CN107632697A - 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 PDF

Info

Publication number
CN107632697A
CN107632697A CN201710919655.2A CN201710919655A CN107632697A CN 107632697 A CN107632697 A CN 107632697A CN 201710919655 A CN201710919655 A CN 201710919655A CN 107632697 A CN107632697 A CN 107632697A
Authority
CN
China
Prior art keywords
application program
sample
probability
sampling periods
sampling
Prior art date
Application number
CN201710919655.2A
Other languages
Chinese (zh)
Other versions
CN107632697B (en
Inventor
曾元清
Original Assignee
广东欧珀移动通信有限公司
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 广东欧珀移动通信有限公司 filed Critical 广东欧珀移动通信有限公司
Priority to CN201710919655.2A priority Critical patent/CN107632697B/en
Publication of CN107632697A publication Critical patent/CN107632697A/en
Application granted granted Critical
Publication of CN107632697B publication Critical patent/CN107632697B/en

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00Details not covered by groups G06F3/00 – G06F13/00 and G06F21/00
    • G06F1/26Power supply means, e.g. regulation thereof
    • G06F1/32Means for saving power
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/34Recording 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for program control, e.g. control units
    • G06F9/06Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44Arrangements for executing specific programs
    • G06F9/445Program loading or initiating
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02DCLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THIR OWN ENERGY USE
    • Y02D10/00Energy efficient computing
    • Y02D10/30Reducing energy consumption in distributed systems
    • Y02D10/34Monitoring

Abstract

The embodiment of the present application discloses a kind of processing method of application program, device, storage medium and electronic equipment.The processing method of the application program, by the use information for obtaining each sampling time point sample application in historical time section, according to sampling time point and use information generation training sample, default mixed Gauss model is trained further according to training sample, based on the mixed Gauss model after training and default 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

Processing method, device, storage medium and the electronic equipment of application program

Technical field

The application is related to technical field of electronic equipment, more particularly to a kind of processing method of application program, device, storage Jie Matter and electronic equipment.

Background technology

With the development of internet and the development of mobile communications network, at the same also along with electronic equipment disposal ability and The fast development of storage capacity, the application of magnanimity have obtained rapid propagation and use;It is conventional 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 Amusement sense in the frequency at end and use.

When electronic equipment, which is opened, multiple application programs, it can seriously take electronics in the application program of running background and set Standby resource, the operation fluency of electronic equipment is reduced, while the power consumption for also resulting in electronic equipment is larger.

The content of the invention

The embodiment of the present application provides a kind of processing method of application program, device, storage medium and electronic equipment, can be with intelligence Energy ground management and 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, applied to electronic equipment, the side Method includes:

Obtain the use information of each sampling time point sample application in historical time section;

According to the sampling time point and use information generation training sample;

Default mixed Gauss model is trained according to the training sample;

Based on the mixed Gauss model after training and default Bayesian model, to the background application in the electronic equipment Program is handled.

Second aspect, the embodiment of the present application provides a kind of processing unit of application program, described applied to electronic equipment Device includes:

Acquisition module, for obtaining the use information of each sampling time point sample application 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 default mixed Gauss model;

Processing module, for based on the mixed Gauss model after training and default Bayesian model, being set to the electronics Background application in standby is handled.

The third aspect, the embodiment of the present application additionally provide a kind of storage medium, a plurality of finger are stored with the storage medium Order, the instruction are suitable to be loaded by processor to perform the processing method of above-mentioned application program.

Fourth aspect, the embodiment of the present application additionally provide a kind of electronic equipment, including processor and memory, the processing Device is electrically connected with the memory, and the memory is used for store instruction and data;Processor is used to perform above-mentioned application The processing method of program.

The embodiment of the present application discloses a kind of processing method of application program, device, storage medium and electronic equipment.It should answer With the processing method of program, by obtaining the use information of each sampling time point sample application in historical time section, root According to sampling time point and use information generation training sample, default mixed Gauss model is instructed further according to training sample Practice, based on the mixed Gauss model after training and default Bayesian model, the background application in electronic equipment is carried 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.

Brief description of the drawings

In order to illustrate more clearly of the technical scheme in the embodiment of the present application, 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 application, 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 scene framework schematic diagram of the processing method for the application program that the embodiment of the present application provides.

Fig. 2 is a kind of schematic flow sheet of the processing method for the application program that the embodiment of the present application provides.

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

Fig. 4 is a kind of schematic diagram for Gauss model that the embodiment of the present application provides.

Fig. 5 is the training schematic diagram for the mixed Gauss model that the embodiment of the present application provides.

Fig. 6 is a kind of schematic diagram for mixed Gauss model that the embodiment of the present application provides.

Fig. 7 is a kind of structural representation of the processing unit for the application program that the embodiment of the present application provides.

Fig. 8 is another structural representation of the processing unit for the application program that the embodiment of the present application provides.

Fig. 9 is another structural representation of the processing unit for the application program that the embodiment of the present application provides.

Figure 10 is the yet another construction schematic diagram of the processing unit for the application program that the embodiment of the present application provides

Figure 11 is a kind of structural representation for the electronic equipment that the embodiment of the present application provides.

Figure 12 is another structural representation for the electronic equipment that the embodiment of the present application provides.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present application, the technical scheme in the embodiment of the present application is carried out clear, complete Site preparation describes, it is clear that described embodiment is only some embodiments of the present application, rather than whole embodiments.It is based on Embodiment in the application, the every other implementation that those skilled in the art are obtained under the premise of creative work is not made Example, belong to the scope of the application protection.

The embodiment of the present application provides a kind of processing method of application program, device, storage medium and electronic equipment.Below will It is described in detail respectively.

Referring to Fig. 1, Fig. 1 is the scene framework schematic diagram of the processing method for the application program that the embodiment of the present application provides.

As schemed, so that the application program to running background is handled for A~E as an example.Data acquisition, record electricity are carried out first Sub- equipment each application program use information, as record opens time of each application program in one month.Then, according to collection To the usage record of application program count each application program and use probability in different time, and by usage time and correspondingly Use probability to be trained to default mixed Gauss model as training sample, 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 the training corresponding to each application program.Based on each Mixed Gauss model after being trained corresponding to application program, does with reference to use of the default Bayesian model to background application Prediction, calculate each background application under time T uses probability.Again from multiple background application A~E really The target background application for being less than predetermined probabilities P using probability is made, and closes target background application.So as to based on use The use habit at family realizes the management and control to background application, reduces occupancy of the application program to electronic equipment resource.

Wherein, electronic equipment can be mobile terminal, such as mobile phone, tablet personal computer, notebook computer, the embodiment of the present application To this without limiting.

In one embodiment, there is provided a kind of processing method of application program, applied to electronic equipment, the electronic equipment can be with For mobile terminals such as smart mobile phone, tablet personal computer, notebook computers.As shown in Fig. 2 flow can be as follows:

101st, the use information of each sampling time point sample application 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 networking application, game application, shopping application etc..

Wherein, sample application program can be multiple in electronic equipment or all mounted application programs.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 expecting the higher result of accuracy, can will be configured more intensive acquisition time, 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 looser, such as every 10min be a sampling time point.

In certain embodiments, self-application program is installed, then can record each use information for having installed application program, is turned Change into the corresponding default storage regions of data Cun Chudao.When the use information for needing to use a certain or some application programs When, then data corresponding with a certain or some application programs can be transferred from the storage region, the data of acquisition are carried out Parsing obtains corresponding information, and using the use information as a certain or some application programs, and this is a certain or some apply journey Sequence is then used as sample application program, the use information the time required to being selected from the use information of acquisition in section.

In certain embodiments, to reduce the power consumption of electronic equipment, the terminal resource of electronic equipment is saved, can directly be set The period of required record, then the use information of each sampling time point sample application is remembered within the period Record, subsequently to use.

102nd, 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 with using probability correspond generation training sample.

103rd, default mixed Gauss model is trained according to training sample.

Specifically, the training sample of above-mentioned generation is inputted into default mixed Gauss model, according to the instruction inputted White silk sample constantly corrects the relevant parameter in default mixed Gauss model, to cause the mixed Gauss model after training to fit For all training samples, a mixed Gauss model finally is trained to each sample application program.Wherein, it is each mixed Gauss model is closed to be made up of more sub- Gauss models.

104th, based on the mixed Gauss model after training and default 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, there is the mixing after 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, target mixed Gauss model is chosen from the mixed Gauss model after N number of training (i.e. for the background application The mixed Gauss model that procedural training goes out), and based on the target mixed Gauss model to the background application.

In certain embodiments, can based on different background applications each it is corresponding train after mixed Gauss model, With reference to the current time, to each background application being calculated using probability at that time.It is each according to what is calculated Individual application program it is each it is self-corresponding use probability, the background application to being met certain condition using probability is cleared up or closed The operation such as close, to reduce occupancy of the application program to 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, training sample is generated according to sampling time point and use information, then Default mixed Gauss model is trained according to training sample, based on the mixed Gauss model after training and default pattra leaves This model, the background application in electronic equipment is handled.The program can reduce the occupancy of electronic equipment end resource, carry The operation fluency of electronic equipment has been risen, has reduced the power consumption of electronic equipment.

In one embodiment, the processing method of another application program is also provided, applied to electronic equipment, the electronic equipment Can be the mobile terminals such as smart mobile phone, tablet personal computer, notebook computer.As shown in figure 3, flow can be as follows:

201st, the use information of each sampling time point sample application in historical time section is obtained.

Sample application program can be multiple in electronic equipment or all mounted application programs.Sampling time point can basis Actual demand is set, if expecting the higher result of accuracy, can will be configured more intensive acquisition time, 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, will can sample Time point is configured looser, 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 can be one month in the past, each time point can be the timestamp of current time.Use ginseng Number can extract from database, can be stored with over application program in a 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

Afterwards, the opening of these application programs is recorded, as various kinds application making in each sampling time point Use information

202nd, time cycle and sampling periods corresponding to each sampling time point are determined, wherein, adopted in every a period of time Sample time point corresponds with sampling periods.

In certain embodiments, historical time section includes multiple time cycles, as historical time section in the past one month, then Time cycle can be then the every day in a middle of the month in the past.Multiple sampling periods can be divided into per a period of time, such as one day In each minute.Specifically, timestamp corresponding to sampling time point can be based on, determines time cycle belonging to it and specific Sampling periods, such as can be the xx xx month, xx days point.In the case of 481 points of September 9 day, September is historical time section, and 9 are week time Phase, 481 points are sampling periods.

Wherein, the collection of sample is can be completed on the terminal devices such as smart mobile phone, tablet personal computer, is obtained every 1 minute The application information being used in present terminal equipment, and the lane database stored to the terminal device, then for One user usage record of one month, it can extract up to ten thousand use information samples.

203rd, use information corresponding to identical sampling periods in the different time cycle is handled, obtains sample application journey Sequence uses probability in sample corresponding to each sampling periods.

In certain embodiments, step is " by corresponding to identical sampling periods in the different time cycle at use information Reason, obtains sample application program and uses probability in sample corresponding to each sampling periods " below scheme can be included:

Judge whether use information meets preparatory condition;

Determine the sampling time that each sample applies in identical sampling periods corresponding use information to meet preparatory condition Point quantity;

Obtaining each sample applies the use information within multiple time cycles to meet that the sampling time point of preparatory condition is total Amount;

According to sampling time point quantity and sampling time point total quantity, each sample application program is calculated in each sampling Sample corresponding to section uses probability.

Specifically, application records can be used according to the above-mentioned user collected, counts the most frequently used N number of sample of user Application, wherein N are configurable.For rational allocation electronic equipment resource, operand, usual N=5 are reduced.

In certain embodiments, use information can be the running state information of sample application program;Then step " judges to use Whether information meets preparatory condition " below scheme can be included:

Judge whether running status is front stage operation;

If so, then judge that use information meets preparatory condition;

If it is not, then judge that use information is unsatisfactory for preparatory condition.

Wherein, running status is in front stage operation, that is, 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 Section (may include 1440 minutes for such as one day, then the 481st minute of the 481st minute of September 1 day and September 31 days is same time period;9 The 1440th minute of the 1440th minute of month 1 day and September 31 days be same time period) in front stage operation sampling time points Amount, is designated as X=[x1, x2, x3…xi..., xn], wherein xiRepresent the use of the i-th daily minutes application program of September part Number.

Such as using 1 day~September of September this 30 days on the 30th as historical time section exemplified by, if at this in 30, wherein having 25 days User has used wechat in 10 minutes at 01 minute to 8 points 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, and it is the 490th minute (8*60+10 to be converted within 10 minutes the period 8 points =490).The wechat use information statistical result of so user can be as shown in table 2 below:

Application program x1 ... x481 ... x490 ... x1440 Wechat 0 0 25 25 25 0 0 QQ ... ... ... ... ... ... ... ... ...

Table 2

In the embodiment of the present application, each sample application program can be used probability in sample corresponding to each sampling periods Definition of probability be Pi, then probability PiSpecific algorithm refer to below equation:

Wherein, xjWith xiShow that definition is identical, all represent the use time of the i-th or j minutes application programs in one day Number.N is the positive integer more than 1.Based on above-mentioned data and probabilistic algorithm, each sample application program is can obtain in each sampling Sample corresponding to period uses the probability distribution of probability, can be as shown in table 3 below:

Application program P1 ... P481 ... P490 ... P1440 Wechat 0 0 0.1 0.1 0.1 0 0 QQ ... ... ... ... ... ... ... ... ...

Table 3

204th, probability generation training sample is used based on sampling periods and corresponding sample.

Specifically, the probability point changed over time according to the sample of each sample application program in above-mentioned table 2 using probability Cloth, sampling time point is corresponded into generation training sample with sample using probability.

In some embodiments, if sampling periods are designated as into t, sampling periods include [t1,t2…tm], sample is made P is designated as with probability, sample includes [P using probability1,P2…Pm].Then specifically the training sample of generation can be designated as (tm,Pm), such as Training sample corresponding to 481st minute is (481,0.1).

205th, training sample is inputted into the first formula, to be trained to the first preset formula, obtains multiple training Sub- Gauss model afterwards.

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, AiRepresent that sample application program i, t represent sampling periods, k represents sub- Gauss model quantity, and k is constant, μk Represent mathematic expectaion, σkRepresent variance, ωkExpression weights, and N (t | μkk) represent that stochastic variable t obeys a mathematic expectaion and is μk, variance σkNormal distribution, P (t | Ai) when can then represent to sample when sample application program i running status is front stage operation Section is t probability.

It is Gaussian Profile probabilistic model.

, can be as the Gauss model of a constructed initialization with reference to figure 4.Then, based on the sampling periods inputted T, sample uses probability P, and the first preset formula is trained, and obtains the sub- Gauss model after multiple training.It is first with reference to figure 5 First the data collected are pre-processed, the probability distribution that each application program is used is obtained, then makees the probability distribution For input, default mixed Gauss model is trained, finally gives suitable mixed Gauss model.

For example mixed Gauss model modeling can be carried out when reading training sample corresponding to the 1st minute;Then the 2nd is read Training sample corresponding to minute, update Gauss model parameter;Training sample corresponding to reading the 3rd minute again, continue renewal mixing By that analogy, after all training samples are all read, renewal Gauss model parameter obtains finally Gauss model parameter ... Mixed Gauss model after training.

Mixed Gauss model is typically formed using 3~5 sub- Gauss models.In modeling process, it is necessary to mixed Gaussian mould Variances sigma in typek, mathematic expectaion μk, weights ωkThe number needed for modeling is obtained Deng some parameter initializations, and by these parameters According to.In initialization procedure, can set variance is as far as possible big, and weights (i.e. ωk) then (such as 0.001) as small as possible.This It is due to that the Gauss model of initialization is a model being inaccurate, it is necessary to ceaselessly reduce his scope that sample, which is set, renewal His parameter value, so as to obtain most probable Gauss model.Variance is set big, be exactly in order to by pixel bag as much as possible Containing to a model the inside, parameter k, corresponding all weights ω are found outk, and in all sub- Gauss models it is each self-corresponding Parameter μkAnd σk

In some embodiments, ω can be determined using maximum likelihood estimatek、μkAnd σkDeng these model parameters.Its In, the likelihood function of mixed Gauss model is:

Using expectation maximization (EM) algorithm, make (μkk) likelihood function maximization.Then ω corresponding to maximumk、μk And σkIt is exactly our estimation.Finally give [(ω111), (ω111) ... (ωkkk)]。

206th, the sub- Gauss model after multiple training is superimposed, with the mixed Gauss model after being trained.

Specifically, by estimated weights ωkAfter each sub- Gauss model weighting processing, by k son after weighting Gauss model overlap-add procedure, with the mixed Gauss model after being trained.With reference to figure 6, resulting mixed Gauss model is by 4 Sub- Gauss model is formed.

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)]。

207th, the background application in electronic equipment is determined.

In certain embodiments, can be in the central processing unit (CPU, central processing unit) of electronic equipment When taking larger larger, running memory resource occupation and/or electronic equipment dump energy deficiency, application program processing can be triggered Instruction.Electronic equipment obtains the application program process instruction, then, determines to transport in backstage according to the application program process instruction Capable background application, subsequently to handle background application.

208th, based on the mixed Gauss model after training corresponding to each application program, 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 Mixed Gauss model afterwards, it can accurately estimate application program and probability is being used corresponding to different time.The application is implemented In example, the expression formula of default Bayesian model is the second preset formula, and second preset formula is as follows:

Wherein, T represents the time, and N represents the quantity of the mixed Gauss model after training, P (Ai| T) expression sampling periods are T When front stage operation application program be application program i probability, P (T | Ai) represent sample application program i running status be before The probability that sampling periods are T during platform operation, and P (T | Aj) represent sampling periods when application program j running status is front stage operation For T probability, P (Ai) represent application program i in historical time section using probability, P (Aj) represent application program j's In historical time section using probability.

Specifically, the mixed Gauss model being primarily based on after training, estimates different application each comfortable object time Corresponding to lower initially 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, can be by application program i in historical time section Interior access times, the ratio of the access times summation with all sample application programs in historical time section are worth to, 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, it is each using each application program according to the algorithm that probability is initially used corresponding to target background application i Self-corresponding mixed Gauss model, calculate the initial of each application program and use probability;Utilize P (Ai) calculation formula, calculate Go out various kinds application in historical time section using probability.

Finally, each item data obtained as above arrived is substituted into the second preset formula (i.e. default Bayesian model), utilized Second preset formula calculates that target background application is corresponding under the object time to use probability, to be lifted using probability Accuracy.

209th, background application is handled according to using probability.

In certain embodiments, the benchmark handled application can be used as by setting probability threshold value.That is, step " being handled according to using probability background application " can include below scheme:

The target background application for being less than predetermined threshold value using probability is determined from current background application program;

Close target background application.

Wherein, the predetermined threshold value 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 one period T of futureiProbability P (T | Ai) being less than 0.5, then cleaning should Background application AiIf not less than 0.5, background application A is keptiContinue in running background.

From the foregoing, it will be observed that the processing method for the application program that the embodiment of the present application provides, every in historical time section by obtaining The use information of one sampling time point sample application, it is then determined that time cycle and sampling corresponding to point of each sampling time Period, then use information corresponding to identical sampling periods in the different time cycle is handled, obtain sample application program and exist Sample corresponding to each sampling periods uses probability.Probability generation training sample is used based on sampling periods and corresponding sample This, and be input in default mixed Gauss model and carry out model training, obtain being made up of the sub- Gauss model after multiple training New mixed Gauss model.Finally, estimate that each backstage should using new mixed Gauss model and default Bayesian model Probability is used under the object time, and corresponding background application is handled according to obtained probability.The program The occupancy of 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 Put to be integrated in the electronic device in the form of software or hardware, and the electronic equipment can specifically include mobile phone, tablet personal computer, pen Remember this apparatus such as computer.As shown in fig. 7, the processing unit 30 of the application program can include receiving module 31, determining module 32, Receiving module 33 and processing module 34, wherein:

Acquisition module 31, for obtaining the use information of each sampling time point sample application 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 default mixed Gauss model;

Processing module 34, for based on the mixed Gauss model after training and default Bayesian model, to electronic equipment In background application handled.

In certain embodiments, historical time section includes multiple time cycles, and multiple samplings are divided into per a period of time Period.With reference to figure 8, generation module 32 can include:

First determines submodule 321, for determining time cycle and sampling periods corresponding to each sampling time point, wherein, Sampling time point corresponds with sampling periods in per a period of time;

Information processing submodule 322, for use information corresponding to identical sampling periods in the different time cycle to be carried out Processing, obtains sample application program and uses probability in sample corresponding to each sampling periods;

Submodule 323 is generated, for using probability generation training sample based on sampling periods and corresponding sample.

In certain embodiments, processing submodule 322 can include:

Judging unit, for judging whether use information meets preparatory condition;

First determining 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, the use information within multiple time cycles is applied to meet adopting for preparatory condition for obtaining each sample Sample time point total quantity;

Computing unit, for according to sampling time point quantity and sampling time point total quantity, calculating each sample application journey Sequence uses probability in sample corresponding to each sampling periods.

In certain embodiments, use information is the running state information of sample application program;Judging unit can be used for:

Judge whether running status is front stage operation;

If so, then judge that use information meets preparatory condition;

If it is not, then judge that use information is unsatisfactory for preparatory condition

In certain embodiments, sampling periods include [t1,t2…tm], sample includes [P using probability1,P2…Pm];With reference to Fig. 9, training module 33 can include:

Input submodule 331, for sampling periods and corresponding sample to be inputted into the first formula using probability, first Preset formula is:

Wherein, AiRepresent that sample application program i, t represent sampling periods, k represents sub- Gauss model quantity, μkRepresent mathematics It is expected, σkRepresent variance, ωkExpression weights, and N (t | μkk) represent that stochastic variable t one mathematic expectaion of obedience is μk, variance be σkNormal distribution, P (t | Ai) represent the probability that sampling periods are t when sample application program i running status is front stage operation;

Submodule 332 is trained, for using probability P based on the sampling periods t, the sample that are inputted, to the first preset formula It is trained, obtains the sub- Gauss model after multiple training;

Submodule 333 is superimposed, for the sub- Gauss model after multiple training to be superimposed, with the mixed Gaussian after being trained Model.

In certain embodiments, each application program is corresponding with the mixed Gauss model after unique training;With reference to figure 10, place Reason module 34 can include:

Acquisition submodule 341, for obtaining application program process instruction;

Second determination sub-module 342, for determining the background application journey in electronic equipment according to application program process instruction Sequence;

Calculating sub module 343, for based on the mixed Gauss model after training corresponding to each application program, utilizing second Preset formula calculates each background application and uses probability in the object time, and the second preset formula is:

Wherein, T represents the time, and N represents the quantity of the mixed Gauss model after training, P (Ai| T) expression sampling periods are T When front stage operation application program be application program i probability, P (T | Ai) represent sample application program i running status be before The probability that sampling periods are T during platform operation, and P (T | Aj) represent sampling periods when application program j running status is front stage operation For T probability, P (Ai) represent application program i in historical time section using probability, P (Aj) represent application program j's In historical time section using probability;

Using processing submodule 344, for being handled according to using probability background application.

In certain embodiments, can include using processing submodule 344:

Second determining unit, for being determined from current background application program after being less than the target of predetermined threshold value using probability Platform application program;

Closing unit, for closing target background application.

From the foregoing, it will be observed that the processing unit for the application program that the embodiment of the present application provides, every in historical time section by obtaining The use information of one sampling time point sample application, training sample, then root are generated according to sampling time point and use information Default mixed Gauss model is trained according to training sample, based on the mixed Gauss model after training and default Bayes Model, the background application in electronic equipment is handled.The program can reduce the occupancy of electronic equipment end resource, lifting The operation fluency of electronic equipment, reduce the power consumption of electronic equipment.

A kind of electronic equipment is also provided in the another embodiment of the application, the electronic equipment can be smart mobile phone, flat board Apparatus such as computer.As shown in figure 11, electronic equipment 400 includes processor 401 and memory 402.Wherein, processor 401 is with depositing Reservoir 402 is electrically connected with.

Processor 401 is the control centre of electronic equipment 400, utilizes various interfaces and the whole 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 the various functions and processing data of electronic equipment being performed, so as to carry out integral monitoring to electronic equipment.

In the present embodiment, processor 401 in electronic equipment 400 can according to the steps, by one or one with On application process corresponding to instruction be loaded into memory 402, and be stored in memory 402 by processor 401 to run In application, so as to realize various functions:

Obtain the use information of each sampling time point sample application in historical time section;

According to sampling time point and use information generation training sample;

Default mixed Gauss model is trained according to training sample;

Based on the mixed Gauss model after training and default Bayesian model, to the background application in electronic equipment Handled.

In certain embodiments, historical time section includes multiple time cycles, and multiple samplings are divided into per a period of time Period;Processor 401 is further used for performing following steps:

Time cycle and sampling periods corresponding to each sampling time point are determined, wherein, per a period of time during interior sampling Between point with sampling periods correspond;

Use information corresponding to identical sampling periods in the different time cycle is handled, sample application program is obtained and exists Sample corresponding to each sampling periods uses probability;

Probability generation training sample is used based on sampling periods and corresponding sample.

In certain embodiments, processor 401 is further used for performing following steps:

Judge whether use information meets preparatory condition;

Determine the sampling time that each sample applies in identical sampling periods corresponding use information to meet preparatory condition Point quantity;

Obtaining each sample applies the use information within multiple time cycles to meet that the sampling time point of preparatory condition is total Amount;

According to sampling time point quantity and sampling time point total quantity, each sample application program is calculated in each sampling Sample corresponding to section uses probability.

In certain embodiments, use information is the running state information of sample application program, and processor 401 is further used In execution following steps:

Judge whether running status is front stage operation;

If so, then judge that use information meets preparatory condition;

If it is not, then judge that use information is unsatisfactory for preparatory condition.

In certain embodiments, sampling periods include [t1,t2…tm], sample includes [P using probability1,P2…Pm];Processing Device 401 is further used for performing following steps:

Sampling periods and corresponding sample are inputted into the first formula using probability, the first preset formula is:

Wherein, AiRepresent that sample application program i, t represent sampling periods, k represents sub- Gauss model quantity, μkRepresent mathematics It is expected, σkRepresent variance, ωkExpression weights, and N (t | μkk) represent that stochastic variable t one mathematic expectaion of obedience is μk, variance be σkNormal distribution, P (t | Ai) represent the probability that sampling periods are t when sample application program i running status is front stage operation;

Probability P is used based on sampling periods t, the sample inputted, the first preset formula is trained, obtains multiple instructions Sub- Gauss model after white silk;

Sub- Gauss model after multiple training is superimposed, with the mixed Gauss model after being trained.

In certain embodiments, each application program is corresponding with the mixed Gauss model after unique training;Processor 401 enters One step is used to perform 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 object time, and the second preset formula is:

Wherein, T represents the time, and N represents the quantity of the mixed Gauss model after training, P (Ai| T) expression sampling periods are T When front stage operation application program be application program i probability, P (T | Ai) represent sample application program i running status be before The probability that sampling periods are T during platform operation, and P (T | Aj) represent sampling periods when application program j running status is front stage operation For T probability, P (Ai) represent application program i in historical time section using probability, P (Aj) represent application program j's In historical time section using probability;

Background application is handled according to using probability.

In certain embodiments, processor 401 is further used for performing following steps:

The target background application for being less than predetermined threshold value using probability is determined from current background application program;

Close target background application.

Memory 402 can be used for storage application and data.Including in the application that memory 402 stores can be within a processor The instruction of execution.Using various functions module can be formed.Processor 401 is stored in the application of memory 402 by operation, from And perform various function application and data processing.

In certain embodiments, as shown in figure 12, electronic equipment 400 also includes:Display screen 403, control circuit 404, penetrate Frequency circuit 405, input block 406, voicefrequency circuit 407, sensor 408 and power supply 409.Wherein, processor 401 is respectively with showing Display screen 403, control circuit 404, radio circuit 405, input block 406, voicefrequency circuit 407, sensor 408 and power supply 409 It is electrically connected with.

Display screen 403 can be used for display by user input information or be supplied to user information and electronic equipment it is each Kind graphical user interface, these graphical user interface can be made up of image, text, icon, video and its any combination.

Control circuit 404 is electrically connected with display screen 403, for the display information of control display screen 403.

Radio circuit 405 is used for transceiving radio frequency signal, to be built by radio communication and the network equipment or other electronic equipments Vertical wireless telecommunications, the receiving and transmitting signal between the network equipment or other electronic equipments.

Input block 406 can be used for numeral, character information or the user's characteristic information (such as fingerprint) for receiving input, and Keyboard, mouse, action bars, optics or the trace ball signal relevant with user's setting and function control is produced to input.Wherein, Input block 406 can include fingerprint recognition module.

Voicefrequency circuit 407 can provide the COBBAIF between user and electronic equipment by loudspeaker, microphone.

Sensor 408 is used to gather external environmental information.Sensor 408 can include ambient light sensor, acceleration Sensor, optical sensor, motion sensor and other sensors.

The all parts that power supply 409 is used for electron equipment 400 are powered.In certain embodiments, power supply 409 can pass through Power-supply management system and processor 401 are logically contiguous, so as to realize management charging, electric discharge, Yi Jigong by power-supply management system The functions such as consumption management.

Although not shown in Figure 12, electronic equipment 400 can also include camera, bluetooth module etc., will not be repeated here.

From the foregoing, it will be observed that the electronic equipment that the embodiment of the present application provides, by obtaining each sampling time in historical time section The use information of point sample application, training sample is generated according to sampling time point and use information, further according to training sample Default 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 certain embodiments, a kind of storage medium is additionally provided, a plurality of instruction is stored with the storage medium, the instruction Suitable for being loaded by processor to perform the processing method of any of the above-described application program.

One of ordinary skill in the art will appreciate that all or part of step in the various methods of above-described embodiment is can To instruct the hardware of correlation to complete by program, the program can be stored in a computer-readable recording medium, storage Medium can include:Read-only storage (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 the concept of description 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 number range when merely by quick method belong to the every of relevant range to refer to Individual 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 clearly opposite prompting, otherwise institute specifically described herein is methodical Step can be performed by any appropriate order.The change of the application is not limited to the step of description order.Unless in addition Advocate, be otherwise all only using any and all example presented herein or exemplary language (for example, " such as ") The concept of the application is better described, and not the scope of the concept of the application is any limitation as.Spirit and model are not being departed from In the case of enclosing, those skilled in the art becomes readily apparent that a variety of modifications and adaptation.

Processing method, device, storage medium and the electronic equipment of the application program provided above the embodiment of the present application It is described in detail, specific case used herein is set forth to the principle and embodiment of the application, and the above is real The explanation for applying example is only intended to help and understands 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 specific embodiments and applications, in summary, in this specification Hold the limitation that should not be construed as to the application.

Claims (16)

  1. A kind of 1. processing method of application program, applied to electronic equipment, it is characterised in that methods described includes:
    Obtain the use information of each sampling time point sample application in historical time section;
    According to the sampling time point and use information generation training sample;
    Default mixed Gauss model is trained according to the training sample;
    Based on the mixed Gauss model after training and default Bayesian model, to the background application in the electronic equipment Handled.
  2. 2. the processing method of application program as claimed in claim 1, it is characterised in that when the historical time section includes multiple Between the cycle, be divided into multiple sampling periods per a period of time;
    According to the step of the sampling time point and use information generation training sample, including:
    Time cycle and sampling periods corresponding to each sampling time point are determined, wherein, sampling time point in every a period of time Corresponded with sampling periods;
    Use information corresponding to identical sampling periods in the different time cycle is handled, obtains sample application program each Sample corresponding to sampling periods uses probability;
    Probability generation training sample is used based on sampling periods and corresponding sample.
  3. 3. the processing method of application program as claimed in claim 2, it is characterised in that by identical sampling in the different time cycle Use information is handled corresponding to period, is obtained sample application program and is used probability in sample corresponding to each sampling periods Step, including:
    Judge whether the use information meets preparatory condition;
    Determine that each sample applies the corresponding use information in identical sampling periods to meet that the sampling time of preparatory condition counts Amount;
    Obtain the sampling time point total quantity that each sample applies within multiple time cycles use information to meet preparatory condition;
    According to the sampling time point quantity and sampling time point total quantity, calculate each sample application program and adopted each Sample corresponding to the sample period uses probability.
  4. 4. the processing method of application program as claimed in claim 3, it is characterised in that the use information is sample application journey The running state information of sequence;Judge the step of whether use information meets preparatory condition, including:
    Judge whether the running status is front stage operation;
    If so, then judge that the use information meets preparatory condition;
    If it is not, then judge that the use information is unsatisfactory for preparatory condition.
  5. 5. the processing method of application program as claimed in claim 2, it is characterised in that the sampling periods include [t1,t2… tm], the sample includes [P using probability1,P2…Pm];
    The step of being trained according to the training sample to default mixed Gauss model, including:
    The sampling periods and corresponding sample are inputted into the first formula using probability, first preset formula is:
    Wherein, AiRepresent that sample application program i, t represent sampling periods, k represents sub- Gauss model quantity, μkRepresent mathematic expectaion, σkRepresent variance, ωkExpression weights, and N (t | μkk) represent that stochastic variable t one mathematic expectaion of obedience is μk, variance σkJust State is distributed, and P (t | Ai) represent the probability that sampling periods are t when sample application program i running status is front stage operation;
    Probability is used based on sampling periods, the sample inputted, first preset formula is trained, obtains multiple training Sub- Gauss model afterwards;
    Sub- Gauss model after multiple training is superimposed, with the mixed Gauss model after being trained.
  6. 6. the processing method of application program as claimed in claim 5, it is characterised in that each application program is corresponding with unique instruction Mixed Gauss model after white silk;
    Based on the mixed Gauss model after training and default Bayesian model, to the background application in the electronic equipment The step of being handled, including:
    Obtain application program process instruction;
    The background application in the electronic equipment is determined according to the application program process instruction;
    Based on the mixed Gauss model after training corresponding to each application program, calculating each backstage using the second preset formula should Probability is used in the object time with program, second preset formula is:
    Wherein, T represents the time, and N represents the quantity of the mixed Gauss model after training, P (Ai| T) represent sampling periods when being T before The probability that the application program of platform operation is application program i, and P (T | Ai) represent that sample application program i running status is transported for foreground During row sampling periods be T probability, P (T | Aj) represent that sampling periods are T's when application program j running status is front stage operation Probability, P (Ai) represent application program i in historical time section using probability, P (Aj) represent application program j in history In period using probability;
    Background application is handled using probability according to described.
  7. 7. the processing method of application program as claimed in claim 6, it is characterised in that answered according to described using probability backstage The step of being handled with program, including:
    The target background application for being less than predetermined threshold value using probability is determined from current background application program;
    Close the target background application.
  8. 8. a kind of processing unit of application program, it is characterised in that described device includes:
    Acquisition module, for obtaining the use information of each sampling time point sample application 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 default mixed Gauss model;
    Processing module, for based on the mixed Gauss model after training and default Bayesian model, in the electronic equipment Background application handled.
  9. 9. the processing unit of application program as claimed in claim 8, it is characterised in that when the historical time section includes multiple Between the cycle, be divided into multiple sampling periods per a period of time;
    The generation module includes:
    First determination sub-module, for determining time cycle and sampling periods corresponding to each sampling time point, wherein, per for the moment Between in the cycle sampling time point corresponded with sampling periods;
    Information processing submodule, for use information corresponding to identical sampling periods in the different time cycle to be handled, obtain To sample application program probability is used in sample corresponding to each sampling periods;
    Submodule is generated, for using probability generation training sample based on sampling periods and corresponding sample.
  10. 10. the processing unit of application program as claimed in claim 9, it is characterised in that the processing submodule includes:
    Judging unit, for judging whether the use information meets preparatory condition;
    First determining unit, for determining that each sample applies the corresponding use information in identical sampling periods to meet to preset The sampling time point quantity of condition;
    Acquiring unit, when applying that use information meets the sampling of preparatory condition within multiple time cycles for obtaining each sample Between put total quantity;
    Computing unit, for according to the sampling time point quantity and sampling time point total quantity, calculating each sample should With program probability is used in sample corresponding to each sampling periods.
  11. 11. the processing unit of application program as claimed in claim 10, it is characterised in that the use information is sample application The running state information of program;The judging unit is used for:
    Judge whether the running status is front stage operation;
    If so, then judge that the use information meets preparatory condition;
    If it is not, then judge that the use information is unsatisfactory for preparatory condition.
  12. 12. the processing unit of application program as claimed in claim 9, it is characterised 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 inputted into the first formula using probability, described One preset formula is:
    Wherein, AiRepresent that sample application program i, t represent sampling periods, k represents sub- Gauss model quantity, μkRepresent mathematic expectaion, σkRepresent variance, ωkExpression weights, and N (t | μkk) represent that stochastic variable t one mathematic expectaion of obedience is μk, variance σkJust State is distributed, and P (t | Ai) represent the probability that sampling periods are t when sample application program i running status is front stage operation;
    Submodule is trained, for using probability based on the sampling periods, the sample that are inputted, first preset formula is instructed Practice, obtain the sub- Gauss model after multiple training;
    Submodule is superimposed, for the sub- Gauss model after multiple training to be superimposed, with the mixed Gauss model after being trained.
  13. 13. the processing unit of application program as claimed in claim 12, it is characterised in that each application program is corresponding with uniquely Mixed Gauss model after training;The processing module includes:
    Acquisition submodule, for obtaining application program process instruction;
    Second determination sub-module, for determining the background application journey in the electronic equipment according to the application program process instruction Sequence;
    Calculating sub module, for based on the mixed Gauss model after training corresponding to each application program, utilizing the second default public affairs Formula calculates each background application and uses probability in the object time, and second preset formula is:
    Wherein, T represents the time, and N represents the quantity of the mixed Gauss model after training, P (Ai| T) represent sampling periods when being T before The probability that the application program of platform operation is application program i, and P (T | Ai) represent that sample application program i running status is transported for foreground During row sampling periods be T probability, P (T | Aj) represent that sampling periods are T's when application program j running status is front stage operation Probability, P (Ai) represent application program i in historical time section using probability, P (Aj) represent application program j in history In period using probability;
    Using processing submodule, for being handled according to described using probability background application.
  14. 14. the processing unit of application program as claimed in claim 13, it is characterised in that the application handles submodule bag Include:
    Second determining unit, for determining that the target backstage for being less than predetermined threshold value using probability should from current background application program Use program;
    Closing unit, for closing the target background application.
  15. A kind of 15. storage medium, it is characterised in that be stored with a plurality of instruction in the storage medium, the instruction be suitable to by Reason device is loaded to perform the processing method of the application program as any one of claim 1-7.
  16. 16. a kind of electronic equipment, it is characterised in that including processor and memory, the processor and the memory are electrical Connection, the memory are used for store instruction and data;The processor is used to perform as any one of claim 1-7 Application program processing method.
CN201710919655.2A 2017-09-30 2017-09-30 Processing method, device, storage medium and the electronic equipment of application program CN107632697B (en)

Priority Applications (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

Applications Claiming Priority (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

Publications (2)

Publication Number Publication Date
CN107632697A true CN107632697A (en) 2018-01-26
CN107632697B CN107632697B (en) 2019-10-25

Family

ID=61104863

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710919655.2A 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)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019062404A1 (en) * 2017-09-30 2019-04-04 Oppo广东移动通信有限公司 Application program processing method and apparatus, storage medium, and electronic device
WO2019062405A1 (en) * 2017-09-30 2019-04-04 Oppo广东移动通信有限公司 Application program processing method and apparatus, storage medium, and electronic device

Citations (6)

* Cited by examiner, † Cited by third party
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
US20170269678A1 (en) * 2016-03-17 2017-09-21 Fujitsu Limited Information processing device, power measuring method, and non-transitory recording medium storing power measuring program

Family Cites Families (4)

* Cited by examiner, † Cited by third party
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
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

Patent Citations (6)

* Cited by examiner, † Cited by third party
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
US20170269678A1 (en) * 2016-03-17 2017-09-21 Fujitsu Limited Information processing device, power measuring method, and non-transitory recording medium storing power measuring program
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

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
孙水发 等: "《视频前景检测及其在水电工程监测中的应用》", 31 December 2014, 国防工业出版社 *
杨旗: "《人体步态及行为识别技术研究》", 31 January 2014, 辽宁科学技术出版社 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019062404A1 (en) * 2017-09-30 2019-04-04 Oppo广东移动通信有限公司 Application program processing method and apparatus, storage medium, and electronic device
WO2019062405A1 (en) * 2017-09-30 2019-04-04 Oppo广东移动通信有限公司 Application program processing method and apparatus, storage medium, and electronic device

Also Published As

Publication number Publication date
WO2019062405A1 (en) 2019-04-04
CN107632697B (en) 2019-10-25

Similar Documents

Publication Publication Date Title
CN106104673B (en) The low-resource of deep neural network occupies adaptation and personalization
CN105468742B (en) The recognition methods of malice order and device
CN102253858B (en) Device and method for managing application programs
US20190138919A1 (en) Methods and systems for preloading applications and generating prediction models
US20160217491A1 (en) Devices and methods for preventing user churn
CN104077515A (en) Terminal device and terminal control program
JP3506068B2 (en) Outlier value calculator
TW201543239A (en) Method and apparatus for grouping contacts
Gordon et al. Energy-efficient activity recognition using prediction
CN104541293A (en) Architecture for client-cloud behavior analyzer
WO2013126244A1 (en) Content pre-fetching for computing devices
US20130066815A1 (en) System and method for mobile context determination
CN107547361B (en) Method, system and the readable medium of equipment are calculated using one or more
JP5175242B2 (en) Method for predicting battery usable time of mobile communication terminal based on usage pattern
CN104049217B (en) Battery dump energy uses the detection method and device of time
Peng et al. A novel hybridization of echo state networks and multiplicative seasonal ARIMA model for mobile communication traffic series forecasting
CN107426432B (en) Resource allocation method and Related product
EP3080754A1 (en) Personalized machine learning models
Moldovan et al. Energy-aware mobile learning: Opportunities and challenges
CN105227626B (en) A kind of content delivery method, device and terminal
WO2015081801A1 (en) Method, server, and system for information push
CN103745193A (en) Skin color detection method and skin color detection device
Donohoo et al. Context-aware energy enhancements for smart mobile devices
US8005654B2 (en) Method, apparatus and computer program product for intelligent workload control of distributed storage
CN105320957A (en) Classifier training method and device

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