of method.

The content of the invention

In view of this, the embodiment of the present application provides one kind and applies method for cleaning, device, storage medium and electronic equipment, The operation fluency of electronic equipment can be improved, reduces power consumption.

In a first aspect, one kind application method for cleaning for providing of the embodiment of the present application, including：

The multidimensional characteristic of application is obtained, obtains the training characteristics set of the application；

Ridge regression model is trained according to the training characteristics set of the application, the ridge regression mould after being trained Type；

The multidimensional characteristic of the application is obtained, obtains the predicted characteristics set of the application；

According to the ridge regression model after the predicted characteristics set and the training, predict whether the application can be clear Reason.

Second aspect, one kind application cleaning plant for providing of the embodiment of the present application, including：

Training characteristics acquiring unit, for obtaining the multidimensional characteristic of application, obtain the training characteristics set of the application；

Training unit, ridge regression model is trained for the training characteristics set according to the application, trained Ridge regression model afterwards；

Predicted characteristics acquiring unit, for obtaining the multidimensional characteristic of the application, obtain the predicted characteristics collection of the application Close；

Predicting unit, for according to the ridge regression model after the predicted characteristics set and the training, described in prediction Using whether can clearing up.

The third aspect, the storage medium that the embodiment of the present application provides, is stored thereon with computer program, when the computer When program is run on computers so that the computer is performed as what the application any embodiment provided applies method for cleaning.

Fourth aspect, the electronic equipment that the embodiment of the present application provides, including processor and memory, the memory have meter Calculation machine program, the processor is by calling the computer program, for performing as what the application any embodiment provided answers Use method for cleaning.

The embodiment of the present application obtains the multidimensional characteristic of application, the training characteristics set being applied；According to the training of application Characteristic set is trained to ridge regression model, the ridge regression model after being trained；The multidimensional characteristic of application is obtained, is answered Predicted characteristics set；According to the ridge regression model after predicted characteristics set and training, whether prediction application can clear up；With Just pair application that can be cleared up is cleared up；The program can realize the automatic cleaning of application, improve the operation stream of electronic equipment Smooth degree, reduces power consumption.

Brief description of the drawings

Technical scheme in order to illustrate the embodiments of the present invention more clearly, make required in being described below to embodiment Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for For those skilled in the art, on the premise of not paying creative work, it can also be obtained according to these accompanying drawings other attached Figure.

Fig. 1 is the application scenarios schematic diagram using method for cleaning that the embodiment of the present application provides.

Fig. 2 is the schematic flow sheet using method for cleaning that the embodiment of the present application provides.

Fig. 3 is another schematic flow sheet using method for cleaning that the embodiment of the present application provides.

Fig. 4 is the structural representation using cleaning plant that the embodiment of the present application provides.

Fig. 5 is another structural representation using cleaning plant that the embodiment of the present application provides.

Fig. 6 is a structural representation of the electronic equipment that the embodiment of the present application provides.

Fig. 7 is another structural representation for the electronic equipment that the embodiment of the present application provides.

Embodiment

Schema is refer to, wherein identical element numbers represent identical component, and the principle of the application is to implement one Illustrated in appropriate computing environment.The following description is based on illustrated the application specific embodiment, and it should not be by It is considered as limitation the application other specific embodiments not detailed herein.

In the following description, the specific embodiment of the application is by with reference to as the step performed by one or multi-section computer And symbol illustrates, unless otherwise stating clearly.Therefore, these steps and operation will have to mention for several times is performed by computer, this paper institutes The computer of finger, which performs, to be included by representing with the computer processing unit of the electronic signal of the data in a structuring pattern Operation.The data or the opening position being maintained in the memory system of the computer are changed in this operation, and its is reconfigurable Or change the running of the computer in a manner of known to the tester of this area in addition.The data structure that the data are maintained For the provider location of the internal memory, it has the particular characteristics as defined in the data format.But the application principle is with above-mentioned text Word illustrates that it is not represented as a kind of limitation, this area tester will appreciate that plurality of step as described below and behaviour Also may be implemented among hardware.

Term as used herein " module " can regard the software object to be performed in the arithmetic system as.It is as described herein Different components, module, engine and service can be regarded as the objective for implementation in the arithmetic system.And device as described herein and side Method can be implemented in a manner of software, can also be implemented certainly on hardware, within the application protection domain.

Term " first ", " second " and " the 3rd " in the application etc. is to be used to distinguish different objects, rather than for retouching State particular order.In addition, term " comprising " and " having " and their any deformations, it is intended that cover non-exclusive include. Such as contain the step of process, method, system, product or the equipment of series of steps or module is not limited to list or Module, but some embodiments also include the step of not listing or module, or some embodiments also include for these processes, Method, product or equipment intrinsic other steps or module.

Referenced herein " embodiment " is it is meant that the special characteristic, structure or the characteristic that describe can wrap in conjunction with the embodiments It is contained at least one embodiment of the application.Each position in the description occur the phrase might not each mean it is identical Embodiment, nor the independent or alternative embodiment with other embodiments mutual exclusion.Those skilled in the art explicitly and Implicitly understand, embodiment described herein can be combined with other embodiments.

The embodiment of the present application provides one kind and applies method for cleaning, and this can be the application using the executive agent of method for cleaning The background application cleaning plant that embodiment provides, or the electronic equipment for applying cleaning plant is integrated with, the wherein application is clear Reason device can be realized by the way of hardware or software.Wherein, electronic equipment can be smart mobile phone, tablet personal computer, the palm The equipment such as upper computer, notebook computer or desktop computer.

Referring to Fig. 1, Fig. 1 is the application scenarios schematic diagram using method for cleaning that the embodiment of the present application provides, with application Exemplified by cleaning plant integrates in the electronic device, electronic equipment can obtain the multidimensional characteristic of application, and the training being applied is special Collection is closed；Ridge regression model is trained according to the training characteristics set of application, the ridge regression model after being trained；Obtain The multidimensional characteristic of application, the predicted characteristics set being applied；According to predicted characteristics set and training after ridge regression model, Whether prediction application can clear up.In addition, electronic equipment can be cleared up with the application that can be cleared up.

Specifically, such as shown in Fig. 1, to judge that the application program a of running background (such as mailbox application, game application) is It is no can clear up exemplified by, can in historical time section, acquisition applications a multidimensional characteristic (such as using a running background when Temporal information long, using a operations etc.), the characteristic set for a that is applied, (such as transported according to characteristic set using a on backstage Capable duration, using temporal information of a operations etc.) ridge regression model is trained, the ridge regression model after being trained；Root It is predicted that multidimensional characteristic corresponding to time (such as t) acquisition applications (such as t application a in the duration of running background, using a Temporal information of operation etc.), the predicted characteristics set for a that is applied；According to the ridge regression mould after predicted characteristics set and training Whether type prediction can clear up using a.In addition, when prediction can clear up using a, electronic equipment using a to clearing up.

Referring to Fig. 2, Fig. 2 is the schematic flow sheet using method for cleaning that the embodiment of the present application provides.The application is implemented The idiographic flow using method for cleaning that example provides can be as follows：

201st, the multidimensional characteristic of application, the training characteristics set being applied are obtained.

It application mentioned by the embodiment of the present application, can be any one application installed on electronic equipment, such as handle official business Using, communications applications, game application, shopping application etc..Wherein, application can include the application of front stage operation, i.e. foreground application, The application of running background, i.e. background application can also be included.

In one embodiment, can receive using cleaning request, clearing up request according to application determines application for clearance, so Afterwards, the multidimensional characteristic of application is obtained, the training characteristics set being applied.

Specifically, the multidimensional characteristic of application can be obtained from property data base, wherein, when multidimensional characteristic can be history Between the multidimensional characteristic that collects, namely history multidimensional characteristic.The a variety of spies applied in historical time are stored with property data base Sign.

Wherein, training characteristics set can include the multidimensional characteristic of application, that is, the multiple features applied.

Wherein, the multidimensional characteristic of application has a dimension of certain length, and corresponding characterize should for the parameter in each of which dimension A kind of characteristic information, the i.e. multidimensional characteristic breath are made up of multiple features.The plurality of feature can include related using itself Characteristic information, such as：Using the duration for being cut into backstage；Using during being cut into backstage, duration is shielded in going out for electronic equipment；Should With the number for entering foreground；Using the time in foreground；Using the time in backstage, using the mode for entering backstage, example Such as switched into by homepage key (home keys), be returned key and switch into, switched into by other application；The type of application, Including one-level (conventional application), two level (other application) etc..

The multidimensional characteristic information can also include the correlated characteristic information of the electronic equipment where application, such as：Electronics is set Whether standby go out screen time, bright screen time, current electric quantity, the wireless network connection status of electronic equipment, electronic equipment are charging State etc..

Wherein, the training sample of application includes the multidimensional characteristic of application.The multidimensional characteristic can be in historical time section, The multiple features gathered according to predeterminated frequency.Historical time section, such as can be 7 days, 10 days in the past；Predeterminated frequency, such as can Be every 10 minutes collection once, per half an hour collection once.It is understood that the multidimensional characteristic number of the application once gathered According to one training characteristics set of composition.

In one embodiment, cleared up for ease of application, can be by the multidimensional characteristic information of application, the unused direct table of numerical value The characteristic information shown is come out with specific numerical quantization, such as the wireless network connection status of electronic equipment this feature letter Breath, can represent normal state with numerical value 1, abnormal state is represented with numerical value 0 (vice versa)；For another example it is directed to electronics Whether equipment can represent charged state with numerical value 1, uncharged state is represented with numerical value 0 in this characteristic information of charged state (vice versa).

202nd, ridge regression model is trained according to the training characteristics set of application, the ridge regression mould after being trained Type.

Wherein, ridge regression model can a kind of machine learning algorithm, ridge regression (ridge regression, Tikhonov Regularization it is a kind of Biased estimator homing method for being exclusively used in synteny data analysis that) algorithm, which is also known as ridge regression, Substantially a kind of least squares estimate of improvement, by abandoning the unbiasedness of least square method, to lose partial information, drop Low precision is that cost acquisition regression coefficient more meets actual, more reliable homing method, and the fitting to ill data is better than Least square method.

In the embodiment of the present application, it can predict to apply whether can clear up using ridge regression model, wherein, ridge regression model Output include can clear up or can not clear up.When that whether can be cleared up using ridge regression model prediction application, it is necessary to utilize existing Characteristic information model is trained, lift the accuracy of prediction.

In one embodiment, the mistake of the ridge regression parameter of ridge regression model is just to solve for the process of ridge regression model training Journey, such as, the ridge regression parameter needed for ridge regression model can be first calculated, then, based on the ridge regression parameter to ridge regression Model is configured.For example step " is trained, stating after being trained according to the training sample of application to ridge regression model Ridge regression model ", it can include：

Establish the error judgment function of ridge regression model；

The target ridge regression parameter of ridge regression model is obtained according to training characteristics set and error judgment function, target ridge is returned Parameter is returned to include ridge parameter and regression parameter；

According to the ridge regression model after target ridge regression parameter and ridge regression model training.

Wherein, ridge regression parameter can include ridge parameter and regression parameter, ridge regression (Ridge Regression) be Increase regular terms on the basis of square error, by determining that λ value can reach balance between variance and deviation：With λ increase, model variance reduces and deviation increases.Ridge parameter can be with regularization parameter λ, and the regression parameter can be to be solved The model parameter w of ridge regression model.

In the embodiment of the present application, error judgment function is the loss function of ridge regression model, for calculating ridge regression model The error between output valve and actual value on sample.

In one embodiment, the error judgment function of ridge regression model can include such as minor function：

Wherein, λ is ridge parameter, i.e. regularization parameter, and x is the feature of sample, and w is the regression parameter of ridge regression model, and n is The dimension of feature.

In one embodiment, the error judgment function of ridge regression model can be deformed, obtains regression parameter acquisition Function, then, function is obtained based on regression parameter to obtain ridge regression parameter.For example error judgment function can be asked Lead, function is obtained to obtain regression parameter, then, function is obtained based on the regression parameter and training characteristics set obtains ridge regression Parameter.

For example, the error judgment function of ridge regression model can include such as minor function：

Derivation can be carried out to error judgment function, obtain function：

2X^T(Y-XW) -2 λ W, X are characterized x matrix or vector, X^TFor X transposition, Y is y matrix or vector；

Then, 2X is made^T(Y-XW) -2 λ W etc. zero, following regression parameter calculation formula can be obtained：

Wherein,For regression parameter to be solved.

, can be to calculate regression parameter based on the formula and training characteristics set after regression parameter calculation formula is obtainedFinally give ridge parameter λ and corresponding regression parameter

In one embodiment, in order to lift forecasting accuracy, multigroup ridge regression parameter can be calculated, then, is chosen most Suitable ridge regression parameter.For example step " obtains the target of ridge regression model according to training characteristics set and error judgment function Ridge regression parameter ", it can include：

Multigroup ridge regression parameter is obtained according to error judgment function, ridge regression parameter includes：Ridge parameter and regression parameter；

According to training characteristics set, ridge regression parameter and error judgment function, the training characteristics under ridge regression parameter are obtained Gather the error for ridge regression model, obtain error corresponding to every group of ridge regression parameter；

According to error corresponding to every group of ridge regression parameter, corresponding target ridge regression ginseng is chosen from multigroup ridge regression parameter Number；

Wherein, error corresponding to ridge regression parameter is the ridge regression model under the ridge regression parameter, inputs training sample set Close the error between the predicted value drawn and actual value.

For example ridge regression parameter can be obtainedIts In, m can be the positive integer more than 2, can set according to the actual requirements, such as, 20,30,40 ....

Then, according to training characteristics set, ridge regression parameterAnd error judgment function, obtain at this Group regression parameterLower training characteristics set obtains every group of ridge regression parameter for the error Fk of ridge regression model Corresponding error such as F1, F2 ... Fk ... Fm.Based on error F corresponding to every group of ridge regression parameter from ridge regression parameterChoose corresponding target ridge regression parameter

In one embodiment, the error judgment function of ridge regression model can be deformed, obtains regression parameter acquisition Function, then, function and multiple default ridge parameter λ are obtained based on regression parameter to obtain regression parameterMultigroup ridge is obtained to return Return parameterFor example derivation can be carried out to error judgment function, function is obtained to obtain regression parameter, then, Function is obtained based on the regression parameter and training characteristics set obtains ridge regression parameter.

Derivation can be carried out to error judgment function, obtain function：

Wherein,For regression parameter to be solved.

After regression parameter calculation formula is obtained, can with based on the formula and multiple default ridge parameter λ come calculate return ParameterFinally give ridge parameter λ and corresponding regression parameter

For example, initialization ridge parameter λ value is 1, formula is utilizedCalculating is tried to achieve corresponding to λ=1Value；λ adds 1, reuses formulaTry to achieve corresponding to λ=2Value；λ adds 1 again, reuses formulaTry to achieve corresponding to λ=3Value ... is until trying to achieve corresponding to λ=mValue, such as m=20.Now, just Can obtain m groups as 20 groups it is differentValue, and then obtain such as 20 groups of m groups

In one embodiment, training characteristics set in order to accuracy and is rapidly obtained for ridge regression model Error, training characteristics set can be divided into more sub- training characteristics set, obtain each sub- training characteristics and be integrated into ridge regression For the error f of ridge regression model under parameter, then, it is integrated into based on each sub- training characteristics under ridge regression parameter for ridge regression The error of model obtains whole training characteristics and is integrated under ridge regression parameter for the error F of ridge regression model.

For example step " according to training characteristics set, ridge regression parameter and error judgment function, is obtained in ridge regression parameter Error of the lower training characteristics set for ridge regression model ", can include：

Training characteristics set is divided into more sub- training characteristics set；

According to sub- training characteristics set, ridge regression parameter and error judgment function, the son instruction under ridge regression parameter is obtained Practice sub- error of the set for ridge regression model, obtain sub- error corresponding to every sub- training characteristics set；

Sub- error corresponding to every sub- training characteristics set, obtain the training characteristics set under ridge regression parameter and returned for ridge Return the error of model.

Wherein, sub- training characteristics set division numbers can be set according to the actual requirements, such as 10,20 etc.. In one embodiment, for the degree of accuracy of lifting error acquisition, the feature quantity that sub- training characteristics set includes is equal, will also train Characteristic set is divided into more sub- training characteristics set.

For example, training characteristics set D can be divided into M sub- training characteristics set, obtain sub- training characteristics set D1, D2……DM；Wherein, M is the positive integer more than 1.Then, according to error judgment function and ridge regression parameter, calculate per height Training characteristics are integrated under ridge regression parameter for the sub- error of ridge regression model, if D1 is in ridge regression parameter Under for ridge regression model sub- error f11, D2 in ridge regression parameterUnder for ridge regression model sub- error F12 ... DM is in ridge regression parameterUnder for ridge regression model sub- error f1M, based on every height train Characteristic set is at this in ridge regression parameterUnder for ridge regression model sub- error, i.e. f11, f12 ... f1M Training characteristics D is obtained in ridge regression parameterUnder for ridge regression model error F1.

Then, according to error judgment function and next group of ridge regression parameter, every sub- training characteristics is calculated and are integrated into ridge For the sub- error of next group of ridge regression model under regression parameter, if D1 is in ridge regression parameter Under for ridge regression Sub- error f21, D2 of model is in ridge regression parameterUnder for ridge regression model sub- error f22 ... DM In ridge regression parameterUnder for ridge regression model sub- error f2M, be integrated into based on every sub- training characteristics Should be in ridge regression parameterUnder for ridge regression model sub- error, i.e. it is special that f21, f22 ... f2M obtains training D is levied in ridge regression parameter Under for ridge regression model error F2.

The like, training characteristics can be calculated and be integrated into error of the m groups ridge regression parameter for ridge regression model, obtained To error F1, F2 ... Fm.

The embodiment of the present application, can be to be obtained after sub- error corresponding to every sub- training characteristics set is obtained based on sub- error A training characteristics set is rounded for the error of ridge regression model, the acquisition modes can have a variety of.Such as in an embodiment In, in order to lift the accuracy of error, the average value of sub- error can be calculated, then, it is special that whole training is obtained based on average value The error for ridge regression model is closed in collection.For example step " according to sub- error corresponding to every sub- training characteristics set, obtains Error of the training characteristics set for ridge regression model under ridge regression parameter ", can include：

According to sub- error corresponding to every sub- training characteristics set, the mean error of sub- training characteristics set is obtained；

Error of the training characteristics set for ridge regression model under ridge regression parameter is obtained according to mean error.

In one embodiment, can using the mean error as under ridge regression parameter training characteristics set for ridge regression The error of model.

Such as using ridge regression parameter asExemplified by, the son in the case where calculating each son for ridge regression model misses Poor f11, D2 are in ridge regression parameterUnder for ridge regression model sub- error f12 ... DM ridge regression join NumberUnder for ridge regression model sub- error f1M, be integrated into this in ridge regression based on every sub- training characteristics ParameterUnder for ridge regression model sub- error, i.e., can be with the average calculation error after f11, f12 ... f1M F '=(f11+f12+ ...+f1M)/M；The f ' is training characteristics D in ridge regression parameterUnder for ridge return Return the error F1 of model.

In one embodiment, in order to lift parameter accuracy and accuracy of forecast, every group of ridge regression ginseng can obtained After error corresponding to number, the target ridge regression that can choose ridge regression parameter corresponding to error minimum as ridge regression model is joined Number, i.e. final argument.

Such as after error such as F1, F2 ... Fk ... Fm corresponding to every group of ridge regression parameter is obtained, it is assumed that Fk is minimum, At this point it is possible to choose ridge regression parameter corresponding to FkTarget ridge regression parameter as ridge regression model.

According to foregoing description, below will using ridge regression parameter as 20 groups, sub- training characteristics collective number be 10 to introduce mesh The selection process of ridge regression parameter, namely the training process of ridge regression model are marked, it is as follows：

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

To carry out derivation, as a result for：

2X^T(Y-XW)-2λW

It is that 0 value that can try to achieve w is to make its value：

(2) value for, initializing λ is 1, according in (3) stepFormula, which calculates, tries to achieve accordinglyValue.

(3), λ adds 1, repeat (2) step try to achieve 20 groups it is differentValue；

(4) characteristic set, is divided into 10 deciles, selected in (3) stepA numerical value, error judgment formula point below Different error amounts of each subcharacter set for ridge regression of 10 deciles are not calculated, obtain 10 different error amounts：

Then the average error value for the error amount of ridge regression by each subcharacter set of 10 deciles is calculated, and will averagely be missed Difference is as characteristic set in selectionWith under λ to the error of ridge regression；

(5) (4) step, is repeated, it is different at 20 groups to calculate characteristic set respectivelyFor the feature of ridge regression under value Error；

(6) the 20 groups of characteristic errors, tried to achieve from (5) are taken corresponding to minimum valueAnd λ value, shouldIt is ridge regression with λ value Fitting obtains the parameter of ridge regression parameter, i.e. ridge regression model final choice.

Ridge regression parameter corresponding to each application can be calculated by above-mentioned steps (1)-(6).

203rd, the multidimensional characteristic of application, the predicted characteristics set being applied are obtained.

Such as can be according to the multidimensional characteristic of predicted time acquisition applications as forecast sample.

Wherein, predicted time can be set according to demand, such as can be current time.

Such as can predicted time point acquisition applications multidimensional characteristic as forecast sample.

In the embodiment of the present application, the multidimensional characteristic gathered in step 201 and 203 is same type feature, such as：Using cutting Enter the duration to backstage；Using during being cut into backstage, duration is shielded in going out for electronic equipment；Using the number for entering foreground；Using Time in foreground；Using the mode for entering backstage.

204th, whether can be cleared up according to the ridge regression model after predicted characteristics set and training, prediction application.

For example the probability that application can clear up can be calculated based on ridge regression model and predicted characteristics set, when probability is big When some threshold value, determine that the application can clear up.

From the foregoing, it will be observed that the embodiment of the present application obtains the multidimensional characteristic of application, the training characteristics set being applied；According to should Training characteristics set is trained to ridge regression model, the ridge regression model after being trained；The multidimensional for obtaining application is special Sign, the predicted characteristics set being applied；According to the ridge regression model after predicted characteristics set and training, whether prediction application It can clear up；So that pair application that can be cleared up is cleared up；The program can realize the automatic cleaning of application, improve electronic equipment Operation fluency, reduce and power consumption and save resource.

Further, believed due in characteristic set, including reflection user using multiple features of the behavioural habits of application Breath, therefore the embodiment of the present application can make it that the cleaning to corresponding application is more personalized and intelligent.

Further, realized based on ridge regression model using cleaning prediction, the accurate of user's behavior prediction can be lifted Property, and then improve the degree of accuracy of cleaning.In addition, the embodiment of the present application can also calculate multigroup ridge regression when to model training Parameter, and using characteristic error choose error most under ridge regression parameter, using the final argument as ridge regression model, Ke Yijin Lift to one step the accuracy that ridge regression model clears up application prediction.

On the basis of the method that will be described below in above-described embodiment, the method for cleaning of the application is described further.Ginseng Fig. 3 is examined, this can include using method for cleaning：

301st, the multidimensional characteristic of application, the training characteristics set being applied are obtained.

For example the multidimensional characteristic of application is obtained from property data base, wherein, multidimensional characteristic can be that historical time gathers The multidimensional characteristic arrived, namely history multidimensional characteristic.The various features applied in historical time are stored with property data base.

One specific training characteristics set can be as follows, includes the characteristic information of multiple dimensions (30 dimensions), needs It is noted that characteristic information as follows is only for example, and in practice, the characteristic information that a training characteristics set is included Quantity, can be more than information as follows quantity, can also be less than information as follows quantity, the specific features taken Information can also be different from characteristic information as follows, are not especially limited herein.

The duration of the last incision backstages of APP till now；

The last incision backstages of APP till now during in, add up screen shut-in time length；

APP enters the number on foreground (by statistics daily) in mono- day；

APP in mono- day (day off is separately counted by working day, day off) enter foreground number, if than current predictive Time is working day, then this feature is average every workday for counting on working day in foreground access times using numerical value；

APP is in the time on foreground in mono- day (by statistics daily)；

Backstage APP counts gained immediately following the number that is opened after current foreground APP regardless of day off on working day；

Backstage APP divides day off on working day to count immediately following the number that is opened after current foreground APP；

The mode that target APP is switched, it is divided into and switches by the switching of home keys, by the switching of recent keys, by other APP；

Target APP one-levels type (conventional application)；

Target APP two-level types (other application)；

Mobile phone screen goes out the screen time；

The mobile phone screen bright screen time；

Current screen light on and off state；

Current electricity；

Current wifi states；

The duration of the last incision backstages of App till now；

The APP last times are used duration on foreground；

The APP upper last times are used duration on foreground；

The upper last time is used duration on foreground on APP；

If 6 periods an of natural gift, every section 4 hours, current predictive time point is morning 8:30, then in the 3rd section, then What this feature represented is target app daily 8:00~12:The time span that 00 this period was used；

Current foreground APP enters backstage and enters foreground by the Mean Time Between Replacement counted daily to target APP；

Current foreground APP entered during backstage enters foreground to target APP by the average screen fall time counted daily；

Target APP is in first bin of backstage dwell histogram (number accounting corresponding to 0-5 minutes)；

Target APP is in first bin of backstage dwell histogram (number accounting corresponding to 5-10 minutes)；

Target APP is in first bin of backstage dwell histogram (number accounting corresponding to 10-15 minutes)；

Target APP is in first bin of backstage dwell histogram (number accounting corresponding to 15-20 minutes)；

Target APP is in first bin of backstage dwell histogram (number accounting corresponding to 25-30 minutes)；

Target APP is in first bin of backstage dwell histogram (number accounting corresponding to after 30 minutes)；

Currently whether have and charging.

302nd, the error judgment function of ridge regression model is established, obtaining corresponding regression parameter according to error judgment function obtains Take function.

For example the error judgment function of ridge regression model can include such as minor function：

Derivation can be carried out to error judgment function, obtain function：

Wherein,For regression parameter to be solved.

303rd, function is obtained according to multiple default ridge parameters and regression parameter, obtains corresponding multiple regression parameters, obtain To multigroup ridge regression parameter.

Wherein, ridge regression parameter includes ridge parameter λ and corresponding regression parameter

304th, training characteristics set is divided into more sub- training characteristics set.

305th, according to sub- training characteristics set, ridge regression parameter and error judgment function, obtain under ridge regression parameter Sub- error of the son training set for ridge regression model.

For example, training characteristics set D can be divided into M sub- training characteristics set, obtain sub- training characteristics set D1, D2……DM；Wherein, M is the positive integer more than 1.Then, according to error judgment function and ridge regression parameter, calculate per height Training characteristics are integrated under ridge regression parameter for the sub- error of ridge regression model, if D1 is in ridge regression parameter Under for ridge regression model sub- error f11, D2 in ridge regression parameterUnder for ridge regression model sub- error F12 ... DM is in ridge regression parameterUnder for ridge regression model sub- error f1M, obtain the training of every height Characteristic set is at this in ridge regression parameterUnder for ridge regression model sub- error, f11, f12 ... f1M.

306th, basis every sub- training set under ridge regression parameter closes the sub- error for ridge regression model, obtains and is returned in ridge Return the error of training characteristics set under parameter for ridge regression model, repeat step 305 and 306 is obtained under every group of ridge regression parameter Error of the training characteristics for ridge regression model.

Such as the sub- error according to corresponding to every sub- training characteristics set, obtain the mean error of sub- training characteristics set； Error of the training characteristics set for ridge regression model under ridge regression parameter is obtained according to mean error.

Then repeat step 305 and 306 can calculate under every group of ridge regression parameter training characteristics set for ridge regression The error of model；Such as ridge regression parameterCorresponding to respectively Error F1, F2 ... Fk ... Fm.

307th, target ridge regression parameter of the ridge regression parameter as ridge regression model corresponding to error minimum is chosen.

In one embodiment, the 301-307 that repeats the above steps can obtain ridge regression parameter corresponding to each application.

308th, relevant parameter in ridge regression model is updated according to target ridge regression parameter, the ridge regression mould after being trained Type.

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

In one embodiment, the 301-308 that repeats the above steps can obtain ridge regression mould after being trained corresponding to each application Type

309th, the multidimensional characteristic of application, the predicted characteristics set being applied are obtained.

In the embodiment of the present application, the multidimensional characteristic of step collection is characterized in same type spy with being obtained in step 301 Sign, namely predicted characteristics set are identical with the characteristic type that training characteristics set is included, such as include：After being cut into The duration of platform；Using during being cut into backstage, duration is shielded in going out for electronic equipment；Using the number for entering foreground；Using before The time of platform；Using the mode for entering backstage.

310th, whether can be cleared up according to the ridge regression model after predicted characteristics set and training, prediction application.

In a specific example, ridge after the training of each background application can be obtained by above-mentioned steps 301-308 Regression model；Then, whether multiple applications of ridge regression model prediction running background may be used after the training based on each background application Cleaning, as shown in table 1, it is determined that the application A1 of running background can be cleared up and using A3, and kept using A2 in running background State it is constant.

Using	Prediction result
		Using A1	It can clear up
Using A2	It can not clear up
		Using A3	It can clear up

Table 1

One kind is additionally provided in one embodiment applies cleaning plant.Referring to Fig. 4, Fig. 4 provides for the embodiment of the present application The structural representation using cleaning plant.Wherein this is applied to electronic equipment using cleaning plant, and this applies cleaning plant bag Training characteristics acquiring unit 401, training unit 402, predicted characteristics acquiring unit 403 and predicting unit 404 are included, it is as follows：

Training characteristics acquiring unit 401, for obtaining the multidimensional characteristic of application, the training characteristics set being applied；

Training unit 402, ridge regression model is trained for the training characteristics set according to the application, instructed Ridge regression model after white silk；

Predicted characteristics acquiring unit 403, for obtaining the multidimensional characteristic of the application, obtain the predicted characteristics of the application Set；

Predicting unit 404, for according to the ridge regression model after the predicted characteristics set and the training, predicting institute Whether state to apply can clear up.

In one embodiment, with reference to figure 5, wherein, training unit 402, including：

Subelement 4021 is established, for establishing the error judgment function of the ridge regression model；

Parameter acquiring subelement 4022, described in being obtained according to the training characteristics set and the error judgment function The target ridge regression parameter of ridge regression model, the target ridge regression parameter include ridge parameter and regression parameter；

Subelement 4023 is trained, after according to the target ridge regression parameter and the ridge regression model training Ridge regression model.

In one embodiment, parameter acquiring subelement 4022, can be used for：

Multigroup ridge regression parameter is obtained according to the error judgment function, the ridge regression parameter includes：Ridge parameter and return Return parameter；

According to the training characteristics set, the ridge regression parameter and the error judgment function, obtain and returned in the ridge The training characteristics set under parameter is returned for the error of the ridge regression model, to obtain corresponding to every group of ridge regression parameter by mistake Difference；

According to error corresponding to every group of ridge regression parameter, corresponding target ridge is chosen from multigroup ridge regression parameter and is returned Return parameter.

In one embodiment, parameter acquiring subelement 4022, can be specifically used for：

Corresponding regression parameter is obtained according to the error judgment function and obtains function；

Function is obtained according to multiple default ridge parameters and the regression parameter, obtains and is returned corresponding to each default ridge parameter Return parameter, obtain multigroup ridge regression parameter.

The training characteristics set is divided into more sub- training characteristics set；

According to the sub- training characteristics set, the ridge regression parameter and the error judgment function, obtain described The sub- training set is obtained corresponding to every sub- training characteristics set for the sub- error of ridge regression model under ridge regression parameter The sub- error；

According to sub- error corresponding to every sub- training characteristics set, the training characteristics under the ridge regression parameter are obtained Gather the error for the ridge regression model.

According to the mean error obtain under the ridge regression parameter training characteristics set for the ridge regression The error of model.

Minimal error is determined from error corresponding to every group of ridge regression parameter；

Ridge regression parameter corresponding to the minimal error is chosen from multigroup ridge regression parameter as target ridge regression Parameter.

Wherein, the method that the step of being performed using each unit in cleaning plant may be referred to the description of above method embodiment walks Suddenly.This can be integrated in the electronic device using cleaning plant, such as mobile phone, tablet personal computer.

When it is implemented, above unit can be realized as independent entity, can also be combined, as Same or several entities realize that the specific implementation of the above each unit can be found in embodiment above, will not be repeated here.

From the foregoing, it will be observed that the present embodiment application cleaning plant can be obtained the multidimensional of application by training characteristics acquiring unit 401 Feature, the training characteristics set being applied；Ridge regression model is entered according to the training characteristics set of application by training unit 402 Row training, the ridge regression model after being trained；The multidimensional characteristic of application is obtained by predicted characteristics acquiring unit 403, is answered Predicted characteristics set；By predicting unit 404 according to the ridge regression model after predicted characteristics set and training, prediction application Whether can clear up；So that pair application that can be cleared up is cleared up；The program can realize the automatic cleaning of application, improve electronics The operation fluency of equipment, reduce power consumption and save resource.

The embodiment of the present application also provides a kind of electronic equipment.Referring to Fig. 6, electronic equipment 500 include processor 501 and Memory 502.Wherein, processor 501 is electrically connected with memory 502.

The processor 500 is the control centre of electronic equipment 500, is set using various interfaces and the whole electronics of connection Standby various pieces, by the computer program of operation or load store in memory 502, and call and be stored in memory Data in 502, the various functions and processing data of electronic equipment 500 are performed, so as to carry out overall prison to electronic equipment 500 Control.

The memory 502 can be used for storage software program and module, and processor 501 is stored in memory by operation 502 computer program and module, so as to perform various function application and data processing.Memory 502 can mainly include Storing program area and storage data field, wherein, storing program area can storage program area, the computer needed at least one function Program (such as sound-playing function, image player function etc.) etc.；Storage data field can store uses institute according to electronic equipment Data of establishment etc..In addition, memory 502 can include high-speed random access memory, non-volatile memories can also be included Device, for example, at least a disk memory, flush memory device or other volatile solid-state parts.Correspondingly, memory 502 can also include Memory Controller, to provide access of the processor 501 to memory 502.

In the embodiment of the present application, the processor 501 in electronic equipment 500 can be according to the steps, by one or one Instruction is loaded into memory 502 corresponding to the process of computer program more than individual, and is stored in by the operation of processor 501 Computer program in reservoir 502, it is as follows so as to realize various functions：

Obtain the multidimensional characteristic of application, the training characteristics set being applied；

According to the ridge regression model after the predicted characteristics set and the training, predict whether the application can be clear.

In some embodiments, ridge regression model is trained in the training sample according to the application, instructed After white silk when stating ridge regression model, processor 501 can specifically perform following steps：

Establish the error judgment function of the ridge regression model；

The target ridge regression of the ridge regression model is obtained according to the training characteristics set and the error judgment function Parameter, the target ridge regression parameter include ridge parameter and regression parameter；

According to the ridge regression model after the target ridge regression parameter and the ridge regression model training.

In some embodiments, the ridge time is being obtained according to the training characteristics set and the error judgment function When returning the target ridge regression parameter of model, processor 501 can specifically perform following steps：

In some embodiments, when obtaining multigroup ridge regression parameter according to the error judgment function, processor 501 Following steps can specifically be performed：

In some embodiments, according to the training characteristics set, the ridge regression parameter and the error judgment Function, obtain under the ridge regression parameter training characteristics set for the ridge regression model error when, processor 501 can specifically perform following steps：

In some embodiments, in the sub- error according to corresponding to every sub- training characteristics set, obtain and returned in the ridge Return the training characteristics set under parameter for the ridge regression model error when, processor 501 can specifically perform following Step：

In some embodiments, in the error according to corresponding to every group of ridge regression parameter, from multigroup ridge regression parameter During the corresponding target ridge regression parameter of middle selection, processor 501 can specifically perform following steps：

From the foregoing, the electronic equipment of the embodiment of the present application, the multidimensional characteristic of application is obtained, the training being applied is special Collection is closed；Ridge regression model is trained according to the training characteristics set of application, the ridge regression model after being trained；Obtain The multidimensional characteristic of application, the predicted characteristics set being applied；According to predicted characteristics set and training after ridge regression model, Whether prediction application can clear up；So that pair application that can be cleared up is cleared up；The program can realize the automatic cleaning of application, carry The high operation fluency of electronic equipment, reduces power consumption.

Also referring to Fig. 7, in some embodiments, electronic equipment 500 can also include：Display 503, radio frequency electrical Road 504, voicefrequency circuit 505 and power supply 506.Wherein, wherein, display 503, radio circuit 504, voicefrequency circuit 505 and Power supply 506 is electrically connected with processor 501 respectively.

The display 503 is displayed for the information inputted by user or is supplied to the information of user and various figures Shape user interface, these graphical user interface can be made up of figure, text, icon, video and its any combination.Display 503 can include display panel, in some embodiments, can use liquid crystal display (Liquid Crystal Display, LCD) or the form such as Organic Light Emitting Diode (Organic Light-Emitting Diode, OLED) match somebody with somebody Put display panel.

The radio circuit 504 can be used for transceiving radio frequency signal, to pass through radio communication and the network equipment or other electricity Sub- equipment establishes wireless telecommunications, the receiving and transmitting signal between the network equipment or other electronic equipments.

The voicefrequency circuit 505 can be used for providing the audio between user and electronic equipment by loudspeaker, microphone Interface.

The power supply 506 is used to all parts power supply of electronic equipment 500.In certain embodiments, power supply 506 Can be logically contiguous by power-supply management system and processor 501, so as to realize management charging by power-supply management system, put The function such as electricity and power managed.

Although not shown in Fig. 7, electronic equipment 500 can also include camera, bluetooth module etc., will not be repeated here.

The embodiment of the present application also provides a kind of storage medium, and the storage medium is stored with computer program, when the meter When calculation machine program is run on computers so that the computer performs in any of the above-described embodiment and applies method for cleaning, than Such as：Obtain the multidimensional characteristic of application, the training characteristics set being applied；According to the training characteristics set of application to ridge regression mould Type is trained, the ridge regression model after being trained；Obtain the multidimensional characteristic of application, the predicted characteristics set being applied； According to the ridge regression model after predicted characteristics set and training, whether prediction application can clear up.

In the embodiment of the present application, storage medium can be magnetic disc, CD, read-only storage (Read Only Memory, ROM) or random access memory (Random Access Memory, RAM) etc..

In the above-described embodiments, the description to each embodiment all emphasizes particularly on different fields, and does not have the portion being described in detail in some embodiment Point, it may refer to the associated description of other embodiment.

It should be noted that for application method for cleaning to the embodiment of the present application, this area common test personnel can be with Understand all or part of flow using method for cleaning for realizing the embodiment of the present application, be that can be controlled by computer program Related hardware is completed, and the computer program can be stored in a computer read/write memory medium, be such as stored in electronics In the memory of equipment, and by least one computing device in the electronic equipment, it may include in the process of implementation such as application The flow of the embodiment of method for cleaning.Wherein, described storage medium can be magnetic disc, CD, read-only storage, arbitrary access note Recall body etc..

For application cleaning plant to the embodiment of the present application, its each functional module can be integrated in a process chip In or modules be individually physically present, can also two or more modules be integrated in a module.It is above-mentioned Integrated module can both be realized in the form of hardware, can also be realized in the form of software function module.It is described integrated If module realized in the form of software function module and as independent production marketing or in use, one can also be stored in In individual computer read/write memory medium, the storage medium is for example read-only storage, disk or CD etc..

One kind application method for cleaning, device, storage medium and the electronic equipment provided above the embodiment of the present application enters Go and be discussed in detail, specific case used herein is set forth to the principle and embodiment of the application, and the above is implemented The explanation of example is only intended to help and understands the present processes and its core concept；Meanwhile for those skilled in the art, according to According to the thought of the application, there will be changes in specific embodiments and applications, in summary, this specification content It should not be construed as the limitation to the application.

Claims

1. one kind applies method for cleaning, it is characterised in that including：

Ridge regression model is trained according to the training characteristics set of the application, the ridge regression model after being trained；

According to the ridge regression model after the predicted characteristics set and the training, predict whether the application can clear up.

2. apply method for cleaning as claimed in claim 1, it is characterised in that according to the training sample of the application to ridge regression Model is trained, and ridge regression model is stated after being trained, including：

Establish the error judgment function of the ridge regression model；

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

3. apply method for cleaning as claimed in claim 2, it is characterised in that according to the training characteristics set and the error Discriminant function obtains the target ridge regression parameter of the ridge regression model, including：

Multigroup ridge regression parameter is obtained according to the error judgment function, the ridge regression parameter includes：Ridge parameter and recurrence are joined Number；

According to the training characteristics set, the ridge regression parameter and the error judgment function, obtain and join in the ridge regression Several lower training characteristics set obtain error corresponding to every group of ridge regression parameter for the error of the ridge regression model；

According to error corresponding to every group of ridge regression parameter, corresponding target ridge regression ginseng is chosen from multigroup ridge regression parameter Number.

4. apply method for cleaning as claimed in claim 3, it is characterised in that multigroup ridge is obtained according to the error judgment function Regression parameter, including：

Function is obtained according to multiple default ridge parameters and the regression parameter, obtains and returns ginseng corresponding to each default ridge parameter Number, obtains multigroup ridge regression parameter.

5. apply method for cleaning as claimed in claim 3, it is characterised in that return according to the training characteristics set, the ridge Return parameter and the error judgment function, obtain under the ridge regression parameter training characteristics set for the ridge regression The error of model, including：

According to the sub- training characteristics set, the ridge regression parameter and the error judgment function, obtain and returned in the ridge The sub- training set under parameter is returned for the sub- error of ridge regression model, to obtain described corresponding to every sub- training characteristics set Sub- error；

According to sub- error corresponding to every sub- training characteristics set, the training characteristics set under the ridge regression parameter is obtained For the error of the ridge regression model.

6. apply method for cleaning as claimed in claim 5, it is characterised in that according to son corresponding to every sub- training characteristics set Error, the error of under the ridge regression parameter training characteristics set for the ridge regression model is obtained, including：

According to the mean error obtain under the ridge regression parameter training characteristics set for the ridge regression model Error.

7. apply method for cleaning as claimed in claim 3, it is characterised in that according to error corresponding to every group of ridge regression parameter, Corresponding target ridge regression parameter is chosen from multigroup ridge regression parameter, including：

8. one kind applies cleaning plant, it is characterised in that including：

Training unit, ridge regression model is trained for the training characteristics set according to the application, after being trained Ridge regression model；

Predicted characteristics acquiring unit, for obtaining the multidimensional characteristic of the application, obtain the predicted characteristics set of the application；

Predicting unit, for according to the ridge regression model after the predicted characteristics set and the training, predicting the application Whether can clear up.

9. apply cleaning plant as claimed in claim 8, it is characterised in that the training unit, including：

Subelement is established, for establishing the error judgment function of the ridge regression model；

Parameter acquiring subelement, for obtaining the ridge regression mould according to the training characteristics set and the error judgment function The target ridge regression parameter of type, the target ridge regression parameter include ridge parameter and regression parameter；

Subelement is trained, for according to the ridge regression mould after the target ridge regression parameter and the ridge regression model training Type.

10. apply cleaning plant as claimed in claim 9, it is characterised in that the parameter acquiring subelement, be used for：

11. apply cleaning plant as claimed in claim 10, it is characterised in that the parameter acquiring subelement, be specifically used for：

12. apply cleaning plant as claimed in claim 10, it is characterised in that the parameter acquiring subelement, be specifically used for：

13. a kind of storage medium, is stored thereon with computer program, it is characterised in that when the computer program is in computer During upper operation so that the computer performs applies method for cleaning as described in any one of claim 1 to 7.

14. a kind of electronic equipment, including processor and memory, the memory have computer program, it is characterised in that described Processor applies method for cleaning by calling the computer program, for performing as described in any one of claim 1 to 7.