CN108076224A - application control method, device and storage medium and mobile terminal - Google Patents
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Abstract
The embodiment of the present application discloses a kind of application control method, device and storage medium and mobile terminal, the described method includes：Every setting time within the setting cycle, current behavioural characteristic is obtained；The default behavior prediction model based on machine learning method generation is obtained, the default behavior prediction model is obtained by the behavioural characteristic sample training of multiple known control operations, is operated for Behavior-based control feature PREDICTIVE CONTROL；The current behavioural characteristic is inputted to the prediction result for the default behavior prediction model, obtaining the default behavior prediction model output；According to the prediction result, automated execution meets the control operation of user behavior custom, and the control operation includes the data scrubbing of application program and data update operation.Technical solution provided by the embodiments of the present application can be performed automatically the application program controlling operation for meeting user behavior custom, improve the intelligence of application program controlling.
Description
Technical field
The invention relates to technical field of mobile terminals more particularly to a kind of application control method, device and
Storage medium and mobile terminal.
Background technology
Function in the mobile terminals such as mobile phone is more and more, provides convenience for the live and work of people, mobile whole
People can install miscellaneous application software in end, and to support the realization of mobile terminal difference in functionality, people can utilize
Mobile phone takes phone, can also listen to music, watch video, chat, plays game etc..User during mobile terminal is used,
Some behavioural habits of itself are had, when controlling in the prior art application program, there is no utilize the user well
Behavioural habits are, it is necessary to improve.
The content of the invention
The embodiment of the present application provides a kind of application control method, device and storage medium and mobile terminal, can be certainly
Dynamic execution meets the application program controlling operation of user behavior custom.
In a first aspect, the embodiment of the present application provides a kind of application control method, including：
Every setting time within the setting cycle, current behavioural characteristic is obtained；
The default behavior prediction model based on machine learning method generation is obtained, the default behavior prediction model is by multiple
The behavioural characteristic sample training of known control operation obtains, and is operated for Behavior-based control feature PREDICTIVE CONTROL；
The current behavioural characteristic is inputted to the default behavior prediction model, obtains the default behavior prediction model
The prediction result of output；
According to the prediction result, automated execution meets the control operation of user behavior custom, and the control operation includes
The data scrubbing of application program and data update operation.
In second aspect, the embodiment of the present application provides a kind of application program controlling device, including：
Behavioural characteristic acquisition module at present every setting time within the setting cycle, obtains current behavioural characteristic；
Default behavior prediction model acquisition module, for obtaining the default behavior prediction mould based on machine learning method generation
Type, the default behavior prediction model is obtained by the behavioural characteristic sample training of multiple known control operations, for Behavior-based control
Feature PREDICTIVE CONTROL operates；
Control operation prediction module for inputting the current behavioural characteristic to the default behavior prediction model, obtains
To the prediction result of the default behavior prediction model output；
Control operation execution module, for meeting the control operation of user behavior custom, the control described in automated execution
Operation includes the data scrubbing of application program and data update operation.
The third aspect, the embodiment of the present application provide a kind of computer readable storage medium, are stored thereon with computer journey
Sequence realizes the application control method provided such as first aspect when the program is executed by processor.
In fourth aspect, the embodiment of the present application provides a kind of mobile terminal, including memory, processor and is stored in
On reservoir and the computer program that can run on a processor, realized when the processor performs as what first aspect was provided answers
Use control method.
The control program of application program provided by the embodiments of the present application, by being obtained within the setting cycle every setting timing
Current behavioural characteristic is taken, current behavioural characteristic is inputted to the default behavior prediction model generated based on machine learning method, is obtained
To the prediction result of the default behavior prediction model output, according to the prediction result, automated execution meets user behavior habit
Used control operation, the control operation include the data scrubbing of application program and data update operation, can be performed automatically symbol
The application program controlling operation of family behavioural habits is shared, improves the intelligence of application program controlling.
Description of the drawings
Fig. 1 is a kind of flow chart of application control method provided by the embodiments of the present application；
Fig. 2 is the flow chart of another application control method provided by the embodiments of the present application；
Fig. 3 is the flow chart of another application control method provided by the embodiments of the present application；
Fig. 4 is a kind of structure diagram of application program controlling device provided by the embodiments of the present application；
Fig. 5 is a kind of structure diagram of mobile terminal provided by the embodiments of the present application；
Fig. 6 is the structure diagram of another mobile terminal provided by the embodiments of the present application.
Specific embodiment
It is specifically real to the application below in conjunction with the accompanying drawings in order to make the purpose, technical scheme and advantage of the application clearer
Example is applied to be described in further detail.It is understood that specific embodiment described herein is used only for explaining the application,
Rather than the restriction to the application.It also should be noted that it illustrates only for ease of description, in attached drawing related to the application
Part rather than full content.It should be mentioned that some exemplary realities before exemplary embodiment is discussed in greater detail
It applies example and is described as the processing described as flow chart or method.Although operations (or step) are described as order by flow chart
Processing, but many of which operation can be implemented concurrently, concomitantly or simultaneously.In addition, the order of operations
It can be rearranged.The processing can be terminated when its operations are completed, be not included in it is also possible to have in attached drawing
Additional step.The processing can correspond to method, function, regulation, subroutine, subprogram etc..
Fig. 1 gives a kind of flow chart of application control method provided by the embodiments of the present application, the side of the present embodiment
Method can be performed by application program controlling device, which can realize that described device can by way of hardware and/or software
The inside of the mobile terminal is arranged on as a mobile terminal part.Mobile terminal described in the embodiment of the present application include but
It is not limited to the equipment such as smart mobile phone, tablet computer or notebook.
As shown in Figure 1, application control method provided in this embodiment comprises the following steps：
Step 101 is being set in the cycle every setting timing, obtains current behavioural characteristic.
During mobile terminal is used, due to mobile terminal species and the difference of performance, different user is directed to oneself
Used mobile terminal has factum custom, for example, the memory of mobile terminal is not very big, user may be frequent
The data cached of some application programs is removed, in another example, user can be set according to the actual demand of oneself and be made a noise using different
Clock.In the prior art, these behavioural habits are all that user oneself is operated manually, not smart enough and need repetitive operation.This
A kind of applicable scene of technical solution that application embodiment provides is exactly：User is learnt by mobile terminal or predetermined server
Historical behavior custom, generate default behavior prediction model, during the use of subsequent movement terminal, obtain mobile terminal one
Behavioural characteristic in the section time based on default behavior prediction model, predicts pending application program controlling operation, automated execution
The control operation.
Wherein, the setting cycle can be one month, and setting time can be a week；The setting cycle can be with
For a week, setting time can be one day；The setting cycle can be one day, and setting time can be 1 hour,
Can be any other situation for meeting setting time and being less than the setting cycle, the present embodiment is to this and is not limited.
Wherein, the behavioural characteristic can include user behavior feature and mobile terminal performance parameter, the mobile terminal
Performance parameter can include memory, electricity and network condition etc..The current behavioural characteristic can be for current setting time forward
The behavioural characteristic possessed in this period started to the setting cycle.
Optionally, if default behavior prediction model is established on mobile terminals, can also include before step 101 with
Lower step：During the operation of mobile terminal, the first user behavior custom of acquisition user's operation mobile terminal；To described first
User behavior custom is analyzed, and obtains the first basic act feature；Using the first basic act feature as training sample,
It is trained based on machine learning method, generates default behavior prediction model.Wherein, the user behavior custom includes user certainly
The behavioural habits of body, further include user occur behavioural habits during mobile terminal performance parameter.
Wherein, when performance parameter of the basic act feature including mobile terminal, application program identification, behavior occur
Between, the behavior end time, behavior scene, in content of the act and behavior frequency at least one of.The current behavioural characteristic
It can be the one or more in basic act feature.Illustratively, basic act is characterized as application program identification, behavior hair
Raw time, behavior end time, behavior scene, content of the act and behavior frequency, behavioural characteristic is application program mark at present
Knowledge, behavior time of origin, behavior scene and content of the act.
Step 102 obtains the default behavior prediction model based on machine learning method generation.The default behavior prediction mould
Type is obtained by the behavioural characteristic sample training of multiple known control operations, is operated for Behavior-based control feature PREDICTIVE CONTROL.
The training generation of the default behavior prediction model based on machine learning method generation and renewal process can move
Dynamic terminal local carries out, and can also be carried out in predetermined server, is finished or more when default behavior prediction model training generates
After new, mobile terminal can be sent directly to and stored or stored in predetermined server, wait standby communication terminals
Active obtaining.Correspondingly, the step 102 can include：From predetermined server or mobile terminal is locally obtained based on machine
The default behavior prediction model of learning method generation.
Optionally, the machine learning method includes neural network method, support vector machine method, traditional decision-tree, patrols
Collect homing method, bayes method and random forest method.Wherein, neutral net (Neural Networks, be abbreviated as NNs)
System refers to artificial neural network, inspires the biological neural network from human brain processing information, it includes input layer, hides
Layer and output layer include three kinds of nodes (elementary cell of neutral net) accordingly：Input node, concealed nodes and output section
Point, input node obtain information from the external world；Concealed nodes and the external world do not contact directly, these nodes utilize activation
Function is calculated, and information is transferred to output node from input node；Output node is used to transfer information to the external world.
In the present embodiment, the source and quantity of user behavior custom sample are not limited, can is that mobile terminal is used
Family is accustomed to by the historical behavior of application program, or targeted user population is accustomed to the historical behavior of application program, also may be used
It is preferably that mobile terminal user is accustomed to the historical behavior of application program to be the combination of the two.Wherein, the target user group
Can be mobile terminal user have same subscriber attribute multiple users, user property can include the age, gender, hobby and
At least one of in occupation.The present embodiment has the mesh of same subscriber attribute with mobile terminal user or with mobile terminal user
The historical behavior custom of user group is marked, is trained as sample, can automatically carry out fitting user agenda custom
Application program controlling operation.
Basic act feature in historical user's behavioural habits as sample based on machine learning method is trained, is obtained
To default behavior prediction model, the input of the default behavior prediction model is behavioural characteristic, exports to wait to hold under behavior feature
The capable control operation to application program.
Optionally, after the default behavior prediction model of generation, further include：Every the setting cycle, system obtains automatically to be worked as
Second user behavioural habits in the preceding setting time cycle；The second user behavioural habits are analyzed, obtain the second base
This behavioural characteristic；The second basic act feature is inputted into the default behavior prediction model, obtains the default row
For the output result of prediction model；If corresponding with the second basic act feature actual control operation of the output result
Difference is beyond default error range, then using the second basic act feature as new training sample to the default behavior
Prediction model carries out training again and updates.Illustratively, the setting cycle can be one day, i.e., pre- in the default behavior of generation
It surveys after model, daily system obtains the user behavior custom in the same day automatically.
Step 103 inputs the current behavioural characteristic to the default behavior prediction model, obtains the default behavior
The prediction result of prediction model output.
Wherein, prediction result may be the control operation for meeting user behavior custom under behavior feature, it is also possible to be
Any control operation is not performed.
Step 104, according to the prediction result, automated execution meet user behavior custom pending control operation, institute
Stating control operation includes the data scrubbing of application program and data update operation.
When meeting the control operation of user behavior custom under prediction result is behavior feature, control described in automated execution
Operation.
Optionally, the data cached cleaning operation of the data scrubbing operation of the application program including application program and/or
The shutoff operation of background application, the data update operation of the application program include the version updating operation of application program
And/or the operating parameter update operation of application program.Illustratively, for the application program of playable audio and video, operating parameter
Can be that receiver plays and loud speaker plays, for alarm clock application program, operating parameter can be that alarm clock sets the time or noisy
Bell ring bell mode etc..
Illustratively, if at present behavioural characteristic in mobile terminal there are 500M-1G in the range of, behavior time of origin is
8 points at night, current behavioural characteristic is inputted in default behavior prediction model, preset the output result of behavior prediction model as application
The data cached cleaning operation of program, the then wechat and day cat application program that mobile terminal automated execution removing user custom is removed
Caching.Illustratively, if it is alarm clock that current behavioural characteristic, which is application program identification, behavior time of origin is evening 10: 30
Point, the behavior end time for 10 points 32 minutes, content of the act for alarm time set and backstage retain operation, by current behavioural characteristic
Into default behavior prediction model, the output result for presetting behavior prediction model is the shutoff operation of background application for input,
The alarm clock that backstage is then retained to operation automatically is closed.
Application control method provided in this embodiment, by current every setting timing acquisition within the setting cycle
Behavioural characteristic inputs current behavioural characteristic to the default behavior prediction model generated based on machine learning method, obtains described
The prediction result of default behavior prediction model output, according to the prediction result, automated execution meets the control of user behavior custom
System operation, the control operation include the data scrubbing of application program and data update operation, can be performed automatically and meet user
The application program controlling operation of behavioural habits, improves the intelligence of application program controlling.
Below exemplified by default behavior prediction model is locally created in mobile terminal, to being based on default behavior prediction model pair
The method that application program is controlled is briefly described.Fig. 2 gives another application program provided by the embodiments of the present application
The flow chart of control method.As shown in Fig. 2, application control method provided in this embodiment comprises the following steps：
Step 201, during the operation of mobile terminal, acquisition user's operation mobile terminal the first user behavior custom.
It is recorded by the behavioural habits that mobile terminal is operated to user's long period, it is exemplary, user's operation is moved
A series of continuous operations of dynamic terminal are recorded and the control operation to mobile terminal execution after this series of continuous operation
It is recorded.
Step 202 analyzes first user behavior custom, obtains the first basic act feature.
Optionally, performance parameter of the first basic act feature including mobile terminal, application program identification, behavior hair
At least one of in raw time, behavior end time, behavior scene, content of the act and behavior frequency.
Step 203, using the first basic act feature as training sample, be trained based on machine learning method,
The default behavior prediction model of generation.
Optionally, which includes：Using the first basic act feature as training sample, based on different engineerings
Learning method is trained, and generates multiple candidate's behavior prediction models；By prediction accuracy in candidate's behavior prediction model most
Behavior prediction model is preset in high conduct.
Step 204 is being set in the cycle every setting timing, obtains current behavioural characteristic.
Step 205 inputs the current behavioural characteristic to the default behavior prediction model, obtains the default behavior
The prediction result of prediction model output.Meet the pending control operation of user behavior custom.
Step 206, according to the prediction result, automated execution meet user behavior custom control operation, the control
Operation includes the data scrubbing of application program and data update operation.
Method provided in this embodiment, by during running in the terminal, the of acquisition user's operation mobile terminal
One user behavior is accustomed to, and the first user behavior custom is analyzed, the first basic act feature is obtained, by the first basic act
Feature is trained based on machine learning method as training sample, generates default behavior prediction model, provide one and meet
The control operation prediction model of user behavior custom, the control that accurately can predict mobile terminal according to current behavioural characteristic are grasped
Make, and automated execution meets the control operation of user behavior custom.
It is exemplified by method is neural network method by machine learning below, to the default row using neural network method generation
For prediction model, the method for carrying out application program controlling is introduced.The neural network method include input layer, hidden layer and
Output layer.Fig. 3 gives the flow chart of another application control method provided by the embodiments of the present application.As shown in figure 3, this
The application control method that embodiment provides comprises the following steps：
Step 301, during the operation of mobile terminal, acquisition user's operation mobile terminal the first user behavior custom.
Step 302 analyzes first user behavior custom, obtains the first basic act feature.
Step 303 inputs the first basic act feature to the input layer, and by respectively being saved with the hidden layer
The calculating of the corresponding activation primitive of point exports intermediate control operation.
Wherein, the activation primitive refers to provide Nonlinear Modeling ability for nerve network system, it is however generally that is non-thread
Property function.Activation primitive can include relu functions, sigmoid functions, tanh functions or maxout functions.
Sigmoid is common nonlinear activation primitive, its mathematical form is as follows：It defeated
Go out the value between 0-1.Tanh with sigmoid still like, in fact, tanh is the deformation of sigmoid：Tanh (x)=
2sigmoid (2x) -1, unlike sigmoid, tanh is 0 average.In recent years, what relu became is becoming increasingly popular.It
Mathematic(al) representation it is as follows：F (x)=max (0, x), wherein, input signal<When 0, output is all 0, input signal>0 situation
Under, output is equal to input.The expression formula of maxout functions is as follows：fi(x)=maxj∈[1,k]Zij.Assuming that input node include x1 and
X2, corresponding weight are respectively w1 and w2, further include weight b, then output node Y=f (w1*x1+w2*x2+b), wherein f is
Activation primitive.In addition, the number of input layer and output layer is usually one, hidden layer can be made of multilayer.
Step 304 utilizes the intermediate control operation actual control operation corresponding with the first basic act feature
Between difference and optimization algorithm the weight in the activation primitive is corrected repeatedly, the control behaviour among described
Make the difference between the actual control operation in default error range, obtain the activation letter of each node of training completion
Number generates default behavior prediction model.
The optimization algorithm includes stochastic gradient descent (Stochastic Gradient Descent, SGD) algorithm, fits
Answering property moments estimation (adaptive moment estimation, adam) algorithm or Momentum algorithms.
Step 305 is being set in the cycle every setting timing, obtains current behavioural characteristic.
Step 306 inputs the current behavioural characteristic to the default behavior prediction model, obtains the default behavior
The prediction result of prediction model output.
Step 307, according to the prediction result, the control operation of user behavior custom is met described in automated execution, it is described
Control operation includes the data scrubbing of application program and data update operation.
Method provided in this embodiment is trained user behavior custom the default behavior of generation using neural network method
Prediction model, by, every setting the current behavioural characteristic of timing acquisition, current behavioural characteristic being inputted pre- within the setting cycle
If behavior prediction model, the prediction result of the default behavior prediction model output is obtained, according to the prediction result, is held automatically
Row meets the control operation of user behavior custom, realizes the application program controlling behaviour that automated execution meets user behavior custom
Make, improve the intelligence of application program controlling.
Fig. 4 is a kind of structure diagram of application program controlling device provided by the embodiments of the present application, which can be by soft
Part and/or hardware realization integrate in the terminal.As shown in figure 4, the device include current behavioural characteristic acquisition module 41,
Default behavior prediction model acquisition module 42, control operation prediction module 43 and control operation execution module 44.
Behavioural characteristic acquisition module 41 at present every setting time within the setting cycle, obtains current behavioural characteristic；
Default behavior prediction model acquisition module 42, for obtaining the default behavior prediction based on machine learning method generation
Model, the default behavior prediction model is obtained by the behavioural characteristic sample training of multiple known control operations, for being based on going
It is characterized PREDICTIVE CONTROL operation；
Control operation prediction module 43, for inputting the current behavioural characteristic to the default behavior prediction model,
Obtain the prediction result of the default behavior prediction model output；
Control operation execution module 44, for meeting the control operation of user behavior custom, the control described in automated execution
System operation includes the data scrubbing of application program and data update operation.
Device provided in this embodiment, will by setting every setting the current behavioural characteristic of timing acquisition in the cycle
Behavioural characteristic is inputted to the default behavior prediction model generated based on machine learning method at present, obtains the default behavior prediction
The prediction result of model output, according to the prediction result, automated execution meets the control operation of user behavior custom, the control
System operation includes the data scrubbing of application program and data update operation, can be performed automatically the application for meeting user behavior custom
Program control operations improve the intelligence of application program controlling.
Optionally, the data cached cleaning operation of the data scrubbing operation of the application program including application program and/or
The shutoff operation of background application, the data update operation of the application program include the version updating operation of application program
And/or the operating parameter update operation of application program.
Optionally, described device further includes：
User behavior is accustomed to acquisition module, for during the operation of mobile terminal, gathering user's operation mobile terminal
First user behavior is accustomed to；
For analyzing first user behavior custom, it is basic to obtain first for basic act feature acquisition module
Behavioural characteristic；
Default behavior prediction model generation module, for using the first basic act feature as training sample, being based on
Machine learning method is trained, and generates default behavior prediction model.
Optionally, when performance parameter of the basic act feature including mobile terminal, application program identification, behavior occur
Between, the behavior end time, behavior scene, in content of the act and behavior frequency at least one of.
Optionally, the machine learning method includes neural network method, support vector machine method, traditional decision-tree, patrols
Collect homing method, bayes method and random forest method.
Optionally, the machine learning method includes neural network method, and the neural network method includes input layer, hidden
It hides layer and output layer, the default behavior prediction model generation module is specifically used for：
The first basic act feature is inputted to the input layer, and by corresponding with each node of the hidden layer
The calculating of activation primitive exports intermediate control operation；
Utilize the difference between intermediate control operation agenda operation corresponding with the first basic act feature
Value and optimization algorithm the weight in the activation primitive is corrected repeatedly, until the intermediate control operation with it is described
Difference between agenda operation obtains the activation primitive of each node of training completion in default error range, and generation is pre-
If behavior prediction model.
Optionally, default behavior prediction model modification module is further included, is specifically used for：
After the default behavior prediction model of generation, every the setting cycle, system obtains the current setting cycle automatically
Interior second user behavioural habits；
The second user behavioural habits are analyzed, obtain the second basic act feature；
The second basic act feature is inputted into the default behavior prediction model, it is pre- to obtain the default behavior
Survey the output result of model；
If the difference for exporting result actual control operation corresponding with the second basic act feature is beyond pre-
If error range, then the default behavior prediction model is carried out using the second basic act feature as new training sample
Training and update again.
The embodiment of the present application also provides a kind of storage medium for including computer executable instructions, and the computer can perform
When being performed by computer processor for performing a kind of application control method, this method includes for instruction：
Every setting time within the setting cycle, current behavioural characteristic is obtained；
The default behavior prediction model based on machine learning method generation is obtained, the default behavior prediction model is by multiple
The behavioural characteristic sample training of known control operation obtains, and is operated for Behavior-based control feature PREDICTIVE CONTROL；
The current behavioural characteristic is inputted to the default behavior prediction model, obtains the default behavior prediction model
The prediction result of output；
According to the prediction result, automated execution meets the control operation of user behavior custom, and the control operation includes
The data scrubbing of application program and data update operation.
Storage medium --- any various types of memory devices or storage device.Term " storage medium " is intended to wrap
It includes：Install medium, such as CD-ROM, floppy disk or magnetic tape equipment；Computer system memory or random access memory, such as
DRAM, DDR RAM, SRAM, EDO RAM, blue Bath (Rambus) RAM etc.；Nonvolatile memory, such as flash memory, magnetic medium
(such as hard disk or optical storage)；Memory component of register or other similar types etc..Storage medium can further include other
The memory of type or its combination.In addition, storage medium can be located at program in the first computer system being wherein performed,
Or can be located in different second computer systems, second computer system is connected to the by network (such as internet)
One computer system.Second computer system can provide program instruction and be used to perform to the first computer." storage is situated between term
Matter " can include may reside in different position two of (such as in different computer systems by network connection) or
More storage mediums.Storage medium can store the program instruction that can be performed by one or more processors and (such as implement
For computer program).
Certainly, a kind of storage medium for including computer executable instructions that the embodiment of the present application is provided, computer
The application program controlling operation that executable instruction is not limited to the described above, can also carry out what the application any embodiment was provided
Relevant operation in application control method.
The embodiment of the present application provides a kind of mobile terminal, and provided by the embodiments of the present application answer can be integrated in the mobile terminal
Use presetting apparatus.Fig. 5 is a kind of structure diagram of mobile terminal provided by the embodiments of the present application.Mobile terminal 500 can
To include：Memory 501, processor 502 and the computer program that is stored on memory 501 and can be run in processor 502,
The processor 502 realizes the application control method as described in the embodiment of the present application when performing the computer program.
Mobile terminal provided by the embodiments of the present application, by obtaining current behavior every setting timing within the setting cycle
Feature inputs current behavioural characteristic to the default behavior prediction model generated based on machine learning method, obtains described default
The prediction result of behavior prediction model output, according to the prediction result, automated execution meets the control behaviour of user behavior custom
Make, the control operation includes the data scrubbing of application program and data update operation, can be performed automatically and meets user behavior
The application program controlling operation of custom, improves the intelligence of application program controlling.
Fig. 6 is the structure diagram of another mobile terminal provided by the embodiments of the present application, as shown in fig. 6, the movement is whole
End can include：Memory 601, central processing unit (Central Processing Unit, CPU) 602 (also known as processor, with
Lower abbreviation CPU), the memory 601, for storing executable program code；The processor 602 is by reading the storage
The executable program code stored in device 601 runs program corresponding with the executable program code, for performing：
It sets in the cycle every setting time, obtains current behavioural characteristic；It is pre- to obtain the default behavior based on machine learning method generation
Model is surveyed, the default behavior prediction model is obtained by the behavioural characteristic sample training of multiple known control operations, for being based on
Behavioural characteristic PREDICTIVE CONTROL operates；The current behavioural characteristic is inputted to the default behavior prediction model, is obtained described pre-
If the prediction result of behavior prediction model output；According to the prediction result, automated execution meets the control of user behavior custom
Operation, the control operation include the data scrubbing of application program and data update operation.
The mobile terminal further includes：Peripheral Interface 603, RF (Radio Frequency, radio frequency) circuit 605, audio-frequency electric
Road 606, loud speaker 611, power management chip 608, input/output (I/O) subsystem 609, touch-screen 612, other input/controls
Control equipment 610 and outside port 604, these components are communicated by one or more communication bus or signal wire 607.
It should be understood that diagram mobile terminal 600 is only an example of mobile terminal, and mobile terminal 600
Can have than more or less components shown in figure, two or more components can be combined or can be with
It is configured with different components.Various parts shown in figure can be including one or more signal processings and/or special
Hardware, software including integrated circuit are realized in the combination of hardware and software.
Just provided in this embodiment for the mobile terminal of application program to be controlled to be described in detail below, the movement is whole
End is by taking mobile phone as an example.
Memory 601, the memory 601 can be by access such as CPU602, Peripheral Interfaces 603, and the memory 601 can
To include high-speed random access memory, nonvolatile memory can also be included, such as one or more disk memory,
Flush memory device or other volatile solid-state parts.
The peripheral hardware that outputs and inputs of equipment can be connected to CPU502 and deposited by Peripheral Interface 603, the Peripheral Interface 603
Reservoir 601.
I/O subsystems 609, the I/O subsystems 609 can be by the input/output peripherals in equipment, such as touch-screen 612
With other input/control devicess 610, Peripheral Interface 603 is connected to.I/O subsystems 609 can include 6091 He of display controller
For controlling one or more input controllers 6092 of other input/control devicess 610.Wherein, one or more input controls
Device 6092 processed receives electric signal from other input/control devicess 610 or sends electric signal to other input/control devicess 610,
Other input/control devicess 610 can include physical button (pressing button, rocker buttons etc.), dial, slide switch, behaviour
Vertical pole clicks on idler wheel.What deserves to be explained is input controller 6092 can with it is following any one be connected：Keyboard, infrared port,
The instruction equipment of USB interface and such as mouse.
Touch-screen 612, the touch-screen 612 are the input interface and output interface between user terminal and user, can
User is shown to depending on output, visual output can include figure, text, icon, video etc..
Display controller 6091 in I/O subsystems 609 receives electric signal from touch-screen 612 or is sent out to touch-screen 612
Electric signals.Touch-screen 612 detects the contact on touch-screen, and the contact detected is converted to and shown by display controller 6091
The interaction of user interface object on touch-screen 612, that is, realize human-computer interaction, the user interface being shown on touch-screen 612
Icon that object can be the icon of running game, be networked to corresponding network etc..What deserves to be explained is equipment can also include light
Mouse, light mouse are the extensions for not showing the touch sensitive surface visually exported or the touch sensitive surface formed by touch-screen.
RF circuits 605 are mainly used for establishing the communication of mobile phone and wireless network (i.e. network side), realize mobile phone and wireless network
The data receiver of network and transmission.Such as transmitting-receiving short message, Email etc..Specifically, RF circuits 605 receive and send RF letters
Number, RF signals are also referred to as electromagnetic signal, and RF circuits 605 convert electrical signals to electromagnetic signal or electromagnetic signal is converted to telecommunications
Number, and communicated by the electromagnetic signal with communication network and other equipment.RF circuits 605 can include performing
The known circuit of these functions includes but not limited to antenna system, RF transceivers, one or more amplifiers, tuner, one
A or multiple oscillators, digital signal processor, CODEC (COder-DECoder, coder) chipset, user identifier mould
Block (Subscriber Identity Module, SIM) etc..
Voicefrequency circuit 606 is mainly used for receiving voice data from Peripheral Interface 603, which is converted to telecommunications
Number, and the electric signal is sent to loud speaker 611.
Loud speaker 611 for the voice signal for receiving mobile phone from wireless network by RF circuits 605, is reduced to sound
And play the sound to user.
Power management chip 608, the hardware for being connected by CPU602, I/O subsystem and Peripheral Interface 603 are supplied
Electricity and power management.
It is arbitrary that application program controlling device, storage medium and the mobile terminal provided in above-described embodiment can perform the application
The application control method that embodiment is provided possesses and performs the corresponding function module of this method and advantageous effect.Not upper
The technical detail of detailed description in embodiment is stated, reference can be made to the application control method that the application any embodiment is provided.
The technical principle that above are only the preferred embodiment of the application and used.The application is not limited to spy described here
Determine embodiment, the various significant changes that can carry out for a person skilled in the art, readjust and substitute all without departing from
The protection domain of the application.Therefore, although being described in further detail by above example to the application, this Shen
Above example please be not limited only to, in the case where not departing from the application design, other more equivalence enforcements can also be included
Example, and scope of the present application is determined by the scope of claim.
Claims (10)
1. a kind of application control method, which is characterized in that including：
Every setting time within the setting cycle, current behavioural characteristic is obtained；
The default behavior prediction model based on machine learning method generation is obtained, the default behavior prediction model is by multiple known
The behavioural characteristic sample training of control operation obtains, and is operated for Behavior-based control feature PREDICTIVE CONTROL；
The current behavioural characteristic is inputted to the default behavior prediction model, obtains the default behavior prediction model output
Prediction result；
According to the prediction result, automated execution meets the control operation of user behavior custom, and the control operation includes application
The data scrubbing of program and data update operation.
2. application control method according to claim 1, which is characterized in that the data scrubbing behaviour of the application program
Make to include the data cached cleaning operation of application program and/or the shutoff operation of background application, the number of the application program
Include the version updating operation of application program according to update operation and/or the operating parameter of application program updates operation.
3. application control method according to claim 1, which is characterized in that further include：
During the operation of mobile terminal, the first user behavior custom of acquisition user's operation mobile terminal；
First user behavior custom is analyzed, obtains the first basic act feature；
Using the first basic act feature as training sample, it is trained based on machine learning method, generates default behavior
Prediction model.
4. application control method according to claim 3, which is characterized in that the basic act feature includes movement
The performance parameter of terminal, application program identification, behavior time of origin, the behavior end time, behavior scene, content of the act and
At least one of in behavior frequency.
5. according to claim 1-4 any one of them application control methods, which is characterized in that the machine learning method
Including neural network method, support vector machine method, traditional decision-tree, logistic regression method, bayes method and random forest
Method.
6. application control method according to claim 3, which is characterized in that the machine learning method includes nerve
Network method, the neural network method includes input layer, hidden layer and output layer, described by the first basic act feature
It as training sample, is trained based on machine learning method, generating default behavior prediction model includes：
The first basic act feature is inputted to the input layer, and passes through activation corresponding with each node of the hidden layer
The calculating of function exports intermediate control operation；
Difference between being operated using intermediate control operation agenda corresponding with the first basic act feature, with
And optimization algorithm corrects the weight in the activation primitive repeatedly, until the intermediate control operation and the actual row
Difference between operation obtains the activation primitive of each node of training completion in default error range, generates default behavior
Prediction model.
7. the application control method according to claim 3 or 6, which is characterized in that in the default behavior prediction mould of generation
After type, further include：
Every the setting cycle, system obtains the second user behavioural habits in the current setting cycle automatically；
The second user behavioural habits are analyzed, obtain the second basic act feature；
The second basic act feature is inputted into the default behavior prediction model, obtains the default behavior prediction mould
The output result of type；
If the difference for exporting result actual control operation corresponding with the second basic act feature is beyond default mistake
Poor scope then carries out again the default behavior prediction model using the second basic act feature as new training sample
Training and update.
8. a kind of application program controlling device, which is characterized in that including：
Behavioural characteristic acquisition module at present every setting time within the setting cycle, obtains current behavioural characteristic；
Default behavior prediction model acquisition module, for obtaining the default behavior prediction model based on machine learning method generation,
The default behavior prediction model is obtained by the behavioural characteristic sample training of multiple known control operations, for Behavior-based control feature
PREDICTIVE CONTROL operates；
Control operation prediction module for inputting the current behavioural characteristic to the default behavior prediction model, obtains institute
State the prediction result of default behavior prediction model output；
Control operation execution module, for meeting the control operation of user behavior custom, the control operation described in automated execution
Data scrubbing and data update operation including application program.
9. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is held by processor
The application control method as described in any in claim 1-7 is realized during row.
10. a kind of mobile terminal including memory, processor and stores the calculating that can be run on a memory and on a processor
Machine program, which is characterized in that the processor is realized when performing the computer program as described in any in claim 1-7
Application control method.
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