CN107809542B - Application program control method and device, storage medium and electronic equipment - Google Patents
Application program control method and device, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN107809542B CN107809542B CN201711122479.6A CN201711122479A CN107809542B CN 107809542 B CN107809542 B CN 107809542B CN 201711122479 A CN201711122479 A CN 201711122479A CN 107809542 B CN107809542 B CN 107809542B
- Authority
- CN
- China
- Prior art keywords
- application
- application program
- preset
- determining
- target application
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
- H04M1/72406—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality by software upgrading or downloading
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
- H04W52/0264—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level by selectively disabling software applications
-
- 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
-
- 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
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The embodiment of the application discloses an application program control method, an application program control device, a storage medium and electronic equipment; the method comprises the following steps: the method comprises the steps of obtaining the current running state of a target application program, judging whether the state meets preset conditions or not, if so, obtaining running parameters of the electronic equipment, wherein the running parameters comprise the current time, foreground application, residual electric quantity, screen-on duration, charging state and network connection state, generating training samples according to the running parameters, training a preset Bayesian model by using the training samples, and controlling the target application program based on the trained Bayesian model. According to the method and the device, whether the application program needs to be cleaned or not can be well predicted according to the service condition of the application program in the past, the method is simple, the flexibility is high, the power consumption of the electronic equipment can be reduced, and the cruising ability of the electronic equipment is improved.
Description
Technical Field
The present application relates to the field of electronic devices, and in particular, to a method and an apparatus for controlling an application program, a storage medium, and an electronic device.
Background
With the development of terminal technology, terminals have begun to change from simply providing telephony devices to a platform for running general-purpose software. The platform no longer aims at providing call management, but provides an operating environment including various application software such as call management, game and entertainment, office events, mobile payment and the like, and with a great deal of popularization, the platform has been deeply developed to the aspects of life and work of people.
At present, people use mobile phones more and more frequently, and the consumption of mobile phone electricity for making calls, sending short messages, playing games, listening to music and the like is larger and larger. Many people enjoy wearing earphones to listen to music, radio or video before sleeping. However, many times, the user sleeps unconsciously while listening, so that the applications in the mobile phone run until the user wakes up and actively closes the program, power consumption is very high, and certain pressure is brought to the endurance of the terminal.
Disclosure of Invention
The embodiment of the application program control method and device, the storage medium and the electronic equipment can reduce the power consumption of the electronic equipment.
In a first aspect, an embodiment of the present application provides an application program control method, including:
acquiring the current running state of a target application program, and judging whether the state meets a preset condition or not;
if so, acquiring operation parameters of the electronic equipment, wherein the operation parameters comprise current time, foreground application, residual electric quantity, screen-on duration, charging state and network connection state;
generating a training sample according to the operation parameters;
training a preset Bayesian model by using the training samples;
and controlling the target application program based on the trained Bayesian model.
In a second aspect, an embodiment of the present application further provides an application control apparatus, including: the device comprises a judging module, an obtaining module, a generating module, a training module and a control module;
the judging module is used for acquiring the current running state of the target application program and judging whether the state meets a preset condition or not;
the acquisition module is used for acquiring the operating parameters of the electronic equipment when the state meets a preset condition, wherein the operating parameters comprise the current time, foreground application, residual electric quantity, the time length of the screen being turned on, a charging state and a network connection state;
the generating module is used for generating a training sample according to the operation parameters;
the training module is used for training a preset Bayesian model by using the training samples;
and the control module is used for controlling the target application program based on the trained Bayesian model.
In a third aspect, an embodiment of the present application further provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the application control method described above.
In a fourth aspect, an embodiment of the present application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the application control method when executing the program.
The application program control method provided by the embodiment of the application program control method comprises the steps of firstly obtaining the current running state of a target application program, judging whether the state meets a preset condition, if so, obtaining running parameters of electronic equipment, wherein the running parameters comprise the current time, foreground application, residual electric quantity, screen-on duration, charging state and network connection state, generating training samples according to the running parameters, training a preset Bayesian model by using the training samples, and controlling the target application program based on the trained Bayesian model. According to the method and the device, whether the application program needs to be cleaned or not can be well predicted according to the service condition of the application program in the past, the method is simple, the flexibility is high, the power consumption of the electronic equipment can be reduced, and the cruising ability of the electronic equipment is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, 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 system diagram of an application control device according to an embodiment of the present disclosure.
Fig. 2 is a schematic flowchart of an application control method according to an embodiment of the present disclosure.
Fig. 3 is another schematic flowchart of an application control method according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an application control device according to an embodiment of the present disclosure.
Fig. 5 is a schematic structural diagram of an application control 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.
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 principles of the present application may be employed in numerous other general-purpose or special-purpose computing, communication environments or configurations. Examples of well known computing systems, environments, and configurations that may be suitable for use with the application include, but are not limited to, hand-held telephones, personal computers, servers, multiprocessor systems, microcomputer-based systems, mainframe-based computers, and distributed computing environments that include any of the above systems or devices.
The details will be described below separately.
The embodiment will be described from the perspective of an application control apparatus, which may be specifically integrated in an electronic device, where the electronic device may be an electronic device with a network communication function, such as a mobile interconnection network device (e.g., a smart phone, a tablet computer).
Referring to fig. 1, fig. 1 is a system schematic diagram of an application control device according to an embodiment of the present disclosure. The application program control device is mainly used for: the method comprises the steps of extracting operation parameters of the electronic equipment from user behavior data, generating training samples according to the operation parameters, training preset Bayesian models by using the training samples, and finally controlling target application programs, such as cleaning or freezing the application programs, based on the trained Bayesian models.
With continuing reference to fig. 2, fig. 2 is a schematic flowchart of an application control method according to an embodiment of the present application, including the following steps:
and step S101, acquiring the current running state of the target application program.
In practical application, taking an intelligent device based on an android system as an example, all the problems are caused by self background starting triggered by software process events. Because the process management mechanism of the android system is event-triggered, for example, when the android system is started, an application process is opened, a network connection is changed, the power is insufficient, time is changed, the power is plugged in, the power is disconnected, and other changes of the states can be regarded as an event, and the android system allows a program to associate the events, for example, after the associated start event, the process management mechanism is equivalent to the general start of the android system. However, the android system can allow one program to associate multiple events, for example, google map, and its associated trigger events include "after power on, program installation, program uninstallation, program update, power on", etc., and when any one of these events changes its state, the system will automatically run the google map in the background. Many self-starting items generated after the software is installed can automatically start the self-starting process according to different triggering conditions. Each application in the terminal is recorded by the process management mechanism when the process is started.
The current running state of each application can be obtained through a process management mechanism, which may include an application manager, in which a usage record of each application in the terminal is recorded, where the usage record may include the running time of the application, and of course, the usage record may also include other usage information of the application, such as an auto-wake time, an application start time, an application close time, a time when the application runs in the background, and so on.
And step S102, judging whether the states meet preset conditions, if so, executing step S103, and if not, ending the current process.
In the embodiment of the present invention, the target application is an audio and video application, and therefore when the target application is in an operating state, it needs to further determine whether the application is playing audio or video. If yes, determining that the application program meets the preset conditions, and if not, ending the current process.
Step S103, obtaining operation parameters of the electronic equipment, wherein the operation parameters comprise current time, foreground application, residual electric quantity, screen-on duration, charging state and network connection state.
In one embodiment, the current time may be in the form of x years, x months, x days, x hours, and x minutes. The charging connection state and the network connection state may include both a connected and an unconnected state.
In the actual application process, the operation parameters may be obtained in real time, for example, the operation of acquiring corresponding data is performed at the sampling time point, that is, the current time, or may be obtained at one time, for example, the electronic device may record, in advance, on/off screen change data, charging state change data, network state change data, and application opening data each time in a historical period in a local database, and then, the operation parameters of the electronic device may be extracted at one time according to the current time.
And step S104, generating a training sample according to the operation parameters.
In an embodiment, the foregoing steps may specifically include:
1-1, determining the sampling date type and the sampling period according to the current time.
In this embodiment, the sampling date type is a division of each week, which may include weekdays and weekends. The sampling period is a division of each day, which may divide the day into 48 periods.
1-2, determining a preset electric quantity range to which the residual electric quantity belongs, and determining a preset duration range to which the screen-on duration belongs.
In this embodiment, the preset electric quantity range and the preset duration range may be set manually, the preset electric quantity range may include three interval ranges indicating a high electric quantity, a medium electric quantity, and a low electric quantity, for example, the high electric quantity may be 70% to 100%, the medium electric quantity may be 40% to 70%, the low electric quantity may be 0% to 40%, and the preset duration range may include three interval ranges indicating a short, a medium, and a long, for example, the long may be more than 10min, the medium may be 5min to 10min, and the short may be 0min to 5 min.
And 1-3, generating a training sample according to the sampling date type, the sampling time period, the preset electric quantity range, the preset duration range, the foreground application, the charging connection state and the network connection state.
In an embodiment, the steps 1 to 3 may specifically include:
1-3-1, and determining the last switching application and the next switching application of the foreground application from the running parameters according to the current time.
In this embodiment, because the foreground application obtained by sampling each time in the history period is known, for the foreground application obtained by sampling any time, different foreground applications obtained before the current time can be regarded as the last switching application of the current foreground application, and different foreground applications obtained after the current time can be regarded as the next switching application of the current foreground application, and generally, the different foreground applications closest to the current time can be taken as the last switching application and the next switching application. In the actual operation process, all foreground applications can be sorted according to the current time, for any three different sequenced foreground applications, the front foreground application can be used as the last switching application of the middle foreground application, and the back foreground application can be used as the next switching application of the middle foreground application.
1-3-2, determining the predicted value of the target application program according to the current time, the next switching application and the foreground application.
In this embodiment, the predicted value may be a value set manually, such as 0 and 1, where 0 may indicate that the target application program may be cleaned, and 1 may indicate that the target application program may not be cleaned. Since all foreground applications collected during the historical period are known, the predicted value of the target application can be determined according to the known foreground applications and the current time.
And 1-3-3, generating a training sample according to the last switching application, the foreground application, the sampling date type, the sampling time period, the preset electric quantity range, the preset duration range, the charging connection state, the network connection state, the target prediction application and the predicted value.
In this embodiment, in order to analyze user behaviors from multiple dimensions, so as to make the trained machine learning model more anthropomorphic, each training sample may be composed of data of a plurality of known feature items and tag items, the known feature items may include the last switching application, foreground application, sampling date type, sampling period, preset electric quantity range, preset duration range, charging connection state, network connection state, and the like, and the tag items are mainly.
For example, the steps 1-3-3 may specifically include:
respectively acquiring characteristic values corresponding to the last switching application, the foreground application, the sampling date type, the sampling time period, the preset electric quantity range, the preset duration range, the charging connection state, the network connection state and the target prediction application;
and generating a training sample according to the characteristic value and the predicted value.
In this embodiment, since the computer program is generally encoded and run in the form of characters, the feature value may be mainly expressed in the form of arabic numerals or letters, such as 1 to 10, and each feature item may also be expressed in the form of letters, such as H for foreground application, B for sampling date type, and so on. When the training sample is generated, the feature value of the feature item can be directly used as a prior condition, and the predicted value of each target application program is used as a posterior result to generate the training sample.
It is easy to understand that the feature value corresponding to each feature item may be preset, and the feature values of different feature items may be the same or different, for example, the feature values of the foreground application and the sampling period may all include 0 to 10, but the meaning of each number indicated in different feature items is different, for example, for the foreground application, 0 may refer to mei-qu, and for the sampling period, 0 may refer to a period of 0:00 to 1: 00.
And step S105, training a preset Bayes model by using the training sample.
For example, the characteristic value may include (q)1，q2…qm) The predicted value may include j1 and j2, and in this case, the step S105 may specifically include:
inputting the predicted value into a first preset formula to obtain the probability of the corresponding predicted value, wherein the first formula is as follows:
where N (j1) represents the number of occurrences of event j1, N (j2) represents the number of occurrences of event j2, and P (j2) represents the probability of occurrence of event j 2;
inputting the characteristic value and the predicted value into a second preset formula to obtain the probability corresponding to the characteristic value and the predicted value, wherein the second formula is as follows:
wherein k is not less than 1 and not more than m, P (q)i| j2) indicates that event q occurs at event j2iProbability of occurrence, N (q)iJ2) represents an event qiAnd the number of times j2 occurred simultaneously.
In this embodiment, the bayesian model may be:
wherein q is1,q2…qmJ is the predicted value of the target prediction application, which is the eigenvalue of the prior condition. To simplify the calculation, assume q1,q2…qmAre independent of each other, then
Thereby obtaining the plainBayes classifier model:
JMAX＝arg max P(J|q1，q2...qm)＝arg maxP(q1|J)P(q2|J)...P(qmjj), where J may represent J1 or J2, the probability value of each feature item is the statistical probability of the number of occurrences, i.e. the above formula:
And step S106, controlling the target application program based on the trained Bayesian model.
In an embodiment, the steps may specifically include:
and 2-1, acquiring an application cleaning instruction.
In this embodiment, the application cleaning instruction may be automatically generated by the electronic device, for example, when a preset time period is reached, the memory occupancy amount reaches a certain limit, or the electric quantity is insufficient, or the running speed is too slow, or the like, the application cleaning instruction is generated, of course, the application cleaning instruction may also be generated by a manual operation of a user, for example, the user may generate the application cleaning instruction by clicking a designated cleaning icon.
2-2, acquiring a target application program and current operating parameters of the electronic equipment according to the application cleaning instruction.
And 2-3, calculating the cleanable rate of the target application program by using the trained Bayesian model and the current operation parameters.
For example, the step 2-3 may specifically include:
determining a current characteristic value according to the current operation parameter;
inputting the current characteristic value into a third preset formula for calculation to obtain a cleanable rate, wherein the third preset formula is used for calculatingThe formula is as follows:
wherein k is more than or equal to 1 and less than or equal to m, qkIs the current eigenvalue.
In this embodiment, similar to the training process, the current feature items of the sampling date type, the sampling period, the preset electric quantity range, the preset duration range, the foreground application, the charging connection state, the network connection state, and the current background application to be predicted may be obtained according to the current operating parameters, and the feature values q corresponding to the feature items may be obtained1,q2…qmThen using the formula:
P(j2|q1，q2...qm)＝P(j2)P(q1|j2)P(q2|j2)...P(qm| j2) to calculate a probability value of the probability of j2 occurring (i.e. whether the current target application program to be predicted needs cleaning) on the premise of the occurrence of the current feature value, as a cleanable rate.
And 2-4, closing the target application program according to the cleanable rate.
For example, the steps 2 to 4 may specifically include:
and when the cleanable rate of the target application program is larger than a preset threshold value, closing the target application program.
In this embodiment, the preset threshold may be set manually, for example, the preset threshold may be 0.5, that is, when P (j2| q) is calculated1，q2...qm) If the number of the target application programs is more than 0.5, the target application programs can be considered to be cleaned, and further the target application programs can be used as cleaning objects to be cleaned.
In practical use, there may be a situation where the user is using the target application, and closing the target application may affect the user's use, so in an embodiment, before closing the target application, a prompt message may be generated and displayed on the terminal screen, for example, the target application is a "XX music" player, the electronic device may determine to pop up "whether to close XX music" when cleaning the application, the user may click a "no" button to prevent closing the target application, and if the pop-up window is popped up for a preset time, for example, 10 seconds, and does not receive a user operation, the target application may be closed.
Therefore, the current running state of the target application program can be obtained, whether the state meets the preset condition or not is judged, if yes, the running parameters of the electronic equipment are obtained, the running parameters comprise the current time, foreground application, the residual electric quantity, the on-screen time, the charging state and the network connection state, a training sample is generated according to the running parameters, the preset Bayesian model is trained by the aid of the training sample, and the target application program is controlled based on the trained Bayesian model. According to the method and the device, whether the application program needs to be cleaned or not can be well predicted according to the service condition of the application program in the past, the method is simple, the flexibility is high, the power consumption of the electronic equipment can be reduced, and the cruising ability of the electronic equipment is improved.
According to the above description of the embodiment, the application control method of the present application will be further explained below.
Referring to fig. 3, fig. 3 is a schematic flowchart of another application control method according to an embodiment of the present application, including the following steps:
in step S201, the electronic device obtains a current running state of the target application.
In one embodiment, the current running state of the target application may be obtained through a process management mechanism, which may include an application manager, in which a usage record of each application in the terminal is recorded, where the usage record may include the running time of the application, and of course, the usage record may also include other usage information of the application, such as an auto-wake time, an application-on time, an application-off time, a time when the application runs in the background, and so on.
In step S202, the electronic device determines whether the target application program is currently in a playing state, if so, step S203 is executed, and if not, the process is ended.
In the embodiment of the present invention, the target application is an audio and video application, and therefore when the target application is in an operating state, it needs to further determine whether the application is playing audio or video. If yes, determining that the application program meets the preset conditions, and if not, ending the current process.
Step S203, the electronic device obtains operation parameters of the electronic device, where the operation parameters include current time, foreground application, remaining power, on-screen duration, charging state, and network connection state.
The operation parameters may be extracted from a database, in which usage records of applications in the electronic device in the past period of time, including on/off records of a screen, charging records, and WiFi connection records, may be stored, and then the operation parameters at each sampling time point may be extracted according to the records. The charging connection state and the network connection state may include both a connected and an unconnected state.
Step S204, the electronic equipment determines the sampling date type and the sampling time period according to the current time, determines the preset electric quantity range to which the residual electric quantity belongs, and determines the preset duration range to which the screen-on duration belongs.
For example, if the sampling time point is 55 minutes at 10 days of 2017, 10 months, 17 days and 10 hours, each day can be divided into 48 time intervals, the day is tuesday, the type of the sampling date is weekday, and the sampling time interval is the 11 th time interval. If the remaining power is 80%, the predetermined power range may be a high power corresponding to 70% to 100%. If the screen-on duration is 3min, the preset duration range may be a short duration corresponding to 0-5 min.
In step S205, the electronic device determines the previous switching application and the next switching application of the foreground application from the running parameters according to the current time.
For example, the target application may be selected by the user, and may first obtain ten applications APP1 and APP2 … APP10 with the highest recent occurrence frequency. Sequencing is carried out on all foreground applications according to sampling time points, for any three adjacent different foreground applications after sequencing, the foreground application in front can be used as the last switching application of the foreground application in the middle, the foreground application behind can be used as the next switching application of the foreground application in the middle, for example, for a certain sampling time point, the foreground application can be APP10, the last switching application can be APP1, and the next switching application can be APP 5.
In step S206, the electronic device determines a predicted value of the target application according to the current time, the next switching application, and the foreground application.
In step S207, the electronic device obtains a previous switching application, a foreground application, a sampling date type, a sampling time period, a preset electric quantity range, a preset duration range, a charging connection state, a network connection state, and a feature value corresponding to the target application program, respectively.
For example, the corresponding relationship between the feature value and the feature item may be as follows:
TABLE 1
In step S208, the electronic device generates a training sample according to the feature value and the predicted value.
Step S209, the electronic device inputs each training sample into a preset bayesian model, and trains the bayesian model.
For example, the characteristic value may include (q)1，q2…qm) The predicted value may include j1 and j2, and in this case, the step S209 may specifically include:
inputting the predicted value into a first preset formula to obtain the probability of the corresponding predicted value, wherein the first formula is as follows:
where N (j1) represents the number of occurrences of event j1, N (j2) represents the number of occurrences of event j2, and P (j2) represents the probability of occurrence of event j 2;
inputting the characteristic value and the predicted value into a second preset formula to obtain the probability corresponding to the characteristic value and the predicted value, wherein the second preset formula is as follows:
wherein i is not less than 1 and not more than m, P (q)i| j2) indicates that event q occurs at event j2iProbability of occurrence, N (q)iJ2) represents an event qiAnd the number of times j2 occurred simultaneously.
In this embodiment, the bayesian model may be:
wherein q is1,q2…qmJ is the predicted value of the target prediction application, which is the eigenvalue of the prior condition. To simplify the calculation, assume q1,q2…qmAre independent of each other, then
Thus, a naive bayes classifier model is obtained:
JMAX＝arg max P(J|q1，q2...qm)＝arg maxP(q1|J)P(q2|J)...P(qmjj), where J may represent J1 or J2, the probability value of each feature item is the statistical probability of the number of occurrences, i.e. the above formula:
wherein j1 is a first predetermined value, and j2 is a second predetermined value. It is easy to know that the process of training the bayesian model is a process of probability statistics, that is, after the bayesian model is trained, probability values of different feature values in each feature item, such as P (q) can be obtained1)、P(q1|j2)。
And step S210, the electronic equipment controls the target application program based on the trained Bayesian model.
In an embodiment, the step of controlling the target application based on the trained bayesian model may specifically include:
acquiring an application cleaning instruction;
acquiring a target application program and current operating parameters of the electronic equipment according to the application cleaning instruction;
calculating the cleanable rate of the target application program by using the trained Bayesian model and the current operation parameters;
and closing the target application program according to the cleanable rate.
The step of calculating the cleanable rate of the target application program by using the trained bayesian model and the current operating parameters may include:
determining a current characteristic value according to the current operation parameter;
inputting the current characteristic value into a third preset formula for calculation to obtain the cleanable rate, wherein the third preset formula is as follows:
wherein k is more than or equal to 1 and less than or equal to m, qkIs the current eigenvalue.
In this embodiment, similar to the training process, the current feature items of the sampling date type, the sampling period, the preset electric quantity range, the preset duration range, the foreground application, the charging connection state, the network connection state, and the current background application to be predicted may be obtained according to the current operating parameters, and the feature values q corresponding to the feature items may be obtained1,q2…qmThen using the formula:
P(j2|q1，q2...qm)＝P(j2)P(q1|j2)P(q2|j2)...P(qm| j2) to calculate the probability value of the probability of j2 occurring (that is, the background application to be predicted currently does not switch to the foreground in a short time) on the premise of the occurrence of the current feature value, as the cleanable rate.
In this embodiment, the preset threshold may be set manually, for example, the preset threshold may be 0.5, that is, when P (j2| q) is calculated1，q2...qm) If the number of the target application programs is more than 0.5, the target application programs can be considered to be cleaned, and further the target application programs can be used as cleaning objects to be cleaned.
In view of the above, an embodiment of the present application provides an application control method, in which an electronic device may obtain a current running state of a target application, determine whether the target application is currently in a playing state, if so, obtain running parameters of the electronic device, where the running parameters include a current time, a foreground application, a remaining power amount, a screen-on duration, a charging state, and a network connection state, determine a sampling date type and a sampling period according to the current time, determine a preset power amount range to which the remaining power amount belongs, determine a preset duration range to which the screen-on duration belongs, determine an upper switching application and a lower switching application of the foreground application from the running parameters according to the current time, determine a predicted value of the target application according to the current time, the lower switching application, and the foreground application, and respectively obtain the upper switching application, the lower switching application, the charging state, and the network connection state, The method comprises the steps that foreground application, a sampling date type, a sampling period, a preset electric quantity range, a preset duration range, a charging connection state, a network connection state and a characteristic value corresponding to a target application program are carried out, training samples are generated according to the characteristic value and a predicted value, each training sample is input into a preset Bayes model, the Bayes model is trained, and finally electronic equipment controls the target application program based on the trained Bayes model. According to the method and the device, whether the application program needs to be cleaned or not can be well predicted according to the service condition of the application program in the past, the method is simple, the flexibility is high, the power consumption of the electronic equipment can be reduced, the cruising ability of the electronic equipment is improved, system resources are saved, and the user experience is good.
In order to better implement the application control method provided by the embodiment of the present application, the embodiment of the present application further provides a device based on the application control method. The terms are the same as those in the application control method, and details of implementation can be referred to the description in the method embodiment.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an application control device according to an embodiment of the present application, where the application control device 30 includes: a judging module 301, an obtaining module 302, a generating module 303, a training module 304 and a control module 305;
the judging module 301 is configured to obtain a current running state of the target application program, and judge whether the state meets a preset condition;
the obtaining module 302 is configured to obtain an operation parameter of the electronic device when the state meets a preset condition, where the operation parameter includes a current time, a foreground application, a remaining power amount, a screen-on duration, a charging state, and a network connection state;
the generating module 303 is configured to generate a training sample according to the operation parameter;
the training module 304 is configured to train a preset bayesian model by using a training sample;
the control module 305 is configured to control the target application based on the trained bayesian model.
With continued reference to fig. 5, in an embodiment, the determining module 301 includes: a judgment sub-module 3011 and a determination sub-module 3012;
the determining sub-module 3011 is configured to determine whether the target application is currently in a playing state;
the determining sub-module 3012 is configured to determine that the status meets a preset condition when the determining sub-module 3011 determines yes.
Further, the generating module 303 may specifically include: a first determining submodule 3031, a second determining submodule 3032 and a generating submodule 3033;
the first determining submodule 3031 is configured to determine a sampling date type and a sampling period according to the current time;
the second determining submodule 3032 is configured to determine a preset electric quantity range to which the remaining electric quantity belongs, and determine a preset duration range to which the screen-on duration belongs;
the generating submodule 3033 is configured to generate a training sample according to a sampling date type, a sampling time period, a preset electric quantity range, a preset time range, a foreground application, a charging connection state, and a network connection state.
As can be seen from the above, the application control device 30 provided in this embodiment of the application may obtain the current running state of the target application through the determining module 301, and determine whether the state meets the preset condition, if so, the obtaining module 302 obtains the running parameters of the electronic device, where the running parameters include current time, foreground application, remaining power, on-screen duration, charging state, and network connection state, the generating module 303 generates a training sample according to the running parameters, the training module 304 trains a preset bayesian model by using the training sample, and the control module 305 controls the target application based on the trained bayesian model. According to the method and the device, whether the application program needs to be cleaned or not can be well predicted according to the service condition of the application program in the past, the method is simple, the flexibility is high, the power consumption of the electronic equipment can be reduced, and the cruising ability of the electronic equipment is improved.
The application also provides a storage medium, on which a computer program is stored, wherein the computer program is used for realizing the application control method provided by the method embodiment when being executed by a processor.
The application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the application control method provided by the method embodiment is realized when the processor executes the program.
In another embodiment of the present application, an electronic device is also provided, and the electronic device may be a smart phone, a tablet computer, or the like. As shown in fig. 6, the electronic device 400 includes a processor 401, a memory 402. The processor 401 is electrically connected to the memory 402.
The processor 401 is a control center of the electronic device 400, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or loading an application program stored in the memory 402 and calling data stored in the memory 402, thereby integrally monitoring the electronic device.
In this embodiment, the processor 401 in the electronic device 400 loads instructions corresponding to processes of one or more application programs into the memory 402 according to the following steps, and the processor 401 runs the application programs stored in the memory 402, thereby implementing various functions:
acquiring the current running state of a target application program, and judging whether the state meets a preset condition or not;
if so, acquiring operation parameters of the electronic equipment, wherein the operation parameters comprise current time, foreground application, residual electric quantity, screen-on duration, charging state and network connection state;
generating a training sample according to the operation parameters;
training a preset Bayesian model by using the training samples;
and controlling the target application program based on the trained Bayesian model.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. The electronic device 500 may include Radio Frequency (RF) circuitry 501, memory 502 including one or more computer-readable storage media, input unit 503, display unit 504, sensor 504, audio circuitry 506, Wireless Fidelity (WiFi) module 507, processor 508 including one or more processing cores, and power supply 509. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 7 does not constitute a limitation of the electronic device and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
The rf circuit 501 may be used for receiving and transmitting information, or receiving and transmitting signals during a call, and in particular, receives downlink information of a base station and then sends the received downlink information to one or more processors 508 for processing; in addition, data relating to uplink is transmitted to the base station. In general, radio frequency circuit 501 includes, but is not limited to, an antenna, at least one Amplifier, a tuner, one or more oscillators, a Subscriber Identity Module (SIM) card, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, the radio frequency circuit 501 may also communicate with a network and other devices through wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Message Service (SMS), and the like.
The memory 502 may be used to store applications and data. Memory 502 stores applications containing executable code. The application programs may constitute various functional modules. The processor 508 executes various functional applications and data processing by executing application programs 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, an application 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 (such as audio data, a phonebook, etc.) created according to the 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 508 and the input unit 503 access to the memory 502.
The input unit 503 may be used to receive input numbers, character information, or user characteristic information (such as a fingerprint), and generate a keyboard, mouse, joystick, optical, or trackball signal input related to user setting and function control. In particular, in one particular embodiment, the input unit 503 may include a touch-sensitive surface as well as other input devices. The touch-sensitive surface, also referred to as a touch display screen or a touch pad, may collect touch operations by a user (e.g., operations by a user on or near the touch-sensitive surface using a finger, a stylus, or any other suitable object or attachment) thereon or nearby, and drive the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface may comprise two parts, a touch detection means and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 508, and can receive and execute commands sent by the processor 508.
The display unit 504 may be used to display information input by or provided to a user and various graphical user interfaces of the electronic device, which may be made up of graphics, text, icons, video, and any combination thereof. The display unit 504 may include a display panel. Alternatively, the display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch-sensitive surface may overlay the display panel, and when a touch operation is detected on or near the touch-sensitive surface, the touch operation is transmitted to the processor 508 to determine the type of touch event, and then the processor 508 provides a corresponding visual output on the display panel according to the type of touch event. Although in FIG. 7 the touch-sensitive surface and the display panel are two separate components to implement input and output functions, in some embodiments the touch-sensitive surface may be integrated with the display panel to implement input and output functions.
The electronic device may also include at least one sensor 505, such as light sensors, motion sensors, and other sensors. In particular, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel according to the brightness of ambient light, and a proximity sensor that may turn off the display panel and/or the backlight when the electronic device is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when the mobile phone is stationary, and can be used for applications of recognizing the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which may be further configured to the electronic device, detailed descriptions thereof are omitted.
The audio circuit 506 may provide an audio interface between the user and the electronic device through a speaker, microphone. The audio circuit 506 can convert the received audio data into an electrical signal, transmit the electrical signal to a speaker, and convert the electrical signal into a sound signal to output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 506 and converted into audio data, which is then processed by the audio data output processor 508 and then sent to another electronic device via the rf circuit 501, or the audio data is output to the memory 502 for further processing. The audio circuit 506 may also include an earbud jack to provide communication of a peripheral headset with the electronic device.
Wireless fidelity (WiFi) belongs to short-distance wireless transmission technology, and electronic equipment can help users to send and receive e-mails, browse webpages, access streaming media and the like through a wireless fidelity module 507, and provides wireless broadband internet access for users. Although fig. 7 shows the wireless fidelity module 507, it is understood that it does not belong to the essential constitution of the electronic device, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The processor 508 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing an application program stored in the memory 502 and calling data stored in the memory 502, thereby integrally monitoring the electronic device. Optionally, processor 508 may include one or more processing cores; preferably, the processor 508 may integrate an application processor, which primarily handles operating systems, user interfaces, application programs, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 508.
The electronic device also includes a power supply 509 (such as a battery) to power the various components. Preferably, the power source may be logically connected to the processor 508 through a power management system, so that the power management system may manage charging, discharging, and power consumption management functions. The power supply 509 may also include any component such as one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
Although not shown in fig. 7, the electronic device may further include a camera, a bluetooth module, and the like, which are not described in detail herein.
In specific implementation, the above modules may be implemented as independent entities, or may be combined arbitrarily to be implemented as the same or several entities, and specific implementation of the above modules may refer to the foregoing method embodiments, which are not described herein again.
It should be noted that, as one of ordinary skill in the art would understand, all or part of the steps in the various methods of the above embodiments may be implemented by relevant hardware instructed by a program, where the program may be stored in a computer-readable storage medium, such as a memory of a terminal, and executed by at least one processor in the terminal, and during the execution, the flow of the embodiments such as the information distribution method may be included. Among others, the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
In the above, detailed descriptions are given to the application control method, the application control device, the storage medium, and the electronic device, and each functional module may be integrated in one processing chip, or each module may exist alone physically, or two or more modules are integrated in one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The principle and the implementation of the present application are explained herein by applying specific examples, and the above description of the 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 (9)
1. An application control method, comprising the steps of:
acquiring the current running state of a target application program, and judging whether the state meets a preset condition or not;
if so, acquiring historical operating parameters of the electronic equipment, wherein the operating parameters comprise sampling time points, foreground application, residual electric quantity, screen-on duration, charging state and network connection state in a historical operating period;
determining the previous switching application and the next switching application of the foreground application from the running parameters according to the sampling time points in the historical running time period;
acquiring current time and determining a predicted value of a target application program according to the current time, next switching application and foreground application;
respectively acquiring a last switching application, a foreground application, a sampling date type, a sampling time period, a preset electric quantity range, a preset duration range, a charging connection state, a network connection state and a characteristic value corresponding to a target application program;
generating a training sample according to the characteristic value and the predicted value;
training a preset Bayesian model by using the training samples;
and calculating the cleanable rate of the target application program based on the trained Bayesian model and the current operating parameters, and closing the target application program according to the cleanable rate.
2. The application control method of claim 1, wherein the step of determining whether the status satisfies a preset condition comprises:
judging whether the target application program is in a playing state currently;
if yes, determining that the state meets a preset condition.
3. The application control method of claim 1, wherein the step of generating training samples based on the operating parameters comprises:
determining a sampling date type and a sampling time period according to the current time;
determining a preset electric quantity range to which the residual electric quantity belongs, and determining a preset duration range to which the screen-on duration belongs;
and generating a training sample according to the sampling date type, the sampling time period, the preset electric quantity range, the preset duration range, the foreground application, the charging connection state and the network connection state.
4. The application control method of claim 1, wherein the controlling the target application based on the trained bayesian model comprises:
acquiring an application cleaning instruction;
acquiring a target application program and current operating parameters of the electronic equipment according to the application cleaning instruction;
calculating the cleanable rate of the target application program by using the trained Bayesian model and the current operating parameters;
and closing the target application program according to the cleanable rate.
5. An application control apparatus, comprising: the device comprises a judging module, an obtaining module, a generating module, a training module and a control module;
the judging module is used for acquiring the current running state of the target application program and judging whether the state meets a preset condition or not;
the acquisition module is used for acquiring historical operating parameters of the electronic equipment when the state meets a preset condition, wherein the operating parameters comprise a sampling time point, foreground application, residual electric quantity, screen-on duration, a charging state and a network connection state in a historical operating period;
the generation module is used for determining the previous switching application and the next switching application of the foreground application from the operation parameters according to the sampling time points in the historical operation period, acquiring the current time, determining the predicted value of the target application program according to the current time, the next switching application and the foreground application, respectively acquiring the previous switching application, the foreground application, the sampling date type, the sampling period, the preset electric quantity range, the preset duration range, the charging connection state, the network connection state and the characteristic value corresponding to the target application program, and generating a training sample according to the characteristic value and the predicted value;
the training module is used for training a preset Bayesian model by using the training samples;
and the control module is used for calculating the cleanable rate of the target application program based on the trained Bayesian model and the current operating parameters, and closing the target application program according to the cleanable rate.
6. The application control device of claim 5, wherein the determining module comprises: a judgment submodule and a determination submodule;
the judgment submodule is used for judging whether the target application program is in a playing state currently;
and the determining submodule is used for determining that the state meets a preset condition when the judging submodule judges that the state meets the preset condition.
7. The application control apparatus of claim 5, wherein the generation module comprises: the device comprises a first determining submodule, a second determining submodule and a generating submodule;
the first determining submodule is used for determining the type of the sampling date and the sampling period according to the current time;
the second determining submodule is used for determining a preset electric quantity range to which the residual electric quantity belongs and determining a preset duration range to which the screen-on duration belongs;
and the generation submodule is used for generating a training sample according to the sampling date type, the sampling time period, the preset electric quantity range, the preset duration range, the foreground application, the charging connection state and the network connection state.
8. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the method according to any one of claims 1-4.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-4 are implemented when the processor executes the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711122479.6A CN107809542B (en) | 2017-11-14 | 2017-11-14 | Application program control method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711122479.6A CN107809542B (en) | 2017-11-14 | 2017-11-14 | Application program control method and device, storage medium and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107809542A CN107809542A (en) | 2018-03-16 |
| CN107809542B true CN107809542B (en) | 2020-06-12 |
Family
ID=61592209
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711122479.6A Active CN107809542B (en) | 2017-11-14 | 2017-11-14 | Application program control method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107809542B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110196805B (en) * | 2018-05-29 | 2021-09-28 | 腾讯科技（深圳）有限公司 | Data processing method, data processing apparatus, storage medium, and electronic apparatus |
| CN108932048B (en) * | 2018-06-11 | 2021-06-01 | Oppo（重庆）智能科技有限公司 | Method for determining usable time of battery and related product |
| CN110602761B (en) * | 2018-06-13 | 2020-12-08 | 华为技术有限公司 | Data transmission method and device |
| CN112997150B (en) * | 2018-12-29 | 2023-12-08 | 深圳市欢太科技有限公司 | Application management method and device, storage medium and electronic equipment |
| CN113439253B (en) * | 2019-04-12 | 2023-08-22 | 深圳市欢太科技有限公司 | Application cleaning method and device, storage medium and electronic equipment |
| CN112152971A (en) * | 2019-06-28 | 2020-12-29 | 北京奇虎科技有限公司 | Method and apparatus for controlling network usage behavior, electronic device, and medium |
| CN111597090A (en) * | 2020-05-18 | 2020-08-28 | 闻泰通讯股份有限公司 | Behavior monitoring method and device, electronic equipment and storage medium |
| CN111708427B (en) | 2020-05-29 | 2022-07-22 | 广州三星通信技术研究有限公司 | Method for managing terminal and terminal |
| CN112799911A (en) * | 2021-01-27 | 2021-05-14 | 苏州浪潮智能科技有限公司 | Node health state detection method, device, equipment and storage medium |
| CN113673080B (en) * | 2021-07-15 | 2023-08-18 | 北京奇艺世纪科技有限公司 | Method, device, equipment and medium for predicting duration of application and training model |
| CN113885944B (en) * | 2021-08-25 | 2022-09-20 | 荣耀终端有限公司 | Application background keep-alive method and device and electronic equipment |
| CN116663610B (en) * | 2023-08-02 | 2023-12-19 | 荣耀终端有限公司 | Scheduling network training method, task scheduling method and related equipment |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521041A (en) * | 2011-12-14 | 2012-06-27 | 华为终端有限公司 | Method for processing application program and wireless handheld device |
| CN103235686A (en) * | 2013-03-29 | 2013-08-07 | 北京智谷睿拓技术服务有限公司 | Application management method and device |
| CN105718027A (en) * | 2016-01-20 | 2016-06-29 | 努比亚技术有限公司 | Management method of background application programs and mobile terminal |
| CN105843650A (en) * | 2016-03-31 | 2016-08-10 | 青岛海信移动通信技术股份有限公司 | Application program management method and device in intelligent terminal |
| CN106557146A (en) * | 2015-09-28 | 2017-04-05 | 北京金山安全软件有限公司 | Power saving method, device and terminal |
| CN106775820A (en) * | 2016-11-10 | 2017-05-31 | 宇龙计算机通信科技(深圳)有限公司 | The method and device of application program management |
| CN107133094A (en) * | 2017-06-05 | 2017-09-05 | 努比亚技术有限公司 | Application management method, mobile terminal and computer-readable recording medium |
| CN107272872A (en) * | 2017-05-31 | 2017-10-20 | 广东欧珀移动通信有限公司 | Electricity-saving control method and Related product |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2932379B1 (en) * | 2012-12-17 | 2019-09-04 | BlackBerry Limited | System and methods for launching an application on an electronic device |
-
2017
- 2017-11-14 CN CN201711122479.6A patent/CN107809542B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521041A (en) * | 2011-12-14 | 2012-06-27 | 华为终端有限公司 | Method for processing application program and wireless handheld device |
| CN103235686A (en) * | 2013-03-29 | 2013-08-07 | 北京智谷睿拓技术服务有限公司 | Application management method and device |
| CN106557146A (en) * | 2015-09-28 | 2017-04-05 | 北京金山安全软件有限公司 | Power saving method, device and terminal |
| CN105718027A (en) * | 2016-01-20 | 2016-06-29 | 努比亚技术有限公司 | Management method of background application programs and mobile terminal |
| CN105843650A (en) * | 2016-03-31 | 2016-08-10 | 青岛海信移动通信技术股份有限公司 | Application program management method and device in intelligent terminal |
| CN106775820A (en) * | 2016-11-10 | 2017-05-31 | 宇龙计算机通信科技(深圳)有限公司 | The method and device of application program management |
| CN107272872A (en) * | 2017-05-31 | 2017-10-20 | 广东欧珀移动通信有限公司 | Electricity-saving control method and Related product |
| CN107133094A (en) * | 2017-06-05 | 2017-09-05 | 努比亚技术有限公司 | Application management method, mobile terminal and computer-readable recording medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107809542A (en) | 2018-03-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107809542B (en) | Application program control method and device, storage medium and electronic equipment | |
| CN109213539B (en) | Memory recovery method and device | |
| CN107734616B (en) | Application program closing method and device, storage medium and electronic equipment | |
| CN109509473B (en) | Voice control method and terminal equipment | |
| WO2019080929A1 (en) | Message prompting method and mobile terminal | |
| CN109844706B (en) | Message processing method and device | |
| CN106303070B (en) | notification message prompting method and device and mobile terminal | |
| CN107277230B (en) | Voice broadcasting method of message and related product | |
| US20210329526A1 (en) | Terminal Behavior Triggering Method and Terminal | |
| CN108135033B (en) | Bluetooth connection method and mobile terminal | |
| CN107222914B (en) | Application control method and related product | |
| CN108156508B (en) | Barrage information processing method and device, mobile terminal, server and system | |
| CN106775809B (en) | Method and device for cleaning memory of mobile terminal and mobile terminal | |
| CN108681498B (en) | CPU occupancy rate monitoring method and device and mobile terminal | |
| CN111050370A (en) | Network switching method and device, storage medium and electronic equipment | |
| CN107562539B (en) | Application program processing method and device, computer equipment and storage medium | |
| CN108112063A (en) | Electric quantity managing method, power management device, terminal and readable storage medium storing program for executing | |
| CN107402808B (en) | Process management method, device, storage medium and electronic equipment | |
| CN108632446A (en) | A kind of information cuing method and mobile terminal | |
| CN109224436A (en) | Virtual key based on interface defines method, terminal and storage medium | |
| CN107317918B (en) | Parameter setting method and related product | |
| CN108334272A (en) | A kind of control method and mobile terminal | |
| CN110069333A (en) | Backstage management-control method, mobile terminal and the computer readable storage medium of application | |
| CN107734618B (en) | Application program closing method and device, storage medium and electronic equipment | |
| CN108920054A (en) | A kind of application recommended method and terminal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: Changan town in Guangdong province Dongguan 523860 usha Beach Road No. 18 Applicant after: GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP., Ltd. Address before: Changan town in Guangdong province Dongguan 523860 usha Beach Road No. 18 Applicant before: GUANGDONG OPPO MOBILE TELECOMMUNICATIONS CORP., Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |