CN107632697B - Processing method, device, storage medium and the electronic equipment of application program - Google Patents
Processing method, device, storage medium and the electronic equipment of application program Download PDFInfo
- Publication number
- CN107632697B CN107632697B CN201710919655.2A CN201710919655A CN107632697B CN 107632697 B CN107632697 B CN 107632697B CN 201710919655 A CN201710919655 A CN 201710919655A CN 107632697 B CN107632697 B CN 107632697B
- Authority
- CN
- China
- Prior art keywords
- application program
- sample
- probability
- sampling
- sampling periods
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
-
- 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
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Quality & Reliability (AREA)
- Computer Hardware Design (AREA)
- Stored Programmes (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
The embodiment of the present application discloses processing method, device, storage medium and the electronic equipment of a kind of application program.The processing method of the application program, by the use information for obtaining each sampling time point sample application program in historical time section, training sample is generated according to sampling time point and use information, preset mixed Gauss model is trained further according to training sample, based on after training mixed Gauss model and preset Bayesian model, the background application in electronic equipment is handled.The program can reduce the occupancy of electronic equipment end resource, improve the operation fluency of electronic equipment, reduce the power consumption of electronic equipment.
Description
Technical field
This application involves technical field of electronic equipment more particularly to a kind of processing method of application program, device, storage Jie
Matter and electronic equipment.
Background technique
With the development of internet with the development of mobile communications network, while also along with the processing capacity of electronic equipment and
The application of the fast development of storage capacity, magnanimity has obtained rapid propagation and use；Commonly apply facilitate user job and
While life, it is no lack of the daily life that application newly developed also enters user, improves the quality of life of user, using end
The frequency at end and the amusement sense in use.
When electronic equipment unlatching there are multiple application programs, electronics can be seriously occupied in the application program of running background and is set
Standby resource reduces the operation fluency of electronic equipment, while the power consumption for also resulting in electronic equipment is larger.
Summary of the invention
The embodiment of the present application provides processing method, device, storage medium and the electronic equipment of a kind of application program, can be with intelligence
Energy ground control application program, reduces powder consumption of electronic equipment.
In a first aspect, the embodiment of the present application provides a kind of processing method of application program, it is applied to electronic equipment, the side
Method includes:
Obtain the use information of each sampling time point sample application program in historical time section；
Training sample is generated according to the sampling time point and the use information；
Preset mixed Gauss model is trained according to the training sample；
Based on after training mixed Gauss model and preset Bayesian model, to the background application in the electronic equipment
Program is handled.
Second aspect, the embodiment of the present application provide a kind of processing unit of application program, are applied to electronic equipment, described
Device includes:
Module is obtained, for obtaining the use information of each sampling time point sample application program in historical time section；
Generation module, for generating training sample according to the sampling time point and the use information；
Training module, for being trained according to the training sample to preset mixed Gauss model；
Processing module, for based on after training mixed Gauss model and preset Bayesian model, the electronics is set
Background application in standby is handled.
The third aspect is stored with a plurality of finger the embodiment of the present application also provides a kind of storage medium in the storage medium
It enables, described instruction is suitable for being loaded by processor to execute the processing method of above-mentioned application program.
Fourth aspect, the embodiment of the present application also provides a kind of electronic equipment, including processor and memory, the processing
Device and the memory are electrically connected, and the memory is for storing instruction and data；Processor is for executing above-mentioned application
The processing method of program.
The embodiment of the present application discloses processing method, device, storage medium and the electronic equipment of a kind of application program.This is answered
With the processing method of program, pass through the use information of each sampling time point sample application program in acquisition historical time section, root
Training sample is generated according to sampling time point and use information, preset mixed Gauss model is instructed further according to training sample
Practice, based on after training mixed Gauss model and preset Bayesian model, in electronic equipment background application carry out
Processing.The program can reduce the occupancy of electronic equipment end resource, improve the operation fluency of electronic equipment, reduce electronics and set
Standby power consumption.
Detailed description of the invention
In order to more clearly explain the technical solutions in the embodiments of the present application, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, the drawings in the following description are only some examples of the present application, for
For those skilled in the art, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is the scene framework schematic diagram of the processing method of application program provided by the embodiments of the present application.
Fig. 2 is a kind of flow diagram of the processing method of application program provided by the embodiments of the present application.
Fig. 3 is another flow diagram of the processing method of application program provided by the embodiments of the present application.
Fig. 4 is a kind of schematic diagram of Gauss model provided by the embodiments of the present application.
Fig. 5 is the training schematic diagram of mixed Gauss model provided by the embodiments of the present application.
Fig. 6 is a kind of schematic diagram of mixed Gauss model provided by the embodiments of the present application.
Fig. 7 is a kind of structural schematic diagram of the processing unit of application program provided by the embodiments of the present application.
Fig. 8 is another structural schematic diagram of the processing unit of application program provided by the embodiments of the present application.
Fig. 9 is another structural schematic diagram of the processing unit of application program provided by the embodiments of the present application.
Figure 10 is the yet another construction schematic diagram of the processing unit of application program provided by the embodiments of the present application
Figure 11 is a kind of structural schematic diagram of electronic equipment provided by the embodiments of the present application.
Figure 12 is another structural schematic diagram of electronic equipment provided by the embodiments of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.It is based on
Embodiment in the application, those skilled in the art's every other implementation obtained without creative efforts
Example, shall fall in the protection scope of this application.
The embodiment of the present application provides processing method, device, storage medium and the electronic equipment of a kind of application program.Below will
It is described in detail respectively.
Referring to Fig. 1, Fig. 1 is the scene framework schematic diagram of the processing method of application program provided by the embodiments of the present application.
As schemed, by taking the application program to running background is handled for A~E as an example.Data acquisition, record electricity are carried out first
Sub- equipment opens the time of each application program as recorded in the use information of each application program in one month.Then, according to acquisition
To the usage record of application program count each application program and use probability in different time, and time and correspondence will be used
Use probability as training sample, preset mixed Gauss model is trained, it is mixed to adjust this according to the sample inputted
The parameter information in Gauss model is closed, to obtain the mixed Gauss model after training corresponding to each application program.Based on each
Mixed Gauss model after the corresponding training of application program does the use of background application in conjunction with preset Bayesian model
Prediction, calculate each background application at time T uses probability.Again from multiple background application A~E really
The target background application for being lower than predetermined probabilities P using probability is made, and closes target background application.To based on use
The use habit at family realizes the control to background application, reduces application program to the occupancy of electronic equipment resource.
Wherein, electronic equipment can be mobile terminal, such as mobile phone, tablet computer, laptop, the embodiment of the present application
To this without limiting.
In one embodiment, a kind of processing method of application program is provided, electronic equipment is applied to, which can be with
For mobile terminals such as smart phone, tablet computer, laptops.As shown in Fig. 2, process can be such that
101, the use information of each sampling time point sample application program in historical time section is obtained.
Application program mentioned by the present embodiment can be any one application program installed on electronic equipment, such as
Office application, social application, game application, shopping application etc..
Wherein, sample application program can be mounted application programs multiple or all in electronic equipment.Application program
Use information can be the usage record of application program, and such as opening time of each application program records.Sampling time point then can root
Set according to actual demand, if expect accuracy it is higher as a result, if can will be configured acquisition time intensively, such as
Every 1min be a sampling time point；If wanting to save the resource of electronic equipment and do not require the accuracy of result, can incite somebody to action
Sampling time point is configured loosely, such as every 10min be a sampling time point.
In some embodiments, self-application program is installed, then can record each use information for having installed application program, is turned
Corresponding data storage is changed into preset storage region.When needing the use information using a certain or certain application programs
When, then data corresponding with a certain or certain application programs can be transferred from the storage region, and the data of acquisition are carried out
Parsing obtains corresponding information, and using the use information as a certain or certain application programs, and this is a certain or certain using journey
Sequence is then used as sample application program, from the use information selected in required time section in the use information of acquisition.
In some embodiments, it is the power consumption for reducing electronic equipment, saves the terminal resource of electronic equipment, can directly set
Then the period of required record is during this period of time remembered the use information of each sampling time point sample application program
Record, so as to subsequent use.
102, training sample is generated according to sampling time point and use information.
Specifically, the use information of accessed sample application program can be pre-processed, calculates each sample
Application program uses probability different sampling stages point, and the use for further obtaining each sample application program changes over time
Probability distribution, by sampling time point and using probability correspond generate training sample.
103, preset mixed Gauss model is trained according to training sample.
Specifically, the training sample of above-mentioned generation is input in preset mixed Gauss model, according to the instruction inputted
Practice sample and constantly correct the relevant parameter in preset mixed Gauss model, so that the mixed Gauss model after training can fit
For all training samples, a mixed Gauss model finally is trained to each sample application program.Wherein, each mixed
Gauss model is closed to be made of multiple sub- Gauss models.
104, based on after training mixed Gauss model and preset Bayesian model, to the background application in electronic equipment
Program is handled.
In the embodiment of the present application, if the number of sample application program has N number of mixing after having N number of training accordingly
Gauss model.The identity information (such as Apply Names, application identities) of each background application is obtained, and according to background application journey
The identity information of sequence chooses target mixed Gauss model from the mixed Gauss model after N number of training and (is directed to the background application
The mixed Gauss model that procedural training goes out), and the background application is handled based on the target mixed Gauss model.
In some embodiments, can based on the mixed Gauss model after different background applications respectively corresponding training,
In conjunction with the current time, to each background application being calculated using probability at that time.It is each according to what is calculated
A application program is corresponding to use probability, and the background application for using probability to meet certain condition is cleared up or closed
The operation such as close, to reduce application program to the occupancy of electronic equipment resource.
From the foregoing, it will be observed that the application is the processing method for the application program that embodiment provides, by obtaining in historical time section
The use information of each sampling time point sample application program generates training sample according to sampling time point and use information, then
Preset mixed Gauss model is trained according to training sample, based on after training mixed Gauss model and preset pattra leaves
This model handles the background application in electronic equipment.The program can reduce the occupancy of electronic equipment end resource, mention
The operation fluency for having risen electronic equipment, reduces the power consumption of electronic equipment.
In one embodiment, the processing method of another application program is also provided, electronic equipment, the electronic equipment are applied to
It can be the mobile terminals such as smart phone, tablet computer, laptop.As shown in figure 3, process can be such that
201, the use information of each sampling time point sample application program in historical time section is obtained.
Sample application program can be mounted application programs multiple or all in electronic equipment.Sampling time point can basis
Actual demand is set, if expecting that accuracy is higher as a result, can will be configured acquisition time intensively, such as every
1min is a sampling time point；If wanting to save the resource of electronic equipment and do not require the accuracy of result, can will sample
Time point is configured loosely, such as every 10min be a sampling time point.The use information of application program can be application program
Relevant information in use.
For example, historical period be can be over one month, each time point can be the timestamp of current time.Use ginseng
Number can be to be extracted from database, can store over application program in month electronic equipment in the database
Opening record, it is as shown in table 1 below:
| Application package name | Open the timestamp of this application program |
| com.tencent.mobileqq | 1457550655465 |
| com.android.settings | 1457605107522 |
| ... | ... |
Table 1
Later, the opening of these application programs is recorded, as various kinds application making in each sampling time point
Use information.
202, each sampling time point corresponding time cycle and sampling periods are determined, wherein adopt in every a period of time
Sample time point and sampling periods correspond.
In some embodiments, historical time section includes multiple time cycles, if historical time section is one month in the past, then
Time cycle can be then the every day in a middle of the month in the past.Every a period of time can be divided into multiple sampling periods, and such as one day
In each minute.Specifically, it can be based on the corresponding timestamp of sampling time point, determine time cycle belonging to it and specific
Sampling periods, such as can for xx days xx month xx divide.In the case of 481 points of September 9 days, September is historical time section, and 9 are week time
Phase, 481 points are sampling periods.
Wherein, the acquisition of sample is can to complete on the terminal devices such as smart phone, tablet computer, is obtained every 1 minute
Application information currently in use in present terminal equipment, and the lane database for arriving the terminal device is stored, then for
One user one month usage record can extract up to ten thousand use information samples.
203, the corresponding use information of sampling periods identical in the different time period is handled, obtains sample application journey
Sequence uses probability in the corresponding sample of each sampling periods.
In some embodiments, step is " at the corresponding use information of sampling periods identical in the different time period
Reason obtains sample application program in the corresponding sample of each sampling periods and uses probability " may include following below scheme:
Judge whether use information meets preset condition；
Determine the sampling time that each sample applies in identical sampling periods corresponding use information to meet preset condition
Point quantity；
Obtain the sampling time point sum that each sample applies within multiple time cycles use information to meet preset condition
Amount；
According to sampling time point quantity and sampling time point total quantity, each sample application program is calculated in each sampling
The corresponding sample of section uses probability.
Specifically, application records can be used according to above-mentioned collected user, counts the most common N number of sample of user
Application, wherein N is configurable.For rational allocation electronic equipment resource, operand, usual N=5 are reduced.
In some embodiments, use information can be the running state information of sample application program；Then " judgement uses step
Whether information meets preset condition " may include following below scheme:
Judge whether operating status is front stage operation；
If so, determining that use information meets preset condition；
If it is not, then determining that use information is unsatisfactory for preset condition.
Wherein, operating status is in front stage operation, this means that the sample application program is used in active user.So
For this N number of sample application program, each sample application program same time in every day within past one month is counted respectively
(such as one day may include 1440 minutes to section, then the 481st minute of the 481st of September 1st minute and September 31st is same time period；9
The 1440th minute of the 1440th minute and September 31st of month 1 day is same time period) in count in the sampling time of front stage operation
Amount, is denoted as X=[x1, x2, x3…xi..., xn], wherein xiIndicate the use of the i-th daily minutes application program of September part
Number.
For example, for using 1 day~September of September this 30 days on the 30th as historical time section, if at this in 30, wherein having 25 days
User has used wechat at 01 minute to 8 points for 10 minutes at 8 points in the morning, and other time does not have to wechat.So statistical is: 8 points
It is the 481st point (8*60+1=481) that 01 point, which is converted into the period, is converted within 10 minutes the period 8 points as the 490th minute (8*60+10
=490).The wechat use information statistical result of so user can be as shown in table 2 below:
| Application program | x1 | ... | x481 | ... | x490 | ... | x1440 |
| 0 | 0 | 25 | 25 | 25 | 0 | 0 | |
| ... | ... | ... | ... | ... | ... | ... | |
| … | |||||||
| … |
Table 2
In the embodiment of the present application, each sample application program can be used probability in the corresponding sample of each sampling periods
Definition of probability be Pi, then probability PiSpecific algorithm can refer to following formula:
Wherein, xjWith xiShow and defines identical, all uses time of i-th or j minutes application program of the expression in one day
Number.N is the positive integer greater than 1.Based on above-mentioned data and probabilistic algorithm, each sample application program can be obtained in each sampling
Period corresponding sample uses the probability distribution of probability, can be as shown in table 3 below:
| Application program | P1 | ... | P481 | ... | P490 | ... | P1440 |
| 0 | 0 | 0.1 | 0.1 | 0.1 | 0 | 0 | |
| ... | ... | ... | ... | ... | ... | ... | |
| … | |||||||
| … |
Table 3
204, training sample is generated using probability based on sampling periods and corresponding sample.
Specifically, the probability point changed over time according to the sample of sample application program each in above-mentioned table 2 using probability
Sampling time point and sample are corresponded using probability and generate training sample by cloth.
In some embodiments, if sampling periods are denoted as t, sampling periods include [t1,t2…tm], sample is made
It is denoted as P with probability, sample includes [P using probability1,P2…Pm].Then specifically the training sample of generation can be denoted as (tm,Pm), such as
481st minute corresponding training sample is (481,0.1).
205, training sample is input in the first preset formula, to be trained to the first preset formula, is obtained multiple
Sub- Gauss model after training.
The probabilistic power spectral density function of the mixed Gauss model substantially of first preset formula in the embodiment of the present application, tool
Body is as follows:
Wherein, AiIndicate that sample application program i, t indicate that sampling periods, k indicate sub- Gauss model quantity, μkIndicate mathematics
It is expected that σkIndicate variance, ωkExpression weight, and N (t | μk,σk) indicate that stochastic variable t obeys a mathematic expectaion as μk, variance be
σkNormal distribution, P (t | Ai) it can then indicate that sampling periods are the general of t when the operating status of sample application program i is front stage operation
Rate.
It is Gaussian Profile probabilistic model.
With reference to Fig. 4, the Gauss model of a constructed initialization can be used as.Then, based on the sampling periods inputted
T, sample uses probability P, is trained to the first preset formula, the sub- Gauss model after obtaining multiple training.It is first with reference to Fig. 5
First collected data are pre-processed, the probability distribution that each application program is used is obtained, then makees the probability distribution
For input, preset mixed Gauss model is trained, suitable mixed Gauss model is finally obtained.
For example, mixed Gauss model modeling can be carried out when reading the 1st minute corresponding training sample；Then the 2nd is read
Minute corresponding training sample, updates Gauss model parameter；The 3rd minute corresponding training sample is read again, continues to update mixing
Gauss model parameter ... and so on updates Gauss model parameter and obtains finally after all training samples are all read
Mixed Gauss model after training.
Mixed Gauss model generally uses 3~5 sub- Gauss models to constitute.In modeling process, need to mixed Gaussian mould
Variances sigma in typek, mathematic expectaion μk, weight ωkThe required number of modeling is found out etc. some parameter initializations, and by these parameters
According to.During initialization, what variance can be arranged is big as far as possible, and weight (i.e. ωk) then (such as 0.001) as small as possible.This
Sample setting be the model being inaccurate due to the Gauss model of initialization, need ceaselessly to reduce his range, update
His parameter value, to obtain most probable Gauss model.Variance setting is big, it is exactly in order to by pixel packet as much as possible
Containing to a model the inside, parameter k, corresponding all weight ω are found outkAnd it is corresponding in all sub- Gauss models
Parameter μkAnd σk。
In some embodiments, maximum likelihood estimate can be used to determine ωk、μkAnd σkDeng these model parameters.Its
In, the likelihood function of mixed Gauss model are as follows:
Using expectation maximization (EM) algorithm, make (μk,σk) likelihood function maximization.The then corresponding ω of maximumk、μk
And σkIt is exactly our estimation.Finally obtain [(ω1,μ1,σ1), (ω1,μ1,σ1) ... (ωk,μk,σk)]。
206, the sub- Gauss model after multiple training is superimposed, with the mixed Gauss model after being trained.
Specifically, by estimated weight ωkAfter each sub- Gauss model weighting processing, by k son after weighting
Gauss model superposition processing, with the mixed Gauss model after being trained.With reference to Fig. 6, obtained mixed Gauss model is by 4
Sub- Gauss model is constituted.
Assuming that user has N number of sample application program, then there is N number of mixed Gauss model, i.e., [P (t | A1), P (t | A2) ... P (t
|AN)]。
207, the background application in electronic equipment is determined.
It in some embodiments, can be in the central processing unit (CPU, central processing unit) of electronic equipment
When occupying larger larger, running memory resource occupation and/or electronic equipment remaining capacity deficiency, application program processing can be triggered
Instruction.Electronic equipment obtains the application program process instruction, then, is determined according to the application program process instruction and is transported in backstage
Capable background application is handled background application so as to subsequent.
208, it based on the mixed Gauss model after training corresponding to each application program, is calculated using the second preset formula every
One background application uses probability the object time.
In the embodiment of the present application, each application program is corresponding with the mixed Gauss model after unique training.Based on training
It is corresponding using probability in different time can to accurately estimate out application program for mixed Gauss model afterwards.The application is implemented
In example, the expression formula of preset Bayesian model is the second preset formula, and second preset formula is as follows:
Wherein, T indicates the time, and N indicates the quantity of the mixed Gauss model after training, P (Ai| T) expression sampling periods be T
When front stage operation application program be application program i probability, P (T | Ai) indicate sample application program i operating status be before
The probability that sampling periods are T when platform operation, and P (T | Aj) indicate sampling periods when the operating status of application program j is front stage operation
For the probability of T, P (Ai) indicate application program i in historical time section using probability, P (Aj) indicate application program j's
In historical time section using probability.
Specifically, the mixed Gauss model after being primarily based on training estimates different application each comfortable object time
Under it is corresponding it is initial using probability (i.e. P (and T | Ai)).Then after, target background application i is calculated in historical time section
Using probability (i.e. P (Ai)).Wherein, P (Ai) can be by data prediction period, it can be by application program i in historical time section
Interior access times, the access times summation with all sample application programs in historical time section ratio obtain, i.e. P (Ai)
Calculation formula it is as follows:
Wherein, S (Ai) it is total access times of the application program i in historical time section, S is that all sample application programs exist
Access times summation in historical time section.
Similarly, each using each application program according to the corresponding initial algorithm using probability of target background application i
Self-corresponding mixed Gauss model calculates the initial of each application program and uses probability；Utilize P (Ai) calculation formula, calculate
Out various kinds application in historical time section using probability.
Finally, all data obtained as above arrived is substituted into the second preset formula (i.e. preset Bayesian model), utilize
Second preset formula calculates that target background application is corresponding using probability under the object time, to be promoted using probability
Accuracy.
209, according to using probability to handle background application.
In some embodiments, cocoa is by setting probability threshold value as the benchmark handled application.That is, step
Suddenly may include following below scheme " according to using probability to handle background application ":
The target background application for being less than preset threshold using probability is determined from current background application program；
Close target background application.
Wherein, which can be set by those skilled in the art or production manufacturer.For example, setting is pre-
If threshold value is 0.5, then if opening background application A in following period TiProbability P (T | Ai) less than 0.5, then cleaning should
Background application AiIf being not less than 0.5, background application A is keptiContinue in running background.
From the foregoing, it will be observed that the processing method of application program provided by the embodiments of the present application, every in historical time section by obtaining
The use information of one sampling time point sample application program, then determines each sampling time point corresponding time cycle and sampling
Period, then the corresponding use information of sampling periods identical in the different time period is handled, it obtains sample application program and exists
The corresponding sample of each sampling periods uses probability.Training sample is generated using probability based on sampling periods and corresponding sample
This, and be input in preset mixed Gauss model and carry out model training, it obtains being made of the sub- Gauss model after multiple training
New mixed Gauss model.Finally, estimating that each backstage is answered using new mixed Gauss model and preset Bayesian model
Probability is used under the object time, and corresponding background application is handled according to obtained probability.The program
The occupancy that electronic equipment end resource can be reduced improves the operation fluency of electronic equipment, reduces the power consumption of electronic equipment.
In the another embodiment of the application, a kind of processing unit of application program, the processing dress of the application program are also provided
Setting can be integrated in the electronic device in the form of software or hardware, which can specifically include mobile phone, tablet computer, pen
Remember this apparatus such as computer.As shown in fig. 7, the processing unit 30 of the application program may include obtain module 31, generation module 32,
Training module 33 and processing module 34, in which:
Module 31 is obtained, for obtaining the use information of each sampling time point sample application program in historical time section；
Generation module 32, for generating training sample according to sampling time point and use information；
Training module 33, for being trained according to training sample to preset mixed Gauss model；
Processing module 34, for based on after training mixed Gauss model and preset Bayesian model, to electronic equipment
In background application handled.
In some embodiments, historical time section includes multiple time cycles, and every a period of time is divided into multiple samplings
Period.With reference to Fig. 8, generation module 32 may include:
First determines submodule 321, for determining each sampling time point corresponding time cycle and sampling periods, wherein
Sampling time point and sampling periods correspond in every a period of time；
Information processing submodule 322, for carrying out the corresponding use information of sampling periods identical in the different time period
Processing obtains sample application program in the corresponding sample of each sampling periods and uses probability；
Submodule 323 is generated, for generating training sample using probability based on sampling periods and corresponding sample.
In some embodiments, processing submodule 322 may include:
Judging unit, for judging whether use information meets preset condition；
First determination unit, for determining that each sample applies the corresponding use information in identical sampling periods to meet in advance
If the sampling time point quantity of condition；
Acquiring unit applies the use information within multiple time cycles to meet adopting for preset condition for obtaining each sample
Sample time point total quantity；
Computing unit, for calculating each sample application journey according to sampling time point quantity and sampling time point total quantity
Sequence uses probability in the corresponding sample of each sampling periods.
In some embodiments, use information is the running state information of sample application program；Judging unit can be used for:
Judge whether operating status is front stage operation；
If so, determining that use information meets preset condition；
If it is not, then determining that use information is unsatisfactory for preset condition
In some embodiments, sampling periods include [t1,t2…tm], sample includes [P using probability1,P2…Pm]；With reference to
Fig. 9, training module 33 may include:
Input submodule 331, for sampling periods and corresponding sample to be input in the first preset formula using probability,
First preset formula are as follows:
Wherein, AiIndicate that sample application program i, t indicate that sampling periods, k indicate sub- Gauss model quantity, μkIndicate mathematics
It is expected that σkIndicate variance, ωkExpression weight, and N (t | μk,σk) indicate that stochastic variable t obeys a mathematic expectaion as μk, variance be
σkNormal distribution, P (t | Ai) indicate the probability that sampling periods are t when the operating status of sample application program i is front stage operation；
Training submodule 332, for using probability P based on the sampling periods t, the sample that are inputted, to the first preset formula
It is trained, the sub- Gauss model after obtaining multiple training；
It is superimposed submodule 333, for the sub- Gauss model after multiple training to be superimposed, with the mixed Gaussian after being trained
Model.
In some embodiments, each application program is corresponding with the mixed Gauss model after unique training；With reference to Figure 10, place
Managing module 34 may include:
Acquisition submodule 341, for obtaining application program process instruction；
Second determines submodule 342, for determining the background application journey in electronic equipment according to application program process instruction
Sequence；
Computational submodule 343, for utilizing second based on the mixed Gauss model after training corresponding to each application program
Preset formula calculates each background application and uses probability, the second preset formula in the object time are as follows:
Wherein, T indicates the time, and N indicates the quantity of the mixed Gauss model after training, P (Ai| T) expression sampling periods be T
When front stage operation application program be application program i probability, P (T | Ai) indicate sample application program i operating status be before
The probability that sampling periods are T when platform operation, and P (T | Aj) indicate sampling periods when the operating status of application program j is front stage operation
For the probability of T, P (Ai) indicate application program i in historical time section using probability, P (Aj) indicate application program j's
In historical time section using probability；
Using processing submodule 344, for according to using probability to handle background application.
In some embodiments, may include: using processing submodule 344
Second determination unit, for after determining the target for using probability to be less than preset threshold in current background application program
Platform application program；
Closing unit, for closing target background application.
From the foregoing, it will be observed that the processing unit of application program provided by the embodiments of the present application, every in historical time section by obtaining
The use information of one sampling time point sample application program generates training sample, then root according to sampling time point and use information
Preset mixed Gauss model is trained according to training sample, based on after training mixed Gauss model and preset Bayes
Model handles the background application in electronic equipment.The program can reduce the occupancy of electronic equipment end resource, be promoted
The operation fluency of electronic equipment, reduces the power consumption of electronic equipment.
A kind of electronic equipment is also provided in the another embodiment of the application, which can be smart phone, plate
Apparatus such as computer.As shown in figure 11, electronic equipment 400 includes processor 401 and memory 402.Wherein, processor 401 with deposit
Reservoir 402 is electrically connected.
Processor 401 is the control centre of electronic equipment 400, utilizes various interfaces and the entire electronic equipment of connection
Various pieces by the application of operation or load store in memory 402, and call the number being stored in memory 402
According to, execute electronic equipment various functions and processing data, thus to electronic equipment carry out integral monitoring.
In the present embodiment, processor 401 in electronic equipment 400 can according to following step, by one or one with
On the corresponding instruction of process of application be loaded into memory 402, and run by processor 401 and be stored in memory 402
In application, to realize various functions:
Obtain the use information of each sampling time point sample application program in historical time section；
Training sample is generated according to sampling time point and use information；
Preset mixed Gauss model is trained according to training sample；
Based on after training mixed Gauss model and preset Bayesian model, to the background application in electronic equipment
It is handled.
In some embodiments, historical time section includes multiple time cycles, and every a period of time is divided into multiple samplings
Period；Processor 401 is further used for executing following steps:
Determine each sampling time point corresponding time cycle and sampling periods, wherein in every a period of time when sampling
Between point with sampling periods correspond；
The corresponding use information of sampling periods identical in the different time period is handled, sample application program is obtained and exists
The corresponding sample of each sampling periods uses probability；
Training sample is generated using probability based on sampling periods and corresponding sample.
In some embodiments, processor 401 is further used for executing following steps:
Judge whether use information meets preset condition；
Determine the sampling time that each sample applies in identical sampling periods corresponding use information to meet preset condition
Point quantity；
Obtain the sampling time point sum that each sample applies within multiple time cycles use information to meet preset condition
Amount；
According to sampling time point quantity and sampling time point total quantity, each sample application program is calculated in each sampling
The corresponding sample of section uses probability.
In some embodiments, use information is the running state information of sample application program, and processor 401 is further used
In execution following steps:
Judge whether operating status is front stage operation；
If so, determining that use information meets preset condition；
If it is not, then determining that use information is unsatisfactory for preset condition.
In some embodiments, sampling periods include [t1,t2…tm], sample includes [P using probability1,P2…Pm]；Processing
Device 401 is further used for executing following steps:
Sampling periods and corresponding sample are input in the first preset formula using probability, the first preset formula are as follows:
Wherein, AiIndicate that sample application program i, t indicate that sampling periods, k indicate sub- Gauss model quantity, μkIndicate mathematics
It is expected that σkIndicate variance, ωkExpression weight, and N (t | μk,σk) indicate that stochastic variable t obeys a mathematic expectaion as μk, variance be
σkNormal distribution, P (t | Ai) indicate the probability that sampling periods are t when the operating status of sample application program i is front stage operation；
Probability P is used based on sampling periods t, the sample inputted, the first preset formula is trained, multiple instructions are obtained
Sub- Gauss model after white silk；
By the sub- Gauss model superposition after multiple training, with the mixed Gauss model after being trained.
In some embodiments, each application program is corresponding with the mixed Gauss model after unique training；Processor 401 into
One step is for executing following steps:
Obtain application program process instruction；
The background application in electronic equipment is determined according to application program process instruction；
Based on corresponding to each application program training after mixed Gauss model, using the second preset formula calculate it is each after
Platform application program uses probability, the second preset formula the object time are as follows:
Wherein, T indicates the time, and N indicates the quantity of the mixed Gauss model after training, P (Ai| T) expression sampling periods be T
When front stage operation application program be application program i probability, P (T | Ai) indicate sample application program i operating status be before
The probability that sampling periods are T when platform operation, and P (T | Aj) indicate sampling periods when the operating status of application program j is front stage operation
For the probability of T, P (Ai) indicate application program i in historical time section using probability, P (Aj) indicate application program j's
In historical time section using probability；
According to using probability to handle background application.
In some embodiments, processor 401 is further used for executing following steps:
The target background application for being less than preset threshold using probability is determined from current background application program；
Close target background application.
Memory 402 can be used for storing application and data.Including in the application that memory 402 stores can be in the processor
The instruction of execution.Using various functional modules can be formed.Processor 401 is stored in the application of memory 402 by operation, from
And perform various functions application and data processing.
In some embodiments, as shown in figure 12, electronic equipment 400 further include: display screen 403, is penetrated at control circuit 404
Frequency circuit 405, input unit 406, voicefrequency circuit 407, sensor 408 and power supply 409.Wherein, processor 401 respectively with it is aobvious
Display screen 403, control circuit 404, radio circuit 405, input unit 406, voicefrequency circuit 407, sensor 408 and power supply 409
It is electrically connected.
Display screen 403 can be used for showing information input by user or be supplied to user information and electronic equipment it is each
Kind graphical user interface, these graphical user interface can be made of image, text, icon, video and any combination thereof.
Control circuit 404 and display screen 403 are electrically connected, and show information for controlling display screen 403.
Radio circuit 405 is used for transceiving radio frequency signal, to build by wireless communication with the network equipment or other electronic equipments
Vertical wireless telecommunications, the receiving and transmitting signal between the network equipment or other electronic equipments.
Input unit 406 can be used for receiving number, character information or the user's characteristic information (such as fingerprint) of input, and
Generate keyboard related with user setting and function control, mouse, operating stick, optics or trackball signal input.Wherein,
Input unit 406 may include fingerprint recognition mould group.
Voicefrequency circuit 407 can provide the audio interface between user and electronic equipment by loudspeaker, microphone.
Sensor 408 is for acquiring external environmental information.Sensor 408 may include ambient light sensor, acceleration
Sensor, optical sensor, motion sensor and other sensors.
All parts of the power supply 409 for electron equipment 400 are powered.In some embodiments, power supply 409 can pass through
Power-supply management system and processor 401 are logically contiguous, to realize management charging, electric discharge, Yi Jigong by power-supply management system
The functions such as consumption management.
Although being not shown in Figure 12, electronic equipment 400 can also include camera, bluetooth module etc., and details are not described herein.
From the foregoing, it will be observed that electronic equipment provided by the embodiments of the present application, by obtaining each sampling time in historical time section
The use information of point sample application generates training sample according to sampling time point and use information, further according to training sample
Preset mixed Gauss model is trained, based on the mixed Gauss model after training to the background application journey in electronic equipment
Sequence is handled.The program can reduce the occupancy of electronic equipment end resource, improve the operation fluency of electronic equipment, reduce
The power consumption of electronic equipment.
In some embodiments, a kind of storage medium is additionally provided, a plurality of instruction is stored in the storage medium, the instruction
Suitable for being loaded by processor to execute the processing method of any of the above-described application program.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
It is completed with instructing relevant hardware by program, which can be stored in a computer readable storage medium, storage
Medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random
Access Memory), disk or CD etc..
Term " one " and " described " and similar word have been used during describing the concept of the application (especially
In the appended claims), it should be construed to not only cover odd number by these terms but also cover plural number.In addition, unless herein
In be otherwise noted, otherwise herein narration numberical range when referred to merely by quick method and belong to the every of relevant range
A independent value, and each independent value is incorporated into this specification, just as these values have individually carried out statement one herein
Sample.In addition, unless otherwise stated herein or context has specific opposite prompt, otherwise institute described herein is methodical
Step can be executed by any appropriate order.The change of the application is not limited to the step of description sequence.Unless in addition
Advocate, is otherwise all only using any and all example or exemplary language presented herein (for example, " such as ")
The concept of the application is better described, and not the range of the concept of the application limited.Spirit and model are not being departed from
In the case where enclosing, those skilled in the art becomes readily apparent that a variety of modifications and adaptation.
Above to the processing method of application program provided by the embodiment of the present application, device, storage medium and electronic equipment
It is described in detail, specific examples are used herein to illustrate the principle and implementation manner of the present application, the above reality
The explanation for applying example is merely used to help understand the present processes and its core concept；Meanwhile for those skilled in the art,
According to the thought of the application, there will be changes in the specific implementation manner and application range, in conclusion in this specification
Hold the limitation that should not be construed as to the application.
Claims (14)
1. a kind of processing method of application program is applied to electronic equipment, which is characterized in that the described method includes:
Obtain the use information of each sampling time point sample application program in historical time section；
Training sample is generated according to the sampling time point and the use information；
Preset mixed Gauss model is trained according to the training sample, wherein each application program is corresponding with uniquely
Mixed Gauss model after training；
Obtain application program process instruction；
The background application in the electronic equipment is determined according to the application program process instruction；
Based on corresponding to each application program training after mixed Gauss model, using preset Bayesian model calculate it is each after
Use probability of the platform application program in the object time, the expression formula of the Bayesian model are the second preset formula, described second
Preset formula are as follows:
Wherein, T indicates the time, and N indicates the quantity of the mixed Gauss model after training, P (Ai| T) indicate sampling periods be T when before
The probability that the application program of platform operation is application program i, and P (T | Ai) indicate the operating status of sample application program i for foreground fortune
When row sampling periods be T probability, P (T | Aj) indicate that sampling periods are T's when the operating status of application program j is front stage operation
Probability, P (Ai) indicate application program i in historical time section using probability, P (Aj) indicate application program j in history
In period using probability；
The background application in the electronic equipment is handled using probability according to described.
2. the processing method of application program as described in claim 1, which is characterized in that when the historical time section includes multiple
Between the period, every a period of time is divided into multiple sampling periods；
The step of generating training sample according to the sampling time point and the use information, comprising:
Determine each sampling time point corresponding time cycle and sampling periods, wherein sampling time point in every a period of time
It is corresponded with sampling periods；
The corresponding use information of sampling periods identical in the different time period is handled, obtains sample application program each
The corresponding sample of sampling periods uses probability；
Training sample is generated using probability based on sampling periods and corresponding sample.
3. the processing method of application program as claimed in claim 2, which is characterized in that by sampling identical in the different time period
Period, corresponding use information was handled, and is obtained sample application program in the corresponding sample of each sampling periods and is used probability
Step, comprising:
Judge whether the use information meets preset condition；
Determine that each sample applies the sampling time that corresponding use information meets preset condition in identical sampling periods to count
Amount；
Obtain the sampling time point total quantity that each sample applies within multiple time cycles use information to meet preset condition；
According to the sampling time point quantity and the sampling time point total quantity, calculates each sample application program and adopted each
Sample period corresponding sample uses probability.
4. the processing method of application program as claimed in claim 3, which is characterized in that the use information is sample application journey
The running state information of sequence；The step of whether use information meets preset condition judged, comprising:
Judge whether the operating status is front stage operation；
If so, determining that the use information meets preset condition；
If it is not, then determining that the use information is unsatisfactory for preset condition.
5. the processing method of application program as claimed in claim 2, which is characterized in that the sampling periods include [t1,t2…
tm], the sample includes [P using probability1,P2…Pm]；
The step of preset mixed Gauss model is trained according to the training sample, comprising:
The sampling periods and corresponding sample are input in the first preset formula using probability, first preset formula
Are as follows:
Wherein, AiIndicate that sample application program i, t indicate that sampling periods, k indicate sub- Gauss model quantity, μkIndicate mathematic expectaion,
σkIndicate variance, ωkExpression weight, and N (t | μk,σk) indicate that stochastic variable t obeys a mathematic expectaion as μk, variance σkJust
State distribution, and P (t | Ai) indicate the probability that sampling periods are t when the operating status of sample application program i is front stage operation；
Probability is used based on sampling periods, the sample inputted, first preset formula is trained, multiple training are obtained
Sub- Gauss model afterwards；
By the sub- Gauss model superposition after multiple training, with the mixed Gauss model after being trained.
6. such as the processing method of application program described in any one of claim 1 to 5, which is characterized in that according to described using general
The step of rate handles the background application in the electronic equipment, comprising:
The target background application for being less than preset threshold using probability is determined from current background application program；
Close the target background application.
7. a kind of processing unit of application program, it is applied to electronic equipment, which is characterized in that described device includes:
Module is obtained, for obtaining the use information of each sampling time point sample application program in historical time section；
Generation module, for generating training sample according to the sampling time point and the use information；
Training module, for being trained according to the training sample to preset mixed Gauss model, wherein each to apply journey
Ordered pair should have the mixed Gauss model after unique training；
Processing module, including acquisition submodule, the second determining submodule, computational submodule and application processing submodule；
The acquisition submodule, for obtaining application program process instruction；
Described second determines submodule, for determining that the backstage in the electronic equipment is answered according to the application program process instruction
Use program；
The computational submodule, for based on the mixed Gauss model after training corresponding to each application program, utilization to be preset
Bayesian model calculates each background application in the probability that uses of object time, and the expression formula of the Bayesian model is the
Two preset formulas, second preset formula are as follows:
Wherein, T indicates the time, and N indicates the quantity of the mixed Gauss model after training, P (Ai| T) indicate sampling periods be T when before
The probability that the application program of platform operation is application program i, and P (T | Ai) indicate the operating status of sample application program i for foreground fortune
When row sampling periods be T probability, P (T | Aj) indicate that sampling periods are T when the operating status of application program j is front stage operation
Probability, P (Ai) indicate application program i in historical time section using probability, P (Aj) indicate going through for application program j
In the history period using probability；
The application handles submodule, for being carried out using probability to the background application in the electronic equipment according to described
Processing.
8. the processing unit of program the use as claimed in claim 7, which is characterized in that when the historical time section includes multiple
Between the period, every a period of time is divided into multiple sampling periods；
The generation module includes:
First determines submodule, for determining each sampling time point corresponding time cycle and sampling periods, wherein per a period of time
Between in the period sampling time point and sampling periods correspond；
Information processing submodule is obtained for handling the corresponding use information of sampling periods identical in the different time period
Probability is used in the corresponding sample of each sampling periods to sample application program；
Submodule is generated, for generating training sample using probability based on sampling periods and corresponding sample.
9. the processing unit of application program as claimed in claim 8, which is characterized in that the processing submodule includes:
Judging unit, for judging whether the use information meets preset condition；
First determination unit, for determining that each sample applies the corresponding use information in identical sampling periods to meet default item
The sampling time point quantity of part；
Acquiring unit, when applying that use information meets the sampling of preset condition within multiple time cycles for obtaining each sample
Between put total quantity；
Computing unit, for calculating each sample and answering according to the sampling time point quantity and the sampling time point total quantity
Probability is used in the corresponding sample of each sampling periods with program.
10. the processing unit of application program as claimed in claim 9, which is characterized in that the use information is sample application
The running state information of program；The judging unit is used for:
Judge whether the operating status is front stage operation；
If so, determining that the use information meets preset condition；
If it is not, then determining that the use information is unsatisfactory for preset condition.
11. the processing unit of application program as claimed in claim 10, which is characterized in that the sampling periods include [t1,
t2…tm], the sample includes [P using probability1,P2…Pm]；The training module includes:
Input submodule, for the sampling periods and corresponding sample to be input in the first preset formula using probability, institute
State the first preset formula are as follows:
Wherein, AiIndicate that sample application program i, t indicate that sampling periods, k indicate sub- Gauss model quantity, μkIndicate mathematic expectaion,
σkIndicate variance, ωkExpression weight, and N (t | μk,σk) indicate that stochastic variable t obeys a mathematic expectaion as μk, variance σkJust
State distribution, and P (t | Ai) indicate the probability that sampling periods are t when the operating status of sample application program i is front stage operation；
Training submodule instructs first preset formula for using probability based on the sampling periods, the sample that are inputted
Practice, the sub- Gauss model after obtaining multiple training；
It is superimposed submodule, for the sub- Gauss model after multiple training to be superimposed, with the mixed Gauss model after being trained.
12. the processing unit of application program as claimed in claim 11, which is characterized in that the application handles submodule packet
It includes:
Second determination unit, for determining that the target backstage for being less than preset threshold using probability is answered from current background application program
Use program；
Closing unit, for closing the target background application.
13. a kind of storage medium, which is characterized in that be stored with a plurality of instruction in the storage medium, described instruction be suitable for by
Device load is managed to execute the processing method such as application program of any of claims 1-6.
14. a kind of electronic equipment, which is characterized in that including processor and memory, the processor and the memory are electrical
Connection, the memory is for storing instruction and data；The processor is for executing as described in any one of claim 1-6
Application program processing method.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710919655.2A CN107632697B (en) | 2017-09-30 | 2017-09-30 | Processing method, device, storage medium and the electronic equipment of application program |
| PCT/CN2018/102011 WO2019062405A1 (en) | 2017-09-30 | 2018-08-23 | Application program processing method and apparatus, storage medium, and electronic device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710919655.2A CN107632697B (en) | 2017-09-30 | 2017-09-30 | Processing method, device, storage medium and the electronic equipment of application program |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107632697A CN107632697A (en) | 2018-01-26 |
| CN107632697B true CN107632697B (en) | 2019-10-25 |
Family
ID=61104863
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710919655.2A Active CN107632697B (en) | 2017-09-30 | 2017-09-30 | Processing method, device, storage medium and the electronic equipment of application program |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN107632697B (en) |
| WO (1) | WO2019062405A1 (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107728772B (en) * | 2017-09-30 | 2020-05-12 | Oppo广东移动通信有限公司 | Application processing method and device, storage medium and electronic equipment |
| CN107632697B (en) * | 2017-09-30 | 2019-10-25 | Oppo广东移动通信有限公司 | Processing method, device, storage medium and the electronic equipment of application program |
| US20190370009A1 (en) * | 2018-06-03 | 2019-12-05 | Apple Inc. | Intelligent swap for fatigable storage mediums |
| CN108932140A (en) * | 2018-07-13 | 2018-12-04 | 重庆邮电大学 | The method of cleaning background application based on Android user behavior habit |
| CN113439253B (en) * | 2019-04-12 | 2023-08-22 | 深圳市欢太科技有限公司 | Application cleaning method and device, storage medium and electronic equipment |
| CN111045507B (en) * | 2019-11-27 | 2022-04-19 | RealMe重庆移动通信有限公司 | List management and control method, device, mobile terminal and storage medium |
| CN112866482B (en) * | 2019-11-27 | 2022-04-15 | 青岛海信移动通信技术股份有限公司 | Method and terminal for predicting behavior habits of objects |
| CN113050783B (en) * | 2019-12-26 | 2023-08-08 | Oppo广东移动通信有限公司 | Terminal control method and device, mobile terminal and storage medium |
| CN114090276A (en) * | 2020-08-25 | 2022-02-25 | 比亚迪股份有限公司 | Controller system, data acquisition method, domain controller, and storage medium |
| CN112650564A (en) * | 2020-12-18 | 2021-04-13 | 北京紫光展锐通信技术有限公司 | Application limiting method and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521041A (en) * | 2011-12-14 | 2012-06-27 | 华为终端有限公司 | Method for processing application program and wireless handheld device |
| CN105718027A (en) * | 2016-01-20 | 2016-06-29 | 努比亚技术有限公司 | Management method of background application programs and mobile terminal |
| CN106201686A (en) * | 2016-06-30 | 2016-12-07 | 北京小米移动软件有限公司 | Management method, device and the terminal of application |
| CN106709298A (en) * | 2017-01-04 | 2017-05-24 | 广东欧珀移动通信有限公司 | Information processing method and device and intelligent terminal |
| CN107133094A (en) * | 2017-06-05 | 2017-09-05 | 努比亚技术有限公司 | Application management method, mobile terminal and computer-readable recording medium |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110307200A1 (en) * | 2010-06-11 | 2011-12-15 | Academia Sinica | Recognizing multiple appliance operating states using circuit-level electrical information |
| CN104317658B (en) * | 2014-10-17 | 2018-06-12 | 华中科技大学 | A kind of loaded self-adaptive method for scheduling task based on MapReduce |
| CN105046429B (en) * | 2015-07-10 | 2018-08-24 | 南京大学 | User's thinking workload assessment method in interactive process based on mobile phone sensor |
| JP2017167930A (en) * | 2016-03-17 | 2017-09-21 | 富士通株式会社 | Information processing device, power measurement method and power measurement program |
| CN107632697B (en) * | 2017-09-30 | 2019-10-25 | Oppo广东移动通信有限公司 | Processing method, device, storage medium and the electronic equipment of application program |
-
2017
- 2017-09-30 CN CN201710919655.2A patent/CN107632697B/en active Active
-
2018
- 2018-08-23 WO PCT/CN2018/102011 patent/WO2019062405A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521041A (en) * | 2011-12-14 | 2012-06-27 | 华为终端有限公司 | Method for processing application program and wireless handheld device |
| CN105718027A (en) * | 2016-01-20 | 2016-06-29 | 努比亚技术有限公司 | Management method of background application programs and mobile terminal |
| CN106201686A (en) * | 2016-06-30 | 2016-12-07 | 北京小米移动软件有限公司 | Management method, device and the terminal of application |
| CN106709298A (en) * | 2017-01-04 | 2017-05-24 | 广东欧珀移动通信有限公司 | Information processing method and device and intelligent terminal |
| CN107133094A (en) * | 2017-06-05 | 2017-09-05 | 努比亚技术有限公司 | Application management method, mobile terminal and computer-readable recording medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107632697A (en) | 2018-01-26 |
| WO2019062405A1 (en) | 2019-04-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107632697B (en) | Processing method, device, storage medium and the electronic equipment of application program | |
| CN107943583B (en) | Application processing method and device, storage medium and electronic equipment | |
| CN107678803B (en) | Application control method and device, storage medium and electronic equipment | |
| CN107704070B (en) | Application cleaning method and device, storage medium and electronic equipment | |
| CN108337358B (en) | Application cleaning method and device, storage medium and electronic equipment | |
| CN109948633A (en) | User gender prediction method, apparatus, storage medium and electronic equipment | |
| CN107678858B (en) | Application processing method, device, storage medium and electronic equipment | |
| CN107678800B (en) | Background application cleaning method and device, storage medium and electronic equipment | |
| CN107911491A (en) | Information recommendation method, device and storage medium, server and mobile terminal | |
| CN107577522B (en) | Application control method, device, storage medium and electronic equipment | |
| CN107832132B (en) | Application control method and device, storage medium and electronic equipment | |
| CN107870810B (en) | Application cleaning method and device, storage medium and electronic equipment | |
| CN107947951A (en) | Groups of users recommends method, apparatus and storage medium and server | |
| CN107408062A (en) | For unloading the technology with the data of loading processing device/coprocessor arrangement | |
| CN107678531B (en) | Application cleaning method and device, storage medium and electronic equipment | |
| CN107728772A (en) | Processing method, device, storage medium and the electronic equipment of application program | |
| CN107894827B (en) | Application cleaning method and device, storage medium and electronic equipment | |
| CN107704876B (en) | Application control method, device, storage medium and electronic equipment | |
| CN107402808B (en) | Process management method, device, storage medium and electronic equipment | |
| CN107943582B (en) | Feature processing method, feature processing device, storage medium and electronic equipment | |
| CN108595573A (en) | page display method, device, storage medium and electronic equipment | |
| CN107643925A (en) | Background application method for cleaning, device, storage medium and electronic equipment | |
| CN107807730B (en) | Using method for cleaning, device, storage medium and electronic equipment | |
| Tiku et al. | Energy-efficient and robust middleware prototyping for smart mobile computing | |
| CN107943537B (en) | Application cleaning method and device, storage medium and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: Changan town in Guangdong province Dongguan 523860 usha Beach Road No. 18 Applicant after: OPPO Guangdong Mobile Communications Co., Ltd. Address before: Changan town in Guangdong province Dongguan 523860 usha Beach Road No. 18 Applicant before: Guangdong OPPO Mobile Communications Co., Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |