CN107943535B - Application cleaning method and device, storage medium and electronic equipment - Google Patents

Application cleaning method and device, storage medium and electronic equipment Download PDF

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CN107943535B
CN107943535B CN201711122517.8A CN201711122517A CN107943535B CN 107943535 B CN107943535 B CN 107943535B CN 201711122517 A CN201711122517 A CN 201711122517A CN 107943535 B CN107943535 B CN 107943535B
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historical
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cleaning
subset
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CN107943535A (en
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梁昆
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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    • 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
    • G06F9/44594—Unloading

Abstract

The embodiment of the application discloses an application cleaning method, an application cleaning device, a storage medium and electronic equipment, wherein a target application set used by current cleaning application is determined, the target application set is a historical application set corresponding to historical application cleaning events, and the target application set comprises a cleanable application subset and an unclonable application subset; when the historical user score corresponding to the historical application cleaning event is smaller than the preset score, selecting corresponding target application from the target application set; acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm; and adjusting the subset in the target application set according to the probability to obtain an adjusted application set. The scheme can realize automatic cleaning of the application, improve the operation smoothness of the electronic equipment and reduce the power consumption.

Description

Application cleaning method and device, storage medium and electronic equipment
Technical Field
The present application relates to the field of communications technologies, and in particular, to an application cleaning method, an application cleaning apparatus, a storage medium, and an electronic device.
Background
At present, a plurality of applications are generally run simultaneously on electronic equipment such as a smart phone, wherein one application runs in a foreground and the other applications run in a background. If the application running in the background is not cleaned for a long time, the available memory of the electronic equipment is reduced, the occupancy rate of a Central Processing Unit (CPU) is too high, and the problems of slow running speed, blockage, too high power consumption and the like of the electronic equipment are caused. Therefore, it is necessary to provide a method to solve the above problems.
Disclosure of Invention
The embodiment of the application cleaning method and device, the storage medium and the electronic equipment can improve the operation smoothness of the electronic equipment and reduce power consumption.
In a first aspect, an embodiment of the present application provides an application cleaning method, including:
determining a target application set used by a current cleaning application, wherein the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set comprises a cleanable application subset and a non-cleanable application subset;
when the historical user score corresponding to the historical application cleaning event is smaller than a preset score, selecting corresponding target application from the target application set;
acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm;
adjusting the subset in the target application set according to the probability to obtain an adjusted application set;
and performing application cleaning according to the adjusted application set.
In a second aspect, an embodiment of the present application provides an application cleaning apparatus, including:
the device comprises a determining unit, a judging unit and a processing unit, wherein the determining unit is used for determining a target application set used by the current cleaning application, the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set comprises a cleanable application subset and a non-cleanable application subset;
the selecting unit is used for selecting corresponding target application from the target application set when the historical user score corresponding to the historical application cleaning event is smaller than a preset score;
the probability obtaining unit is used for obtaining the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm;
the adjusting unit is used for adjusting the subset in the target application set according to the probability to obtain an adjusted application set;
and the cleaning unit is used for cleaning the application according to the adjusted application set.
In a third aspect, a storage medium is provided in this application, where a computer program is stored, and when the computer program runs on a computer, the computer is caused to execute an application cleaning method as provided in any embodiment of this application.
In a fourth aspect, an electronic device provided in an embodiment of the present application includes a processor and a memory, where the memory has a computer program, and the processor is configured to execute the application cleaning method provided in any embodiment of the present application by calling the computer program.
Determining a target application set used by a current cleaning application, wherein the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set comprises a cleanable application subset and an unclonable application subset; when the historical user score corresponding to the historical application cleaning event is smaller than the preset score, selecting corresponding target application from the target application set; acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm; adjusting the subset in the target application set according to the probability to obtain an adjusted application set; and performing application cleaning according to the adjusted application set. The scheme can realize automatic cleaning of the application, improve the operation smoothness of the electronic equipment and reduce the power consumption.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of an application scenario of an application cleaning method according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of an application cleaning method according to an embodiment of the present application.
Fig. 3 is another schematic flow chart of an application cleaning method according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an application cleaning apparatus according to an embodiment of the present application.
Fig. 5 is another schematic structural diagram of an application cleaning apparatus according to an embodiment of the present application.
Fig. 6 is another schematic structural diagram of an application cleaning apparatus according to an embodiment of the present application.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 8 is another schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Referring to the drawings, wherein like reference numbers refer to like elements, the principles of the present application are illustrated as being implemented in a suitable computing environment. The following description is based on illustrated embodiments of the application and should not be taken as limiting the application with respect to other embodiments that are not detailed herein.
In the description that follows, specific embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise indicated. Accordingly, these steps and operations will be referred to, several times, as being performed by a computer, the computer performing operations involving a processing unit of the computer in electronic signals representing data in a structured form. This operation transforms the data or maintains it at locations in the computer's memory system, which may be reconfigured or otherwise altered in a manner well known to those skilled in the art. The data maintains a data structure that is a physical location of the memory that has particular characteristics defined by the data format. However, while the principles of the application have been described in language specific to above, it is not intended to be limited to the specific form set forth herein, and it will be recognized by those of ordinary skill in the art that various of the steps and operations described below may be implemented in hardware.
The term module, as used herein, may be considered a software object executing on the computing system. The various components, modules, engines, and services described herein may be viewed as objects implemented on the computing system. The apparatus and method described herein may be implemented in software, but may also be implemented in hardware, and are within the scope of the present application.
The terms "first", "second", and "third", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules listed, but rather, some embodiments may include other steps or modules not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
An execution main body of the application cleaning method may be the application cleaning device provided in the embodiment of the present application, or an electronic device integrated with the application cleaning device, where the application cleaning device may be implemented in a hardware or software manner. The electronic device may be a smart phone, a tablet computer, a palm computer, a notebook computer, or a desktop computer.
Referring to fig. 1, fig. 1 is a schematic view of an application scenario of an application cleaning method according to an embodiment of the present application, taking an example that an application cleaning device is integrated in an electronic device, where the electronic device may determine a target application set used by a current cleaning application, where the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set includes a cleanable application subset and a unclonable application subset; when the historical user score corresponding to the historical application cleaning event is smaller than the preset score, selecting corresponding target application from the target application set; acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm; adjusting the subset in the target application set according to the probability to obtain an adjusted application set; and performing application cleaning according to the adjusted application set.
Specifically, for example, as shown in fig. 1, taking cleaning an application program (such as a mailbox application, a game application, and the like) running in a background as an example, a target application set M used by a current cleaning application may be determined, where the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set includes a cleanable application subset a and a non-cleanable application subset B; when the historical user score s corresponding to the historical application cleaning event is smaller than a preset score, selecting a corresponding target application a from the target application set A; and (3) acquiring the cleanable probability p of the target application a according to the multidimensional characteristic information of the target application a (such as the running time of the application a in the background, the running time information of the application a and the like) and a Bayesian algorithm, adjusting A and/or B based on the probability, and cleaning the application according to the adjusted set M.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating 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 application cleaning method can be as follows:
201. determining a target application set used by a current cleaning application, wherein the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set comprises a cleanable application subset and a non-cleanable application subset.
The application to which the present application relates may be an application installed on the electronic device, such as an office application, a communication application, a game application, a shopping application, and the like. The applications may include foreground applications, background applications, and/or the like.
The historical application cleaning event may be an event that the application cleaning is performed at a historical time, such as an operation of performing the application cleaning at the historical time.
When or after a historical application clean-up event occurs, i.e., when or after an application is cleaned up at a historical time, there will be a corresponding set of historical applications. For example, when the historical application event is triggered, application cleaning can be performed based on the historical application set, that is, the application set used by the historical application cleaning event. For another example, after the historical application event is triggered, a corresponding historical application set is generated when the application cleaning is completed; i.e. the set of applications generated after the historical application time trigger.
The historical application set corresponding to the historical application cleaning event can comprise a historical cleanable application subset and/or a historical unclonable application subset. The subset of history cleanable applications includes applications cleanable at a historical time, and the subset of history unclonable applications includes applications unclonable at the historical time.
In the embodiment of the application, after each historical application cleaning event is triggered or occurs, the user can score the event according to the satisfaction degree of the historical application cleaning event to obtain the historical user score of each historical application cleaning event.
The user score of the application cleaning event may represent the degree of satisfaction of the user with the application cleaning event, for example, the higher the user score is, the higher the degree of satisfaction is, and the lower the degree of satisfaction is, the lower the user score is.
In one embodiment, the user score may be a percentile, i.e., a full score of 100, with higher scores representing higher user satisfaction. In an embodiment, the user score may also select a class for the gear or level, e.g., "unsatisfied," "general," "satisfied," etc. When the user's satisfaction with the application cleaning event is not high or low at some point, the median value of the score values may be taken, for example, 50 points for the percentile system and "normal" for the gear selection class.
There are various ways to determine the target application set, for example, the target application set may be selected from a historical application set of historical application cleaning events. In an embodiment, the selection may be based on user comments of historical application cleaning events. Determining the set of target applications currently used by the cleaning application as step "may include:
obtaining historical user scores of a plurality of candidate historical application cleaning events;
selecting corresponding reference historical application cleaning events from the plurality of candidate historical application cleaning events according to the historical user scores of the candidate historical application cleaning events;
and determining a target application set used by the current cleaning application according to the historical application set corresponding to the reference historical application cleaning event.
For example, the candidate historical application cleaning event with the highest historical user score may be selected as the reference historical application cleaning event.
For example, the user score for candidate historical application cleaning event 1 is 60, the user score for candidate historical application cleaning event 2 is 70, and the user score for candidate historical application cleaning event 3 is 90; at this time, the candidate historical application cleaning event 3 may be selected as the reference historical application cleaning event.
In an embodiment, after the reference historical application cleaning event is obtained, a historical application set corresponding to the reference historical application cleaning event may be used as a target application set used by the current cleaning application.
In an embodiment, the reference historical app clean event may also be selected based on the historical time of the candidate historical app clean event. For example, a candidate historical application cleaning event with a historical time closest to the current time may be selected as the reference historical application cleaning event.
202. And when the historical user score corresponding to the historical application cleaning event is smaller than the preset score, selecting corresponding target application from the target application set.
For example, the target application set M includes a cleanable application subset a { application a1, application a2 … … application an }, and a uncleanable application subset B { application B1, application B2 … … application bn }. At this time, when the score is smaller than the preset score, the corresponding target application ai may be selected from the cleanable application subset a, or the corresponding target application bj may be selected from the unpermittable application subset B.
The preset score can be set according to actual requirements, such as the satisfaction degree of a user on application cleaning.
When the historical user score corresponding to the historical application cleaning event is smaller than the preset score, the user is indicated to be unsatisfied or not completely satisfied with the target application set used for cleaning currently, and at the moment, the target application set needs to be adjusted.
According to the method and the device, when the score is smaller than the preset score, the corresponding application can be selected from the target application set to be cleaned and judged, and then the target application set is adjusted.
The target application is an application in a target application set, and the application may be an application causing a user score to be lower than a preset score, and may be called a problem application. If the application which the user does not want to clean is located in the cleanable application subset or the application which the user wants to clean is located in the unclonable application subset, the application which the user does not want to clean may be cleaned, and the application which the user wants to clean is not cleaned, and at this time, the user is not satisfied with the historical application cleaning event itself or the generated result, so the score is low.
In practical applications, a general problem application frequently appears in an application set of low-grade historical application cleaning events, for example, an application appears in a cleanable application subset or an uncleanable application subset of the low-grade historical application cleaning events every time, and then the application may be a problem application, and the placed subsets are not right.
Therefore, in an embodiment, cleaning events with scores less than a preset score may be acquired, and then, applications that appear in the application set of these events at the same time are selected as target applications. For example, the step "selecting a corresponding target application from the target application set" includes:
selecting a target historical application cleaning event of which the historical user score is smaller than a preset score from a plurality of candidate historical application cleaning events;
acquiring a target historical application set corresponding to a target historical application cleaning event;
and selecting applications appearing in the target application set and the target historical application set at the same time as target applications.
For example, the user score for candidate historical app clear event 1 is 30, the user score for candidate historical app clear event 2 is 20, and the user score for candidate historical app clear event 3 is 60; when the preset score is 50 and the target application set is the set of the candidate historical application cleaning events 3, the applications appearing in the historical application sets corresponding to the candidate historical application cleaning events 1, 2 and 3 at the same time can be selected as the target applications.
In one embodiment, the target historical application set may include a target historical cleanable application subset, such as a list of historical cleanable applications, at which point applications that are present in both the cleanable application subset and the target historical cleanable application subset may be selected as target applications.
For example, the user score for candidate historical app clear event 1 is 30, the user score for candidate historical app clear event 2 is 20, and the user score for candidate historical app clear event 3 is 60; when the preset score is 50 and the target application set is the set of candidate historical application cleaning events 3, applications appearing in the historical cleanable application subset (e.g., the historical cleanable application list) corresponding to the candidate historical application cleaning events 1, 2, and 3 at the same time may be selected as the target applications.
In one embodiment, the target historical application set may include a target historical unclonable application subset, such as a list of historical keep-alive applications, at which point applications that are present in both the unclonable application subset and the target historical unclonable application subset may be selected as target applications.
For example, the user score for candidate historical app clear event 1 is 30, the user score for candidate historical app clear event 2 is 20, and the user score for candidate historical app clear event 3 is 60; when the preset score is 50 and the target application set is the set of the candidate historical application cleaning events 3, the applications appearing in the historical unclonable application subset (such as the historical keep-alive application list) corresponding to the candidate historical application cleaning events 1, 2 and 3 at the same time can be selected as the target applications.
203. And acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm.
The applied multidimensional feature has dimensions with a certain length, and the parameter on each dimension corresponds to one feature information for representing the application, namely the multidimensional feature information is composed of a plurality of features. The plurality of feature information may include application-related feature information, such as: applying the duration of the cut-in to the background; the screen-off duration of the electronic equipment is prolonged when the application is switched into the background; the number of times the application enters the foreground; the time the application is in the foreground; the mode that the application enters the background, such as being switched into by a home key, being switched into by a return key, being switched into by other applications, and the like; types of applications, including primary (common applications), secondary (other applications); the histogram information of the background stay time is applied, for example, the first bin (the number of times corresponding to 0-5 minutes) of the histogram of the background stay time is applied.
The multidimensional feature information may also include relevant feature information of the electronic device where the application is located, such as: the screen-off time, the screen-on time and the current electric quantity of the electronic equipment, the wireless network connection state of the electronic equipment, whether the electronic equipment is in a charging state or not and the like.
For example, a plurality of characteristic information of the application may be collected according to a preset frequency in a historical time period. Historical time periods, such as the past 7 days, 10 days; the preset frequency may be, for example, one acquisition every 10 minutes, one acquisition every half hour.
In one embodiment, in order to facilitate application shutdown, feature information that is not directly represented by a numerical value in the multidimensional feature information of the application may be quantized by a specific numerical value, for example, the feature information of a wireless network connection state of an electronic device may be represented by a numerical value 1 to indicate a normal state, and may be represented by a numerical value 0 to indicate an abnormal state (or vice versa); for another example, the characteristic information of whether the electronic device is in the charging state may be represented by a value 1, and a value 0 to represent the non-charging state (or vice versa).
The electronic device can collect a plurality of applied feature information in each time period and store the feature information in the feature database, so that the embodiment of the application can extract the plurality of applied feature information from the feature database.
The Bayesian algorithm is a Bayesian classification algorithm, and is a machine learning algorithm. The Bayesian classification algorithm is also a statistical classification method, and is an algorithm for classification by using probability statistical knowledge. In many cases, naive Bayes (A), (B)
Figure BDA0001467678400000094
Bayes, NB) classification algorithm can be compared with decision tree and neural network classification algorithm, the algorithm can be applied to a large database, and the method is simple, high in classification accuracy and fast in speed.
The bayesian algorithms may include a Naive Bayes algorithm, a Gaussian Naive Bayes (Gaussian Naive Bayes) algorithm, a polynomial Naive Bayes (multinominal Naive Bayes) algorithm, a bernoulli Naive Bayes (bernoulli Naive Bayes) algorithm, and the like.
Taking a naive bayes algorithm as an example, the bayes model can be:
Figure BDA0001467678400000091
wherein m is the number of the characteristic information, q1,q2…qmSample vector of a priori condition, qiJ is the predicted tagged result (including cleanable or uncleanable) of the target application for the sample vector corresponding to the ith feature information.
To simplify the calculation, assume q1,q2…qmAre independent of each other, then
Figure BDA0001467678400000092
Thus, a naive bayes classifier model is obtained:
JMAX＝arg max P(J|q1,q2…qm)＝arg maxP(q1|J)P(q2|J)…P(qm|J)，
wherein J may represent J1 or J2, and the probability value of each feature information is a statistical probability of the occurrence times, that is, the above formula:
Figure BDA0001467678400000093
where j1 is a first predetermined value (indicating that the application is cleanable or uncleanable) and j2 is a second predetermined value (indicating that the application is uncleanable or cleanable).
And forming the multi-dimensional characteristic information of the target application into a sample vector, and then calculating the cleanable probability of the target application based on a naive Bayes algorithm.
204. And adjusting the subset in the target application set according to the probability to obtain an adjusted application set.
Adjusting the subset within the target application set may include deleting, adding, or transferring applications within the subset (e.g., cleanable application subset, and/or unclonable application subset), among others.
In one embodiment, the step of "adjusting the subset of the target application set according to the probability" may include:
when the target application belongs to the cleanable application subset and the probability is smaller than the preset probability, moving the target application from the cleanable application subset to the uncleanable application subset;
and when the target application belongs to the uncleanable application subset and the probability is not less than the preset probability, moving the target application from the uncleanable application subset to the cleanable application subset according to the probability.
For example, the target application set M includes a cleanable application subset a { application a1, application a2 … … application an }, and an unsolicited application subset B { application B1, application B2 … … application bn }. At this time, when the score is smaller than the preset score, the corresponding target application ai can be selected from the cleanable application subset a; based on the multidimensional feature information of the target application ai and the Bayesian algorithm, the probability P (ai) that the target application ai can be cleaned is calculated to be less than P', which indicates that the user does not want to clean the target application ai, but the target application ai is located in the cleanable application subset A, and at this time, the target application ai can be moved from the cleanable application subset A to the uncleanable application subset B, wherein B comprises { application B1, application B2 … …, bn and ai }.
For another example, a corresponding target application bj is selected from the unpurifiable application subset B, and based on the multidimensional feature information of the target application bj and the bayesian algorithm, a probability P (bj) that the target application bj can be cleaned is calculated to be greater than P', which indicates that the user wishes to clean the target application bj, but the target application bj is located in the unpurifiable application subset a, at this time, the target application bj can be moved from the unpurifiable application subset B to the unpurifiable application subset a, where a includes { application a1, application a2 … … application an, bj }.
205. And performing application cleaning according to the adjusted application set.
For example, in one embodiment, applications located within a cleanable subset of applications of the adjusted set of applications may be cleaned.
For another example, in an embodiment, applications that are not located within the unclonable application subset of the adjusted application set may be cleaned. In addition, applications located within the unclonable application subset may also be kept alive.
In an embodiment, when the historical user score corresponding to the historical application cleaning event is not less than the preset score, the application cleaning is performed according to the target application set. No adjustments to the set of applications are required.
As can be seen from the above, the target application set used by the current cleaning application is determined in the embodiment of the present application, where the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set includes a cleanable application subset and an unclonable application subset; when the historical user score corresponding to the historical application cleaning event is smaller than the preset score, selecting corresponding target application from the target application set; acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm; adjusting the subset in the target application set according to the probability to obtain an adjusted application set; and performing application cleaning according to the adjusted application set. The scheme realizes automatic cleaning of the application, improves the operation smoothness of the electronic equipment, reduces power consumption and saves resources.
Further, when an application set of historical application cleaning events with lower scores is used, the cleanable probability of the application can be calculated based on multidimensional characteristic information of the application and a Bayesian algorithm, and the target application set is adjusted based on the cleanable probability; the accuracy of application cleaning can be improved, and the real requirements of users are met.
Furthermore, as the multi-dimensional characteristic information of the application comprises a plurality of characteristic information reflecting the behavior habit of the user using the application, the cleaning of the corresponding application can be more personalized and intelligent.
The cleaning method of the present application will be further described below on the basis of the method described in the above embodiment. Referring to fig. 3, the application cleaning method may include:
301. determining a target application set used by a current cleaning application, wherein the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set comprises a cleanable application subset and a non-cleanable application subset.
The application to which the present application relates may be an application installed on the electronic device, such as an office application, a communication application, a game application, a shopping application, and the like. The applications may include foreground applications, background applications, and/or the like.
The historical application cleaning event may be an event that the application cleaning is performed at a historical time, such as an operation of performing the application cleaning at the historical time.
When or after a historical application clean-up event occurs, i.e., when or after an application is cleaned up at a historical time, there will be a corresponding set of historical applications. For example, when the historical application event is triggered, application cleaning can be performed based on the historical application set, that is, the application set used by the historical application cleaning event. For another example, after the historical application event is triggered, a corresponding historical application set is generated when the application cleaning is completed; i.e. the set of applications generated after the historical application time trigger.
For example, historical user scores of a plurality of candidate historical application cleaning events may be obtained, and corresponding reference historical application cleaning events are selected from the plurality of candidate historical application cleaning events according to the historical user scores of the candidate historical application cleaning events; and determining a target application set used by the current cleaning application according to the historical application set corresponding to the reference historical application cleaning event.
302. And judging whether the historical user score corresponding to the historical application cleaning event is smaller than a preset score, if so, executing the step 303, and if not, executing the step.
The preset score can be set according to actual requirements, such as the satisfaction degree of a user on application cleaning. Such as 70 points, etc.
303. And selecting a target historical application cleaning event of which the historical user score is smaller than the preset score from the plurality of candidate historical application cleaning events.
The target application is an application in a target application set, and the application may be an application causing a user score to be lower than a preset score, and may be called a problem application. If the application which the user does not want to clean is located in the cleanable application subset or the application which the user wants to clean is located in the unclonable application subset, the application which the user does not want to clean may be cleaned, and the application which the user wants to clean is not cleaned, and at this time, the user is not satisfied with the historical application cleaning event itself or the generated result, so the score is low.
Therefore, it is possible to acquire cleaning events having a score smaller than a preset score, and then select an application appearing in an application set of these events at the same time as a target application.
304. And acquiring a target historical application set corresponding to the target historical application cleaning event.
The target history application set comprises a target history cleanable application subset and a target history unclonable application subset.
305. And selecting applications appearing in the target application set and the target historical application set at the same time as target applications.
In one embodiment, applications that are simultaneously present in the cleanable application subset and the target history cleanable application subset may be selected as target applications.
For example, the user score for candidate historical app clear event 1 is 30, the user score for candidate historical app clear event 2 is 20, and the user score for candidate historical app clear event 3 is 60; when the preset score is 50 and the target application set is the set of candidate historical application cleaning events 3, applications appearing in the historical cleanable application subset (e.g., the historical cleanable application list) corresponding to the candidate historical application cleaning events 1, 2, and 3 at the same time may be selected as the target applications.
In one embodiment, an application that is simultaneously present in the unclonable application subset and the target history unclonable application subset is selected as the target application.
For example, the user score for candidate historical app clear event 1 is 30, the user score for candidate historical app clear event 2 is 20, and the user score for candidate historical app clear event 3 is 60; when the preset score is 50 and the target application set is the set of the candidate historical application cleaning events 3, the applications appearing in the historical unclonable application subset (such as the historical keep-alive application list) corresponding to the candidate historical application cleaning events 1, 2 and 3 at the same time can be selected as the target applications.
306. And acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm.
The applied multidimensional feature has dimensions with a certain length, and the parameter on each dimension corresponds to one feature information for representing the application, namely the multidimensional feature information is composed of a plurality of features. The plurality of feature information may include application-related feature information, such as: applying the duration of the cut-in to the background; the screen-off duration of the electronic equipment is prolonged when the application is switched into the background; the number of times the application enters the foreground; the time the application is in the foreground; the mode that the application enters the background, such as being switched into by a home key, being switched into by a return key, being switched into by other applications, and the like; types of applications, including primary (common applications), secondary (other applications); the histogram information of the background stay time is applied, for example, the first bin (the number of times corresponding to 0-5 minutes) of the histogram of the background stay time is applied.
The plurality of feature information may further include related feature information of the electronic device where the application is located, for example: the screen-off time, the screen-on time and the current electric quantity of the electronic equipment, the wireless network connection state of the electronic equipment, whether the electronic equipment is in a charging state or not and the like.
For example, the applied plurality of feature information may include the following 30-dimensional features, and it should be noted that the feature information shown below is only an example, and the number of the feature information actually included may be greater than or less than the number of the feature information shown below, and the specific feature information may be different from that shown below, and is not limited in detail here. The 30-dimensional features include:
the last time the APP switches into the background to the current time;
the last time the APP switches into the background to the current time;
the number of times the APP enters the foreground in one day (counted per day);
the number of times that the APP enters the foreground in one day (the rest days are counted separately according to the working days and the rest days), for example, if the current predicted time is the working day, the feature usage value is the average usage number of the foreground in each working day counted by the working days;
the time of day (counted daily) of APP in the foreground;
the background APP is opened for times following the current foreground APP, and the times are obtained by statistics on the rest days without dividing into working days;
the background APP is opened for times following the current foreground APP, and statistics is carried out according to working days and rest days;
the switching modes of the target APP are divided into home key switching, receiver key switching and other APP switching;
target APP primary type (common application);
target APP secondary type (other applications);
the screen off time of the mobile phone screen;
the screen lightening time of the mobile phone screen;
the current screen is in a bright or dark state;
the current amount of power;
a current wifi state;
the last time that App switches into the background to the present time;
the last time the APP is used in the foreground;
the last time the APP is used in the foreground;
the last time the APP is used in the foreground;
if 6 time periods are divided in one day, each time period is 4 hours, the current prediction time point is 8:30 in the morning, and the current prediction time point is in the 3 rd period, the characteristic represents the time length of the target app used in the time period of 8: 00-12: 00 every day;
counting the average interval time of each day from the current foreground APP entering the background to the target APP entering the foreground;
counting average screen-off time per day from the current foreground APP entering the background to the target APP entering the foreground;
target APP in the background residence time histogram first bin (0-5 minutes corresponding times ratio);
target APP in the background residence time histogram first bin (5-10 minutes corresponding times ratio);
target APP in the first bin of the background residence time histogram (10-15 minutes corresponding times in proportion);
target APP in the first bin of the background residence time histogram (15-20 minutes corresponding times in proportion);
target APP in the first bin of the background residence time histogram (15-20 minutes corresponding times in proportion);
target APP in the first bin of the background residence time histogram (25-30 minutes corresponding times in proportion);
target APP in the first bin of the background dwell time histogram (corresponding number of times after 30 minutes is a ratio);
whether there is charging currently.
The Bayesian algorithm is a Bayesian classification algorithm, and is a machine learning algorithm.
Taking a naive bayes algorithm as an example, the bayes model can be:
Figure BDA0001467678400000151
wherein m is the number of the characteristic information, q1,q2…qmSample vector of a priori condition, qiJ is the predicted tagged result (including cleanable or uncleanable) of the target application for the sample vector corresponding to the ith feature information.
To simplify the calculation, assume q1,q2…qmAre independent of each other, then
Figure BDA0001467678400000152
Thus, a naive bayes classifier model is obtained:
JMAX＝arg max P(J|q1,q2…qm)＝arg maxP(q1|J)P(q2|J)…P(qm|J)，
wherein J may represent J1 or J2, and the probability value of each feature information is a statistical probability of the occurrence times, that is, the above formula:
Figure BDA0001467678400000153
where j1 is a first predetermined value (indicating that the application is cleanable or uncleanable) and j2 is a second predetermined value (indicating that the application is uncleanable or cleanable).
And forming the multi-dimensional characteristic information of the target application into a sample vector, and then calculating the cleanable probability of the target application based on a naive Bayes algorithm.
307. And adjusting the subset in the target application set according to the probability to obtain an adjusted application set.
When the target application belongs to the cleanable application subset and the probability is smaller than the preset probability, moving the target application from the cleanable application subset to the uncleanable application subset;
and when the target application belongs to the uncleanable application subset and the probability is not less than the preset probability, moving the target application from the uncleanable application subset to the cleanable application subset according to the probability.
For example, the target application set M includes a cleanable application subset a { application a1, application a2 … … application an }, and an unsolicited application subset B { application B1, application B2 … … application bn }. At this time, when the score is smaller than the preset score, the corresponding target application ai can be selected from the cleanable application subset a; based on the multidimensional feature information of the target application ai and the Bayesian algorithm, the probability P (ai) that the target application ai can be cleaned is calculated to be less than P', which indicates that the user does not want to clean the target application ai, but the target application ai is located in the cleanable application subset A, and at this time, the target application ai can be moved from the cleanable application subset A to the uncleanable application subset B, wherein B comprises { application B1, application B2 … …, bn and ai }.
308. And performing application cleaning according to the adjusted application set.
For example, applications that are located within a cleanable subset of applications of the adjusted set of applications may be cleaned.
For another example, applications that are not located within the unclonable application subset of the adjusted application set may be cleaned.
309. And performing application cleaning according to the target application set.
For example, applications located within a cleanable subset of applications of the target set of applications may be cleaned.
In a specific example, the following cleanable application list can be obtained by using the method of the embodiment of the present application, and the applications a1, a2 and a3 can be cleaned according to the list shown in table 1.
Applications of Operation of
Application a1 Can be cleaned
Application a2 Can be cleaned
Application a3 Can be cleaned
TABLE 1
As can be seen from the above, the target application set used by the current cleaning application is determined in the embodiment of the present application, where the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set includes a cleanable application subset and an unclonable application subset; when the historical user score corresponding to the historical application cleaning event is smaller than the preset score, selecting corresponding target application from the target application set; acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm; adjusting the subset in the target application set according to the probability to obtain an adjusted application set; and performing application cleaning according to the adjusted application set. The scheme realizes automatic cleaning of the application, improves the operation smoothness of the electronic equipment, reduces power consumption and saves resources.
Further, when an application set of historical application cleaning events with lower scores is used, the cleanable probability of the application can be calculated based on multidimensional characteristic information of the application and a Bayesian algorithm, and the target application set is adjusted based on the cleanable probability; the accuracy of application cleaning can be improved, and the real requirements of users are met.
Furthermore, as the multi-dimensional characteristic information of the application comprises a plurality of characteristic information reflecting the behavior habit of the user using the application, the cleaning of the corresponding application can be more personalized and intelligent.
In one embodiment, an application cleaning device is also provided. Referring to fig. 4, fig. 4 is a schematic structural diagram of an application cleaning apparatus according to an embodiment of the present application. The application cleaning apparatus is applied to an electronic device, and includes a determining unit 401, a selecting unit 402, a probability obtaining unit 403, a second adjusting unit 404, and a cleaning unit 405, as follows:
a determining unit 401, configured to determine a target application set used by a current cleaning application, where the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set includes a cleanable application subset and a non-cleanable application subset;
a selecting unit 402, configured to select a corresponding target application from the target application set when a historical user score corresponding to the historical application cleaning event is smaller than a preset score;
a probability obtaining unit 403, configured to obtain a cleanable probability of the target application according to the multidimensional feature information of the target application and a bayesian algorithm;
an adjusting unit 404, configured to adjust a subset in the target application set according to the probability, to obtain an adjusted application set;
a cleaning unit 405, configured to perform application cleaning according to the adjusted application set.
In an embodiment, the adjusting unit 404 may be configured to:
when the target application belongs to the cleanable application subset and the probability is smaller than a preset probability, moving the target application from the cleanable application subset to the uncleanable application subset;
and when the target application belongs to the uncleanable application subset and the probability is not less than a preset probability, moving the target application from the uncleanable application subset to the cleanable application subset according to the probability.
In an embodiment, referring to fig. 5, the selecting unit 402 may include:
an event selecting subunit 4021, configured to select a target historical application cleaning event of which the historical user score is smaller than the preset score from the multiple candidate historical application cleaning events;
a set obtaining subunit 4022, configured to obtain a target history application set corresponding to the target history application cleaning event;
an application selecting subunit 4023, configured to select an application appearing in both the target application set and the target historical application set as a target application.
In one embodiment, the set of target historical applications includes: the target history can clear the application subset; the application selection subunit 4023 may be configured to: and selecting applications appearing in the cleanable application subset and the target history cleanable application subset at the same time as target applications.
In one embodiment, the set of target historical applications includes: a target history unclonable application subset; the application selection subunit 4023 may be configured to: and selecting applications appearing in the unclonable application subset and the target history unclonable application subset at the same time as target applications.
In an embodiment, referring to fig. 6, the determining unit 401 may include:
the score obtaining sub-unit 4011 is configured to obtain historical user scores of a plurality of candidate historical application cleaning events;
a reference event selecting subunit 4012, configured to select, according to the historical user scores of the candidate historical application cleaning events, corresponding reference historical application cleaning events from the multiple candidate historical application cleaning events;
the set determining subunit 4013 is configured to determine, according to the historical application set corresponding to the reference historical application cleaning event, a target application set used by the current cleaning application.
In an embodiment, the cleaning unit 405 may further be configured to: and when the historical user score corresponding to the historical application cleaning event is not less than the preset score, performing application cleaning according to the target application set.
The steps performed by each unit in the application cleaning device may refer to the method steps described in the above method embodiments. The application cleaning device can be integrated in electronic equipment such as a mobile phone, a tablet computer and the like.
In a specific implementation, the above units may be implemented as independent entities, or may be combined arbitrarily to be implemented as the same or several entities, and the specific implementation of the above units may refer to the foregoing embodiments, which are not described herein again.
As can be seen from the above, in the application cleaning apparatus in this embodiment, the determining unit 401 may determine, by the application cleaning apparatus in this embodiment, a target application set used by a current cleaning application, where the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set includes a cleanable application subset and an unclonable application subset; when the historical user score corresponding to the historical application cleaning event is smaller than the preset score, the selecting unit 402 selects the corresponding target application from the target application set; the probability obtaining unit 403 obtains the cleanable probability of the target application according to the multidimensional feature information of the target application and the bayesian algorithm; adjusting the subset in the target application set by an adjusting unit 404 according to the probability to obtain an adjusted application set; application cleaning is performed by the cleaning unit 405 according to the adjusted application set. The scheme can realize automatic cleaning of the application, improve the operation smoothness of the electronic equipment and reduce the power consumption.
The embodiment of the application also provides the electronic equipment. Referring to fig. 7, an 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, connects various parts of the whole electronic device by using various interfaces and lines, executes various functions of the electronic device 500 and processes data by running or loading a computer program stored in the memory 502 and calling data stored in the memory 502, thereby performing overall monitoring of the electronic device 500.
The memory 502 may be used to store software programs and modules, and the processor 501 executes various functional applications and data processing by operating the computer programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, a computer program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 502 may include high speed random access memory, and may 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, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.
In this embodiment, the processor 501 in the electronic device 500 loads instructions corresponding to one or more processes of the computer program into the memory 502, and the processor 501 runs the computer program stored in the memory 502, so as to implement various functions as follows:
determining a target application set used by a current cleaning application, wherein the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set comprises a cleanable application subset and a non-cleanable application subset;
when the historical user score corresponding to the historical application cleaning event is smaller than a preset score, selecting corresponding target application from the target application set;
acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm;
adjusting the subset in the target application set according to the probability to obtain an adjusted application set;
and performing application cleaning according to the adjusted application set.
In some embodiments, when adjusting the subset of the target application set according to the probability, the processor 501 may specifically perform the following steps:
when the target application belongs to the cleanable application subset and the probability is smaller than a preset probability, moving the target application from the cleanable application subset to the uncleanable application subset;
and when the target application belongs to the uncleanable application subset and the probability is not less than a preset probability, moving the target application from the uncleanable application subset to the cleanable application subset according to the probability.
In some embodiments, when selecting a corresponding target application from the target application set, the processor 501 may specifically perform the following steps:
selecting a target historical application cleaning event of which the historical user score is smaller than the preset score from a plurality of candidate historical application cleaning events;
acquiring a target historical application set corresponding to the target historical application cleaning event;
and selecting applications appearing in the target application set and the target historical application set at the same time as target applications.
In some embodiments, the set of target historical applications comprises: the target history can clear the application subset; when selecting applications appearing in the target application set and the target historical application set at the same time as target applications, the processor 501 may further specifically perform the following steps:
and selecting applications appearing in the cleanable application subset and the target history cleanable application subset at the same time as target applications.
In some embodiments, the set of target historical applications comprises: a target history unclonable application subset; when selecting applications appearing in the target application set and the target historical application set at the same time as target applications, the processor 501 may further specifically perform the following steps:
and selecting applications appearing in the unclonable application subset and the target history unclonable application subset at the same time as target applications.
In some embodiments, in determining the target application set used by the current cleaning application, the processor 501 may specifically perform the following steps:
obtaining historical user scores of a plurality of candidate historical application cleaning events;
selecting corresponding reference historical application cleaning events from the plurality of candidate historical application cleaning events according to the historical user scores of the candidate historical application cleaning events;
and determining a target application set used by the current cleaning application according to the historical application set corresponding to the reference historical application cleaning event.
In some embodiments, the processor 501 may further specifically perform the following steps:
and when the historical user score corresponding to the historical application cleaning event is not less than the preset score, performing application cleaning according to the target application set.
As can be seen from the above, the target application set used by the current cleaning application is determined in the embodiment of the present application, where the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set includes a cleanable application subset and an unclonable application subset; when the historical user score corresponding to the historical application cleaning event is smaller than the preset score, selecting corresponding target application from the target application set; acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm; adjusting the subset in the target application set according to the probability to obtain an adjusted application set; and performing application cleaning according to the adjusted application set. The scheme can realize automatic cleaning of the application, improve the operation smoothness of the electronic equipment and reduce the power consumption.
Referring to fig. 8, in some embodiments, the electronic device 500 may further include: a display 503, radio frequency circuitry 504, audio circuitry 505, and a power supply 506. The display 503, the rf circuit 504, the audio circuit 505, and the power source 506 are electrically connected to the processor 501.
The display 503 may be used to display information entered by or provided to the user as well as various graphical user interfaces, which may be made up of graphics, text, icons, video, and any combination thereof. The display 503 may include a display panel, and in some embodiments, the display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The rf circuit 504 may be used for transceiving rf signals to establish wireless communication with a network device or other electronic devices via wireless communication, and for transceiving signals with the network device or other electronic devices.
The audio circuit 505 may be used to provide an audio interface between a user and an electronic device through a speaker, microphone.
The power source 506 may be used to power various components of the electronic device 500. In some embodiments, power supply 506 may be logically coupled to processor 501 through a power management system, such that functions of managing charging, discharging, and power consumption are performed through the power management system.
Although not shown in fig. 8, the electronic device 500 may further include a camera, a bluetooth module, and the like, which are not described in detail herein.
An 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, the computer is caused to execute the application cleaning method in any one of the above embodiments, for example: determining a target application set used by the current cleaning application, wherein the target application set is a historical application set corresponding to a historical application cleaning event, and the target application set comprises a cleanable application subset and a non-cleanable application subset; when the historical user score corresponding to the historical application cleaning event is smaller than the preset score, selecting corresponding target application from the target application set; acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm; adjusting the subset in the target application set according to the probability to obtain an adjusted application set; and performing application cleaning according to the adjusted application set.
In the embodiment of the present application, the storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It should be noted that, for the application cleaning method in the embodiment of the present application, it can be understood by a person skilled in the art that all or part of the process of implementing the application cleaning method in the embodiment of the present application can be completed by controlling the relevant hardware through a computer program, where the computer program can be stored in a computer readable storage medium, such as a memory of an electronic device, and executed by at least one processor in the electronic device, and during the execution process, the process of implementing the embodiment of the application cleaning method can be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.
For the application cleaning device in the embodiment of the present application, each functional module may be integrated into one processing chip, or each module may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium, such as a read-only memory, a magnetic or optical disk, or the like.
The application cleaning method, the application cleaning device, the storage medium and the electronic device provided by the embodiments of the present application are described in detail above, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims (12)

1. An application cleaning method, comprising:
determining a target application set used by a current cleaning application, wherein the target application set is a historical application set corresponding to a historical application cleaning event, the historical application set corresponding to the historical application cleaning event refers to a historical application set corresponding to the historical application cleaning event each time, and the target application set comprises a cleanable application subset and an unclonable application subset; the historical application set corresponding to the historical application cleaning event comprises a historical cleanable application subset and a historical unclonable application subset; the history cleanable application subset comprises applications cleanable at a history time, and the history unclonable application subset comprises applications unclonable at the history time;
when the historical user score corresponding to the historical application cleaning event is smaller than a preset score, selecting corresponding target application from the target application set;
acquiring the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm;
adjusting the subset in the target application set according to the probability to obtain an adjusted application set, including: when the target application belongs to the cleanable application subset and the probability is smaller than a preset probability, moving the target application from the cleanable application subset to the uncleanable application subset; when the target application belongs to the uncleanable application subset and the probability is not smaller than a preset probability, moving the target application from the uncleanable application subset to the cleanable application subset according to the probability;
and performing application cleaning according to the adjusted application set.
2. The application cleaning method of claim 1, wherein selecting a corresponding target application from the set of target applications comprises:
selecting a target historical application cleaning event of which the historical user score is smaller than the preset score from a plurality of candidate historical application cleaning events;
acquiring a target historical application set corresponding to the target historical application cleaning event;
and selecting applications appearing in the target application set and the target historical application set at the same time as target applications.
3. The application cleansing method of claim 2, wherein the set of target historical applications comprises: the target history can clear the application subset;
selecting applications appearing in the target application set and the target historical application set at the same time as target applications, wherein the selecting of the applications includes:
and selecting applications appearing in the cleanable application subset and the target history cleanable application subset at the same time as target applications.
4. The application cleansing method of claim 2, wherein the set of target historical applications comprises: a target history unclonable application subset;
selecting applications appearing in the target application set and the target historical application set at the same time as target applications, wherein the selecting of the applications includes:
and selecting applications appearing in the unclonable application subset and the target history unclonable application subset at the same time as target applications.
5. The application scrubbing method of claim 1, wherein determining a set of target applications used by a current scrubbing application comprises:
obtaining historical user scores of a plurality of candidate historical application cleaning events;
selecting corresponding reference historical application cleaning events from the plurality of candidate historical application cleaning events according to the historical user scores of the candidate historical application cleaning events;
and determining a target application set used by the current cleaning application according to the historical application set corresponding to the reference historical application cleaning event.
6. The application cleaning method of claim 1, further comprising:
and when the historical user score corresponding to the historical application cleaning event is not less than the preset score, performing application cleaning according to the target application set.
7. An application cleaning apparatus, comprising:
the determining unit is used for determining a target application set used by a current cleaning application, wherein the target application set is a historical application set corresponding to a historical application cleaning event, the historical application set corresponding to the historical application cleaning event refers to a historical application set corresponding to each historical application cleaning event, and the target application set comprises a cleanable application subset and an uncleanable application subset; the historical application set corresponding to the historical application cleaning event comprises a historical cleanable application subset and a historical unclonable application subset; the history cleanable application subset comprises applications cleanable at a history time, and the history unclonable application subset comprises applications unclonable at the history time;
the selecting unit is used for selecting corresponding target application from the target application set when the historical user score corresponding to the historical application cleaning event is smaller than a preset score;
the probability obtaining unit is used for obtaining the cleanable probability of the target application according to the multidimensional characteristic information of the target application and a Bayesian algorithm;
the adjusting unit is used for adjusting the subset in the target application set according to the probability to obtain an adjusted application set;
a cleaning unit, configured to perform application cleaning according to the adjusted application set;
wherein the adjusting unit is configured to:
when the target application belongs to the cleanable application subset and the probability is smaller than a preset probability, moving the target application from the cleanable application subset to the uncleanable application subset;
and when the target application belongs to the uncleanable application subset and the probability is not less than a preset probability, moving the target application from the uncleanable application subset to the cleanable application subset according to the probability.
8. The application cleaning apparatus of claim 7, wherein the selecting unit comprises:
the event selection subunit is used for selecting a target historical application cleaning event of which the historical user score is smaller than the preset score from the plurality of candidate historical application cleaning events;
the set acquisition subunit is used for acquiring a target historical application set corresponding to the target historical application cleaning event;
and the application selection subunit is used for selecting the applications appearing in the target application set and the target historical application set at the same time as the target applications.
9. The application cleaning apparatus according to claim 7, wherein the determining unit includes:
the score obtaining subunit is used for obtaining historical user scores of a plurality of candidate historical application cleaning events;
a reference event selecting subunit, configured to select, according to the historical user score of the candidate historical application cleaning event, a corresponding reference historical application cleaning event from the plurality of candidate historical application cleaning events;
and the set determining subunit is used for determining a target application set used by the current cleaning application according to the historical application set corresponding to the reference historical application cleaning event.
10. The application cleaning device of claim 7, wherein the cleaning unit is further configured to perform application cleaning according to the target application set when a historical user score corresponding to the historical application cleaning event is not less than a preset score.
11. A storage medium having stored thereon a computer program, characterized in that, when the computer program is run on a computer, it causes the computer to execute the application cleaning method according to any one of claims 1 to 7.
12. An electronic device comprising a processor and a memory, said memory having a computer program, wherein said processor is adapted to perform the application cleaning method of any of claims 1 to 7 by invoking said computer program.
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