CN107748682A - Background application management-control method, device, storage medium and electronic equipment - Google Patents
Background application management-control method, device, storage medium and electronic equipment Download PDFInfo
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Abstract
This application discloses a kind of background application management-control method, device, storage medium and electronic equipment, methods described includes：Sample data is inputted into algorithm model, obtains multiple first prediction results；When multiple first prediction results include correctly predicted result and error prediction result, the fisrt feature parameter value that target signature parameter in sample data corresponds to correctly predicted result, and the second feature parameter value of corresponding error prediction result are obtained respectively；Initial preset offset is obtained according to fisrt feature parameter value and second feature parameter value calculation；By sample data and initial preset offset, input algorithm model is trained, and obtains target predesigned compensation value；By the current multiple characteristic parameters of default background application and target predesigned compensation value, algorithm model is inputted, obtains target prediction result, and management and control is carried out to default background application according to target prediction result.The accuracy being predicted to presetting background application is improved, lifts the accuracy of background application management and control.
Description
Technical field
The application is related to communication technical field, more particularly to a kind of background application management-control method, device, storage medium and electricity
Sub- equipment.
Background technology
Cleaning background application is method that is a kind of conventional and effectively reducing EMS memory occupation, reduce power consumption.But background application
It can not arbitrarily clear up, if the background application next will be used, but be cleaned, then need to restart, start time length,
Power consumption also accordingly increases.Therefore, it is necessary to accurately differentiate background application whether can clear up it is significant.Tradition judges that backstage should
It is Statistics-Based Method with the method that can be cleared up, for example retains the most frequently used application, clears up the application being of little use.But this is clear
The problem of precision of prediction is inadequate be present in reason method.
The content of the invention
The application provides a kind of background application management-control method, device, storage medium and electronic equipment, can be lifted to application
Program carries out the accuracy of management and control.
In a first aspect, the embodiment of the present application provides a kind of background application management-control method, applied to electronic equipment, including step
Suddenly：
Sample data is inputted into algorithm model, obtains multiple first prediction results；
When the multiple first prediction result includes correctly predicted result and error prediction result, respectively described in acquisition
Target signature parameter corresponds to the fisrt feature parameter value of correctly predicted result in sample data, and corresponds to error prediction result
Second feature parameter value；
Initial preset offset is obtained according to the fisrt feature parameter value and the second feature parameter value calculation；
By the sample data and the initial preset offset, input the algorithm model and be trained, obtain mesh
Mark predesigned compensation value；
By the current multiple characteristic parameters of default background application and the target predesigned compensation value, the algorithm mould is inputted
Type, target prediction result is obtained, and management and control is carried out to the default background application according to the target prediction result.
Second aspect, the embodiment of the present application provides a kind of background application control device, applied to electronic equipment, including：
First prediction result acquiring unit, for sample data to be inputted into algorithm model, obtain multiple first prediction results；
Characteristic ginseng value acquiring unit, for including correctly predicted result and mistake when the multiple first prediction result
During prediction result, the fisrt feature parameter that target signature parameter in the sample data corresponds to correctly predicted result is obtained respectively
Value, and the second feature parameter value of corresponding error prediction result；
Initial compensation value acquiring unit, for according to the fisrt feature parameter value and the second feature parameter value calculation
Obtain initial preset offset；
Target predesigned compensation value acquiring unit, for by the sample data and the initial preset offset, input
The algorithm model is trained, and obtains target predesigned compensation value；
Control unit, for by the current multiple characteristic parameters of default background application and the target predesigned compensation value,
The algorithm model is inputted, obtains target prediction result, and the default background application is entered according to the target prediction result
Row management and control.
The third aspect, the embodiment of the present application provide a kind of storage medium, computer program are stored thereon with, when the calculating
When machine program is run on computers so that the computer performs above-mentioned background application management-control method.
Fourth aspect, the embodiment of the present application provide a kind of electronic equipment, including processor and memory, the memory have
Computer program, the processor is by calling the computer program, for performing above-mentioned background application management-control method.
Background application management-control method, device, storage medium and the electronic equipment that the embodiment of the present application provides, by by sample
Data input algorithm model, obtain multiple first prediction results；When multiple first prediction results include correctly predicted result and
During error prediction result, the fisrt feature parameter that target signature parameter in sample data corresponds to correctly predicted result is obtained respectively
Value, and the second feature parameter value of corresponding error prediction result；According to fisrt feature parameter value and second feature parameter value meter
Calculation obtains initial preset offset；By sample data and initial preset offset, input algorithm model is trained, and obtains mesh
Mark predesigned compensation value；By the current multiple characteristic parameters of default background application and target predesigned compensation value, algorithm model is inputted,
Target prediction result is obtained, and management and control is carried out to default background application according to target prediction result.It can improve to default backstage
Using the accuracy being predicted, so as to lift the accuracy that management and control is carried out to the application program for entering backstage.
Brief description of the drawings
In order to illustrate more clearly of the technical scheme in the embodiment of the present application, make required in being described below to embodiment
Accompanying drawing is briefly described.It should be evident that drawings in the following description are only some embodiments of the present application, for
For those skilled in the art, on the premise of not paying creative work, it can also be obtained according to these accompanying drawings other attached
Figure.
Fig. 1 is the system schematic for the background application control device that the embodiment of the present application provides；
Fig. 2 is the application scenarios schematic diagram for the background application control device that the embodiment of the present application provides；
Fig. 3 is the schematic flow sheet for the background application management-control method that the embodiment of the present application provides；
Fig. 4 is the schematic flow sheet for the selection target signature parameter that the embodiment of the present application provides；
Fig. 5 obtains the schematic flow sheet of initial preset offset for what the embodiment of the present application provided；
Fig. 6 obtains another schematic flow sheet of initial preset offset for what the embodiment of the present application provided；
Fig. 7 obtains the schematic flow sheet of target predesigned compensation value for what the embodiment of the present application provided；
Fig. 8 is the first structural representation for the background application control device that the embodiment of the present application provides；
Fig. 9 is second of structural representation of the background application control device that the embodiment of the present application provides；
Figure 10 is the third structural representation for the background application control device that the embodiment of the present application provides；
Figure 11 is the 4th kind of structural representation of the background application control device that the embodiment of the present application provides；
Figure 12 is the 5th kind of structural representation of the background application control device that the embodiment of the present application provides；
Figure 13 is the structural representation for the electronic equipment that the embodiment of the present application provides；
Figure 14 is another structural representation for the electronic equipment that the embodiment of the present application provides.
Embodiment
Schema is refer to, wherein identical element numbers represent identical component, and the principle of the application is to implement one
Illustrated in appropriate computing environment.The following description is based on illustrated the application specific embodiment, and it should not be by
It is considered as limitation the application other specific embodiments not detailed herein.
In the following description, the specific embodiment of the application is by with reference to as the step performed by one or multi-section computer
And symbol illustrates, unless otherwise stating clearly.Therefore, these steps and operation will have to mention for several times is performed by computer, this paper institutes
The computer of finger, which performs, to be included by representing with the computer processing unit of the electronic signal of the data in a structuring pattern
Operation.The data or the opening position being maintained in the memory system of the computer are changed in this operation, and its is reconfigurable
Or change the running of the computer in a manner of known to the tester of this area in addition.The data structure that the data are maintained
For the provider location of the internal memory, it has the particular characteristics as defined in the data format.But the application principle is with above-mentioned text
Word illustrates that it is not represented as a kind of limitation, and this area tester will appreciate that following plurality of step and operation also
It may be implemented among hardware.
Term as used herein " module " can regard the software object to be performed in the arithmetic system as.This paper difference
Component, module, engine and service can be regarded as the objective for implementation in the arithmetic system.And this paper device and method can be with
The mode of software is implemented, and can also be implemented certainly on hardware, within the application protection domain.
Term " first ", " second " and " the 3rd " in the application etc. is to be used to distinguish different objects, rather than for retouching
State particular order.In addition, term " comprising " and " having " and their any deformations, it is intended that cover non-exclusive include.
Such as contain the step of process, method, system, product or the equipment of series of steps or module is not limited to list or
Module, but some embodiments also include the step of not listing or module, or some embodiments also include for these processes,
Method, product or equipment intrinsic other steps or module.
Referenced herein " embodiment " is it is meant that the special characteristic, structure or the characteristic that describe can wrap in conjunction with the embodiments
It is contained at least one embodiment of the application.Each position in the description occur the phrase might not each mean it is identical
Embodiment, nor the independent or alternative embodiment with other embodiments mutual exclusion.Those skilled in the art explicitly and
Implicitly understand, embodiment described herein can be combined with other embodiments.
Referring to Fig. 1, Fig. 1 is the system schematic for the background application control device that the embodiment of the present application provides.The backstage
It is mainly used in using control device：Sample data is inputted into algorithm model, obtains multiple first prediction results；When multiple first pre-
When surveying result includes correctly predicted result and error prediction result, it is corresponding just that target signature parameter in sample data is obtained respectively
The fisrt feature parameter value of true prediction result, and the second feature parameter value of corresponding error prediction result；According to fisrt feature
Parameter value and second feature parameter value calculation obtain initial preset offset；It is defeated by sample data and initial preset offset
Enter algorithm model to be trained, obtain target predesigned compensation value；By default background application current multiple characteristic parameters and mesh
Predesigned compensation value is marked, inputs algorithm model, obtains target prediction result, and default background application is entered according to target prediction result
Row management and control.Such as close or freeze.
Specifically, referring to Fig. 2, the application scenarios that Fig. 2 is the background application control device that the embodiment of the present application provides show
It is intended to.For example background application control device detects the application in the running background of electronic equipment when receiving management and control request
Program includes default background application a, default background application b and default background application c；Then corresponding default background application is obtained
A, background application b and default background application c multiple characteristic parameters are preset, multiple characteristic parameters are inputted into algorithm model；Point
Probability a ', probability b ' and probability c ' are not obtained；Then according to probability a ', probability b ' and probability c ' respectively to the pre- of running background
If background application a, default background application b and default background application c carry out management and control, such as should by the minimum default backstage of probability
Closed with b.
The embodiment of the present application provides a kind of background application management-control method, and the executive agent of the background application management-control method can be with
It is the background application control device that the embodiment of the present application provides, or is integrated with the electronic equipment of the background application control device,
Wherein the background application control device can be realized by the way of hardware or software.
The embodiment of the present application will be described from the angle of background application control device, and the background application control device is specific
It can integrate in the electronic device.The background application management-control method includes：Sample data is inputted into algorithm model, obtains multiple
One prediction result；When multiple first prediction results include correctly predicted result and error prediction result, sample is obtained respectively
Target signature parameter corresponds to the fisrt feature parameter value of correctly predicted result, and the second of corresponding error prediction result in data
Characteristic ginseng value；Initial preset offset is obtained according to fisrt feature parameter value and second feature parameter value calculation；By sample number
According to this and initial preset offset, input algorithm model are trained, and obtain target predesigned compensation value；Default background application is worked as
Preceding multiple characteristic parameters and target predesigned compensation value, algorithm model is inputted, obtains target prediction result, and it is pre- according to target
Survey result and management and control is carried out to default background application.
Referring to Fig. 3, Fig. 3 is the schematic flow sheet for the background application management-control method that the embodiment of the present application provides.The application
The background application management-control method that embodiment provides is applied to electronic equipment, and idiographic flow can be as follows：
Step 101, sample data is inputted into algorithm model, obtains multiple first prediction results.
Sample data includes the set of characteristic parameters of multiple dimensions.Sample data is the training sample data obtained in advance,
Characteristic parameter in sample data corresponds to the operational factor of background application, specific sample data can with as shown in table 1 below,
, it is necessary to illustrate, the characteristic parameter shown in table 1 is only for example characteristic information including multiple dimensions, in practice, a sample
The quantity for the characteristic parameter that data are included, the quantity than characteristic parameter shown in table 1 can be more than, can also be less than shown in table 1
The quantity of characteristic parameter, the specific features parameter taken can also be different from shown in table 1, are not especially limited herein.
Table 1
The characteristic parameter that sample data inputs algorithm model every time can be that all, it can also be chosen from sample data
What Partial Feature parameter was formed, as shown in table 2, an input data is that 10 characteristic parameters are chosen from sample data.
| Dimension | Characteristic parameter |
| 1 | During being cut into backstage, the screen duration that goes out of electronic equipment |
| 2 | The time on foreground is in daily |
| 3 | The type of application, including one-level (conventional application), two level (other application) |
| 4 | The bright screen time of electronic equipment |
| 5 | The current electric quantity of electronic equipment |
| 6 | Current wireless network state |
| 7 | Using the duration used every time on foreground |
| 8 | Current foreground application enters the average time interval that backstage enters foreground to intended application |
| 9 | Current foreground application enters being averaged the screen time of going out during backstage enters foreground to intended application |
| 10 | Apply the number accounting of each pre-set interval in the histogram of backstage residence time |
Table 2
It should be noted that the dimension in table 2 is only the citing to characteristic parameter in an input data, it is not offered as pair
The dimension of characteristic parameter is defined.In some embodiments, can be according to being actually needed selection characteristic parameter.
Training sample data include multiple characteristic parameters, and the characteristic ginseng value included in each characteristic parameter is different, each
Sample data includes multiple characteristic parameters, and each characteristic parameter corresponds to one or more characteristic ginseng values, by these sample datas
It is separately input to as training data in algorithm model, algorithm model is multiple first pre- according to corresponding to obtaining these sample datas
Survey result.
It should be noted that the characteristic parameter of same sample data can correspond to different characteristic ginseng values.One spy
The characteristic ginseng value for levying parameter is as shown in table 3.
Table 3:Bright screen, which goes out, shields record
Step 102, when multiple first prediction results include correctly predicted result and error prediction result, obtain respectively
Target signature parameter corresponds to the fisrt feature parameter value of correctly predicted result in sample data, and corresponds to error prediction result
Second feature parameter value.
First prediction result is that algorithm model corresponds to the result that different input datas is predicted to obtain, and input data is not
Together, the result obtained may also be different.Wherein just include correctly predicted result and error prediction result.When the multiple first prediction knots
When fruit includes correctly predicted result and error prediction result, it is corresponding correct pre- that target signature parameter in sample data is obtained respectively
Survey the fisrt feature parameter value of result, and the second feature parameter value of corresponding error prediction result.
Also referring to Fig. 4, Fig. 4 is the schematic flow sheet for the selection target signature parameter that the embodiment of the present application provides.
In present embodiment, the method for acquisition target signature parameter, idiographic flow can be as follows：
Step 1021, modification inputs the weight of each characteristic parameter of algorithm model successively.
The input data of input algorithm model includes multiple characteristic parameters, one of characteristic parameter is changed every time, by this
The weight of characteristic parameter constantly adjusts, and such as constantly reduces or improves constantly, inputs in algorithm model and be predicted again after having adjusted,
Continue to obtain the first prediction result.Wherein, this feature parameter can be removed up to dropping to zero by constantly reducing weight.
Step 1022, if the first prediction result changes, it is determined that corresponding characteristic parameter is target signature parameter.
First prediction result changes, i.e., becomes error prediction result from correctly predicted result, or become from error prediction result
Into correctly predicted result, illustrate that this feature parameter can play the crucial influence of comparison to the correctness of prediction result, it is determined that
Corresponding characteristic parameter is target signature parameter.
Step 103, initial preset offset is obtained according to fisrt feature parameter value and second feature parameter value calculation.
Initial preset offset can be obtained by obtaining the difference of fisrt feature parameter value and second feature parameter value,
Fisrt feature parameter value and second feature parameter value can be multiplied by after different weighted values respectively and subtract each other to obtain initial preset benefit
Repay value.
Please refer to fig. 5, Fig. 5 obtains the schematic flow sheet of initial preset offset for what the embodiment of the present application provided.
In the present embodiment, the method for obtaining initial preset offset, idiographic flow can be as follows：
Step 10131, multiple fisrt feature parameter values that target signature parameter corresponds to multiple correctly predicted results are obtained.
Step 10132, multiple second feature parameter values that target signature parameter corresponds to multiple error prediction results are obtained.
Step 10133, according to the average value of multiple fisrt feature parameter values and the average value of multiple second feature parameter values
Initial preset offset is calculated.
By being averaged respectively to multiple fisrt feature parameter values and multiple second feature parameter values, then subtract each other again
To initial preset offset.It is of course also possible to the average value of the average value and second feature parameter value to fisrt feature parameter value
Subtract each other to obtain initial preset offset after being multiplied by different weighted values respectively.
Also referring to Fig. 6, Fig. 6 is another flow for the obtaining initial preset offset signal that the embodiment of the present application provides
Figure.In the present embodiment, the method for obtaining initial preset offset, idiographic flow can be as follows：
Step 10134, the characteristic ginseng value of target signature parameter, and multiple ginsengs with characteristic ginseng value into gradient are obtained
Examine characteristic ginseng value.
Gradient can be incremental gradient, or the gradient successively decreased.First obtain the characteristic parameter of target signature parameter
Value, then using this feature parameter value as radix, then on the basis of this radix, obtain a number for being incremented by and/or successively decreasing
Row.The value of gradient can be based on 1/10th, half etc..It can be the gradient of an equal difference, may not be
Difference, change with the order of magnitude of data in ordered series of numbers, as the bigger difference of data is also bigger, the smaller difference of data is also smaller.
Step 10135, multiple fixed reference feature parameter values are inputted into algorithm model, obtains the second prediction result.
Then multiple fixed reference feature parameter values are inputted into algoritic module respectively, obtains multiple second prediction results.Accordingly,
Characteristic ginseng value corresponding to other characteristic parameters is constant in input data.
Step 10136, if the second prediction result changes, adjacent correctly predicted result and error prediction knot are obtained respectively
Fisrt feature parameter value corresponding to fruit and second feature parameter value.
Second prediction result is as a result also different equally because input data is different.Second prediction result becomes wrong from correct
By mistake, or from mistake become correct when, obtain fisrt feature parameter corresponding to adjacent correctly predicted result and error prediction result
Value and second feature parameter value.Two adjacent data i.e. in gradient data.
Step 10137, initial preset offset is obtained according to the difference of fisrt feature parameter value and second feature parameter value.
By subtracting each other to obtain initial preset offset to fisrt feature parameter value and second feature parameter value.Certainly, also may be used
Subtract each other to obtain initial preset compensation to be multiplied by after different weighted values fisrt feature parameter value and second feature parameter value respectively
Value.
Step 104, sample data and initial preset offset, input algorithm model are trained, it is pre- obtains target
If offset.
Target signature parameter in sample data is superimposed with initial preset offset before algorithm model is inputted, then defeated
Enter algorithm model prediction, learn by the training of a large amount of numbers, obtain a target predesigned compensation value, can allow more input
Data improve the accuracy of prediction after target predesigned compensation value is superimposed.
Also referring to Fig. 7, Fig. 7 obtains the schematic flow sheet of target predesigned compensation value for what the embodiment of the present application provided.
In the present embodiment, the method for obtaining target predesigned compensation value, idiographic flow can be as follows：
Step 1041, according to multiple fisrt feature parameter values, the first span corresponding to acquisition.
From multiple fisrt feature parameter values, first span can be obtained.
Step 1042, according to multiple second feature parameter values, the second span corresponding to acquisition.
Likewise, from multiple second feature parameter values, second span can be obtained.It must can also even beat
One the 3rd span, with the second span respectively in the both sides of the first span.
Step 1043, the first object predesigned compensation value of corresponding first span, and corresponding second value model are obtained
The the second target predesigned compensation value enclosed.
Corresponding different span sets different target predesigned compensation values.Object feature value in first span
It is correct during corresponding prediction result, then need not compensate for or only need less compensation, the target in the second span is special
Sign parameter value needs to compensate, it may be possible to which increase is also likely to be to reduce.
Step 105, by the current multiple characteristic parameters of default background application and target predesigned compensation value, algorithm mould is inputted
Type, target prediction result is obtained, and management and control is carried out to default background application according to target prediction result.
Before being predicted to default background application, the current multiple characteristic parameters of default background application are first obtained, will be more
Target signature parameter in individual characteristic parameter is superimposed corresponding target predesigned compensation value, then inputs algorithm model.Need to illustrate
, target signature parameter can be including multiple, and target predesigned compensation value corresponds with target signature parameter.
Target prediction result can be to clear up a probable value of the default background application, and/or do not clear up the backstage and answer
One probable value, background application is then preset to this according to target prediction result and carries out management and control, after such as closing or keeping this
Platform application.
The plurality of characteristic parameter is inputted in algorithm model, algorithm model corresponds to the spy of wherein one or more characteristic parameters
Levy target predesigned compensation value corresponding to parameter value superposition.For example, 10 characteristic parameters are inputted in algorithm model, including two
Individual target signature parameter：Current electric quantity and running background duration, the wherein characteristic ginseng value of current electric quantity are 10%, running background
The characteristic ginseng value of duration is 10 minutes, is then superimposed corresponding target predesigned compensation to the characteristic ginseng value 10% of current electric quantity
Value-%2, i.e. 10%-2%=8%, the characteristic ginseng value for inputting algorithm model is 8%, to the characteristic parameter of running background duration
Target predesigned compensation value 5 minutes, i.e. ,+5 minutes 10 minutes=15 minutes, input algorithm model corresponding to value superposition in 10 minutes
Characteristic ginseng value is 15 minutes.Then algorithm model is carried out pre- according to the characteristic ginseng value after the corresponding target predesigned compensation value of superposition
Survey, obtain prediction result.It should be noted that this example is not merely to the citing that understanding is carried out, is limited the application
System, the application can also utilize target predesigned compensation value in other ways.
It should be noted that the training process of algorithm model can also can be completed in server end at electronic equipment end.
Training process, actual prediction process when algorithm model are all when server end is completed, it is necessary to use the algorithm model after optimization
When, the use state of multiple periods before default background application current time can be input to server, server is actual
After the completion of prediction, prediction result is sent to electronic equipment end, the default backstage should further according to prediction result management and control for electronic equipment
With.
Training process, actual prediction process when algorithm model are all when electronic equipment end is completed, it is necessary to after using optimization
Algorithm model when, the use state of multiple periods before default background application current time can be input to electronics and set
It is standby, after the completion of electronic equipment actual prediction, electronic equipment default background application according to prediction result management and control.
When the training process of algorithm model is completed in server end, the actual prediction process of algorithm model is at electronic equipment end
, can be by multiple periods before default background application current time during completion, it is necessary to when using the algorithm model after optimization
Use state is input to electronic equipment, and after the completion of electronic equipment actual prediction, this is default according to prediction result management and control for electronic equipment
Background application.Optionally, the algorithm model file trained (model files) can be transplanted on smart machine, if desired
Judge whether current background application can clear up, then obtain multiple periods before default background application current time uses shape
State, the algorithm model file (model files) trained is input to, calculates and can obtain predicted value.
Above-mentioned all technical schemes, any combination can be used to form the alternative embodiment of the application, it is not another herein
One repeats.
From the foregoing, it will be observed that the background application management-control method that the embodiment of the present application provides, by the way that sample data is inputted into algorithm mould
Type, obtain multiple first prediction results；When multiple first prediction results include correctly predicted result and error prediction result,
The fisrt feature parameter value that target signature parameter in sample data corresponds to correctly predicted result is obtained respectively, and corresponding mistake is in advance
Survey the second feature parameter value of result；Initial preset compensation is obtained according to fisrt feature parameter value and second feature parameter value calculation
Value；By sample data and initial preset offset, input algorithm model is trained, and obtains target predesigned compensation value；Will be pre-
If the current multiple characteristic parameters of background application and target predesigned compensation value, input algorithm model, obtain target prediction result,
And management and control is carried out to default background application according to target prediction result.Can improve default background application is predicted it is accurate
Property, so as to lift the accuracy that management and control is carried out to the application program for entering backstage.
Referring to Fig. 8, Fig. 8 is the first structural representation for the background application control device that the embodiment of the present application provides.
Wherein the background application control device 300 is applied to electronic equipment, and the background application control device 300 includes the first prediction result
Acquiring unit 301, characteristic ginseng value acquiring unit 302, initial compensation value acquiring unit 303, target predesigned compensation value obtain single
Member 304 and control unit 305.Wherein：
First prediction result acquiring unit 301, for sample data to be inputted into algorithm model, obtain multiple first prediction knots
Fruit.
Sample data includes the set of characteristic parameters of multiple dimensions.Sample data is the training sample data obtained in advance,
Characteristic parameter in sample data corresponds to the operational factor of background application, and a specific sample data includes the spy of multiple dimensions
Reference ceases.
The characteristic parameter that sample data inputs algorithm model every time can be that all, it can also be chosen from sample data
What Partial Feature parameter was formed.
Training sample data include multiple characteristic parameters, and the characteristic ginseng value included in each characteristic parameter is different, each
Sample data includes multiple characteristic parameters, and each characteristic parameter corresponds to one or more characteristic ginseng values, by these sample datas
It is separately input to as training data in algorithm model, algorithm model is multiple first pre- according to corresponding to obtaining these sample datas
Survey result.
Characteristic ginseng value acquiring unit 302, for including correctly predicted result and mistake when multiple first prediction results
During prediction result, the fisrt feature parameter value that target signature parameter in sample data corresponds to correctly predicted result is obtained respectively, with
And the second feature parameter value of corresponding error prediction result.
First prediction result is that algorithm model corresponds to the result that different input datas is predicted to obtain, and input data is not
Together, the result obtained may also be different.Wherein just include correctly predicted result and error prediction result.When the multiple first prediction knots
When fruit includes correctly predicted result and error prediction result, it is corresponding correct pre- that target signature parameter in sample data is obtained respectively
Survey the fisrt feature parameter value of result, and the second feature parameter value of corresponding error prediction result.
Referring to Fig. 9, Fig. 9 is second of structural representation of the background application control device that the embodiment of the present application provides.
In the present embodiment, characteristic ginseng value acquiring unit 302 includes Weight Acquisition subelement 3021 and target signature parameter acquiring
Subelement 3022.
Weight Acquisition subelement 3021, the weight of each characteristic parameter for changing input algorithm model successively.
The input data of input algorithm model includes multiple characteristic parameters, one of characteristic parameter is changed every time, by this
The weight of characteristic parameter constantly adjusts, and such as constantly reduces or improves constantly, inputs in algorithm model and be predicted again after having adjusted,
Continue to obtain the first prediction result.Wherein, this feature parameter can be removed up to dropping to zero by constantly reducing weight.
Target signature parameter acquiring subelement 3022, if changing for the first prediction result, it is determined that corresponding feature ginseng
Number is target signature parameter.
First prediction result changes, i.e., becomes error prediction result from correctly predicted result, or become from error prediction result
Into correctly predicted result, illustrate that this feature parameter can play the crucial influence of comparison to the correctness of prediction result, it is determined that
Corresponding characteristic parameter is target signature parameter.
Initial compensation value acquiring unit 303, for being obtained according to fisrt feature parameter value and second feature parameter value calculation
Initial preset offset.
Initial preset offset can be obtained by obtaining the difference of fisrt feature parameter value and second feature parameter value,
Fisrt feature parameter value and second feature parameter value can be multiplied by after different weighted values respectively and subtract each other to obtain initial preset benefit
Repay value.
Referring to Fig. 10, Figure 10 is the third structural representation for the background application control device that the embodiment of the present application provides
Figure.In the present embodiment, initial compensation value acquiring unit 303 includes fisrt feature parameter value acquisition subelement 3031, second
Characteristic ginseng value obtains subelement 3032 and initial compensation value obtains subelement 3033. wherein：
Fisrt feature parameter value obtains subelement 3031, and multiple correctly predicted results are corresponded to for obtaining target signature parameter
Multiple fisrt feature parameter values.
Second feature parameter value obtains subelement 3032, and multiple error prediction results are corresponded to for obtaining target signature parameter
Multiple second feature parameter values.
Initial compensation value obtains subelement 3033, for the average value according to multiple fisrt feature parameter values and multiple second
The mean value calculation of characteristic ginseng value obtains initial preset offset.
By being averaged respectively to multiple fisrt feature parameter values and multiple second feature parameter values, then subtract each other again
To initial preset offset.It is of course also possible to the average value of the average value and second feature parameter value to fisrt feature parameter value
Subtract each other to obtain initial preset offset after being multiplied by different weighted values respectively.
Figure 11 is referred to, Figure 11 is the 4th kind of structural representation of the background application control device that the embodiment of the present application provides
Figure.In the present embodiment, initial compensation value acquiring unit 303 includes fixed reference feature parameter value acquisition subelement 3034, second
Prediction result obtains subelement 3035, characteristic ginseng value obtains subelement 3036 and initial preset offset obtains subelement
3033.Wherein：
Fixed reference feature parameter value obtains subelement 3034, for obtaining the characteristic ginseng value of target signature parameter, Yi Jiyu
Multiple fixed reference feature parameter values of the characteristic ginseng value into gradient.
Gradient can be incremental gradient, or the gradient successively decreased.First obtain the characteristic parameter of target signature parameter
Value, then using this feature parameter value as radix, then on the basis of this radix, obtain a number for being incremented by and/or successively decreasing
Row.The value of gradient can be based on 1/10th, half etc..It can be the gradient of an equal difference, may not be
Difference, change with the order of magnitude of data in ordered series of numbers, as the bigger difference of data is also bigger, the smaller difference of data is also smaller.
Second prediction result obtains subelement 3035, for multiple fixed reference feature parameter values to be inputted into algorithm model, obtains
Second prediction result.
Then multiple fixed reference feature parameter values are inputted into algoritic module respectively, obtains multiple second prediction results.Accordingly,
Characteristic ginseng value corresponding to other characteristic parameters is constant in input data.
Characteristic ginseng value obtains subelement 3036, if changing for the second prediction result, obtains respectively adjacent correct
Fisrt feature parameter value and second feature parameter value corresponding to prediction result and error prediction result.
Second prediction result is as a result also different equally because input data is different.Second prediction result becomes wrong from correct
By mistake, or from mistake become correct when, obtain fisrt feature parameter corresponding to adjacent correctly predicted result and error prediction result
Value and second feature parameter value.Two adjacent data i.e. in gradient data.
Initial preset offset obtains subelement 3033, for according to fisrt feature parameter value and second feature parameter value
Difference obtains initial preset offset.
By subtracting each other to obtain initial preset offset to fisrt feature parameter value and second feature parameter value.Certainly, also may be used
Subtract each other to obtain initial preset compensation to be multiplied by after different weighted values fisrt feature parameter value and second feature parameter value respectively
Value.
Target predesigned compensation value acquiring unit 304, for by sample data and initial preset offset, inputting algorithm mould
Type is trained, and obtains target predesigned compensation value.
Target signature parameter in sample data is superimposed with initial preset offset before algorithm model is inputted, then defeated
Enter algorithm model prediction, learn by the training of a large amount of numbers, obtain a target predesigned compensation value, can allow more input
Data improve the accuracy of prediction after target predesigned compensation value is superimposed.
Figure 12 is referred to, Figure 12 is the 5th kind of structural representation of the background application control device that the embodiment of the present application provides
Figure.In the present embodiment, target predesigned compensation value acquiring unit 304 includes：
First span obtains subelement 3041, for according to multiple fisrt feature parameter values, first corresponding to acquisition
Span.
From multiple fisrt feature parameter values, first span can be obtained.
Second span obtains subelement 3042, for according to multiple second feature parameter values, second corresponding to acquisition
Span.
Likewise, from multiple second feature parameter values, second span can be obtained.It must can also even beat
One the 3rd span, with the second span respectively in the both sides of the first span.
First object predesigned compensation value determination subelement 3043, the first object for obtaining corresponding first span are pre-
If offset.
Corresponding different span sets different target predesigned compensation values.Object feature value in first span
It is correct during corresponding prediction result, then need not compensate for or only need less compensation, the target in the second span is special
Sign parameter value needs to compensate, it may be possible to which increase is also likely to be to reduce.
Second target predesigned compensation value determination subelement 3044, the second target for obtaining corresponding second span are pre-
If offset.
Control unit 305, it is defeated for by the current multiple characteristic parameters of default background application and target predesigned compensation value
Enter algorithm model, obtain target prediction result, and management and control is carried out to default background application according to target prediction result.
Before being predicted to default background application, the current multiple characteristic parameters of default background application are first obtained, will be more
Target signature parameter in individual characteristic parameter is superimposed corresponding target predesigned compensation value, then inputs algorithm model.Need to illustrate
, target signature parameter can be including multiple, and target predesigned compensation value corresponds with target signature parameter.
Target prediction result can be to clear up a probable value of the default background application, and/or do not clear up the backstage and answer
One probable value, background application is then preset to this according to target prediction result and carries out management and control, after such as closing or keeping this
Platform application.
Above-mentioned all technical schemes, any combination can be used to form the alternative embodiment of the application, it is not another herein
One repeats.
From the foregoing, it will be observed that the background application control device that the embodiment of the present application provides, by the way that sample data is inputted into algorithm mould
Type, obtain multiple first prediction results；When multiple first prediction results include correctly predicted result and error prediction result,
The fisrt feature parameter value that target signature parameter in sample data corresponds to correctly predicted result is obtained respectively, and corresponding mistake is in advance
Survey the second feature parameter value of result；Initial preset compensation is obtained according to fisrt feature parameter value and second feature parameter value calculation
Value；By sample data and initial preset offset, input algorithm model is trained, and obtains target predesigned compensation value；Will be pre-
If the current multiple characteristic parameters of background application and target predesigned compensation value, input algorithm model, obtain target prediction result,
And management and control is carried out to default background application according to target prediction result.Can improve default background application is predicted it is accurate
Property, so as to lift the accuracy that management and control is carried out to the application program for entering backstage.
In the embodiment of the present application, background application control device belongs to same with the background application management-control method in foregoing embodiments
One design, can run the either method provided in background application management-control method embodiment on background application control device, its
Specific implementation process refers to the embodiment of background application management-control method, and here is omitted.
The embodiment of the present application also provides a kind of electronic equipment.Refer to Figure 13, electronic equipment 400 include processor 401 with
And memory 402.Wherein, processor 401 is electrically connected with memory 402.
Processor 400 is the control centre of electronic equipment 400, utilizes various interfaces and the whole electronic equipment of connection
Various pieces, by the computer program of operation or load store in memory 402, and call and be stored in memory 402
Interior data, the various functions and processing data of electronic equipment 400 are performed, so as to carry out integral monitoring to electronic equipment 400.
Memory 402 can be used for storage software program and module, and processor 401 is stored in memory 402 by operation
Computer program and module, so as to perform various function application and data processing.Memory 402 can mainly include storage
Program area and storage data field, wherein, storing program area can storage program area, the computer program needed at least one function
(such as sound-playing function, image player function etc.) etc.；Storage data field can store to be created according to using for electronic equipment
Data etc..In addition, memory 402 can include high-speed random access memory, nonvolatile memory, example can also be included
Such as at least one disk memory, flush memory device or other volatile solid-state parts.Correspondingly, memory 402 may be used also
With including Memory Controller, to provide access of the processor 401 to memory 402.
In the embodiment of the present application, the processor 401 in electronic equipment 400 can be according to the steps, by one or one
Instruction is loaded into memory 402 corresponding to the process of computer program more than individual, and is stored in by the operation of processor 401
Computer program in reservoir 402, it is as follows so as to realize various functions：
Sample data is inputted into algorithm model, obtains multiple first prediction results；
When multiple first prediction results include correctly predicted result and error prediction result, sample data is obtained respectively
Middle target signature parameter corresponds to the fisrt feature parameter value of correctly predicted result, and the second feature of corresponding error prediction result
Parameter value；
Initial preset offset is obtained according to fisrt feature parameter value and second feature parameter value calculation；
By sample data and initial preset offset, input algorithm model is trained, and obtains target predesigned compensation value；
By the current multiple characteristic parameters of default background application and target predesigned compensation value, algorithm model is inputted, is obtained
Target prediction result, and management and control is carried out to default background application according to target prediction result.
In some embodiments, processor 401 is additionally operable to perform following steps：
Obtain multiple fisrt feature parameter values that target signature parameter corresponds to multiple correctly predicted results；
Obtain multiple second feature parameter values that target signature parameter corresponds to multiple error prediction results；
Obtained just according to the mean value calculation of the average value of multiple fisrt feature parameter values and multiple second feature parameter values
Beginning predesigned compensation value.
In some embodiments, processor 401 is additionally operable to perform following steps：
Obtain the characteristic ginseng value of target signature parameter, and multiple fixed reference feature parameters with characteristic ginseng value into gradient
Value；
Multiple fixed reference feature parameter values are inputted into algorithm model, obtain the second prediction result；
If the second prediction result changes, the is obtained corresponding to adjacent correctly predicted result and error prediction result respectively
One characteristic ginseng value and second feature parameter value；
Initial preset offset is obtained according to the difference of fisrt feature parameter value and second feature parameter value.
In some embodiments, processor 401 is additionally operable to perform following steps：
The weight of each characteristic parameter of modification input algorithm model successively；
If the first prediction result changes, it is determined that corresponding characteristic parameter is target signature parameter.
In some embodiments, processor 401 is additionally operable to perform following steps：
Obtain multiple fisrt feature parameter values of target signature parameter and multiple second feature parameter values；
According to multiple fisrt feature parameter values, the first span corresponding to acquisition；
According to multiple second feature parameter values, the second span corresponding to acquisition；
Obtain the first object predesigned compensation value of corresponding first span, and the second mesh of corresponding second span
Mark predesigned compensation value.
From the foregoing, the electronic equipment that the embodiment of the present application provides, by the way that sample data is inputted into algorithm model, is obtained
Multiple first prediction results；When multiple first prediction results include correctly predicted result and error prediction result, obtain respectively
Target signature parameter corresponds to the fisrt feature parameter value of correctly predicted result, and corresponding error prediction result in sampling notebook data
Second feature parameter value；Initial preset offset is obtained according to fisrt feature parameter value and second feature parameter value calculation；Will
Sample data and initial preset offset, input algorithm model are trained, and obtain target predesigned compensation value；By default backstage
Using current multiple characteristic parameters and target predesigned compensation value, algorithm model is inputted, obtains target prediction result, and according to
Target prediction result carries out management and control to default background application.The accuracy being predicted to presetting background application can be improved, from
And lift the accuracy that management and control is carried out to the application program for entering backstage.
Also referring to Figure 14, in some embodiments, electronic equipment 400 can also include：Display 403, radio frequency
Circuit 404, voicefrequency circuit 405 and power supply 406.Wherein, wherein, display 403, radio circuit 404, voicefrequency circuit 405 with
And power supply 406 is electrically connected with processor 401 respectively.
Display 403 is displayed for the information inputted by user or the information for being supplied to user and various figures are used
Family interface, these graphical user interface can be made up of figure, text, icon, video and its any combination.Display 403
Display panel can be included, in some embodiments, can use liquid crystal display (Liquid Crystal Display,
LCD) or the form such as Organic Light Emitting Diode (Organic Light-Emitting Diode, OLED) configures display surface
Plate.
Radio circuit 404 can be used for transceiving radio frequency signal, to be set by radio communication and the network equipment or other electronics
It is standby to establish wireless telecommunications, the receiving and transmitting signal between the network equipment or other electronic equipments.
Voicefrequency circuit 405 can be used for providing the COBBAIF between user and electronic equipment by loudspeaker, microphone.
Power supply 406 is used to all parts power supply of electronic equipment 400.In some embodiments, power supply 406 can
With logically contiguous by power-supply management system and processor 401, so that charged, discharged by power-supply management system realization management,
And the function such as power managed.
Although not shown in Figure 14, electronic equipment 400 can also include camera, bluetooth module etc., will not be repeated here.
The embodiment of the present application also provides a kind of storage medium, and storage medium is stored with computer program, works as computer program
When running on computers so that computer performs the application program management-control method in any of the above-described embodiment, such as：Passing through will
Sample data inputs algorithm model, obtains multiple first prediction results；When multiple first prediction results include correctly predicted knot
When fruit and error prediction result, the fisrt feature ginseng that target signature parameter in sample data corresponds to correctly predicted result is obtained respectively
Numerical value, and the second feature parameter value of corresponding error prediction result；According to fisrt feature parameter value and second feature parameter value
Initial preset offset is calculated；By sample data and initial preset offset, input algorithm model is trained, obtained
Target predesigned compensation value；By the current multiple characteristic parameters of default background application and target predesigned compensation value, algorithm mould is inputted
Type, target prediction result is obtained, and management and control is carried out to default background application according to target prediction result.
In the embodiment of the present application, storage medium can be magnetic disc, CD, read-only storage (Read Only Memory,
) or random access memory (Random Access Memory, RAM) etc. ROM.
In the above-described embodiments, the description to each embodiment all emphasizes particularly on different fields, and does not have the portion being described in detail in some embodiment
Point, it may refer to the associated description of other embodiment.
It should be noted that for the background application management-control method of the embodiment of the present application, this area common test personnel
It is that can pass through computer program it is appreciated that realizing all or part of flow of the embodiment of the present application background application management-control method
To control the hardware of correlation to complete, computer program can be stored in a computer read/write memory medium, be such as stored in electricity
In the memory of sub- equipment, and by least one computing device in the electronic equipment, it may include in the process of implementation as rear
The flow of the embodiment of platform application management-control method.Wherein, storage medium can be magnetic disc, CD, read-only storage, arbitrary access
Memory body etc..
For the background application control device of the embodiment of the present application, its each functional module can be integrated in a processing core
In piece or modules are individually physically present, can also two or more modules be integrated in a module.On
Stating integrated module can both be realized in the form of hardware, can also be realized in the form of software function module.Integrated
If module is realized in the form of software function module and as independent production marketing or in use, can also be stored in one
In computer read/write memory medium, storage medium is for example read-only storage, disk or CD etc..
A kind of background application management-control method, device, storage medium and the electronics provided above the embodiment of the present application is set
Standby to be described in detail, specific case used herein is set forth to the principle and embodiment of the application, the above
The explanation of embodiment is only intended to help and understands the present processes and its core concept；Meanwhile for those skilled in the art
Member, according to the thought of the application, there will be changes in specific embodiments and applications, in summary, this explanation
Book content should not be construed as the limitation to the application.
Claims (12)
- A kind of 1. background application management-control method, applied to electronic equipment, it is characterised in that including step：Sample data is inputted into algorithm model, obtains multiple first prediction results；When the multiple first prediction result includes correctly predicted result and error prediction result, the sample is obtained respectively Target signature parameter corresponds to the fisrt feature parameter value of correctly predicted result, and the second of corresponding error prediction result in data Characteristic ginseng value；Initial preset offset is obtained according to the fisrt feature parameter value and the second feature parameter value calculation；By the sample data and the initial preset offset, input the algorithm model and be trained, it is pre- to obtain target If offset；By the current multiple characteristic parameters of default background application and the target predesigned compensation value, the algorithm model is inputted, Target prediction result is obtained, and management and control is carried out to the default background application according to the target prediction result.
- 2. background application management-control method according to claim 1, it is characterised in that described according to the fisrt feature parameter The step of value and the second feature parameter value calculation obtain initial preset offset, including：Obtain multiple fisrt feature parameter values that the target signature parameter corresponds to multiple correctly predicted results；Obtain multiple second feature parameter values that the target signature parameter corresponds to multiple error prediction results；Obtained according to the mean value calculation of the average value of the multiple fisrt feature parameter value and the multiple second feature parameter value To initial preset offset.
- 3. background application management-control method according to claim 1, it is characterised in that described according to the fisrt feature parameter The step of value and the second feature parameter value calculation obtain initial preset offset, including：Obtain the characteristic ginseng value of the target signature parameter, and multiple fixed reference features with the characteristic ginseng value into gradient Parameter value；The multiple fixed reference feature parameter value is inputted into algorithm model, obtains the second prediction result；If second prediction result changes, the is obtained corresponding to adjacent correctly predicted result and error prediction result respectively One characteristic ginseng value and second feature parameter value；Initial preset offset is obtained according to the difference of the fisrt feature parameter value and the second feature parameter value.
- 4. background application management-control method according to claim 1, it is characterised in that the step of obtaining target signature parameter, Including：Modification inputs the weight of each characteristic parameter of the algorithm model successively；If first prediction result changes, it is determined that the corresponding characteristic parameter is target signature parameter.
- 5. background application management-control method according to claim 1, it is characterised in that the target predesigned compensation value of obtaining Step, including：Obtain multiple fisrt feature parameter values of the target signature parameter and multiple second feature parameter values；According to multiple fisrt feature parameter values, the first span corresponding to acquisition；According to multiple second feature parameter values, the second span corresponding to acquisition；Obtain the first object predesigned compensation value of corresponding first span, and the of corresponding second span Two target predesigned compensation values.
- A kind of 6. background application control device, applied to electronic equipment, it is characterised in that described device includes：First prediction result acquiring unit, for sample data to be inputted into algorithm model, obtain multiple first prediction results；Characteristic ginseng value acquiring unit, for including correctly predicted result and error prediction when the multiple first prediction result When as a result, the fisrt feature parameter value that target signature parameter in the sample data corresponds to correctly predicted result is obtained respectively, with And the second feature parameter value of corresponding error prediction result；Initial compensation value acquiring unit, for being obtained according to the fisrt feature parameter value and the second feature parameter value calculation Initial preset offset；Target predesigned compensation value acquiring unit, for by the sample data and the initial preset offset, described in input Algorithm model is trained, and obtains target predesigned compensation value；Control unit, for by the current multiple characteristic parameters of default background application and the target predesigned compensation value, input The algorithm model, target prediction result is obtained, and pipe is carried out to the default background application according to the target prediction result Control.
- 7. background application control device according to claim 6, it is characterised in that the initial compensation value acquiring unit bag Include：Fisrt feature parameter value obtains subelement, and the more of multiple correctly predicted results are corresponded to for obtaining the target signature parameter Individual fisrt feature parameter value；Second feature parameter value obtains subelement, and the more of multiple error prediction results are corresponded to for obtaining the target signature parameter Individual second feature parameter value；Initial compensation value obtains subelement, for the average value according to the multiple fisrt feature parameter value and the multiple second The mean value calculation of characteristic ginseng value obtains initial preset offset.
- 8. background application control device according to claim 6, it is characterised in that the initial compensation value acquiring unit bag Include：Fixed reference feature parameter value obtains subelement, for obtaining the characteristic ginseng value of the target signature parameter, and with it is described Multiple fixed reference feature parameter values of the characteristic ginseng value into gradient；Second prediction result obtains subelement, for the multiple fixed reference feature parameter value to be inputted into algorithm model, obtains second Prediction result；Characteristic ginseng value obtains subelement, if changing for second prediction result, obtains respectively adjacent correctly predicted As a result with error prediction result corresponding to fisrt feature parameter value and second feature parameter value；Initial preset offset obtains subelement, for according to the fisrt feature parameter value and the second feature parameter value Difference obtains initial preset offset.
- 9. background application control device according to claim 6, it is characterised in that the characteristic ginseng value acquiring unit bag Include：Weight Acquisition subelement, for changing the weight for each characteristic parameter for inputting the algorithm model successively；Target signature parameter acquiring subelement, if changing for first prediction result, it is determined that the corresponding feature ginseng Number is target signature parameter.
- 10. background application control device according to claim 6, it is characterised in that the characteristic ginseng value acquiring unit, It is additionally operable to obtain multiple fisrt feature parameter values of the target signature parameter and multiple second feature parameter values；The target predesigned compensation value acquiring unit includes：First span obtains subelement, for according to multiple fisrt feature parameter values, the first value corresponding to acquisition Scope；Second span obtains subelement, for according to multiple second feature parameter values, the second value corresponding to acquisition Scope；First object predesigned compensation value determination subelement, the default benefit of first object for obtaining corresponding first span Repay value；Second target predesigned compensation value determination subelement, the default benefit of the second target for obtaining corresponding second span Repay value.
- 11. a kind of storage medium, is stored thereon with computer program, it is characterised in that when the computer program is in computer During upper operation so that the computer performs the background application management-control method as described in any one of claim 1 to 5.
- 12. a kind of electronic equipment, including processor and memory, the memory have computer program, it is characterised in that described Processor is by calling the computer program, for performing the background application management and control side as described in any one of claim 1 to 5 Method.
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