WO2019062405A1 - Procédé et appareil de traitement de programme d'application, support de stockage et dispositif électronique - Google Patents

Procédé et appareil de traitement de programme d'application, support de stockage et dispositif électronique Download PDF

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Publication number
WO2019062405A1
WO2019062405A1 PCT/CN2018/102011 CN2018102011W WO2019062405A1 WO 2019062405 A1 WO2019062405 A1 WO 2019062405A1 CN 2018102011 W CN2018102011 W CN 2018102011W WO 2019062405 A1 WO2019062405 A1 WO 2019062405A1
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Prior art keywords
application
sample
sampling
probability
period
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PCT/CN2018/102011
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English (en)
Chinese (zh)
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曾元清
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Oppo广东移动通信有限公司
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Publication of WO2019062405A1 publication Critical patent/WO2019062405A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • 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

Definitions

  • the present application relates to the field of electronic device technologies, and in particular, to a method, an apparatus, a storage medium, and an electronic device for processing an application.
  • the application running in the background will seriously occupy the resources of the electronic device, reduce the running fluency of the electronic device, and cause the power consumption of the electronic device to be large.
  • the embodiment of the present application provides a processing method, an apparatus, a storage medium, and an electronic device of an application program, which can intelligently control an application program and reduce power consumption of the electronic device.
  • an embodiment of the present application provides a processing method of an application, which is applied to an electronic device, where the method includes:
  • the background application in the electronic device is processed based on the trained mixed Gaussian model and the preset Bayesian model.
  • the embodiment of the present application provides an application processing device, which is applied to an electronic device, where the device includes:
  • An obtaining module configured to acquire usage information of a sample application at each sampling time point in a historical time period
  • a generating module configured to generate a training sample according to the sampling time point and the usage information
  • a training module configured to train the preset mixed Gaussian model according to the training sample
  • a processing module configured to process a background application in the electronic device based on the trained mixed Gaussian model and a preset Bayesian model.
  • the embodiment of the present application further provides a storage medium, where the storage medium stores a plurality of instructions, and the instructions are adapted to be loaded by a processor to perform the following steps:
  • the background application in the electronic device is processed based on the trained mixed Gaussian model and the preset Bayesian model.
  • the embodiment of the present application further provides an electronic device, including a processor and a memory, the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is configured to perform the following steps. :
  • the background application in the electronic device is processed based on the trained mixed Gaussian model and the preset Bayesian model.
  • FIG. 1 is a schematic diagram of a scenario structure of a processing method of an application provided by an embodiment of the present application.
  • FIG. 2 is a schematic flowchart of a method for processing an application provided by an embodiment of the present application.
  • FIG. 3 is another schematic flowchart of a method for processing an application provided by an embodiment of the present application.
  • FIG. 4 is a schematic diagram of a Gaussian model provided by an embodiment of the present application.
  • FIG. 5 is a schematic diagram of training of a hybrid Gaussian model provided by an embodiment of the present application.
  • FIG. 6 is a schematic diagram of a hybrid Gaussian model provided by an embodiment of the present application.
  • FIG. 7 is a schematic structural diagram of a processing device of an application program according to an embodiment of the present application.
  • FIG. 8 is another schematic structural diagram of a processing device of an application program according to an embodiment of the present application.
  • FIG. 9 is still another schematic structural diagram of a processing device of an application program according to an embodiment of the present application.
  • FIG. 10 is still another schematic structural diagram of a processing device of an application program according to an embodiment of the present application.
  • FIG. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
  • FIG. 12 is another schematic structural diagram of an electronic device according to an embodiment of the present application.
  • the embodiment of the present application provides a processing method, device, storage medium, and electronic device of an application program. The details will be described separately below.
  • the embodiment of the present application provides a processing method of an application, which is applied to an electronic device, where the method includes:
  • the background application in the electronic device is processed based on the trained mixed Gaussian model and the preset Bayesian model.
  • the historical time period includes a plurality of time periods, each time period being divided into a plurality of sampling periods;
  • the step of generating a training sample according to the sampling time point and the usage information includes:
  • the usage information corresponding to the same sampling period in different time periods is processed to obtain a sample usage probability corresponding to the sample application in each sampling period;
  • the training samples are generated based on the sampling period and the corresponding sample usage probability.
  • the processing information corresponding to the same sampling period in different time periods is processed to obtain a sample usage probability corresponding to the sample application in each sampling period, including:
  • a sample usage probability corresponding to each sample application period is calculated according to the number of sampling time points and the total number of sampling time points.
  • the usage information is running state information of the sample application; and the step of determining whether the usage information meets a preset condition comprises:
  • the sampling period includes [t 1 , t 2 ... t m ], and the sample usage probability includes [P 1 , P 2 . . . P m ];
  • the step of training the preset mixed Gaussian model according to the training sample includes:
  • a i represents the sample application i
  • t represents the sampling period
  • k represents the number of sub-Gaussian models
  • ⁇ k represents the mathematical expectation
  • ⁇ k represents the variance
  • ⁇ k represents the weight
  • ⁇ k , ⁇ k ) represents The random variable t obeys a normal distribution with a mathematical expectation of ⁇ k and a variance of ⁇ k
  • a i ) represents the probability that the running state of the sample application i is the sampling period of the foreground running time is t;
  • a plurality of trained sub-Gaussian models are superimposed to obtain a mixed Gaussian model after training.
  • each application corresponds to a uniquely trained mixed Gaussian model
  • the usage probability of each background application at the target time is calculated by using the second preset formula based on the mixed Gaussian model corresponding to each application, and the second preset formula is:
  • T is the time
  • N is the number of mixed Gaussian models after training
  • T) is the probability that the application running in the foreground is the application i when the sampling period is T
  • a i ) is expressed.
  • the running state of the sample application i is the probability that the sampling period is T in the foreground running
  • a j ) indicates the running state of the application j is the probability that the sampling period is T in the foreground running
  • P(A i ) indicates the application.
  • the application use probability of the program i in the historical time period, P(A j ) represents the application use probability of the application j in the historical time period;
  • the background application is processed according to the usage probability.
  • the step of processing the background application according to the usage probability includes:
  • FIG. 1 is a schematic diagram of a scenario structure of an application processing method according to an embodiment of the present application.
  • the processing of the application running in the background is A to E as an example.
  • data collection is performed to record the usage information of the electronic device in each application, such as recording the time when each application is opened within one month.
  • the usage probability of each application at different times is counted, and the usage time and the corresponding usage probability are used as training samples, and the preset mixed Gaussian model is trained according to the input
  • the sample adjusts the parameter information in the mixed Gaussian model to obtain a trained mixed Gaussian model corresponding to each application.
  • the use of the background application is predicted, and the usage probability of each background application at time T is calculated.
  • the target background application whose usage probability is lower than the preset probability P is determined from the plurality of background applications A to E, and the target background application is closed. Therefore, the control of the background application is implemented based on the user's usage habits, and the occupation of the electronic device resources by the application is reduced.
  • the electronic device may be a mobile terminal, such as a mobile phone, a tablet computer, a notebook computer, etc., which is not limited in this embodiment.
  • a processing method of an application is provided, which is applied to an electronic device, and the electronic device may be a mobile terminal such as a smart phone, a tablet computer, or a notebook computer.
  • the process can be as follows:
  • the application mentioned in this embodiment may be any application installed on the electronic device, such as an office application, a social application, a game application, a shopping application, and the like.
  • the sample application can be multiple or all installed applications in the electronic device.
  • the application usage information can be a usage record of the application, such as the opening time record of each application.
  • the sampling time point can be set according to actual needs. If you want to get higher accuracy results, you can set the collection time point to be more dense, such as every 1min as a sampling time point; if you want to save resources of electronic equipment If the accuracy of the result is not required, the sampling time point can be set loosely, for example, every 10 minutes is a sampling time point.
  • the usage information of each installed application may be recorded and converted into corresponding data storage into a preset storage area.
  • data corresponding to the certain application or some applications may be retrieved from the storage area, and the acquired data is parsed to obtain corresponding information, as The usage information of the application or the application, and the application or the application is used as a sample application, and the usage information in the required time period is selected from the obtained usage information.
  • the time period of the required recording may be directly set, and then the usage information of the sample application for each sampling time point is selected within the time period. Record it for later use.
  • the usage information of the obtained sample application may be preprocessed, the usage probability of each sample application at different sampling time points is calculated, and the probability distribution of the usage of each sample application over time is further obtained.
  • the training sample is generated by one-to-one correspondence between the sampling time point and the usage probability.
  • each mixed Gaussian model is composed of multiple sub-Gaussian models.
  • the background application in the electronic device is processed based on the trained mixed Gaussian model and the preset Bayesian model.
  • N mixed Gaussian models after training.
  • identity information such as application name, application identifier, etc.
  • N the identity information of each background application
  • select a target mixed Gaussian model from the N trained mixed Gaussian models according to the identity information of the background application (ie, for the background application)
  • Train the mixed Gaussian model and blend the Gaussian model based on the target against the background application.
  • the usage probability of each background application at the time may be calculated based on the mixed Gaussian model corresponding to each of the different background applications, combined with the current time. According to the calculated usage probabilities of the respective applications, the background application whose usage probability satisfies certain conditions is cleaned or closed to reduce the occupation of the electronic device resources by the application.
  • the application is a processing method of an application provided by an embodiment, which acquires usage information of a sample application at each sampling time point in a historical time period, generates a training sample according to a sampling time point and usage information, and then, according to the training sample.
  • the preset mixed Gaussian model is trained, and the background application in the electronic device is processed based on the trained mixed Gaussian model and the preset Bayesian model.
  • the solution can reduce the occupation of the final resources of the electronic device, improve the running fluency of the electronic device, and reduce the power consumption of the electronic device.
  • another application processing method is also provided, which is applied to an electronic device, and the electronic device may be a mobile terminal such as a smart phone, a tablet computer, or a notebook computer.
  • the process can be as follows:
  • the sample application can be multiple or all installed applications in an electronic device.
  • the sampling time point can be set according to actual needs. If you want to get higher accuracy results, you can set the collection time point to be more dense, such as every 1min as a sampling time point; if you want to save resources of electronic equipment, the result is If the accuracy is not required, the sampling time point can be loosely set, such as every 10 minutes as a sampling time point.
  • the usage information of the application can be related information of the application during use.
  • the historical time period can be the past month
  • each time point can be the time stamp of the current time.
  • the usage parameters can be extracted from the database, which can store the open records of the applications in the electronic device in the past month, as shown in Table 1 below:
  • the historical time period includes a plurality of time periods, such as the historical time period being the past month, and the time period may be each of the past one month.
  • Each time period can be divided into multiple sampling periods, such as every minute of the day.
  • the time period to which the sampling time point corresponds and the specific sampling period may be determined, such as xx months xx days xx minutes. Take September 481 as an example, September is the historical time period, 9 is the time period, and 481 is the sampling period.
  • the sample collection can be completed on a terminal device such as a smart phone or a tablet computer, and the application information currently being used on the current terminal device is acquired every 1 minute and stored in the database of the terminal device, then one for one user.
  • the monthly usage record can extract tens of thousands of usage information samples.
  • the step of “processing the usage information corresponding to the same sampling period in different time periods to obtain the sample usage probability corresponding to the sample application in each sampling period” may include the following processes:
  • the sample usage probability corresponding to each sample application period is calculated according to the number of sampling time points and the total number of sampling time points.
  • the usage information may be operational status information of the sample application; then the step “determining whether the usage information satisfies the preset condition” may include the following processes:
  • the running state is running in the foreground, which means that the current user is using the sample application.
  • each sample application is counted for the same time period in each day of the past month (eg, 1440 minutes per day, then 481 minutes and September 31st of September 1st)
  • the probability of the sample use probability corresponding to each sample application period in each sampling period may be defined as P i , and the specific algorithm of the probability P i may refer to the following formula:
  • x j and x i are defined the same, both indicate the number of times the application is used during the ith or j minutes of the day.
  • n is a positive integer greater than one.
  • the sampling time point and the sample use probability are generated in one-to-one correspondence to generate a training sample.
  • the sampling period if the sampling period is recorded as t, the sampling period includes [t 1 , t 2 ... t m ], the sample usage probability is denoted as P, and the sample usage probability includes [P 1 , P 2 ... P m ].
  • the generated training sample can be recorded as (t m , P m ), and the training sample corresponding to the 481th minute is (481, 0.1).
  • a i represents the sample application i
  • t represents the sampling period
  • k represents the number of sub-Gaussian models
  • k is a constant
  • ⁇ k represents the mathematical expectation
  • ⁇ k represents the variance
  • ⁇ k represents the weight
  • ⁇ k , ⁇ k ) indicates that the random variable t obeys a normal distribution with a mathematical expectation of ⁇ k and a variance of ⁇ k
  • a i ) can indicate that the running state of the sample application i is the foreground running time sampling period is t. Probability.
  • the first preset formula is trained to obtain a plurality of trained sub-Gaussian models.
  • the collected data is preprocessed to obtain a probability distribution used by each application, and then the probability distribution is used as an input to train the preset mixed Gaussian model to obtain a suitable mixed Gaussian model.
  • the mixed Gaussian model can be modeled when the training sample corresponding to the first minute is read; then the training sample corresponding to the second minute is read, the Gaussian model parameters are updated; the training sample corresponding to the third minute is read, and the update is continued. Mixing Gaussian model parameters... and so on, until all training samples are read, update the Gaussian model parameters to get the final trained mixed Gaussian model.
  • the mixed Gaussian model is generally constructed using 3 to 5 sub-Gaussian models.
  • some parameters such as variance ⁇ k , mathematical expectation ⁇ k , weight ⁇ k in the mixed Gaussian model need to be initialized, and the data required for modeling is obtained through these parameters.
  • the variance can be set as large as possible, and the weight (ie ⁇ k ) is as small as possible (such as 0.001).
  • This setup is due to the fact that the initialized Gaussian model is an inaccurate model that needs to constantly shrink its range and update its parameter values to get the most probable Gaussian model.
  • To set the variance larger in order to include as many pixels as possible into a model, find the parameter k, the corresponding weights ⁇ k , and the corresponding parameters ⁇ k and ⁇ k in all the sub-Gaussian models.
  • maximum likelihood estimation may be employed to determine these model parameters such as ⁇ k , ⁇ k , and ⁇ k .
  • the likelihood function of the mixed Gaussian model is:
  • the weighted k sub-Gaussian models are superimposed to obtain a mixed Gaussian model after training.
  • the resulting mixed Gaussian model is composed of four sub-Gaussian models.
  • N mixed Gaussian models namely [P(t
  • the application processing instructions may be triggered when the central processing unit (CPU) of the electronic device occupies a large amount, the running memory resource is occupied, and/or the remaining power of the electronic device is insufficient.
  • the electronic device acquires the application processing instruction, and then determines a background application running in the background according to the application processing instruction, so as to subsequently process the background application.
  • each application corresponds to a uniquely trained mixed Gaussian model. Based on the mixed Gaussian model after training, the probability of use of the application at different times can be accurately estimated.
  • the expression of the preset Bayesian model is the second preset formula, and the second preset formula is as follows:
  • T is the time
  • N is the number of mixed Gaussian models after training
  • T) is the probability that the application running in the foreground is the application i when the sampling period is T
  • a i ) is expressed.
  • the running state of the sample application i is the probability that the sampling period is T in the foreground running
  • a j ) indicates the running state of the application j is the probability that the sampling period is T in the foreground running
  • P(A i ) indicates the application.
  • the application use probability of program i in the historical time period, P(A j ) represents the application use probability of the application j in the historical time period.
  • a i )) corresponding to each application at the target time is estimated.
  • the application use probability (ie, P(A i )) of the target background application i in the historical time period is calculated.
  • P(A i ) can be obtained by the data preprocessing period, which can be obtained by the ratio of the number of uses of the application i in the historical time period to the sum of the usage times of all the sample applications in the historical time period, that is, P(A i )
  • the formula is as follows:
  • S(A i ) is the total number of uses of application i in the historical time period
  • S is the sum of the usage times of all sample applications in the historical time period.
  • the initial usage probability of each application is calculated by using the mixed Gaussian model corresponding to each application; and the calculation formula of P(A i ) is used to calculate The probability of application usage for each sample application over the historical time period.
  • the data obtained above is substituted into the second preset formula (ie, the preset Bayesian model), and the second preset formula is used to calculate the corresponding use probability of the target background application at the target time to improve The accuracy of the probability of use.
  • the second preset formula ie, the preset Bayesian model
  • the probability threshold can be set as a basis for processing the application. That is, the step "Processing the background application according to the probability of use” may include the following process:
  • the preset threshold may be set by a person skilled in the art or a product manufacturer. For example, the predetermined threshold is set to 0.5, then T is opened when the background application probability P of A i in the next period (T
  • the processing method of the application program obtains the usage information of the sample application at each sampling time point in the historical time period, and then determines the time period and the sampling period corresponding to each sampling time point, and then The usage information corresponding to the same sampling period in different time periods is processed to obtain a sample usage probability corresponding to the sample application in each sampling period.
  • the training samples are generated based on the sampling period and the corresponding sample use probability, and are input into a preset mixed Gaussian model for model training, and a new mixed Gaussian model composed of a plurality of trained sub-Gaussian models is obtained.
  • the new mixed Gaussian model and the preset Bayesian model are used to estimate the usage probability of each background application at the target time, and the corresponding background application is processed according to the obtained probability.
  • the solution can reduce the occupation of the final resources of the electronic device, improve the running fluency of the electronic device, and reduce the power consumption of the electronic device.
  • a processing device for an application is further provided.
  • the processing device of the application may be integrated into the electronic device in the form of software or hardware, and the electronic device may specifically include a mobile phone, a tablet computer, and a notebook computer. And other equipment.
  • the processing device 30 of the application may include a receiving module 31, a determining module 32, a receiving module 33, and a processing module 34, where:
  • the obtaining module 31 is configured to acquire usage information of the sample application at each sampling time point in the historical time period;
  • a generating module 32 configured to generate a training sample according to the sampling time point and the usage information
  • the training module 33 is configured to train the preset mixed Gaussian model according to the training sample
  • the processing module 34 is configured to process the background application in the electronic device based on the trained mixed Gaussian model and the preset Bayesian model.
  • the historical time period includes a plurality of time periods, each time period being divided into a plurality of sampling periods.
  • the generating module 32 may include:
  • the first determining submodule 321 is configured to determine a time period and a sampling period corresponding to each sampling time point, wherein the sampling time point corresponds to the sampling period one-to-one in each time period;
  • the information processing sub-module 322 is configured to process the usage information corresponding to the same sampling period in different time periods to obtain a sample usage probability corresponding to the sample application in each sampling period;
  • the generating submodule 323 is configured to generate a training sample based on the sampling period and the corresponding sample usage probability.
  • processing sub-module 322 can include:
  • a determining unit configured to determine whether the usage information meets a preset condition
  • a first determining unit configured to determine, by each sample, a number of sampling time points that the corresponding usage information in the same sampling period meets the preset condition
  • An obtaining unit configured to acquire a total number of sampling time points in which each sample application uses the information to satisfy the preset condition in multiple time periods;
  • the calculating unit is configured to calculate, according to the number of sampling time points and the total number of sampling time points, a sample usage probability corresponding to each sample application period in each sampling period.
  • the usage information is operational state information of the sample application; the determining unit can be used to:
  • the sampling period includes [t 1 , t 2 ... t m ], and the sample usage probability includes [P 1 , P 2 . . . P m ]; referring to FIG. 9 , the training module 33 may include:
  • the input sub-module 331 is configured to input the sampling period and the corresponding sample usage probability into the first formula, where the first preset formula is:
  • a i represents the sample application i
  • t represents the sampling period
  • k represents the number of sub-Gaussian models
  • ⁇ k represents the mathematical expectation
  • ⁇ k represents the variance
  • ⁇ k represents the weight
  • ⁇ k , ⁇ k ) represents The random variable t obeys a normal distribution with a mathematical expectation of ⁇ k and a variance of ⁇ k
  • a i ) represents the probability that the running state of the sample application i is the sampling period of the foreground running time is t;
  • the training sub-module 332 is configured to train the first preset formula based on the input sampling period t and the sample usage probability P to obtain a plurality of trained sub-Gaussian models;
  • the superposition sub-module 333 is configured to superimpose the plurality of trained sub-Gaussian models to obtain the trained mixed Gaussian model.
  • each application corresponds to a uniquely trained mixed Gaussian model; with reference to FIG. 10, the processing module 34 can include:
  • the obtaining submodule 341 is configured to acquire an application processing instruction
  • a second determining submodule 342 configured to determine a background application in the electronic device according to the application processing instruction
  • the calculation sub-module 343 is configured to calculate the usage probability of each background application at the target time by using the second preset formula based on the mixed Gaussian model corresponding to each application, and the second preset formula is:
  • T is the time
  • N is the number of mixed Gaussian models after training
  • T) is the probability that the application running in the foreground is the application i when the sampling period is T
  • a i ) is expressed.
  • the running state of the sample application i is the probability that the sampling period is T in the foreground running
  • a j ) indicates the running state of the application j is the probability that the sampling period is T in the foreground running
  • P(A i ) indicates the application.
  • the application use probability of the program i in the historical time period, P(A j ) represents the application use probability of the application j in the historical time period;
  • the application processing sub-module 344 is configured to process the background application according to the usage probability.
  • the application processing sub-module 344 can include:
  • a second determining unit configured to determine, from the current background application, a target background application whose usage probability is less than a preset threshold
  • the processing device of the application program provided by the embodiment of the present application generates the training sample according to the sampling time point and the usage information by acquiring the usage information of the sample application at each sampling time point in the historical time period, and then according to the training sample pair.
  • the preset mixed Gaussian model is trained, and the background application in the electronic device is processed based on the trained mixed Gaussian model and the preset Bayesian model.
  • the solution can reduce the occupation of the final resources of the electronic device, improve the running fluency of the electronic device, and reduce the power consumption of the electronic device.
  • an electronic device is further provided, and the electronic device may be a device such as a smart phone or a tablet computer.
  • the electronic device 400 includes a processor 401 and a memory 402.
  • the processor 401 is electrically connected to the memory 402.
  • the processor 401 is a control center of the electronic device 400, and connects various parts of the entire electronic device using various interfaces and lines, executes the electronic device by running or loading an application stored in the memory 402, and calling data stored in the memory 402. The various functions and processing of data to provide overall monitoring of the electronic device.
  • the processor 401 in the electronic device 400 loads the instructions corresponding to the process of one or more applications into the memory 402 according to the following steps, and is stored in the memory 402 by the processor 401.
  • the background application in the electronic device is processed based on the trained mixed Gaussian model and the preset Bayesian model.
  • the historical time period includes a plurality of time periods, each time period being divided into a plurality of sampling periods; the processor 401 is further configured to perform the following steps:
  • the usage information corresponding to the same sampling period in different time periods is processed to obtain a sample usage probability corresponding to the sample application in each sampling period;
  • the training samples are generated based on the sampling period and the corresponding sample usage probability.
  • the processor 401 is further configured to perform the following steps:
  • the sample usage probability corresponding to each sample application period is calculated according to the number of sampling time points and the total number of sampling time points.
  • the usage information is operational state information of the sample application
  • the processor 401 is further configured to perform the following steps:
  • the sampling period includes [t 1 , t 2 ... t m ], and the sample usage probability includes [P 1 , P 2 . . . P m ]; the processor 401 is further configured to perform the following steps:
  • the sampling period and the corresponding sample usage probability are input into the first formula, and the first preset formula is:
  • a i represents the sample application i
  • t represents the sampling period
  • k represents the number of sub-Gaussian models
  • ⁇ k represents the mathematical expectation
  • ⁇ k represents the variance
  • ⁇ k represents the weight
  • ⁇ k , ⁇ k ) represents The random variable t obeys a normal distribution with a mathematical expectation of ⁇ k and a variance of ⁇ k
  • a i ) represents the probability that the running state of the sample application i is the sampling period of the foreground running time is t;
  • the first preset formula is trained based on the input sampling period t and the sample usage probability P to obtain a plurality of trained sub-Gaussian models;
  • a plurality of trained sub-Gaussian models are superimposed to obtain a mixed Gaussian model after training.
  • each application corresponds to a uniquely trained mixed Gaussian model; the processor 401 is further configured to perform the following steps:
  • the second preset formula is used to calculate the usage probability of each background application at the target time.
  • the second preset formula is:
  • T is the time
  • N is the number of mixed Gaussian models after training
  • T) is the probability that the application running in the foreground is the application i when the sampling period is T
  • a i ) is expressed.
  • the running state of the sample application i is the probability that the sampling period is T in the foreground running
  • a j ) indicates the running state of the application j is the probability that the sampling period is T in the foreground running
  • P(A i ) indicates the application.
  • the application use probability of the program i in the historical time period, P(A j ) represents the application use probability of the application j in the historical time period;
  • the background application is processed according to the probability of use.
  • the processor 401 is further configured to perform the following steps:
  • Memory 402 can be used to store applications and data.
  • the application stored in memory 402 contains instructions that are executable in the processor.
  • Applications can form various functional modules.
  • the processor 401 executes various functional applications and data processing by running an application stored in the memory 402.
  • the electronic device 400 further includes a display screen 403, a control circuit 404, a radio frequency circuit 405, an input unit 406, an audio circuit 407, a sensor 408, and a power source 409.
  • the processor 401 is electrically connected to the display screen 403, the control circuit 404, the radio frequency circuit 405, the input unit 406, the audio circuit 407, the sensor 408, and the power source 409, respectively.
  • the display screen 403 can be used to display information entered by the user or information provided to the user as well as various graphical user interfaces of the electronic device, which can be composed of images, text, icons, video, and any combination thereof.
  • the control circuit 404 is electrically connected to the display screen 403 for controlling the display screen 403 to display information.
  • the radio frequency circuit 405 is configured to transmit and receive radio frequency signals to establish wireless communication with network devices or other electronic devices through wireless communication, and to transmit and receive signals with network devices or other electronic devices.
  • the input unit 406 can be configured to receive input digits, character information, or user characteristic information (eg, fingerprints), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function controls.
  • the input unit 406 can include a fingerprint identification module.
  • the audio circuit 407 can provide an audio interface between the user and the electronic device through a speaker and a microphone.
  • Sensor 408 is used to collect external environmental information.
  • Sensor 408 can include ambient brightness sensors, acceleration sensors, light sensors, motion sensors, and other sensors.
  • Power source 409 is used to power various components of electronic device 400.
  • the power supply 409 can be logically coupled to the processor 401 through a power management system to enable functions such as managing charging, discharging, and power management through the power management system.
  • the electronic device 400 may further include a camera, a Bluetooth module, and the like, and details are not described herein again.
  • the electronic device provided by the embodiment of the present application generates the training sample according to the sampling time point and the usage information by acquiring the usage information of the sample application at each sampling time point in the historical time period, and then presets according to the training sample.
  • the mixed Gaussian model is trained to process the background application in the electronic device based on the trained mixed Gaussian model.
  • the solution can reduce the occupation of the final resources of the electronic device, improve the running fluency of the electronic device, and reduce the power consumption of the electronic device.
  • a storage medium having stored therein a plurality of instructions adapted to be loaded by a processor to perform a processing method of any of the applications described above.
  • the program may be stored in a computer readable storage medium, and the storage medium may include: Read Only Memory (ROM), Random Access Memory (RAM), disk or optical disk.
  • ROM Read Only Memory
  • RAM Random Access Memory

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  • 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

L'invention concerne un procédé et un appareil de traitement de programme d'application, un support de stockage et un dispositif électronique. Le procédé de traitement comprend : l'acquisition d'informations d'utilisation concernant un programme d'application échantillon à chaque instant d'échantillonnage dans une période de temps historique (101) ; la génération d'un échantillon d'apprentissage selon l'instant d'échantillonnage et les informations d'utilisation (102) ; l'entraînement d'un modèle de mélange gaussien prédéfini selon l'échantillon d'apprentissage (103) ; le traitement d'un programme d'application d'arrière-plan dans un dispositif électronique sur la base du modèle de mélange gaussien entraîné et d'un modèle bayésien prédéfini (104).
PCT/CN2018/102011 2017-09-30 2018-08-23 Procédé et appareil de traitement de programme d'application, support de stockage et dispositif électronique WO2019062405A1 (fr)

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