CN107870811A - Using method for cleaning, device, storage medium and electronic equipment - Google Patents
Using method for cleaning, device, storage medium and electronic equipment Download PDFInfo
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- CN107870811A CN107870811A CN201711047124.5A CN201711047124A CN107870811A CN 107870811 A CN107870811 A CN 107870811A CN 201711047124 A CN201711047124 A CN 201711047124A CN 107870811 A CN107870811 A CN 107870811A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/329—Power saving characterised by the action undertaken by task scheduling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/48—Indexing scheme relating to G06F9/48
- G06F2209/482—Application
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
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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 multiple characteristic informations of application；Choose multiple identical forecast models；Characteristic information corresponding to each forecast model is chosen from multiple characteristic informations, the characteristic information set of each forecast model is obtained, wherein, the characteristic information set of each forecast model differs；Whether can be cleared up according to forecast model and its application of characteristic information ensemble prediction, obtain the prediction result of each forecast model；Finally determined, using that whether can clear up, to clear up application to clear up, the automatic cleaning of application is realized with this, improve the operation fluency of electronic equipment, reduce power consumption according to the prediction result of each forecast model.
Description
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
The embodiment of the present application provides one kind and applies method for cleaning, device, storage medium and electronic equipment, it is possible to increase electricity
The operation fluency of sub- equipment, reduce power consumption.
In a first aspect, one kind application method for cleaning for providing of the embodiment of the present application, including：
Obtain multiple characteristic informations of application；
Choose multiple identical forecast models；
Characteristic information corresponding to each forecast model is chosen from the multiple characteristic information, obtain each forecast model
Characteristic information set, wherein, the characteristic information set of each forecast model differs；
Using that whether can clear up according to the forecast model and its characteristic information ensemble prediction, each prediction mould is obtained
The prediction result of type；
Finally determine whether the application can clear up according to the prediction result of each forecast model.
Second aspect, one kind application cleaning plant for providing of the embodiment of the present application, including：
Feature acquiring unit, for obtaining multiple characteristic informations of application；
Model chooses unit, for choosing multiple identical forecast models；
Feature Selection unit, for choosing characteristic information corresponding to each forecast model from the multiple characteristic information,
The characteristic information set of each forecast model is obtained, wherein, the characteristic information set of each forecast model differs；
Predicting unit, whether can be cleared up for being applied according to the forecast model and its characteristic information ensemble prediction,
Obtain the prediction result of each forecast model；
Determining unit, for finally determining whether the application can clear up according to the prediction result of each forecast model.
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 multiple characteristic informations of application；Choose multiple identical forecast models；From multiple features
Characteristic information corresponding to each forecast model is chosen in information, the characteristic information set of each forecast model is obtained, wherein, each
The characteristic information set of forecast model differs；Whether can be cleared up according to forecast model and its application of characteristic information ensemble prediction,
Obtain the prediction result of each forecast model；Finally determine to apply whether can clear up according to the prediction result of each forecast model,
To clear up the automatic cleaning that can be cleared up using application is realized with this, the operation fluency of electronic equipment is improved, is reduced
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
What embodiment provided applies cleaning plant, or is integrated with the electronic equipment for applying cleaning plant, and the wherein application cleaning fills
Putting can be realized by the way of hardware or software.Wherein, electronic equipment can be smart mobile phone, tablet personal computer, palm electricity
The equipment such as brain, 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 multiple characteristic informations of application；Choose multiple identical
Forecast model；Characteristic information corresponding to each forecast model is chosen from multiple characteristic informations, obtain each forecast model
Characteristic information set, wherein, the characteristic information set of each forecast model differs；According to forecast model and its characteristic information collection
Close whether prediction application can clear up, obtain the prediction result of each forecast model；According to the prediction result of each forecast model most
Determine whether application can clear up eventually.
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 be with acquisition applications a multiple characteristic informations multidimensional characteristic (such as using a running background when
Temporal information long, using a operations etc.)；Multiple identical forecast models (such as decision-tree model) are chosen, are believed from multiple features
Characteristic information corresponding to each forecast model is chosen in breath, the characteristic information set of each forecast model is obtained, wherein, it is each pre-
Whether the characteristic information set for surveying model differs, can be cleared up, obtained according to forecast model and its application of characteristic information ensemble prediction
To the prediction result of each forecast model；Finally determine whether can clear up using a according to the prediction result of each forecast model.This
Outside, 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, multiple characteristic informations of application are obtained.
Application mentioned by the present embodiment, can be that any one installed on electronic equipment is applied, such as office application,
Communications applications, game application, shopping application etc..In addition, the application can be foreground application, or background application.
Wherein, multiple characteristic informations of application are the multidimensional characteristic information of application, can be adopted during the use of application
Collection.
The multidimensional characteristic of application has a dimension of certain length, and the parameter in each of which dimension is corresponding to characterize the one of application
Kind characteristic information, i.e. multidimensional characteristic breath are made up of various features.The plurality of characteristic information 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；Using
Into the number on foreground；Using the time in foreground；Switch using the mode for entering backstage, such as by homepage key (home keys)
Into, be returned key and switch into, switched into by other application；The type of application, including one-level (conventional application), two level
(other application)；Apply in backstage stay time histogram information, such as apply in first bin of backstage dwell histogram
(number accounting corresponding to 0-5 minutes) etc..
The plurality of 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..
Such as can be in historical time section, according to multiple characteristic informations of predeterminated frequency acquisition applications.Historical time
Section, such as can be 7 days, 10 days in the past；Predeterminated frequency, such as can gather once within every 10 minutes, one is gathered per half an hour
It is secondary.
In one embodiment, closed 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).
Wherein, electronic equipment can in each period acquisition applications multiple characteristic informations, and be stored in property data base
In, therefore, the embodiment of the present application can extract multiple characteristic informations of application from property data base.
202nd, multiple identical forecast models are chosen.
Wherein, forecast model is a kind of machine learning algorithm, for predicting the generation of some event, such as, it can predict
Using whether can clearing up.The forecast model can include：Decision-tree model, Logic Regression Models, Bayesian model, nerve net
Network model, Clustering Model etc..
In the embodiment of the present application, multiple identical forecast models can be chosen and make application cleaning prediction, such as choose M prediction
Model, M are the positive integer more than 1, can be 2,3,4 ... 9,10 etc..
The embodiment of the present application can pre-set n forecast model, can be pre- from n when needing to carry out using cleaning
Survey and M forecast model is chosen in model.
Sequential between step 201 and 202 is not limited by sequence number, can be that step 202 performs before step 201,
Can perform simultaneously.
Wherein, the quantity of forecast model can be set according to the actual requirements, such as, in one embodiment, can be based on should
Characteristic information quantity determines.That is, step " choosing multiple identical forecast models " can include：
The destination number of forecast model according to needed for determining the quantity of characteristic information, destination number are less than the number of characteristic information
Amount；
Multiple identical forecast models are chosen according to destination number.
In one embodiment, the quantity of forecast model can be less than the quantity of characteristic information, for example, using sharing 10 spies
Reference ceases, then the quantity of forecast model can be with 8.
Wherein, the mode of the true forecast model quantity of the quantity of feature based information has a variety of, such as, it is accurate in order to lift prediction
True property, the quantity of forecast model is The more the better, and in one embodiment, the quantity of forecast model can be only than the quantity of characteristic information
It is small by 1.That is, the quantity M=feature quantities t-1 of forecast model.
In one embodiment, be also based on feature quantity and each forecast model corresponding to feature quantity, it is determined that
The destination number of required forecast model.
For example using shared K feature, K is the positive integer more than 1, it is assumed that the feature quantity of each forecast model is to set
I are put, at this point it is possible to choose I feature from K feature, is sharedKind is followed the example of, namely the quantity of forecast model
For example, using a total of 10 features, then can select that (wherein each model is made with 9 dimensional features with 9 models
For input).9 dimensional features are chosen from 10 virals, it is a total ofFollow the example of, it is possible to choose 9 models.
In one embodiment, cleaning predetermined speed and success rate are applied in order to be lifted, it is determined that the quantity of forecast model
When also need to consider related storage information, the current amount of memory such as the space complexity and electronic equipment of forecast model
Deng.Wherein, step " destination number of forecast model according to needed for determining the quantity of characteristic information ", can include：
Obtain the space complexity of forecast model；
The prediction according to needed for determining the current amount of memory of space complexity, electronic equipment and the quantity of characteristic information
The destination number of model.
Wherein, the measurement of required memory space when space complexity finger counting method performs in electronic equipment；Typically use S (n)
Represent.Space complexity S (n) is defined as the memory space spent by the algorithm, and it is also problem scale n function.Asymptotic sky
Between complexity also usually be referred to as space complexity.
Wherein, the current amount of memory of electronic equipment is the current memory space of electronic equipment or residual memory space
Measurement, the memory space can be including memory headroom etc..
In one embodiment, space complexity and current amount of memory that can be based on forecast model calculate most
The forecast model that can be chosen greatly, then, maximum quantity determine the model quantity finally chosen with feature quantity.Such as step
" according to forecast model needed for the current amount of memory of space complexity, electronic equipment and the determination of the quantity of characteristic information
Destination number ", it can include：
According to the current amount of memory of space complexity and electronic equipment, the maximum number that can use forecast model is obtained
Amount；
When maximum quantity be less than characteristic information quantity when, by maximum quantity be used as needed for forecast model destination number.
Wherein, the maximum quantity Mmax of forecast model is tried to achieve based on amount of memory D/ space complexities S.
, can be in the hope of maximum predicted model quantity, when maximum predicted model quantity is less than characteristic in the embodiment of the present application
During amount, it is suitable to show the maximum quantity, can meet feature and model requirements, ensures the accuracy of prediction result, and and can is full
Space requirement needed for sufficient forecast model, lift the success rate of prediction.
For example, application shares 30 characteristic informations, the space complexity of forecast model is 20KB, current memory space
Measure as 500KB, at this point it is possible to which calculating the maximum of forecast model chooses quantity 500/20=25, it is seen that maximum chooses quantity 25
Less than feature quantity 30, then application cleaning prediction can be made to choose 25 forecast models.
When maximum predicted model quantity is more than feature quantity, show that current feature quantity can not meet forecast model
Demand, at this point it is possible to the selection quantity of forecast model is redefined, such as, in one embodiment,
When maximum predicted model quantity is more than feature quantity, default feature quantity and forecast model quantity can be based on
Between difference determine model choose quantity.That is, step is " according to the current amount of memory of space complexity, electronic equipment
And the destination number of forecast model needed for the quantity determination of characteristic information " can also include：
When maximum quantity is more than the quantity of characteristic information, the number of targets of forecast model according to needed for determining predetermined number difference
Amount, predetermined number difference are characterized the number differences between the quantity of information and the quantity of required forecast model.
Wherein, predetermined number difference can be set according to the actual requirements, for example be 1,2,3,4 etc..
For example, for example, application shares 30 characteristic informations, the space complexity of forecast model is 10KB, and current deposits
Storage amount of space is 500KB, at this point it is possible to which calculating the maximum of forecast model chooses quantity 500/10=50, it is seen that maximum is chosen
Quantity 50 is more than feature quantity 30, it is assumed that default difference between feature quantity and forecast model quantity is 1, then now, can
Make application cleaning prediction to choose 30-1=29 forecast model.
203rd, each forecast model is chosen from multiple characteristic informations corresponding to characteristic information, obtain each forecast model
Characteristic information set, wherein, the characteristic information set of each forecast model differs.
Wherein, the characteristic information set of forecast model differ can be any two forecast model characteristic information set
Middle Partial Feature differs, or whole features differ.In addition, the spy that the characteristic information set of each forecast model is included
Sign information content be able to can also be differed with identical.
For example, the characteristic information set of forecast model 1 includes feature 1, feature 2, feature 3, feature 4, feature 5, mould is predicted
The characteristic information set of type 2 includes feature 1, feature 2, feature 3, feature 7, feature 8.
Again for example, the characteristic information set of forecast model includes feature 1, feature 2, feature 3, feature 4, feature 5, predicts mould
The characteristic information set of type 2 includes feature 6, feature 7, feature 8, feature 9, feature 10.
In one embodiment, it is determined that after the quantity of forecast model, the quantity can be based on and choose each forecast model pair
The characteristic information answered.That is, step " target signature information corresponding to each forecast model is chosen from multiple characteristic informations " can
With including：Target signature information corresponding to from multiple characteristic informations choosing each forecast model according to destination number.
Wherein, characteristic information quantity corresponding to each forecast model can be identical with the quantity of forecast model, in some realities
Apply in example, the two can also be differed.
For example using shared K feature, K is the positive integer more than 1, it is assumed that the forecast model quantity of selection is M, often
The feature quantity of individual forecast model is M, at this point it is possible to choose M feature from K feature, is sharedKind is followed the example of.
Assuming that have using a total of 10 features, then can select that (wherein each model is with 9 dimensional features with 9 models
As input).Now, 9 dimensional features are chosen from 10 virals, it is a total ofFollow the example of.
204th, whether can be cleared up according to forecast model and its application of characteristic information ensemble prediction, obtain each forecast model
Prediction result.
For each forecast model, it can predict whether application can be clear according to forecast model and its character pair information aggregate
Reason, can so obtain multiple prediction results.Wherein, prediction result includes：Using can clear up or application can not clear up.
Such as when forecast model is decision-tree model, based on each decision-tree model and its characteristic information prediction application
Whether can clear up；Specifically, corresponding leaf node can be determined according to feature and decision-tree model, by the defeated of the leaf node
Go out as prediction output result.Such as determined using target signature according to the branch condition (characteristic value for dividing feature) of decision tree
Current leaf node, take result of the output of the leaf node as prediction.Due to leaf node output include can clear up,
Or it can not clear up.
For example, when the quantity of forecast model is M, can be according to each forecast model and its corresponding characteristic information collection
Close, obtain the prediction result of each forecast model, you can to obtain M prediction result.
205th, finally determine to apply whether can clear up according to the prediction result of each forecast model.
After the prediction result of each forecast model is obtained, can the prediction result based on each forecast model finally determine
Using whether can clearing up.
For example the first prediction result quantity that application can clear up and the second prediction knot that application can not clear up can be obtained
Fruit quantity, when the first prediction result quantity is more than the second prediction result quantity, it is determined that application can clear up, conversely, determining application
It can not clear up.
For example, after obtaining M prediction result, it is assumed that there is J prediction result to be cleared up for application, M-J prediction result is
Using that can not clear up, if during J ＞ M-J, can finally determine that application can clear up, otherwise determine that application can not clear up.
From the foregoing, it will be observed that the embodiment of the present application obtains multiple characteristic informations of application；Choose multiple identical forecast models；From
Characteristic information corresponding to each forecast model is chosen in multiple characteristic informations, the characteristic information set of each forecast model is obtained,
Wherein, the characteristic information set of each forecast model differs；It is according to forecast model and its characteristic information ensemble prediction application
It is no to clear up, obtain the prediction result of each forecast model；Finally determined according to the prediction result of each forecast model using being
No to clear up, to clear up the automatic cleaning that can be cleared up using application is realized with this, the operation for improving electronic equipment is smooth
Degree, reduces power consumption.
Further, the more of the behavioural habits of application are used due in each sample of sample set, including reflection user
Individual characteristic information, therefore the embodiment of the present application can make it that the cleaning to corresponding application is more personalized and intelligent.
Further, using multiple identical forecast models, and for each forecast model using different characteristic informations come
Application cleaning prediction is realized, the accuracy of user's behavior prediction can be lifted, and then improves the degree of accuracy of cleaning；In addition, using
Multiple identical forecast models and the application cleaning of Partial Feature information parallel anticipation, relative to using a forecast model and entirely
The mode of portion's characteristic information prediction application cleaning, can lift the speed using cleaning prediction, shorten predicted time.
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, multiple characteristic informations of application are obtained.
Application mentioned by the present embodiment, can be that any one installed on electronic equipment is applied, such as office application,
Communications applications, game application, shopping application etc..In addition, the application can be foreground application, or background application.
Wherein, multiple characteristic informations of application are the multidimensional characteristic information of application, can be adopted during the use of application
Collection.
The multidimensional characteristic of application has a dimension of certain length, and the parameter in each of which dimension is corresponding to characterize the one of application
Kind characteristic information, i.e. multidimensional characteristic breath are made up of various features.The plurality of characteristic information 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；Using
Into the number on foreground；Using the time in foreground；Switch using the mode for entering backstage, such as by homepage key (home keys)
Into, be returned key and switch into, switched into by other application；The type of application, including one-level (conventional application), two level
(other application)；Apply in backstage stay time histogram information, such as apply in first bin of backstage dwell histogram
(number accounting corresponding to 0-5 minutes) etc..
The plurality of 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..
For example multiple characteristic informations of application can include following 30 dimensional feature, it is necessary to explanation, spy as follows
Reference breath is only for example, the quantity of the characteristic information included in reality, can be more than the quantity than information as follows, also may be used
With the quantity less than information as follows, the specific features information taken can also not made specific herein with difference as follows
Limit.30 dimensional features include：
The duration of the last incision backstages of APP till now；
The duration of the last incision backstages of APP till now；
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 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, according to the quantity of characteristic information determine needed for forecast model destination number.
Wherein, forecast model is a kind of machine learning algorithm, for predicting the generation of some event, such as, it can predict
Using whether can clearing up.The forecast model can include：Decision-tree model, Logic Regression Models, Bayesian model, nerve net
Network model, Clustering Model etc..
In one embodiment, the quantity of forecast model can be less than the quantity of characteristic information, for example, using sharing 10 spies
Reference ceases, then the quantity of forecast model can be with 8.
Wherein, the mode of the true forecast model quantity of the quantity of feature based information has a variety of, such as, it is accurate in order to lift prediction
True property, the quantity of forecast model is The more the better, and in one embodiment, the quantity of forecast model can be only than the quantity of characteristic information
It is small by 1.That is, the quantity M=feature quantities t-1 of forecast model.
In one embodiment, be also based on feature quantity and each forecast model corresponding to feature quantity, it is determined that
The destination number of required forecast model.
For example using shared K feature, K is the positive integer more than 1, it is assumed that the feature quantity of each forecast model is to set
I are put, at this point it is possible to choose I feature from K feature, is sharedKind is followed the example of, namely the quantity of forecast model
For example, using a total of 10 features, then can select that (wherein each model is made with 9 dimensional features with 9 models
For input).9 dimensional features are chosen from 10 virals, it is a total ofFollow the example of, it is possible to choose 9 models.
303rd, multiple identical forecast models are chosen from forecast model database according to destination number.
Wherein, multiple identical forecast models are saved in forecast model database, it is determined that required using forecast model
, can be with the forecast model of the selection respective numbers from the database after quantity.
For example, destination number is M, at this point it is possible to choose M identical forecast model from database.
304th, each forecast model is chosen from multiple characteristic informations according to destination number corresponding to target signature information, obtain
To the characteristic information set of each forecast model, wherein, the characteristic information set of each forecast model differs.
Wherein, the characteristic information set of forecast model differ can be any two forecast model characteristic information set
Middle Partial Feature differs, or whole features differ.In addition, the spy that the characteristic information set of each forecast model is included
Sign information content be able to can also be differed with identical.
For example, the characteristic information set of forecast model 1 includes feature 1, feature 2, feature 3, feature 4, feature 5, mould is predicted
The characteristic information set of type 2 includes feature 1, feature 2, feature 3, feature 7, feature 8.
Again for example, the characteristic information set of forecast model includes feature 1, feature 2, feature 3, feature 4, feature 5, predicts mould
The characteristic information set of type 2 includes feature 6, feature 7, feature 8, feature 9, feature 10.
For example using shared K feature, K is the positive integer more than 1, it is assumed that the forecast model quantity of selection is M, often
The feature quantity of individual forecast model is M, at this point it is possible to choose M feature from K feature, is sharedKind is followed the example of.
Assuming that have using a total of 10 features, then can select that (wherein each model is with 9 dimensional features with 9 models
As input).Now, 9 dimensional features are chosen from 10 virals, it is a total ofFollow the example of.
305th, whether can be cleared up according to forecast model and its application of characteristic information ensemble prediction, obtain each forecast model
Prediction result.
Wherein, prediction result includes：Using can clear up or application can not clear up.
For example obtained based on target signature information and Logic Regression Models and apply closable probability；When probability is more than
During predetermined probabilities value, it is determined that application can clear up, otherwise it can not clear up.
For example, when the quantity of forecast model is M, can be according to each forecast model and its corresponding characteristic information collection
Close, obtain the prediction result of each forecast model, you can to obtain M prediction result.
306th, finally determine to apply whether can clear up according to the prediction result of each forecast model.
For example, after obtaining M prediction result, it is assumed that there is J prediction result to be cleared up for application, M-J prediction result is
Using that can not clear up, if during J ＞ M-J, can finally determine that application can clear up, otherwise determine that application can not clear up.
In a specific example, can utilize the embodiment of the present application method prediction running background multiple applications whether
It can clear up, as shown in table 1, it is determined that the application A1 of running background can be cleared up and using A3, and keep transporting on backstage using A2
Capable state 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
From the foregoing, it will be observed that the embodiment of the present application obtains multiple characteristic informations of application；Choose multiple identical forecast models；From
Characteristic information corresponding to each forecast model is chosen in multiple characteristic informations, the characteristic information set of each forecast model is obtained,
Wherein, the characteristic information set of each forecast model differs；It is according to forecast model and its characteristic information ensemble prediction application
It is no to clear up, obtain the prediction result of each forecast model；Finally determined according to the prediction result of each forecast model using being
No to clear up, to clear up the automatic cleaning that can be cleared up using application is realized with this, the operation for improving electronic equipment is smooth
Degree, reduces power consumption.
Further, the more of the behavioural habits of application are used due in each sample of sample set, including reflection user
Individual characteristic information, therefore the embodiment of the present application can make it that the cleaning to corresponding application is more personalized and intelligent.
Further, using multiple identical forecast models, and for each forecast model using different characteristic informations come
Application cleaning prediction is realized, the accuracy of user's behavior prediction can be lifted, and then improves the degree of accuracy of cleaning；In addition, using
Multiple identical forecast models and the application cleaning of Partial Feature information parallel anticipation, relative to using a forecast model and entirely
The mode of portion's characteristic information prediction application cleaning, can lift the speed using cleaning prediction, shorten predicted time.
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
Include feature acquiring unit 401, model chooses unit 402, Feature Selection unit 403, predicting unit 404 and determining unit 405,
It is as follows：
Feature acquiring unit 401, for obtaining multiple characteristic informations of application；
Model chooses unit 402, for choosing multiple identical forecast models；
Feature Selection unit 403, believe for choosing feature corresponding to each forecast model from the multiple characteristic information
Breath, obtains the characteristic information set of each forecast model, wherein, the characteristic information set of each forecast model differs；
Whether predicting unit 404, can be clear for being applied according to the forecast model and its characteristic information ensemble prediction
Reason, obtains the prediction result of each forecast model；
Determining unit 405, for finally determining whether the application can clear up according to the prediction result of each forecast model.
In one embodiment, unit 402 is chosen, can be included with reference to figure 5, the model：
Quantity determination subelement 4021, the number of targets for the forecast model according to needed for the determination of the quantity of the characteristic information
Amount；
Subelement 4022 is chosen, for choosing multiple identical forecast models according to the destination number.
In one embodiment, subelement 4022 is chosen, specifically can be used for：According to the destination number from the multiple spy
Target signature information corresponding to each forecast model is chosen in reference breath.
In one embodiment, quantity determination subelement 4021, specifically can be used for：
Obtain the space complexity of the forecast model；
Quantity is according to the current amount of memory of the space complexity, electronic equipment and the quantity of the characteristic information
It is determined that the destination number of required forecast model.
In one embodiment, quantity determination subelement 4021, specifically can be used for：
According to the current amount of memory of space complexity and the electronic equipment, forecast model can be used most by obtaining
Big quantity；
When the maximum quantity be less than the characteristic information quantity when, using the maximum quantity as needed for forecast model
Destination number；
When the maximum quantity is more than the quantity of the characteristic information, the forecast model according to needed for determining predetermined number difference
Destination number, the predetermined number difference is characterized the number differences between the quantity of information and the quantity of required forecast model.
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 multiple features of application by feature acquiring unit 401
Information；Unit 402 is chosen by model and chooses multiple identical forecast models；By Feature Selection unit 403 from multiple characteristic informations
Characteristic information corresponding to the middle each forecast model of selection, obtains the characteristic information set of each forecast model, wherein, each prediction
The characteristic information set of model differs；It is according to forecast model and its characteristic information ensemble prediction application by predicting unit 404
It is no to clear up, obtain the prediction result of each forecast model；By determining unit 405 according to the prediction result of each forecast model most
Determine whether application can clear up eventually, to clear up the automatic cleaning that can be cleared up using application is realized with this, improve electronics and set
Standby operation fluency, reduces power consumption.
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 multiple characteristic informations of application；
Choose multiple identical forecast models；
Characteristic information corresponding to each forecast model is chosen from the multiple characteristic information, obtain each forecast model
Characteristic information set, wherein, the characteristic information set of each forecast model differs；
Using that whether can clear up according to the forecast model and its characteristic information ensemble prediction, each prediction mould is obtained
The prediction result of type；
Finally determine whether the application can clear up according to the prediction result of each forecast model.
In some embodiments, when choosing multiple identical forecast models, processor 501 can specifically perform following
Step：
The destination number of forecast model according to needed for determining the quantity of the characteristic information；
Multiple identical forecast models are chosen according to the destination number.
In some embodiments, target signature corresponding to each forecast model is being chosen from the multiple characteristic information
During information, processor 501 can specifically perform following steps：
Target signature letter corresponding to from the multiple characteristic information choosing each forecast model according to the destination number
Breath.
In some embodiments, the destination number of forecast model needed for being determined according to the quantity of the characteristic information
When, processor 501 can specifically perform following steps：
Obtain the space complexity of the forecast model；
Determined according to the current amount of memory of the space complexity, electronic equipment and the quantity of the characteristic information
The destination number of required forecast model.
In some embodiments, according to the current amount of memory of the space complexity, electronic equipment and institute
When stating the destination number of forecast model needed for the quantity determination of characteristic information, processor 501 can also specifically perform following steps：
According to the current amount of memory of space complexity and the electronic equipment, forecast model can be used most by obtaining
Big quantity；
When the maximum quantity be less than the characteristic information quantity when, using the maximum quantity as needed for forecast model
Destination number.
In some embodiments, according to the current amount of memory of the space complexity, electronic equipment and institute
When stating the destination number of forecast model needed for the quantity determination of characteristic information, processor 501 can also specifically perform following steps：
When the maximum quantity is more than the quantity of the characteristic information, the forecast model according to needed for determining predetermined number difference
Destination number, the predetermined number difference is characterized the number differences between the quantity of information and the quantity of required forecast model.
From the foregoing, the electronic equipment of the embodiment of the present application, multiple characteristic informations of application are obtained；Choose multiple identical
Forecast model；Characteristic information corresponding to each forecast model is chosen from multiple characteristic informations, obtain each forecast model
Characteristic information set, wherein, the characteristic information set of each forecast model differs；According to forecast model and its characteristic information collection
Close whether prediction application can clear up, obtain the prediction result of each forecast model；According to the prediction result of each forecast model most
Determine whether application can clear up eventually, to clear up the automatic cleaning that can be cleared up using application is realized with this, improve electronics and set
Standby operation fluency, 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 multiple characteristic informations of application；Choose multiple identical forecast models；Each prediction is chosen from multiple characteristic informations
Characteristic information corresponding to model, the characteristic information set of each forecast model is obtained, wherein, the characteristic information of each forecast model
Set differs；Whether can be cleared up according to forecast model and its application of characteristic information ensemble prediction, obtain each forecast model
Prediction result；Finally determine to apply whether can clear up according to the prediction result of each forecast model.
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 (13)
1. one kind applies method for cleaning, it is characterised in that including：
Obtain multiple characteristic informations of application；
Choose multiple identical forecast models；
Characteristic information corresponding to each forecast model is chosen from the multiple characteristic information, obtain the feature of each forecast model
Information aggregate, wherein, the characteristic information set of each forecast model differs；
Using that whether can clear up according to the forecast model and its characteristic information ensemble prediction, each forecast model is obtained
Prediction result；
Finally determine whether the application can clear up according to the prediction result of each forecast model.
2. apply method for cleaning as claimed in claim 1, it is characterised in that multiple identical forecast models are chosen, including：
The destination number of forecast model according to needed for determining the quantity of the characteristic information；
Multiple identical forecast models are chosen according to the destination number.
3. apply method for cleaning as claimed in claim 2, it is characterised in that chosen from the multiple characteristic information each pre-
Target signature information corresponding to model is surveyed, including：
Target signature information corresponding to from the multiple characteristic information choosing each forecast model according to the destination number.
4. apply method for cleaning as claimed in claim 2, it is characterised in that according to needed for determining the quantity of the characteristic information
The destination number of forecast model, including：
Obtain the space complexity of the forecast model；
According to needed for determining the current amount of memory of the space complexity, electronic equipment and the quantity of the characteristic information
The destination number of forecast model.
5. apply method for cleaning as claimed in claim 4, it is characterised in that work as according to the space complexity, electronic equipment
The destination number of forecast model needed for the quantity determination of preceding amount of memory and the characteristic information, including：
According to the current amount of memory of space complexity and the electronic equipment, the maximum number that can use forecast model is obtained
Amount；
When the maximum quantity is less than the quantity of the characteristic information, using the mesh of maximum quantity forecast model as needed for
Mark quantity.
6. apply method for cleaning as claimed in claim 5, it is characterised in that work as according to the space complexity, electronic equipment
The destination number of forecast model needed for the quantity determination of preceding amount of memory and the characteristic information, in addition to：
When the maximum quantity is more than the quantity of the characteristic information, the mesh of forecast model according to needed for determining predetermined number difference
Quantity is marked, the predetermined number difference is characterized the number differences between the quantity of information and the quantity of required forecast model.
7. one kind applies cleaning plant, it is characterised in that including：
Feature acquiring unit, for obtaining multiple characteristic informations of application；
Model chooses unit, for choosing multiple identical forecast models；
Feature Selection unit, for characteristic information corresponding to choosing each forecast model from the multiple characteristic information, obtain
The characteristic information set of each forecast model, wherein, the characteristic information set of each forecast model differs；
Predicting unit, for, using that whether can clear up, being obtained according to the forecast model and its characteristic information ensemble prediction
The prediction result of each forecast model；
Determining unit, for finally determining whether the application can clear up according to the prediction result of each forecast model.
8. apply cleaning plant as claimed in claim 7, it is characterised in that the model chooses unit, including：
Quantity determination subelement, the destination number for the forecast model according to needed for the determination of the quantity of the characteristic information；
Subelement is chosen, for choosing multiple identical forecast models according to the destination number.
9. apply cleaning plant as claimed in claim 8, it is characterised in that the selection subelement, be specifically used for：According to institute
State destination number and target signature information corresponding to each forecast model is chosen from the multiple characteristic information.
10. apply cleaning plant as claimed in claim 8, it is characterised in that the quantity determination subelement, be specifically used for：
Obtain the space complexity of the forecast model；
Quantity determines according to the current amount of memory of the space complexity, electronic equipment and the quantity of the characteristic information
The destination number of required forecast model.
11. apply cleaning plant as claimed in claim 10, it is characterised in that the quantity determination subelement, be used for：
According to the current amount of memory of space complexity and the electronic equipment, the maximum number that can use forecast model is obtained
Amount；
When the maximum quantity is less than the quantity of the characteristic information, using the mesh of maximum quantity forecast model as needed for
Mark quantity；
When the maximum quantity is more than the quantity of the characteristic information, the mesh of forecast model according to needed for determining predetermined number difference
Quantity is marked, the predetermined number difference is characterized the number differences between the quantity of information and the quantity of required forecast model.
12. 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 6.
13. 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 6.
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