WO2019085754A1 - Procédé et appareil de nettoyage d'application, et support d'informations et dispositif électronique - Google Patents

Procédé et appareil de nettoyage d'application, et support d'informations et dispositif électronique Download PDF

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Publication number
WO2019085754A1
WO2019085754A1 PCT/CN2018/110632 CN2018110632W WO2019085754A1 WO 2019085754 A1 WO2019085754 A1 WO 2019085754A1 CN 2018110632 W CN2018110632 W CN 2018110632W WO 2019085754 A1 WO2019085754 A1 WO 2019085754A1
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Prior art keywords
ridge regression
error
parameter
feature set
application
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PCT/CN2018/110632
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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 WO2019085754A1 publication Critical patent/WO2019085754A1/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
    • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
    • G06F1/3234—Power saving characterised by the action undertaken
    • G06F1/329—Power saving characterised by the action undertaken by task scheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
    • G06F9/5022—Mechanisms to release resources
    • 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 invention relates to the field of electronic device communication technologies, and in particular, to an application cleaning method, device, storage medium, and electronic device.
  • the embodiment of the present application provides an application cleaning method, a device, a storage medium, and an electronic device, which can improve the running fluency of the electronic device and reduce power consumption.
  • an application cleaning method provided by the embodiment of the present application includes:
  • the ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained;
  • an application cleaning apparatus provided by the embodiment of the present application includes:
  • a training feature acquiring unit configured to acquire a multi-dimensional feature of the application, and obtain a training feature set of the application
  • a training unit configured to train the ridge regression model according to the applied training feature set, and obtain a trained ridge regression model
  • a prediction feature acquiring unit configured to acquire a multi-dimensional feature of the application, to obtain a predicted feature set of the application
  • a prediction unit configured to predict whether the application is cleanable according to the predicted feature set and the trained ridge regression model.
  • a storage medium provided by an embodiment of the present application has a computer program stored thereon, and when the computer program is run on a computer, the computer is caused to perform an application cleaning method according to any embodiment of the present application.
  • an electronic device provided by an embodiment of the present application includes a processor and a memory, where the memory has a computer program, and the processor is used to execute an application provided by any embodiment of the present application by calling the computer program. Cleaning method.
  • FIG. 1 is a schematic diagram of an application scenario of an application cleaning method according to an embodiment of the present disclosure.
  • FIG. 2 is a schematic flowchart of an application cleaning method provided by an embodiment of the present application.
  • FIG. 3 is another schematic flowchart of an application cleaning method provided by an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of an application cleaning device according to an embodiment of the present application.
  • FIG. 5 is another schematic structural diagram of an application cleaning device according to an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
  • FIG. 7 is another schematic structural diagram of an electronic device according to an embodiment of the present application.
  • references to "an embodiment” herein mean that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the present application.
  • the appearances of the phrases in various places in the specification are not necessarily referring to the same embodiments, and are not exclusive or alternative embodiments that are mutually exclusive. Those skilled in the art will understand and implicitly understand that the embodiments described herein can be combined with other embodiments.
  • An embodiment of the present application provides an application cleaning method, including:
  • the ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained;
  • the ridge regression model is trained according to the applied training samples to obtain a trained ridge regression model, including:
  • a trained ridge regression model is obtained according to the target ridge regression parameter and the ridge regression model.
  • acquiring a target ridge regression parameter of the ridge regression model according to the training feature set and the error determination function includes:
  • the ridge regression parameters including: a ridge parameter and a regression parameter;
  • Corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters.
  • acquiring a plurality of sets of ridge regression parameters according to the error determination function comprises:
  • regression parameters corresponding to each preset ridge parameter are obtained, and multiple sets of ridge regression parameters are obtained.
  • the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to the training feature set, the ridge regression parameter, and the error judgment function, including:
  • the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to a sub-error corresponding to each sub-training feature set, including:
  • the corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters, including:
  • a ridge regression parameter corresponding to the minimum error is selected from the plurality of sets of ridge regression parameters as a target ridge regression parameter.
  • obtaining an error of the training feature set for the ridge regression model under the ridge regression parameter according to the average error comprises:
  • the average error is directly taken as the error of the training feature set for the ridge regression model under the ridge regression parameter.
  • predicting whether the application is cleanable based on the predicted feature set and the trained ridge regression model comprises:
  • the embodiment of the present application provides an application cleaning method, which may be a background application cleaning device provided by an embodiment of the present application, or an electronic device integrated with the application cleaning device, where the application cleaning device may adopt hardware. Or software implementation.
  • the electronic device may be a device such as a smart phone, a tablet computer, a palmtop computer, a notebook computer, or a desktop computer.
  • FIG. 1 is a schematic diagram of an application scenario of an application cleaning method according to an embodiment of the present application.
  • the application device is integrated into an electronic device as an example, and the electronic device can acquire a multi-dimensional feature of the application and obtain a training feature set of the application.
  • the ridge regression model is trained to obtain the trained ridge regression model; the applied multi-dimensional features are obtained to obtain the applied prediction feature set; and the predicted feature set and the trained ridge regression model are used to predict whether the application can be used. Clean up.
  • electronic devices can be cleaned up with cleanable applications.
  • the multi-dimensional feature of the application a can be collected in a historical time period (for example, the application a is The duration of the background running, the time information of the application a, etc.), the feature set of the application a is obtained, and the ridge regression model is trained according to the feature set (for example, the time length of the application a running in the background, the time information of the application a running, etc.)
  • the ridge regression model after training; collecting the multi-dimensional features corresponding to the application according to the prediction time (such as t) (for example, the time length of the application a running in the background at time t, the time information of the application a running, etc.), and obtaining the predicted feature set of the application a; It is predicted whether the application a can be cleaned based on the predicted feature set and the trained ridge regression model.
  • the prediction time such as t
  • FIG. 2 is a schematic flowchart of an application cleaning method according to an embodiment of the present application.
  • the specific process of the application cleaning method provided by the embodiment of the present application may be as follows:
  • the application mentioned in the embodiment of the present application may be any application installed on the electronic device, such as an office application, a communication application, a game application, a shopping application, and the like.
  • the application may include an application running in the foreground, that is, a foreground application, and may also include an application running in the background, that is, a background application.
  • the application cleanup request may be received, the application to be cleaned is determined according to the application cleanup request, and then the multidimensional feature of the application is obtained, and the applied training feature set is obtained.
  • the multi-dimensional feature of the application may be obtained from the feature database, wherein the multi-dimensional feature may be a multi-dimensional feature collected by the historical time, that is, a historical multi-dimensional feature.
  • the feature database stores various features of the application at historical time.
  • the training feature set may include multi-dimensional features of the application, that is, multiple features of the application.
  • the multi-dimensional feature of the application has a dimension of a certain length, and the parameters in each dimension correspond to a feature information that represents the application, that is, the multi-dimensional feature is composed of multiple features.
  • the plurality of features may include feature information related to the application itself, for example, the duration of the application cutting into the background; the duration of the electronic device when the application is cut into the background; the number of times the application enters the foreground; the time when the application is in the foreground; the application is in the background The time, the way the application enters the background, such as being switched by the home button (home button), being switched back by the return button, being switched in by other applications, etc.; the type of application, including the first level (common application), the second level (other applications) )Wait.
  • the multi-dimensional feature information may further include related feature information of the electronic device where the application is located, for example, the time when the electronic device is off, the time of the bright screen, the current power, the wireless network connection status of the electronic device, whether the electronic device is in the charging state, or the like.
  • the applied training samples include multi-dimensional features of the application.
  • the multi-dimensional feature may be a plurality of features acquired at a preset frequency during a historical time period.
  • the historical time period may be, for example, the past 7 days or 10 days;
  • the preset frequency may be, for example, collected every 10 minutes and collected every half hour. It can be understood that the multi-dimensional feature data of the once collected application constitutes a training feature set.
  • the feature information that is not directly represented by the value in the multi-dimensional feature information of the application may be quantized by a specific value, for example, the feature information of the wireless network connection state of the electronic device may be used.
  • the value 1 indicates the normal state, and the value 0 indicates the abnormal state (or vice versa); for example, for the characteristic information of whether the electronic device is in the charging state, the value 1 indicates the state of charge, and the value 0 indicates the uncharged state ( The opposite is also possible).
  • the ridge regression model can be a machine learning algorithm, ridge regression (Tikhonov regularization) algorithm, also known as ridge regression is a biased estimation regression method dedicated to collinear data analysis, which is essentially an improved
  • the least squares estimation method by abandoning the unbiasedness of the least squares method, obtains a more realistic and reliable regression method with the regression coefficient at the cost of losing part of the information and reducing the accuracy, and fits the ill-conditioned data more strongly than the least squares method. .
  • the ridge regression model can be used to predict whether the application can be cleaned, wherein the output of the ridge regression model includes cleanable or non-cleanable.
  • the ridge regression model it is necessary to use the existing feature information to train the model to improve the accuracy of the prediction.
  • the process of training the ridge regression model is a process of solving the ridge regression parameter of the ridge regression model.
  • the ridge regression parameter required by the ridge regression model may be calculated first, and then the ridge is determined based on the ridge regression parameter.
  • the regression model is set.
  • the step "training the ridge regression model according to the applied training samples to obtain the post-training ridge regression model” may include:
  • the target ridge regression parameter of the ridge regression model is obtained according to the training feature set and the error judgment function, and the target ridge regression parameter includes a ridge parameter and a regression parameter;
  • the trained ridge regression model is obtained according to the target ridge regression parameter and the ridge regression model.
  • the ridge regression parameter may include a ridge parameter and a regression parameter
  • Ridge Regression is to add a regular term on the basis of the square error, and the balance between the variance and the deviation can be achieved by determining the value of ⁇ : with ⁇
  • the ridge parameter can normalize the parameter ⁇ , which can be the model parameter w of the ridge regression model to be solved.
  • the error judgment function is a loss function of the ridge regression model, and is used to calculate an error between the output value and the true value of the ridge regression model on the sample.
  • the error judgment function of the ridge regression model may include the following functions:
  • ⁇ is the ridge parameter, ie the regularization parameter, x is the characteristic of the sample, w is the regression parameter of the ridge regression model, and n is the dimension of the feature.
  • the error judgment function of the ridge regression model may be deformed to obtain a regression parameter acquisition function, and then the ridge regression parameter is obtained based on the regression parameter acquisition function.
  • the error judgment function can be derived to obtain a regression parameter acquisition function, and then the ridge regression parameter is obtained based on the regression parameter acquisition function and the training feature set.
  • the error judgment function of the ridge regression model may include the following functions:
  • the error judgment function can be derived to obtain a function:
  • the regression parameter can be calculated based on the formula and the training feature set. Finally, the ridge parameter ⁇ and the corresponding regression parameters are obtained.
  • the step "acquiring the target ridge regression parameter of the ridge regression model according to the training feature set and the error judgment function" may include:
  • the ridge regression parameters include: ridge parameters and regression parameters;
  • the ridge regression parameter and the error judgment function the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained, and the error corresponding to each set of ridge regression parameters is obtained;
  • the corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters
  • the trained ridge regression model is obtained according to the target ridge regression parameter and the ridge regression model.
  • the error corresponding to the ridge regression parameter is the ridge regression model under the ridge regression parameter, and the error between the predicted value and the true value obtained by inputting the training sample set.
  • m can be a positive integer greater than 2, which can be set according to actual needs, for example, 20, 30, 40...
  • the ridge regression parameter And an error judgment function to obtain regression parameters in the set
  • the errors corresponding to each set of ridge regression parameters are obtained, such as F1, F2, ..., Fk...Fm. Error F from the ridge regression parameters based on the regression parameters of each set of ridges Select the corresponding target ridge regression parameter
  • the error judgment function of the ridge regression model may be deformed to obtain a regression parameter acquisition function, and then the regression parameter is obtained based on the regression parameter acquisition function and the plurality of preset ridge parameters ⁇ .
  • Get multiple sets of ridge regression parameters For example, the error judgment function may be derived to obtain a regression parameter acquisition function, and then the ridge regression parameter is obtained based on the regression parameter acquisition function and the training feature set.
  • the error judgment function of the ridge regression model may include the following functions:
  • the error judgment function can be derived to obtain a function:
  • the regression parameter can be calculated based on the formula and a plurality of preset ridge parameters ⁇ . Finally, the ridge parameter ⁇ and the corresponding regression parameters are obtained.
  • the training feature set may be divided into a plurality of sub-training feature sets, and each sub-training feature set is obtained under the ridge regression parameter.
  • the error f of the ridge regression model is then obtained based on the error of the sub-training feature set for the ridge regression model under the ridge regression parameter to obtain the error F of the entire training feature set for the ridge regression model under the ridge regression parameter.
  • the step "acquiring the error of the training feature set to the ridge regression model under the ridge regression parameter according to the training feature set, the ridge regression parameter, and the error judgment function" may include:
  • the sub-training feature set the ridge regression parameter and the error judgment function, the sub-errors of the sub-training set for the ridge regression model under the ridge regression parameter are obtained, and the sub-error corresponding to each sub-training feature set is obtained;
  • the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained.
  • the number of sub-training feature set partitions can be set according to actual needs, such as 10, 20, and so on.
  • the sub-training feature set includes the same number of features, that is, the training feature set is equally divided into a plurality of sub-training feature sets.
  • the training feature set D may be divided into M sub-training feature sets to obtain sub-training feature sets D1, D2, . . . DM; wherein M is a positive integer greater than one.
  • M is a positive integer greater than one.
  • the error of the training feature set in the m-group ridge regression parameter for the ridge regression model can be calculated, and the errors F1, F2, ... Fm are obtained.
  • the embodiment of the present application can obtain the error of the entire training feature set for the ridge regression model based on the sub-error, and the obtaining manner can be various.
  • the average value of the sub-errors may be calculated, and then the error of the entire training feature set for the ridge regression model is obtained based on the average value.
  • the step “acquiring the error of the training feature set to the ridge regression model under the ridge regression parameter according to the sub-error corresponding to each sub-training feature set” may include:
  • the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to the average error.
  • the average error can be used as an error of the training feature set for the ridge regression model under the ridge regression parameter.
  • the ridge regression parameter is For example, in the calculation of the sub-errors f11, D2 for the ridge regression model under the ridges in the ridge regression parameters Sub-errors f12, ... DM in the ridge regression model
  • the sub-error f1M for the ridge regression model is based on the ridge regression parameter for each sub-training feature set.
  • the error F1 for the ridge regression model is For example, in the calculation of the sub-errors f11, D2 for the ridge regression model under the ridges in the ridge regression parameters.
  • the sub-error f1M for the ridge regression model is based on the ridge regression parameter for each sub-training feature set.
  • the average error f' (f11+f12+...+
  • the ridge regression parameter corresponding to the smallest error may be selected as the target ridge regression parameter of the ridge regression model, that is, finally parameter.
  • the ridge regression parameter corresponding to Fk can be selected.
  • the target ridge regression parameter as a ridge regression model.
  • the selection process of the target ridge regression parameters that is, the training process of the ridge regression model, will be introduced as follows: the ridge regression parameter is 20 groups and the number of sub-training feature sets is 10.
  • step A the following error judgment formula respectively calculates the different error values of each sub-feature set of 10 equal parts for the ridge regression, and obtains 10 different error values:
  • the 20 sets of characteristic errors obtained from (5) take the minimum value corresponding to And ⁇ value
  • the The ⁇ value is the ridge regression fitting to obtain the ridge regression parameters, that is, the parameters finally selected by the ridge regression model.
  • the ridge regression parameters corresponding to each application can be calculated by the above steps (1)-(6).
  • the multidimensional features of the application can be collected as prediction samples based on the predicted time.
  • the prediction time can be set according to requirements, such as the current time.
  • a multi-dimensional feature of an application can be acquired as a prediction sample at a predicted time point.
  • the multi-dimensional features collected in steps 201 and 203 are the same type of features, for example, the length of time when the application is cut into the background; the time when the application is cut into the background, the duration of the electronic device; the number of times the application enters the foreground; The time at the front desk; the way the app enters the background.
  • the probability that the application can be cleaned can be calculated based on the ridge regression model and the predicted feature set, and when the probability is greater than a certain threshold, the application can be cleaned and the like.
  • the embodiment of the present application obtains the multi-dimensional features of the application, and obtains the applied training feature set; the ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained; the multi-dimensional feature of the application is obtained, and the application is obtained.
  • the embodiment of the present application can make the cleaning of the corresponding application more personalized and intelligent.
  • the application of the cleanup prediction based on the ridge regression model can improve the accuracy of the user behavior prediction, thereby improving the accuracy of the cleanup.
  • multiple sets of ridge regression parameters can be calculated during the training of the model, and the ridge regression parameter with the lowest error of the feature error is used as the final parameter of the ridge regression model, and the ridge regression model can be further improved. The accuracy of the forecast for application cleanup.
  • the application cleaning method may include:
  • the multi-dimensional feature of the application is obtained from the feature database, wherein the multi-dimensional feature can be a multi-dimensional feature collected by the historical time, that is, a historical multi-dimensional feature.
  • the feature database stores various features of the application at historical time.
  • the training feature set may include multi-dimensional features of the application, that is, multiple features of the application.
  • the multi-dimensional feature of the application has a dimension of a certain length, and the parameters in each dimension correspond to a feature information that represents the application, that is, the multi-dimensional feature is composed of multiple features.
  • the plurality of features may include feature information related to the application itself, for example, the duration of the application cutting into the background; the duration of the electronic device when the application is cut into the background; the number of times the application enters the foreground; the time when the application is in the foreground; the application is in the background The time, the way the application enters the background, such as being switched by the home button (home button), being switched back by the return button, being switched in by other applications, etc.; the type of application, including the first level (common application), the second level (other applications) )Wait.
  • the multi-dimensional feature information may further include related feature information of the electronic device where the application is located, for example, the time when the electronic device is off, the time of the bright screen, the current power, the wireless network connection status of the electronic device, whether the electronic device is in the charging state, or the like.
  • a specific training feature set may be as follows, including feature information of multiple dimensions (30 dimensions). It should be noted that the feature information shown below is only an example. In practice, a feature included in a training feature set is included. The number of the information may be more than the number of the information shown below, or may be less than the number of the information shown below. The specific feature information may be different from the feature information shown below, and is not specifically limited herein.
  • the number of times the APP enters the foreground in the day (the rest day is divided by the working day and the rest day). For example, if the current forecasting time is the working day, the feature usage value is the average number of working days in the foreground for each working day.
  • the background APP is followed by the number of times the current foreground APP is opened, regardless of the workday rest day statistics;
  • the background APP is closely followed by the number of times the current foreground APP is opened, and is counted as a workday rest day;
  • the way in which the target APP is switched is divided into a home key switch, a recent key switch, and another APP switch;
  • the screen of the mobile phone is off;
  • the current screen is on and off
  • the feature indicates that the target app is being used every day from 8:00 to 12:00. Length of time used;
  • the current front-end APP enters the background to the target APP and enters the foreground according to the average interval of daily statistics;
  • the current foreground APP goes to the background until the target APP enters the foreground and the average screen is extinguished by daily statistics;
  • the target APP stays in the background for the first bin of the histogram (the proportion of times corresponding to 0-5 minutes);
  • the target APP stays in the background for the first bin of the histogram (5-10 minutes corresponds to the proportion of times);
  • the target APP stays in the background for the first bin of the histogram (10-15 minutes corresponds to the proportion of times);
  • the target APP stays in the background for the first bin of the histogram (15-20 minutes corresponds to the proportion of times);
  • the target APP stays in the background for the first bin of the histogram (15-20 minutes corresponds to the proportion of times);
  • the target APP stays in the background for the first bin of the histogram (25-30 minutes corresponds to the proportion of times);
  • the target APP stays in the background for the first bin of the histogram (the proportion of the corresponding number of times after 30 minutes);
  • the ridge regression model can be a machine learning algorithm, ridge regression (Tikhonov regularization) algorithm, also known as ridge regression is a biased estimation regression method dedicated to collinear data analysis, which is essentially an improved
  • the least squares estimation method by abandoning the unbiasedness of the least squares method, obtains a more realistic and reliable regression method with the regression coefficient at the cost of losing part of the information and reducing the accuracy, and fits the ill-conditioned data more strongly than the least squares method. .
  • the error judgment function of the ridge regression model may include the following functions:
  • ⁇ is the ridge parameter, ie the regularization parameter, x is the characteristic of the sample, w is the regression parameter of the ridge regression model, and n is the dimension of the feature.
  • the error judgment function can be derived to obtain a function:
  • the ridge regression parameters include the ridge parameter ⁇ and the corresponding regression parameters
  • the number of sub-training feature set partitions can be set according to actual needs, such as 10, 20, and so on.
  • the sub-training feature set includes the same number of features, that is, the training feature set is equally divided into a plurality of sub-training feature sets.
  • the training feature set D may be divided into M sub-training feature sets to obtain sub-training feature sets D1, D2, . . . DM; wherein M is a positive integer greater than one.
  • M is a positive integer greater than one.
  • the sub-errors f11 and D2 for the ridge regression model are in the ridge regression parameters.
  • Sub-errors f12, ... DM in the ridge regression model For the sub-error f1M of the ridge regression model, the sub-training feature set is obtained in the ridge regression parameter.
  • each sub-training set for the ridge regression model under the ridge regression parameter obtains the error of the training feature set for the ridge regression model under the ridge regression parameter, and repeat steps 305 and 306 to obtain the training characteristics of each set of ridge regression parameters.
  • the error for the ridge regression model obtains the error of the training feature set for the ridge regression model under the ridge regression parameter, and repeat steps 305 and 306 to obtain the training characteristics of each set of ridge regression parameters.
  • the average error of the sub-training feature set is obtained; and the error of the training feature set for the ridge regression model under the ridge regression parameter is obtained according to the average error.
  • the average error can be used as an error of the training feature set for the ridge regression model under the ridge regression parameter.
  • the ridge regression parameter is For example, in the calculation of the sub-errors f11, D2 for the ridge regression model under the ridges in the ridge regression parameters Sub-errors f12, ... DM in the ridge regression model
  • the sub-error f1M for the ridge regression model is based on the ridge regression parameter for each sub-training feature set.
  • the error F1 for the ridge regression model is For example, in the calculation of the sub-errors f11, D2 for the ridge regression model under the ridges in the ridge regression parameters.
  • the sub-error f1M for the ridge regression model is based on the ridge regression parameter for each sub-training feature set.
  • the average error f' (f11+f12+...+
  • the ridge regression parameter corresponding to Fk can be selected.
  • the target ridge regression parameter as a ridge regression model.
  • the above steps 301-307 are repeated to obtain the ridge regression parameters corresponding to each application.
  • the value of the regression parameter w in the ridge regression model is updated.
  • repeating the above steps 301-308 can obtain a post-training ridge regression model corresponding to each application.
  • the prediction time can be set according to requirements, such as the current time.
  • a multi-dimensional feature of an application can be acquired as a prediction sample at a predicted time point.
  • the multi-dimensional feature collected in the step is the same type of feature as the feature acquired in step 301, that is, the predicted feature set and the feature set included in the training feature set are the same, for example, the time length of the application is cut into the background.
  • the time when the application is cut into the background the duration of the electronic device; the number of times the application enters the foreground; the time the application is in the foreground; and the way the application enters the background.
  • the probability that the application can be cleaned can be calculated based on the ridge regression model and the predicted feature set, and when the probability is greater than a certain threshold, the application can be cleaned and the like.
  • the post-training ridge regression model of each background application can be obtained through the above steps 301-308; then, based on the post-training ridge regression model of each background application, it is predicted whether multiple applications running in the background can be cleaned up, As shown in Table 1, it is determined that the application A1 and the application A3 running in the background can be cleaned, while the state in which the application A2 is running in the background is maintained.
  • the embodiment of the present application obtains the multi-dimensional features of the application, and obtains the applied training feature set; the ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained; the multi-dimensional feature of the application is obtained, and the application is obtained.
  • the embodiment of the present application can make the cleaning of the corresponding application more personalized and intelligent.
  • the application of the cleanup prediction based on the ridge regression model can improve the accuracy of the user behavior prediction, thereby improving the accuracy of the cleanup.
  • multiple sets of ridge regression parameters can be calculated during the training of the model, and the ridge regression parameter with the lowest error of the feature error is used as the final parameter of the ridge regression model, and the ridge regression model can be further improved. The accuracy of the forecast for application cleanup.
  • the embodiment of the present application further provides an application cleaning device, including:
  • a training feature acquiring unit configured to acquire a multi-dimensional feature of the application, and obtain a training feature set of the application
  • a training unit configured to train the ridge regression model according to the applied training feature set, and obtain a trained ridge regression model
  • a prediction feature acquiring unit configured to acquire a multi-dimensional feature of the application, to obtain a predicted feature set of the application
  • a prediction unit configured to predict whether the application is cleanable according to the predicted feature set and the trained ridge regression model.
  • the training unit includes:
  • a parameter obtaining subunit configured to acquire a target ridge regression parameter of the ridge regression model according to the training feature set and the error judgment function, where the target ridge regression parameter includes a ridge parameter and a regression parameter;
  • a training subunit configured to obtain a trained ridge regression model according to the target ridge regression parameter and the ridge regression model.
  • the parameter acquisition subunit is configured to:
  • the ridge regression parameters including: a ridge parameter and a regression parameter;
  • Corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters.
  • the parameter acquisition subunit is specifically configured to:
  • regression parameters corresponding to each preset ridge parameter are obtained, and multiple sets of ridge regression parameters are obtained.
  • the parameter acquisition subunit is specifically configured to:
  • the parameter acquisition subunit is specifically configured to:
  • the ridge regression parameters including: a ridge parameter and a regression parameter;
  • a ridge regression parameter corresponding to the minimum error is selected from the plurality of sets of ridge regression parameters as a target ridge regression parameter.
  • the parameter acquisition subunit is specifically configured to: directly use the average error as an error of the training feature set for the ridge regression model under the ridge regression parameter.
  • the predicting unit is configured to:
  • FIG. 4 is a schematic structural diagram of an application cleaning apparatus according to an embodiment of the present application.
  • the application cleaning device is applied to an electronic device, and the application cleaning device includes a training feature acquisition unit 401, a training unit 402, a prediction feature acquisition unit 403, and a prediction unit 404, as follows:
  • the training feature acquiring unit 401 is configured to acquire a multi-dimensional feature of the application, and obtain a training feature set of the application;
  • the training unit 402 is configured to train the ridge regression model according to the applied training feature set to obtain a trained ridge regression model
  • a prediction feature acquisition unit 403 configured to acquire a multi-dimensional feature of the application, to obtain a prediction feature set of the application;
  • the prediction unit 404 is configured to predict whether the application is cleanable according to the predicted feature set and the trained ridge regression model.
  • the training unit 402 includes:
  • a parameter obtaining sub-unit 4022 configured to acquire a target ridge regression parameter of the ridge regression model according to the training feature set and the error determination function, where the target ridge regression parameter includes a ridge parameter and a regression parameter;
  • the training sub-unit 4023 is configured to obtain a trained ridge regression model according to the target ridge regression parameter and the ridge regression model.
  • the parameter acquisition subunit 4022 can be used to:
  • the ridge regression parameters including: a ridge parameter and a regression parameter;
  • Corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters.
  • the parameter obtaining subunit 4022 may be specifically configured to:
  • regression parameters corresponding to each preset ridge parameter are obtained, and multiple sets of ridge regression parameters are obtained.
  • the parameter obtaining subunit 4022 may be specifically configured to:
  • the parameter obtaining subunit 4022 may be specifically configured to:
  • the parameter obtaining subunit 4022 may be specifically configured to:
  • a ridge regression parameter corresponding to the minimum error is selected from the plurality of sets of ridge regression parameters as a target ridge regression parameter.
  • the parameter obtaining subunit 4022 may be specifically configured to:
  • the parameter obtaining subunit 4022 may be specifically configured to:
  • the ridge regression parameters including: a ridge parameter and a regression parameter;
  • a ridge regression parameter corresponding to the minimum error is selected from the plurality of sets of ridge regression parameters as a target ridge regression parameter.
  • the parameter obtaining sub-unit 4022 may be specifically configured to: directly use the average error as an error of the training feature set for the ridge regression model under the ridge regression parameter.
  • the predicting unit 404 is configured to:
  • the steps performed by each unit in the application cleaning device may refer to the method steps described in the foregoing method embodiments.
  • the application cleaning device can be integrated in an electronic device such as a mobile phone, a tablet computer, or the like.
  • the foregoing various units may be implemented as an independent entity, and may be implemented in any combination, and may be implemented as the same entity or a plurality of entities.
  • the foregoing units refer to the foregoing embodiments, and details are not described herein again.
  • module unit
  • module may be taken to mean a software object that is executed on the computing system.
  • the different components, modules, engines, and services described herein can be considered as implementation objects on the computing system.
  • the apparatus and method described herein may be implemented in software, and may of course be implemented in hardware, all of which are within the scope of the present application.
  • the application cleaning device can obtain the multi-dimensional feature of the application by the training feature acquiring unit 401, and obtain the applied training feature set; the training unit 402 trains the ridge regression model according to the applied training feature set, and obtains the training.
  • the ridge regression model; the multi-dimensional feature of the application is obtained by the prediction feature acquisition unit 403 to obtain the applied prediction feature set; the prediction unit 404 predicts whether the application can be cleaned according to the predicted feature set and the trained ridge regression model;
  • the application is cleaned; the solution can automatically clean the application, improve the running fluency of the electronic device, reduce power consumption and save resources.
  • the electronic device 500 includes a processor 501 and a memory 502.
  • the processor 501 is electrically connected to the memory 502.
  • the processor 500 is a control center of the electronic device 500 that connects various portions of the entire electronic device using various interfaces and lines, by running or loading a computer program stored in the memory 502, and recalling data stored in the memory 502, The various functions of the electronic device 500 are performed and the data is processed to perform overall monitoring of the electronic device 500.
  • the memory 502 can be used to store software programs and modules, and the processor 501 executes various functional applications and data processing by running computer programs and modules stored in the memory 502.
  • the memory 502 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, a computer program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be stored according to Data created by the use of electronic devices, etc.
  • memory 502 can include high speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, memory 502 can also include a memory controller to provide processor 501 access to memory 502.
  • the processor 501 in the electronic device 500 loads the instructions corresponding to the process of one or more computer programs into the memory 502 according to the following steps, and is stored in the memory 502 by the processor 501.
  • the computer program in which to implement various functions, as follows:
  • the ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained;
  • the processor 501 may specifically perform the following steps:
  • a trained ridge regression model is obtained according to the target ridge regression parameter and the ridge regression model.
  • the processor 501 when acquiring the target ridge regression parameter of the ridge regression model according to the training feature set and the error judgment function, the processor 501 may specifically perform the following steps:
  • the ridge regression parameters including: a ridge parameter and a regression parameter;
  • Corresponding target ridge regression parameters are selected from the plurality of sets of ridge regression parameters according to the error corresponding to each set of ridge regression parameters.
  • the processor 501 may specifically perform the following steps:
  • regression parameters corresponding to each preset ridge parameter are obtained, and multiple sets of ridge regression parameters are obtained.
  • the processor 501 can specifically perform the following steps:
  • the processor 501 may specifically perform the following steps. :
  • the processor 501 may specifically perform the following steps:
  • a ridge regression parameter corresponding to the minimum error is selected from the plurality of sets of ridge regression parameters as a target ridge regression parameter.
  • the processor 501 when acquiring an error of the training feature set for the ridge regression model under the ridge regression parameter according to the average error, the processor 501 may specifically perform the following steps:
  • the average error is directly taken as the error of the training feature set for the ridge regression model under the ridge regression parameter.
  • the processor 501 may specifically perform the following steps:
  • the electronic device in the embodiment of the present application acquires the multi-dimensional feature of the application, obtains the applied training feature set, and trains the ridge regression model according to the applied training feature set to obtain the trained ridge regression model;
  • Feature obtain the applied feature set; according to the predicted feature set and the trained ridge regression model, predict whether the application can be cleaned; to clean up the cleanable application; the solution can automatically clean the application and improve the electronic device Smooth operation and reduced power consumption.
  • the electronic device 500 may further include: a display 503, a radio frequency circuit 504, an audio circuit 505, and a power source 506.
  • the display 503, the radio frequency circuit 504, the audio circuit 505, and the power source 506 are electrically connected to the processor 501, respectively.
  • the display 503 can be used to display information entered by a user or information provided to a user, as well as various graphical user interfaces, which can be composed of graphics, text, icons, video, and any combination thereof.
  • the display 503 can include a display panel.
  • the display panel can be configured in the form of a liquid crystal display (LCD) or an organic light-emitting diode (OLED).
  • LCD liquid crystal display
  • OLED organic light-emitting diode
  • the radio frequency circuit 504 can be used to transmit and receive radio frequency signals to establish wireless communication with a network device or other electronic device through wireless communication, and to transmit and receive signals with a network device or other electronic device.
  • the audio circuit 505 can be used to provide an audio interface between a user and an electronic device through a speaker or a microphone.
  • the power source 506 can be used to power various components of the electronic device 500.
  • the power source 506 can be logically coupled to the processor 501 through a power management system to enable functions such as managing charging, discharging, and power management through the power management system.
  • the electronic device 500 may further include a camera, a Bluetooth module, and the like, and details are not described herein again.
  • the embodiment of the present application further provides a storage medium, where the storage medium stores a computer program, and when the computer program runs on a computer, causes the computer to execute an application cleaning method in any of the above embodiments, such as: obtaining Applying the multi-dimensional characteristics, the applied training feature set is obtained; the ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained; the applied multi-dimensional features are obtained, and the applied predicted feature set is obtained; according to the predicted feature set And the trained ridge regression model to predict whether the application can be cleaned up.
  • an application cleaning method in any of the above embodiments, such as: obtaining Applying the multi-dimensional characteristics, the applied training feature set is obtained; the ridge regression model is trained according to the applied training feature set, and the trained ridge regression model is obtained; the applied multi-dimensional features are obtained, and the applied predicted feature set is obtained; according to the predicted feature set And the trained ridge regression model to predict whether the application can be cleaned up.
  • the storage medium may be a magnetic disk, an optical disk, a read only memory (ROM), or a random access memory (RAM).
  • ROM read only memory
  • RAM random access memory
  • the computer program may be stored in a computer readable storage medium, such as in a memory of the electronic device, and executed by at least one processor in the electronic device, and may include, for example, an application cleaning method during execution.
  • the storage medium may be a magnetic disk, an optical disk, a read only memory, a random access memory, or the like.
  • each functional module may be integrated into one processing chip, or each module may exist physically separately, or two or more modules may be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or in the form of software functional modules.
  • the integrated module if implemented in the form of a software functional module and sold or used as a standalone product, may also be stored in a computer readable storage medium, such as a read only memory, a magnetic disk or an optical disk, etc. .

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Abstract

La présente invention concerne un procédé et un appareil de nettoyage d'application, ainsi qu'un support d'informations et un dispositif électronique. Le procédé selon les modes de réalisation de la présente invention consiste : à acquérir des caractéristiques multidimensionnelles d'une application afin d'obtenir un ensemble de caractéristiques d'apprentissage de l'application; à entraîner un modèle de régression d'arête en fonction de l'ensemble de caractéristiques d'apprentissage afin d'obtenir un modèle de régression d'arête entraîné; à acquérir les caractéristiques multidimensionnelles de l'application afin d'obtenir un ensemble de caractéristiques prédictives de l'application; et à prédire si l'application peut être nettoyée en fonction de l'ensemble de caractéristiques prédictives et du modèle de régression d'arête entraîné.
PCT/CN2018/110632 2017-10-31 2018-10-17 Procédé et appareil de nettoyage d'application, et support d'informations et dispositif électronique WO2019085754A1 (fr)

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