CN107748682B

CN107748682B - Background application control method and device, storage medium and electronic equipment

Info

Publication number: CN107748682B
Application number: CN201711044966.5A
Authority: CN
Inventors: 曾元清
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date: 2017-10-31
Filing date: 2017-10-31
Publication date: 2020-11-13
Anticipated expiration: 2037-10-31
Also published as: CN107748682A

Abstract

The application discloses a background application control method, a background application control device, a storage medium and electronic equipment, wherein the method comprises the following steps: inputting the sample data into an algorithm model to obtain a plurality of first prediction results; when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results; calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value; inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value; and applying a plurality of current characteristic parameters and a target preset compensation value to a preset background, inputting the current characteristic parameters and the target preset compensation value into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result. The accuracy of predicting the preset background application is improved, and the accuracy of background application control is improved.

Description

Background application control method and device, storage medium and electronic equipment

Technical Field

The present application relates to the field of communications technologies, and in particular, to a background application control method and apparatus, a storage medium, and an electronic device.

Background

Background application cleaning is a common and effective method for reducing memory occupation and power consumption. However, the background application cannot be cleaned up at will, and if the background application is to be used next but cleaned up, the application needs to be restarted, so that the starting time is long, and the power consumption is correspondingly increased. Therefore, it is important to accurately determine whether the background application can be cleaned. The traditional method for judging cleanability of background applications is a statistical-based method, such as retaining the most common applications and cleaning the less common applications. However, this cleaning method has a problem that the prediction accuracy is not sufficient.

Disclosure of Invention

The application provides a background application control method and device, a storage medium and an electronic device, and accuracy of control over an application program can be improved.

In a first aspect, an embodiment of the present application provides a background application management and control method, which is applied to an electronic device, and includes the steps of:

inputting the sample data into an algorithm model to obtain a plurality of first prediction results;

when the first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring first characteristic parameter values of target characteristic parameters in the sample data, which correspond to the correct prediction results, and second characteristic parameter values, which correspond to the wrong prediction results;

calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value;

inputting the sample data and the initial preset compensation value into the algorithm model for training to obtain a target preset compensation value;

and inputting a plurality of current characteristic parameters of a preset background application and the target preset compensation value corresponding to the target characteristic parameter into the algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result.

In a second aspect, an embodiment of the present application provides a background application management and control device, which is applied to an electronic device, and includes:

the first prediction result acquisition unit is used for inputting the sample data into the algorithm model to obtain a plurality of first prediction results;

a characteristic parameter value obtaining unit, configured to, when the multiple first prediction results include correct prediction results and incorrect prediction results, respectively obtain first characteristic parameter values of target characteristic parameters in the sample data, where the target characteristic parameters correspond to the correct prediction results, and second characteristic parameter values corresponding to the incorrect prediction results;

the initial compensation value acquisition unit is used for calculating an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value;

a target preset compensation value obtaining unit, configured to input the sample data and the initial preset compensation value into the algorithm model for training to obtain a target preset compensation value;

and the control unit is used for inputting a plurality of current characteristic parameters of a preset background application and the target preset compensation value corresponding to the target characteristic parameter into the algorithm model to obtain a target prediction result, and controlling the preset background application according to the target prediction result.

In a third aspect, an embodiment of the present application provides a storage medium, on which a computer program is stored, and when the computer program runs on a computer, the computer is caused to execute the above background application management and control method.

In a fourth aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, where the memory has a computer program, and the processor is configured to execute the foregoing background application management and control method by calling the computer program.

According to the background application control method and device, the storage medium and the electronic equipment, a plurality of first prediction results are obtained by inputting sample data into the algorithm model; when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results; calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value; inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value; and applying a plurality of current characteristic parameters and a target preset compensation value to a preset background, inputting the current characteristic parameters and the target preset compensation value into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result. The accuracy of predicting the preset background application can be improved, and therefore the accuracy of managing and controlling the application program entering the background is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

Fig. 1 is a system schematic diagram of a background application management and control device according to an embodiment of the present disclosure;

fig. 2 is a schematic view of an application scenario of a background application management and control device according to an embodiment of the present application;

fig. 3 is a schematic flowchart of a background application management and control method according to an embodiment of the present application;

fig. 4 is a schematic flow chart illustrating a process of selecting a target feature parameter according to an embodiment of the present application;

fig. 5 is a schematic flow chart illustrating obtaining an initial preset compensation value according to an embodiment of the present disclosure;

FIG. 6 is another schematic flow chart illustrating the process of obtaining an initial default compensation value according to an embodiment of the present disclosure;

fig. 7 is a schematic flowchart of a process for obtaining a target preset compensation value according to an embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of a background application management and control device according to an embodiment of the present disclosure;

fig. 9 is a second structural schematic diagram of a background application management and control device according to an embodiment of the present application;

fig. 10 is a schematic structural diagram of a background application management and control apparatus according to an embodiment of the present application;

fig. 11 is a fourth schematic structural diagram of a background application management and control device according to an embodiment of the present application;

fig. 12 is a schematic structural diagram of a fifth background application management and control device according to an embodiment of the present application;

fig. 13 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

fig. 14 is another schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Referring to the drawings, wherein like reference numbers refer to like elements, the principles of the present application are illustrated as being implemented in a suitable computing environment. The following description is based on illustrated embodiments of the application and should not be taken as limiting the application with respect to other embodiments that are not detailed herein.

In the description that follows, specific embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise indicated. Accordingly, these steps and operations will be referred to, several times, as being performed by a computer, the computer performing operations involving a processing unit of the computer in electronic signals representing data in a structured form. This operation transforms the data or maintains it at locations in the computer's memory system, which may be reconfigured or otherwise altered in a manner well known to those skilled in the art. The data maintains a data structure that is a physical location of the memory that has particular characteristics defined by the data format. However, while the principles of the application have been described in language specific to above, it is not intended to be limited to the specific embodiments shown, and it will be recognized by those of ordinary skill in the art that various of the steps and operations described below may be implemented in hardware.

The term module, as used herein, may be considered a software object executing on the computing system. The various components, modules, engines, and services herein may be viewed as objects implemented on the computing system. The apparatus and method herein can be implemented in software, and certainly can be implemented in hardware, which is within the scope of the present application.

The terms "first", "second", and "third", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules listed, but rather, some embodiments may include other steps or modules not listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

Referring to fig. 1, fig. 1 is a system schematic diagram of a background application management and control device according to an embodiment of the present disclosure. This management and control device is used to backstage supporter mainly used: inputting the sample data into an algorithm model to obtain a plurality of first prediction results; when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results; calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value; inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value; and applying a plurality of current characteristic parameters and a target preset compensation value to a preset background, inputting the current characteristic parameters and the target preset compensation value into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result. Such as shut down, or freeze, etc.

Specifically, please refer to fig. 2, and fig. 2 is a schematic view of an application scenario of a background application management and control apparatus according to an embodiment of the present application. For example, when receiving a control request, the background application control device detects that an application program running in a background of the electronic device includes a preset background application a, a preset background application b, and a preset background application c; then acquiring a plurality of characteristic parameters corresponding to the preset background application a, the preset background application b and the preset background application c, and inputting the plurality of characteristic parameters into an algorithm model; respectively obtaining a probability a ', a probability b ' and a probability c '; and then respectively managing and controlling the preset background application a, the preset background application b and the preset background application c which run in the background according to the probability a ', the probability b ' and the probability c ', for example, closing the preset background application b with the lowest probability.

An execution main body of the background application control method may be the background application control device provided in the embodiment of the present application, or an electronic device integrated with the background application control device, where the background application control device may be implemented in a hardware or software manner.

The embodiments of the present application will be described from the perspective of a background application management and control device, which may be specifically integrated in an electronic device. The background application control method comprises the following steps: inputting the sample data into an algorithm model to obtain a plurality of first prediction results; when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results; calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value; inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value; and applying a plurality of current characteristic parameters and a target preset compensation value to a preset background, inputting the current characteristic parameters and the target preset compensation value into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result.

Referring to fig. 3, fig. 3 is a schematic flowchart illustrating a background application management and control method according to an embodiment of the present application. The background application control method provided by the embodiment of the application is applied to the electronic device, and the specific process can be as follows:

step 101, inputting sample data into an algorithm model to obtain a plurality of first prediction results.

The sample data comprises a set of feature parameters for a plurality of dimensions. The sample data is training sample data obtained in advance, the characteristic parameters in the sample data correspond to the operating parameters of the background application, a specific sample data may be shown in table 1 below and include characteristic information of multiple dimensions, it should be noted that the characteristic parameters shown in table 1 are only examples, and in practice, the number of the characteristic parameters included in a sample data may be more than the number of the characteristic parameters shown in table 1 or less than the number of the characteristic parameters shown in table 1, and the specific characteristic parameters may be different from those shown in table 1, which is not limited specifically here.

TABLE 1

The characteristic parameters of the algorithm model input by the sample data each time can be all, or can be formed by selecting partial characteristic parameters from the sample data, as shown in table 2, one input data is 10 characteristic parameters selected from the sample data.

Dimension (d) of	Characteristic parameter
		1	The screen-off duration of the electronic device during the cut-in period to the background
2	Time of day in foreground
		3	Types of applications, including primary (common applications), secondary (other applications)
4	Bright screen time of electronic device
		5	Current power of electronic device
6	Current wireless network state
		7	Duration of time that application is used in foreground each time
8	Average time interval from the current foreground application entering the background to the target application entering the foreground
		9	Average screen-off time from the current foreground application entering the background to the target application entering the foreground
10	Number of times per preset interval in histogram of application of background stay time

TABLE 2

It should be noted that the dimensions in table 2 are merely examples of the feature parameters in one input data, and do not represent that the dimensions of the feature parameters are limited. In some embodiments, the characteristic parameters may be selected according to actual needs.

The training sample data comprises a plurality of characteristic parameters, the characteristic parameter values included in each characteristic parameter are different, each sample data comprises a plurality of characteristic parameters, each characteristic parameter corresponds to one or more characteristic parameter values, the sample data is used as training data and is respectively input into the algorithm model, and the algorithm model obtains a plurality of corresponding first prediction results according to the sample data.

It should be noted that the characteristic parameters of the same sample data may correspond to different characteristic parameter values.

The values of the characteristic parameters of one characteristic parameter are shown in table 3.

TABLE 3 record of on screen and off screen

And 102, when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results.

The first prediction result is obtained by predicting the algorithm model corresponding to different input data, and the obtained result may be different when the input data is different. Including correct predictions and incorrect predictions. When the plurality of first prediction results comprise correct prediction results and wrong prediction results, first characteristic parameter values of target characteristic parameters in the sample data, corresponding to the correct prediction results, and second characteristic parameter values, corresponding to the wrong prediction results, are respectively obtained.

Referring to fig. 4, fig. 4 is a schematic flow chart illustrating the process of selecting the target feature parameter according to an embodiment of the present disclosure. In this embodiment, the specific process of the method for obtaining the target characteristic parameter may be as follows:

step 1021, modifying the weight of each characteristic parameter of the input algorithm model in sequence.

The input data of the input algorithm model comprises a plurality of characteristic parameters, one characteristic parameter is modified each time, the weight of the characteristic parameter is continuously adjusted, such as continuously reduced or continuously improved, the adjusted characteristic parameter is input into the algorithm model for prediction, and a first prediction result is continuously obtained. Wherein, the weight is continuously decreased until the weight is reduced to zero, i.e. the characteristic parameter is removed.

In step 1022, if the first prediction result changes, the corresponding feature parameter is determined to be the target feature parameter.

And changing the first prediction result, namely changing the correct prediction result into the wrong prediction result or changing the wrong prediction result into the correct prediction result, wherein the first prediction result shows that the characteristic parameters can play a more critical influence on the correctness of the prediction result, and then determining the corresponding characteristic parameters as the target characteristic parameters.

And 103, calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value.

The initial preset compensation value can be obtained by obtaining a difference value between the first characteristic parameter value and the second characteristic parameter value, or the first characteristic parameter value and the second characteristic parameter value can be multiplied by different weight values respectively and then subtracted to obtain the initial preset compensation value.

Referring to fig. 5, fig. 5 is a schematic flow chart illustrating a process of obtaining an initial predetermined compensation value according to an embodiment of the present disclosure. In this embodiment, the specific process of the method for obtaining the initial preset compensation value may be as follows:

step 10131, a plurality of first characteristic parameter values of the target characteristic parameter corresponding to the plurality of correct prediction results are obtained.

Step 10132, a plurality of second characteristic parameter values of the target characteristic parameter corresponding to the plurality of misprediction results are obtained.

Step 10133, calculating to obtain an initial preset compensation value according to the average value of the plurality of first characteristic parameter values and the average value of the plurality of second characteristic parameter values.

And respectively averaging the plurality of first characteristic parameter values and the plurality of second characteristic parameter values, and then subtracting to obtain an initial preset compensation value. Of course, the initial preset compensation value may also be obtained by subtracting the average value of the first characteristic parameter value and the average value of the second characteristic parameter value after multiplying the average values by different weight values respectively.

Referring to fig. 6, fig. 6 is another schematic flow chart of obtaining an initial default compensation value according to an embodiment of the present disclosure. In this embodiment, the specific process of the method for obtaining the initial preset compensation value may be as follows:

step 10134, a characteristic parameter value of the target characteristic parameter and a plurality of reference characteristic parameter values having a gradient with the characteristic parameter value are obtained.

The gradient may be an increasing gradient or a decreasing gradient. The characteristic parameter value of the target characteristic parameter is obtained, then the characteristic parameter value is used as a base number, and an increasing and/or decreasing number sequence is obtained on the basis of the base number. The value of the gradient may be one tenth, one half, etc. of the base. The gradient may be an arithmetic gradient or may not be arithmetic, and the magnitude of the data in the array varies, such that the larger the data, the larger the difference, and the smaller the difference.

Step 10135, inputting the plurality of reference characteristic parameter values into the algorithm model to obtain a second prediction result.

And then, respectively inputting the plurality of reference characteristic parameter values into an algorithm module to obtain a plurality of second prediction results. Correspondingly, the characteristic parameter values corresponding to other characteristic parameters in the input data are unchanged.

In step 10136, if the second prediction result changes, a first characteristic parameter value and a second characteristic parameter value corresponding to the adjacent correct prediction result and the adjacent incorrect prediction result are obtained.

The second prediction results are also different because the input data are different. And when the second prediction result changes from correct to wrong or from wrong to correct, acquiring a first characteristic parameter value and a second characteristic parameter value corresponding to the adjacent correct prediction result and the incorrect prediction result. I.e. two adjacent ones of the gradient data.

And 10137, obtaining an initial preset compensation value according to the difference value between the first characteristic parameter value and the second characteristic parameter value.

And subtracting the first characteristic parameter value and the second characteristic parameter value to obtain an initial preset compensation value. Of course, the first characteristic parameter value and the second characteristic parameter value may be multiplied by different weight values respectively and then subtracted to obtain an initial preset compensation value.

And 104, inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value.

Target characteristic parameters in the sample data are superposed with an initial preset compensation value before being input into the algorithm model, then the target characteristic parameters are input into the algorithm model for prediction, and a target preset compensation value is obtained through training and learning for a large number of times, so that the accuracy of prediction can be improved after more input data are superposed with the target preset compensation value.

Referring to fig. 7, fig. 7 is a schematic flow chart illustrating obtaining a target default compensation value according to an embodiment of the present disclosure. In this embodiment, the specific process of the method for obtaining the target preset compensation value may be as follows:

step 1041, obtaining a corresponding first value range according to the plurality of first characteristic parameter values.

From the plurality of first characteristic parameter values, a first value range can be obtained.

And 1042, acquiring a corresponding second value range according to the plurality of second characteristic parameter values.

Similarly, a second value range can be obtained from the plurality of second characteristic parameter values. Even a third value range can be set, and the third value range and the second value range are respectively arranged at two sides of the first value range.

Step 1043, obtaining a first target preset compensation value corresponding to the first value range and a second target preset compensation value corresponding to the second value range.

And setting different target preset compensation values corresponding to different value ranges. The prediction result corresponding to the target characteristic value in the first value range is correct, so that compensation is not needed or less compensation is needed, and the target characteristic parameter value in the second value range needs compensation, and may be increased or decreased.

And 105, inputting the current multiple characteristic parameters of the preset background application and the target preset compensation value corresponding to the target characteristic parameters into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result.

Before the prediction of the preset background application is carried out, a plurality of current characteristic parameters of the preset background application are obtained, target characteristic parameters in the characteristic parameters are overlapped with corresponding target preset compensation values, and then the target characteristic parameters are input into an algorithm model. It should be noted that the target characteristic parameters may include a plurality of target characteristic parameters, and the target preset compensation values correspond to the target characteristic parameters one to one.

The target prediction result may be a probability value for cleaning the preset background application and/or a probability value for not cleaning the background application, and then the preset background application is controlled according to the target prediction result, such as closing or keeping the background application.

And inputting the characteristic parameters into an algorithm model, wherein the algorithm model superposes corresponding target preset compensation values on the characteristic parameter values of one or more characteristic parameters. For example, 10 feature parameters are input into the algorithm model, which includes two target feature parameters: the method comprises the steps of obtaining a current electric quantity and a background running time, wherein a characteristic parameter value of the current electric quantity is 10%, a characteristic parameter value of the background running time is 10 minutes, then superposing 10% of the characteristic parameter value of the current electric quantity with a corresponding target preset compensation value-% 2, namely 10% -2% ═ 8%, the characteristic parameter value input into an algorithm model is 8%, superposing 10 minutes of the characteristic parameter value of the background running time with the corresponding target preset compensation value 5 minutes, namely 10 minutes +5 minutes ═ 15 minutes, and inputting the characteristic parameter value of the algorithm model into the algorithm model for 15 minutes. And then, the algorithm model predicts according to the characteristic parameter value superposed with the corresponding target preset compensation value to obtain a prediction result. It should be noted that this example is only for understanding and is not a limitation to the present application, and the present application may also use the target preset compensation value in other ways.

It should be noted that the training process of the algorithm model may be completed at the server side or the electronic device side. When the training process and the actual prediction process of the algorithm model are completed at the server side and the optimized algorithm model needs to be used, the using states of the preset background application in a plurality of time periods before the current time can be input into the server, the server sends the prediction result to the electronic equipment side after the actual prediction is completed, and the electronic equipment controls the preset background application according to the prediction result.

When the training process and the actual prediction process of the algorithm model are completed at the electronic equipment end and the optimized algorithm model needs to be used, the use states of the preset background application in a plurality of time periods before the current time can be input into the electronic equipment, and after the actual prediction of the electronic equipment is completed, the electronic equipment controls the preset background application according to the prediction result.

When the training process of the algorithm model is completed at the server side, the actual prediction process of the algorithm model is completed at the electronic equipment side, and the optimized algorithm model needs to be used, the use states of the preset background application in a plurality of time periods before the current time can be input into the electronic equipment, and after the actual prediction of the electronic equipment is completed, the electronic equipment controls the preset background application according to the prediction result. Optionally, the trained algorithm model file (model file) may be transplanted to the intelligent device, if it is required to determine whether the current background application is cleanable, the usage states of a plurality of time periods before the current time of the preset background application are obtained, the usage states are input to the trained algorithm model file (model file), and the predicted value is obtained through calculation.

All the above technical solutions can be combined arbitrarily to form the optional embodiments of the present application, and are not described herein again.

As can be seen from the above, the background application control method provided in the embodiment of the present application obtains a plurality of first prediction results by inputting sample data into the algorithm model; when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results; calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value; inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value; and applying a plurality of current characteristic parameters and a target preset compensation value to a preset background, inputting the current characteristic parameters and the target preset compensation value into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result. The accuracy of predicting the preset background application can be improved, and therefore the accuracy of managing and controlling the application program entering the background is improved.

Referring to fig. 8, fig. 8 is a schematic structural diagram of a background application management and control device according to an embodiment of the present disclosure. The background application management and control apparatus 300 is applied to an electronic device, and the background application management and control apparatus 300 includes a first prediction result obtaining unit 301, a characteristic parameter value obtaining unit 302, an initial compensation value obtaining unit 303, a target preset compensation value obtaining unit 304, and a management and control unit 305. Wherein:

the first prediction result obtaining unit 301 is configured to input sample data into the algorithm model to obtain a plurality of first prediction results.

The sample data comprises a set of feature parameters for a plurality of dimensions. The sample data is training sample data acquired in advance, the characteristic parameters in the sample data correspond to the operating parameters of the background application, and one specific sample data comprises characteristic information with multiple dimensions.

The characteristic parameters of the algorithm model input by the sample data each time can be all, or can be formed by selecting partial characteristic parameters from the sample data.

A characteristic parameter value obtaining unit 302, configured to, when the multiple first prediction results include correct prediction results and incorrect prediction results, obtain a first characteristic parameter value corresponding to the correct prediction results and a second characteristic parameter value corresponding to the incorrect prediction results of the target characteristic parameter in the sample data, respectively.

Referring to fig. 9, fig. 9 is a schematic structural diagram of a background application management and control device according to an embodiment of the present disclosure. In the present embodiment, the characteristic parameter value acquisition unit 302 includes a weight acquisition subunit 3021 and a target characteristic parameter acquisition subunit 3022.

A weight obtaining subunit 3021, configured to modify the weights of the characteristic parameters of the input algorithm model in sequence.

A target characteristic parameter obtaining subunit 3022, configured to determine, if the first prediction result changes, that the corresponding characteristic parameter is the target characteristic parameter.

An initial compensation value obtaining unit 303, configured to obtain an initial preset compensation value by calculating according to the first characteristic parameter value and the second characteristic parameter value.

Referring to fig. 10, fig. 10 is a schematic structural diagram of a background application management and control device according to an embodiment of the present application. In the present embodiment, the initial compensation value acquisition unit 303 includes a first characteristic parameter value acquisition sub-unit 3031, a second characteristic parameter value acquisition sub-unit 3032, and an initial compensation value acquisition sub-unit 3033, where:

the first characteristic parameter value obtaining subunit 3031 is configured to obtain a plurality of first characteristic parameter values of a plurality of correct prediction results corresponding to the target characteristic parameter.

The second characteristic parameter value obtaining sub-unit 3032 is configured to obtain a plurality of second characteristic parameter values of the target characteristic parameter corresponding to the plurality of misprediction results.

The initial compensation value obtaining subunit 3033 is configured to obtain an initial preset compensation value by calculating according to the average value of the plurality of first characteristic parameter values and the average value of the plurality of second characteristic parameter values.

Referring to fig. 11, fig. 11 is a fourth structural schematic diagram of a background application management and control device according to an embodiment of the present application. In the present embodiment, the initial compensation value acquisition unit 303 includes a reference characteristic parameter value acquisition sub-unit 3034, a second prediction result acquisition sub-unit 3035, a characteristic parameter value acquisition sub-unit 3036, and an initial preset compensation value acquisition sub-unit 3033. Wherein:

a reference characteristic parameter value obtaining subunit 3034, configured to obtain a characteristic parameter value of the target characteristic parameter, and a plurality of reference characteristic parameter values that are in a gradient with the characteristic parameter value.

A second prediction result obtaining sub-unit 3035, configured to input the plurality of reference feature parameter values into the algorithm model to obtain a second prediction result.

A characteristic parameter value obtaining subunit 3036, configured to, if the second prediction result changes, obtain a first characteristic parameter value and a second characteristic parameter value corresponding to the adjacent correct prediction result and the adjacent incorrect prediction result, respectively.

An initial preset compensation value obtaining subunit 3033, configured to obtain an initial preset compensation value according to a difference between the first characteristic parameter value and the second characteristic parameter value.

And a target preset compensation value obtaining unit 304, configured to input the sample data and the initial preset compensation value into an algorithm model for training, so as to obtain a target preset compensation value.

Referring to fig. 12, fig. 12 is a schematic diagram illustrating a fifth structure of a background application management and control device according to an embodiment of the present application. In the present embodiment, the target preset compensation value acquisition unit 304 includes:

the first value range obtaining subunit 3041 is configured to obtain a corresponding first value range according to the plurality of first characteristic parameter values.

The second value range obtaining subunit 3042 is configured to obtain a corresponding second value range according to the plurality of second characteristic parameter values.

The first target preset compensation value determining subunit 3043 is configured to obtain a first target preset compensation value corresponding to the first value range.

The second target preset compensation value determining subunit 3044 is configured to obtain a second target preset compensation value corresponding to the second value range.

The control unit 305 is configured to input a plurality of current characteristic parameters and a target preset compensation value of the preset background application into the algorithm model to obtain a target prediction result, and control the preset background application according to the target prediction result.

As can be seen from the above, the background application control device provided in the embodiment of the present application obtains a plurality of first prediction results by inputting sample data into the algorithm model; when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results; calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value; inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value; and applying a plurality of current characteristic parameters and a target preset compensation value to a preset background, inputting the current characteristic parameters and the target preset compensation value into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result. The accuracy of predicting the preset background application can be improved, and therefore the accuracy of managing and controlling the application program entering the background is improved.

In this embodiment of the application, the background application management and control device and the background application management and control method in the foregoing embodiments belong to the same concept, and any one of the methods provided in the background application management and control method embodiment may be run on the background application management and control device, and a specific implementation process thereof is described in detail in the background application management and control method embodiment, and is not described herein again.

The embodiment of the application also provides the electronic equipment. Referring to fig. 13, the electronic device 400 includes a processor 401 and a memory 402. The processor 401 is electrically connected to the memory 402.

The processor 400 is a control center of the electronic device 400, connects various parts of the entire electronic device using various interfaces and lines, performs various functions of the electronic device 400 by running or loading a computer program stored in the memory 402 and calling data stored in the memory 402, and processes the data, thereby monitoring the electronic device 400 as a whole.

The memory 402 may be used to store software programs and modules, and the processor 401 executes various functional applications and data processing by operating the computer programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, a computer program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 access to the memory 402.

In this embodiment, the processor 401 in the electronic device 400 loads instructions corresponding to one or more processes of the computer program into the memory 402 according to the following steps, and the processor 401 runs the computer program stored in the memory 402, so as to implement various functions, as follows:

when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results;

inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value;

and applying a plurality of current characteristic parameters and a target preset compensation value to a preset background, inputting the current characteristic parameters and the target preset compensation value into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result.

In some embodiments, the processor 401 is further configured to perform the following steps:

obtaining a plurality of first characteristic parameter values of a target characteristic parameter corresponding to a plurality of correct prediction results;

obtaining a plurality of second characteristic parameter values of a plurality of error prediction results corresponding to the target characteristic parameter;

and calculating to obtain an initial preset compensation value according to the average value of the plurality of first characteristic parameter values and the average value of the plurality of second characteristic parameter values.

acquiring characteristic parameter values of target characteristic parameters and a plurality of reference characteristic parameter values which are in gradient with the characteristic parameter values;

inputting the multiple reference characteristic parameter values into an algorithm model to obtain a second prediction result;

if the second prediction result changes, respectively acquiring a first characteristic parameter value and a second characteristic parameter value corresponding to the adjacent correct prediction result and the adjacent incorrect prediction result;

and obtaining an initial preset compensation value according to the difference value of the first characteristic parameter value and the second characteristic parameter value.

sequentially modifying the weight of each characteristic parameter input into the algorithm model;

and if the first prediction result is changed, determining the corresponding characteristic parameter as the target characteristic parameter.

acquiring a plurality of first characteristic parameter values and a plurality of second characteristic parameter values of a target characteristic parameter;

acquiring a corresponding first value range according to the plurality of first characteristic parameter values;

acquiring a corresponding second value range according to the plurality of second characteristic parameter values;

and acquiring a first target preset compensation value corresponding to the first value range and a second target preset compensation value corresponding to the second value range.

As can be seen from the above, the electronic device provided in the embodiment of the present application obtains a plurality of first prediction results by inputting sample data into the algorithm model; when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results; calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value; inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value; and applying a plurality of current characteristic parameters and a target preset compensation value to a preset background, inputting the current characteristic parameters and the target preset compensation value into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result. The accuracy of predicting the preset background application can be improved, and therefore the accuracy of managing and controlling the application program entering the background is improved.

Referring also to fig. 14, in some embodiments, the electronic device 400 may further include: a display 403, radio frequency circuitry 404, audio circuitry 405, and a power supply 406. The display 403, the rf circuit 404, the audio circuit 405, and the power source 406 are electrically connected to the processor 401.

The display 403 may be used to display information entered by or provided to the user as well as various graphical user interfaces, which may be made up of graphics, text, icons, video, and any combination thereof. The Display 403 may include a Display panel, and in some embodiments, the Display panel may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.

The rf circuit 404 may be used for transceiving rf signals to establish wireless communication with a network device or other electronic devices through wireless communication, and for transceiving signals with the network device or other electronic devices.

The audio circuit 405 may be used to provide an audio interface between the user and the electronic device through a speaker, microphone.

The power supply 406 may be used to power various components of the electronic device 400. In some embodiments, the power source 406 may be logically connected to the processor 401 through a power management system, so that functions of managing charging, discharging, and power consumption are implemented through the power management system.

Although not shown in fig. 14, the electronic device 400 may further include a camera, a bluetooth module, and the like, which are not described in detail herein.

An embodiment of the present application further provides a storage medium, where the storage medium stores a computer program, and when the computer program runs on a computer, the computer is caused to execute the application program management and control method in any one of the above embodiments, for example: inputting sample data into an algorithm model to obtain a plurality of first prediction results; when the plurality of first prediction results comprise correct prediction results and wrong prediction results, respectively acquiring a first characteristic parameter value of a target characteristic parameter in sample data, which corresponds to the correct prediction results, and a second characteristic parameter value, which corresponds to the wrong prediction results; calculating to obtain an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value; inputting the sample data and the initial preset compensation value into an algorithm model for training to obtain a target preset compensation value; and applying a plurality of current characteristic parameters and a target preset compensation value to a preset background, inputting the current characteristic parameters and the target preset compensation value into an algorithm model to obtain a target prediction result, and managing and controlling the preset background application according to the target prediction result.

In the embodiment of the present application, the storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a Random Access Memory (RAM), or the like.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

It should be noted that, for the background application management and control method in the embodiment of the present application, it can be understood by a person skilled in the art that all or part of the process for implementing the background application management and control method in the embodiment of the present application can be completed by controlling related hardware through a computer program, where the computer program can be stored in a computer readable storage medium, such as a memory of an electronic device, and executed by at least one processor in the electronic device, and the process of executing the process may include the process of the embodiment of the background application management and control method. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.

For the background application management and control device in the embodiment of the present application, each functional module may be integrated in one processing chip, or each module may exist alone physically, or two or more modules are integrated in one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium, such as a read-only memory, a magnetic or optical disk, or the like.

The background application control method, the background application control device, the storage medium and the electronic device provided by the embodiment of the application are introduced in detail, a specific example is applied in the description to explain the principle and the implementation manner of the application, and the description of the embodiment is only used for helping to understand the method and the core idea of the application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. A background application control method is applied to electronic equipment and is characterized by comprising the following steps:

2. The background application management and control method according to claim 1, wherein the step of calculating an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value includes:

obtaining a plurality of first characteristic parameter values of a plurality of correct prediction results corresponding to the target characteristic parameter;

3. The background application management and control method according to claim 1, wherein the step of calculating an initial preset compensation value according to the first characteristic parameter value and the second characteristic parameter value includes:

acquiring a characteristic parameter value of the target characteristic parameter and a plurality of reference characteristic parameter values which are in gradient with the characteristic parameter value;

inputting the reference characteristic parameter values into an algorithm model to obtain a second prediction result;

if the second prediction result is changed, respectively acquiring a first characteristic parameter value and a second characteristic parameter value corresponding to the adjacent correct prediction result and the adjacent incorrect prediction result;

4. The background application management and control method according to claim 1, wherein the step of obtaining the target feature parameters includes:

and if the first prediction result is changed, determining the corresponding characteristic parameter as a target characteristic parameter.

5. The background application management and control method according to claim 1, wherein the step of obtaining the target preset compensation value includes:

acquiring a plurality of first characteristic parameter values and a plurality of second characteristic parameter values of the target characteristic parameter;

acquiring a corresponding first value range according to a plurality of first characteristic parameter values;

6. The utility model provides a management and control device is used to backstage supporter, is applied to electronic equipment, its characterized in that, the device includes:

7. The background application management and control device according to claim 6, wherein the initial compensation value obtaining unit includes:

a first characteristic parameter value obtaining subunit, configured to obtain a plurality of first characteristic parameter values of a plurality of correct prediction results corresponding to the target characteristic parameter;

a second characteristic parameter value obtaining subunit, configured to obtain a plurality of second characteristic parameter values of a plurality of misprediction results corresponding to the target characteristic parameter;

and the initial compensation value obtaining subunit is used for calculating an initial preset compensation value according to the average value of the plurality of first characteristic parameter values and the average value of the plurality of second characteristic parameter values.

8. The background application management and control device according to claim 6, wherein the initial compensation value obtaining unit includes:

a reference characteristic parameter value obtaining subunit, configured to obtain a characteristic parameter value of the target characteristic parameter and a plurality of reference characteristic parameter values that are in a gradient with the characteristic parameter value;

a second prediction result obtaining subunit, configured to input the multiple reference feature parameter values into an algorithm model to obtain a second prediction result;

a characteristic parameter value obtaining subunit, configured to, if the second prediction result changes, obtain a first characteristic parameter value and a second characteristic parameter value corresponding to an adjacent correct prediction result and an adjacent incorrect prediction result, respectively;

and the initial preset compensation value obtaining subunit is configured to obtain an initial preset compensation value according to a difference between the first characteristic parameter value and the second characteristic parameter value.

9. The background application management and control device according to claim 6, wherein the feature parameter value obtaining unit includes:

the weight obtaining subunit is used for sequentially modifying the weight of each characteristic parameter input into the algorithm model;

and the target characteristic parameter acquiring subunit is configured to determine, if the first prediction result changes, that the corresponding characteristic parameter is a target characteristic parameter.

10. The background application management and control device according to claim 6, wherein the feature parameter value obtaining unit is further configured to obtain a plurality of the first feature parameter values and a plurality of the second feature parameter values of the target feature parameter;

the target preset compensation value obtaining unit includes:

a first value range obtaining subunit, configured to obtain, according to the plurality of first characteristic parameter values, a corresponding first value range;

a second value range obtaining subunit, configured to obtain a corresponding second value range according to the plurality of second characteristic parameter values;

a first target preset compensation value determining subunit, configured to obtain a first target preset compensation value corresponding to the first value range;

and the second target preset compensation value determining subunit is used for acquiring a second target preset compensation value corresponding to the second value range.

11. A storage medium having a computer program stored thereon, wherein when the computer program runs on a computer, the computer is caused to execute the background application management and control method according to any one of claims 1 to 5.

12. An electronic device comprising a processor and a memory, the memory having a computer program, wherein the processor is configured to execute the background application management and control method according to any one of claims 1 to 5 by calling the computer program.