CN107886119B - Feature extraction method, application control method, device, medium and electronic equipment - Google Patents

Feature extraction method, application control method, device, medium and electronic equipment Download PDF

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Publication number
CN107886119B
CN107886119B CN201711055031.7A CN201711055031A CN107886119B CN 107886119 B CN107886119 B CN 107886119B CN 201711055031 A CN201711055031 A CN 201711055031A CN 107886119 B CN107886119 B CN 107886119B
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sample
parameter
preset threshold
application
characteristic parameter
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CN107886119A (en
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曾元清
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for recognising patterns
    • G06K9/62Methods or arrangements for pattern recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for program control, e.g. control units
    • G06F9/06Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46Multiprogramming arrangements
    • G06F9/48Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806Task transfer initiation or dispatching
    • G06F9/4843Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for recognising patterns
    • G06K9/62Methods or arrangements for pattern recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6228Selecting the most significant subset of features
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for recognising patterns
    • G06K9/62Methods or arrangements for pattern recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G06K9/6277Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on a parametric (probabilistic) model, e.g. based on Neyman-Pearson lemma, likelihood ratio, receiver operating characteristic [ROC] curve plotting a false acceptance rate [FAR] versus a false reject rate [FRR]
    • G06K9/6278Bayesian classification

Abstract

The embodiment of the application discloses a feature extraction method, an application control device, a medium and electronic equipment, wherein a first sample of a preset application program is obtained, and the first sample comprises at least one feature parameter; judging whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold value or not; if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold value, determining the first sample as a target sample; and if the parameter value of the characteristic parameter in the first sample is different from the first preset threshold value, acquiring a second sample of the preset application program, wherein the second sample comprises at least one characteristic parameter, and determining the first sample and the second sample as the target sample together. The embodiment of the application can improve the intelligence and accuracy of management and control of the application.

Description

Feature extraction method, application control method, device, medium and electronic equipment
Technical Field
The application belongs to the technical field of communication, and particularly relates to a feature extraction method application control method, device, medium and electronic equipment.
Background
With the development of electronic technology, people usually install many applications on electronic devices. When a user opens multiple application programs in the electronic device, if the user returns to a desktop of the electronic device or stays at an application interface of a certain application program or controls a screen of the electronic device, the multiple application programs opened by the user still run in a background of the electronic device. However, the application running in the background can severely occupy the memory of the electronic device, and the power consumption of the electronic device is increased, and the running smoothness of the electronic device is reduced.
Disclosure of Invention
The application provides a feature extraction method, an application management and control device, a medium and electronic equipment, and can improve the intelligence and accuracy of management and control of an application program.
In a first aspect, an embodiment of the present application provides a feature extraction method, applied to an electronic device, including:
obtaining a first sample of a preset application program, wherein the first sample comprises at least one characteristic parameter;
judging whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold value or not;
if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold value, determining the first sample as a target sample;
and if the parameter value of the characteristic parameter in the first sample is different from the first preset threshold value, acquiring a second sample of the preset application program, wherein the second sample comprises at least one characteristic parameter, and determining the first sample and the second sample as a target sample together.
In a second aspect, an embodiment of the present application provides a feature extraction apparatus, which is applied to an electronic device, and includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a first sample of a preset application program, and the first sample comprises at least one characteristic parameter;
the first judging module is used for judging whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold value or not;
the determining module is used for determining the first sample as a target sample if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold;
a second obtaining module, configured to obtain a second sample of the preset application program if a parameter value of a feature parameter in the first sample is different from the first preset threshold, where the second sample includes at least one feature parameter;
the determining module is further configured to determine the first sample and the second sample together as a target sample.
In a third aspect, an embodiment of the present application provides an application management and control method, applied to an electronic device, including:
determining a target sample according to a preset application program, wherein the target sample is the target sample in any one of the above items;
inputting the target sample into a preset algorithm model for prediction, and obtaining a prediction result;
and controlling the preset application program according to the prediction result.
In a fourth aspect, an embodiment of the present application provides an application management and control apparatus, applied to an electronic device, including:
the extraction module is used for determining a target sample according to a preset application program, wherein the target sample is any one of the target samples;
the prediction module is used for inputting the target sample into a preset algorithm model for prediction and obtaining a prediction result;
and the control module is used for controlling the preset application program according to the prediction result.
In a fifth aspect, an embodiment of the present application provides a medium including a computer program, which when run on a computer, causes the computer to execute the feature extraction method described in any one of the above.
In a sixth aspect, an embodiment of the present application further provides a medium, where the medium includes a computer program, and when the computer program runs on a computer, the computer is caused to execute the application management and control method described above.
In a seventh aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, which are electrically connected to each other, where the memory stores a computer program, and the processor is configured to execute the feature extraction method described in any one of the above by calling the computer program.
In an eighth aspect, an embodiment of the present application further provides an electronic device, which includes a processor and a memory, which are electrically connected to each other, where the memory stores a computer program, and the processor is configured to execute the application management and control method described above by calling the computer program.
According to the feature extraction method, the application control device, the medium and the electronic equipment, the target sample is determined according to the parameter values of the feature parameters in the first sample, then the target sample is input into the preset algorithm model for prediction, and a prediction result is obtained; according to the prediction result, the preset application program is managed and controlled, so that the extraction amount in the feature extraction process can be saved, the calculation amount is saved, the accuracy of prediction of the preset application program can be improved, and the intelligence and the accuracy of management and control of the application program entering the background are 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 feature extraction apparatus according to an embodiment of the present application.
Fig. 2 is a schematic view of an application scenario of the feature extraction device according to the embodiment of the present application.
Fig. 3 is a schematic flow chart of a feature extraction method according to an embodiment of the present application.
Fig. 4 is a schematic view of another scenario of the feature extraction method according to the embodiment of the present application.
Fig. 5 is another schematic flow chart of the feature extraction method according to the embodiment of the present application.
Fig. 6 is a schematic structural diagram of a feature extraction device according to an embodiment of the present application.
Fig. 7 is another schematic structural diagram of a feature extraction apparatus according to an embodiment of the present application.
Fig. 8 is a flowchart illustrating an application management and control method according to an embodiment of the present application.
Fig. 9 is a schematic structural diagram of an application management and control apparatus according to an embodiment of the present application.
Fig. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 11 is another schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Referring to the drawings, wherein like reference numbers refer to like elements, the principles of the present application are illustrated as being implemented in a suitable computing environment. The following description is based on illustrated embodiments of the application and should not be taken as limiting the application with respect to other embodiments that are not detailed herein.
In the description that follows, specific embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise indicated. Accordingly, these steps and operations will be referred to, several times, as being performed by a computer, the computer performing operations involving a processing unit of the computer in electronic signals representing data in a structured form. This operation transforms the data or maintains it at locations in the computer's memory system, which may be reconfigured or otherwise altered in a manner well known to those skilled in the art. The data maintains a data structure that is a physical location of the memory that has particular characteristics defined by the data format. However, while the principles of the application have been described in language specific to above, it is not intended to be limited to the specific form set forth herein, and it will be recognized by those of ordinary skill in the art that various of the steps and operations described below may be implemented in hardware.
The term module, as used herein, may be considered a software object executing on the computing system. The various components, modules, engines, and services described herein may be viewed as objects implemented on the computing system. The apparatus and method described herein may be implemented in software, but may also be implemented in hardware, and are within the scope of the present application.
The terms "first", "second", and "third", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules listed, but rather, some embodiments may include other steps or modules not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In the prior art, when a background application is managed and controlled, part of the background application is generally cleaned directly according to the memory occupation condition of the electronic device and the priority of each application, so as to release the memory. However, some applications are important to the user, or some applications need to be used again by the user in a short time, and if the applications are cleaned up when cleaning up the applications later, the process of reloading the applications by the electronic device is required when the user uses the applications again, which consumes a lot of time and memory resources. The electronic device may be a smart phone, a tablet computer, a desktop computer, a notebook computer, or a palm computer.
Referring to fig. 1, fig. 1 is a system schematic diagram of a feature extraction device according to an embodiment of the present disclosure. The feature extraction device is mainly used for: obtaining a first sample of a preset application program, wherein the first sample comprises at least one characteristic parameter; judging whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold value or not; if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold value, determining the first sample as a target sample; and if the parameter value of the characteristic parameter in the first sample is different from the first preset threshold value, acquiring a second sample of the preset application program, wherein the second sample comprises at least one characteristic parameter, and determining the first sample and the second sample as the target sample together. Therefore, whether other samples are obtained or not is determined by the parameters of the characteristic parameters in the first sample obtained firstly so as to determine the target sample, so that the extraction amount of the characteristic extraction process can be saved, the calculation amount can be saved, the calculation speed can be increased, the efficiency of controlling the current or background application programs can be improved, and the electric quantity of the electronic equipment can be saved.
Specifically, please refer to fig. 2, and fig. 2 is a schematic view of an application scenario of the feature extraction apparatus according to the embodiment of the present application. The scenario may comprise an electronic device 1, the electronic device 1 may comprise a feature extraction apparatus 10, and the feature extraction apparatus 10 may be integrated in the electronic device 1. The feature extraction device 10 may include at least two samples, such as a first sample, a second sample, and the like. The sample may include one, two, or more characteristic parameters, and the characteristic parameter may be a status characteristic parameter of the electronic device, or the characteristic parameter may be a characteristic parameter related to the electronic device, such as: the method comprises the steps of obtaining the current state (in a screen-on state or in a screen-off state) of a screen in the electronic equipment, the screen-off duration, the screen-on duration, the brightness level, the geographic position, the residual electric quantity, the charging state, the network state and the like. The characteristic parameter may also be an operation characteristic parameter of the preset application, or the characteristic parameter may also be a characteristic parameter related to the preset application, such as: the type of application, whether there is a traffic upload, whether there is a traffic download, how long it runs in the foreground, how many times it cuts into the foreground during a day, how long it cuts into the foreground during a day, or how it cuts into the background (e.g., switched by the HOME key (i.e., the HOME key), switched by the return key, switched by other applications), etc. It should be noted that the characteristic parameter may also be a status characteristic parameter of the electronic device and an operation characteristic parameter of a preset application program at the same time.
In the process of extracting a sample by the feature extraction device 10, the feature extraction device 10 includes two samples. The feature extraction apparatus 10 first obtains a first sample, where the first sample may include one, two, or more feature parameters, and further, the first sample includes at least two feature parameters, such as a current screen state in the electronic device, whether an application has a traffic upload, and whether an application has a traffic download. And comparing the parameter value of the characteristic parameter in the first sample with a first preset threshold value, and judging whether the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold value. The first preset threshold is the last state of the characteristic parameter in the first sample, that is, the characteristic parameter in the first sample obtained at present is compared with the characteristic parameter in the first sample obtained at the last time, whether the characteristic parameter and the characteristic parameter are the same or not is judged, if the characteristic parameter and the characteristic parameter are the same, the first sample is directly used as the target sample, other samples do not need to be obtained, and the workload of obtaining the characteristic parameter can be saved. If the difference is not the same, further obtaining other samples, such as a second sample, where the second sample may include the type of the application, the start time, the shutdown time, the remaining power of the electronic device, and the charging state of the electronic device. And the first sample and the second sample are collectively used as one target sample. It should be noted that, in the actual operation process, a certain error may occur, and within the error range, the current characteristic parameter in the first sample is considered to be the same as the previous characteristic parameter, that is, the first sample is not changed. Compare each time and all need extract a large amount of feature parameters, this application embodiment only needs to extract partial feature parameter each time, can save the extraction volume of feature parameter.
An execution main body of the feature extraction method may be the feature extraction device provided in the embodiment of the present application, or an electronic device integrated with the feature extraction device, where the feature extraction device may be implemented in a hardware, software, or a combination of software and hardware.
Embodiments of the present application will be described in terms of a feature extraction apparatus, which may be specifically integrated in an electronic device. The feature extraction method comprises the following steps: obtaining a first sample of a preset application program, wherein the first sample comprises at least one characteristic parameter; judging whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold value or not; if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold value, determining the first sample as a target sample; and if the parameter value of the characteristic parameter in the first sample is different from the first preset threshold value, acquiring a second sample of the preset application program, wherein the second sample comprises at least one characteristic parameter, and determining the first sample and the second sample as the target sample together. Therefore, whether other samples are obtained again or not is determined by the parameter of the characteristic parameter in the first sample obtained firstly so as to determine the target sample, and the extraction amount of the characteristic extraction process can be saved.
Referring to fig. 3, fig. 3 is a schematic flow chart of a feature extraction method according to an embodiment of the present disclosure. The feature extraction method is applied to electronic equipment, and the feature extraction method in the embodiment of the application comprises the following specific steps:
in step 101, a first sample of a predetermined application program is obtained, wherein the first sample comprises at least one characteristic parameter.
The predetermined application may be any application installed in the electronic device, such as a communication application, a multimedia application, a game application, an information application, or a shopping application.
It should be noted that, in the embodiment of the present application, a plurality of samples may be obtained, where a first sample is obtained first, and the characteristic parameters included in the first sample may refer to the above contents, and are not described herein again. In the embodiment of the present application, different priorities may be set for the multiple samples, and the priority of the first sample is set to be the highest, which is defined as the first priority, and the priorities decrease from the first sample to the next. Thus, the characteristic parameters included in the first sample are also given the same priority.
In step 102, it is determined whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold.
The first preset threshold and the relationship between the parameter value of the characteristic parameter in the first sample and the first preset threshold may refer to the above contents, and are not described herein again.
In step 103, if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold, the first sample is determined as the target sample.
The parameter value of the characteristic parameter in the first sample is the same as the first preset threshold, which can be referred to above, and is not described herein again.
In step 104, if the parameter value of the characteristic parameter in the first sample is different from the first preset threshold, a second sample of the preset application program is obtained, wherein the second sample includes at least one characteristic parameter, and the first sample and the second sample are jointly determined as the target sample.
For example, the parameter value of the characteristic parameter in the first sample is different from the first preset threshold, which is not described herein again. The second sample and the characteristic parameters in the second sample may refer to the above contents, and are not described herein again. The second sample is given a second priority, the second priority is only next to the first priority, and the characteristic parameters included in the second sample are also given the same priority.
In some embodiments, the characteristic parameter in the first sample is different from the characteristic parameter in the second sample.
As can be seen from the above, in the embodiment of the present application, whether to obtain another sample is determined by using the parameter of the feature parameter in the first sample obtained first, so as to determine the target sample, and the extraction amount in the feature extraction process can be saved.
It should be noted that when the characteristic parameter in the first sample and the characteristic parameter in the second sample both change, that is, when the characteristic parameters change from the previous time, other samples may be obtained again. In the actual operation process, the change size can be determined, and if the change is not large, other samples can not be obtained. The method comprises the following specific steps:
referring to fig. 4, fig. 4 is a schematic view of another scenario of the feature extraction method according to the embodiment of the present application. The feature extraction apparatus in the scenario of fig. 4 may include a plurality of samples, a first sample, a second sample. It should be noted that the sum of the feature parameters in all samples is smaller than the feature parameters in the feature parameter set. For example, the feature parameter set includes 30 feature parameters, the first sample includes 3 feature parameters, the second sample includes 5 feature parameters, the third sample includes 4 feature parameters, and the fourth sample includes 15 feature parameters, so that N is 4. Also for example: the first sample comprises 3 feature parameters, the second sample comprises 5 feature parameters, the third sample comprises 4 feature parameters, the fourth sample comprises 5 feature parameters, the fifth sample comprises 12 feature parameters, and N is 5. For another example: the first sample comprises 3 feature parameters, the second sample comprises 5 feature parameters, the third sample comprises 4 feature parameters, the fourth sample comprises 5 feature parameters, the fifth sample comprises 5 feature parameters, the sixth sample comprises 6 feature parameters, and then N is 6. It should be noted that N may also be a natural number of 7 or more than 7, which is not illustrated here.
The following description will take three samples as an example.
Referring to fig. 5, fig. 5 is another schematic flow chart of a feature extraction method according to an embodiment of the present disclosure. The feature extraction method specifically comprises the following steps:
in step 201, a first sample of a predetermined application program is obtained, wherein the first sample includes at least one characteristic parameter. See step 101 in particular.
In step 202, it is determined whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold. See step 102 in particular.
In step 203, if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold, the first sample is determined as the target sample. See step 103 in particular.
In step 204, if the parameter value of the feature parameter in the first sample is not the same as the first preset threshold, a second sample of the preset application program is obtained, where the second sample includes at least one feature parameter.
The above may be referred to as the parameter value of the characteristic parameter in the first sample being different from the first preset threshold. The second sample and the characteristic parameters in the second sample may refer to the above contents, and are not described herein again.
In step 205, it is determined whether the parameter value of the characteristic parameter in the second sample is the same as the second preset threshold.
The second preset threshold is the last state of the characteristic parameter in the second sample, that is, the characteristic parameter in the second sample obtained at present is compared with the characteristic parameter in the second sample obtained at last time, and whether the two are the same or not is judged.
In step 206, if the parameter value of the characteristic parameter in the second sample is the same as the second preset threshold, the first sample and the second sample are determined as the target sample together.
It should be noted that, in the actual operation process, a certain error may occur, and the parameter value of the characteristic parameter in the second sample is assumed to be the same as the second preset threshold within the error range. Therefore, the embodiment of the application can save the extraction of the number of the characteristic parameters on the premise of ensuring the accuracy rate of extracting the characteristic parameters.
In step 207, if the parameter value of the characteristic parameter in the second sample is not the same as the second preset threshold, a third sample of the preset application program is obtained, where the third sample includes at least one characteristic parameter, and the first sample, the second sample, and the third sample are determined as the target sample together.
The characteristic parameter in the third sample may be a foreground running time of the application, a background running time, a number of times of switching into the foreground in one day, and a time of switching into the foreground in one day. In some embodiments, the third sample is given a third priority, which is inferior to the priority of the second sample. Note that the feature parameters in the third sample have the same priority.
In some embodiments, the feature extraction method may further include: and judging whether the parameter value of the characteristic parameter in the third sample is the same as a third preset threshold value, and if the parameter value of the characteristic parameter in the third sample is the same as the third preset threshold value, determining the first sample, the second sample and the third sample as the target sample together. The third preset threshold is a last state of the characteristic parameter in the third sample, that is, the characteristic parameter in the currently obtained third sample is compared with the characteristic parameter in the last obtained third sample, and whether the two are the same or not is judged.
Therefore, the embodiment of the application searches for a balance point on the feature extraction workload and the feature extraction accuracy, and improves the feature extraction accuracy as much as possible under the condition that the feature extraction workload is as small as possible. The pressure of the system of the electronic equipment can be reduced, the speed can be increased, and the electric quantity can be saved.
In order to better implement the feature extraction method provided by the embodiment of the present application, the embodiment of the present application further provides a feature extraction device. The meaning of the noun is the same as the above feature extraction method, and the details of the specific implementation can refer to the description in the method embodiment.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a feature extraction device according to an embodiment of the present disclosure. The feature extraction apparatus 10 is applied to an electronic device, and the feature extraction apparatus 10 may include a first obtaining module 110, a first judging module 120, a second obtaining module 130, and a determining module 140.
The first obtaining module 110 is configured to obtain a first sample of a preset application program, where the first sample includes at least one feature parameter.
The predetermined application may be any application installed in the electronic device, such as a communication application, a multimedia application, a game application, an information application, or a shopping application.
It should be noted that, in the embodiment of the present application, a plurality of samples may be obtained, where a first sample is obtained first, and the characteristic parameters included in the first sample may refer to the above contents, and are not described herein again. In the embodiment of the present application, different priorities may be set for the multiple samples, and the priority of the first sample is set to be the highest, which is defined as the first priority, and the priorities decrease from the first sample to the next. Thus, the characteristic parameters included in the first sample are also given the same priority.
The first determining module 120 is configured to determine whether a parameter value of the characteristic parameter in the first sample is the same as a first preset threshold.
The first preset threshold and the relationship between the parameter value of the characteristic parameter in the first sample and the first preset threshold may refer to the above contents, and are not described herein again.
The determining module 140 is configured to determine the first sample as the target sample if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold.
The parameter value of the characteristic parameter in the first sample is the same as the first preset threshold, which can be referred to above, and is not described herein again.
The second obtaining module 130 obtains a second sample of the preset application program if the parameter value of the characteristic parameter in the first sample is different from the first preset threshold, where the second sample includes at least one characteristic parameter.
For example, the parameter value of the characteristic parameter in the first sample is different from the first preset threshold, which is not described herein again. The second sample and the characteristic parameters in the second sample may refer to the above contents, and are not described herein again. The second sample is given a second priority, the second priority is only next to the first priority, and the characteristic parameters included in the second sample are also given the same priority.
In some embodiments, the characteristic parameter in the first sample is different from the characteristic parameter in the second sample.
The determining module 140 is further configured to determine the first sample and the second sample together as the target sample.
As can be seen from the above, in the embodiment of the present application, whether to obtain another sample is determined according to the parameter of the feature parameter in the first sample obtained by the first obtaining module 110, so as to determine the target sample, and the extraction amount in the feature extraction process can be saved.
It should be noted that when the characteristic parameter in the first sample and the characteristic parameter in the second sample both change, that is, when the characteristic parameters change from the previous time, other samples may be obtained again. In the actual operation process, the change size can be determined, and if the change is not large, other samples can not be obtained.
In some embodiments, please refer to fig. 7, and fig. 7 is a schematic structural diagram of a feature extraction apparatus according to an embodiment of the present disclosure. The feature extraction apparatus 10 may further include a second determination module 150.
The second determining module 150 is configured to determine whether a parameter value of the characteristic parameter in the second sample is the same as a second preset threshold.
The second preset threshold is the last state of the characteristic parameter in the second sample, that is, the characteristic parameter in the second sample obtained at present is compared with the characteristic parameter in the second sample obtained at last time, and whether the two are the same or not is judged.
The determining module 140 is further configured to determine the first sample and the second sample as the target sample together if the parameter value of the characteristic parameter in the second sample is the same as the second preset threshold.
It should be noted that, in the actual operation process, a certain error may occur, and the parameter value of the characteristic parameter in the second sample is assumed to be the same as the second preset threshold within the error range. Therefore, the embodiment of the application can save the extraction of the number of the characteristic parameters on the premise of ensuring the accuracy rate of extracting the characteristic parameters.
In some embodiments, the feature extraction apparatus 10 may further include a third acquisition module 160.
The third obtaining module 160 is configured to obtain a third sample of the preset application program if the parameter value of the feature parameter in the second sample is different from the second preset threshold, where the third sample includes at least one feature parameter. The characteristic parameter in the third sample may be a foreground running time of the application, a background running time, a number of times of switching into the foreground in one day, and a time of switching into the foreground in one day. In some embodiments, the third sample is given a third priority, which is inferior to the priority of the second sample. Note that the feature parameters in the third sample have the same priority.
The determining module 140 is further configured to determine the first sample, the second sample, and the third sample together as the target sample.
In some embodiments, the feature extraction apparatus 10 may further include a third determination module 170.
The third determining module 170 is configured to determine whether a parameter value of the characteristic parameter in the third sample is the same as a third preset threshold.
The third preset threshold may be higher than the first preset threshold, and is not described herein again.
The determining module 140 is further configured to determine the first sample, the second sample, and the third sample as the target sample together if the parameter value of the characteristic parameter in the third sample is the same as a third preset threshold.
In some embodiments, the feature extraction device may include a plurality of samples, a first sample, a second sample. It should be noted that the sum of the feature parameters in all samples is smaller than the feature parameters in the feature parameter set. For example, the feature parameter set includes 30 feature parameters, the first sample includes 3 feature parameters, the second sample includes 5 feature parameters, the third sample includes 4 feature parameters, and the fourth sample includes 15 feature parameters, so that N is 4. Also for example: the first sample comprises 3 feature parameters, the second sample comprises 5 feature parameters, the third sample comprises 4 feature parameters, the fourth sample comprises 5 feature parameters, the fifth sample comprises 12 feature parameters, and N is 5. For another example: the first sample comprises 3 feature parameters, the second sample comprises 5 feature parameters, the third sample comprises 4 feature parameters, the fourth sample comprises 5 feature parameters, the fifth sample comprises 5 feature parameters, the sixth sample comprises 6 feature parameters, and then N is 6. It should be noted that N may also be a natural number of 7 or more than 7, which is not illustrated here.
In some embodiments, after the target sample is determined according to the above manner of extracting the characteristic parameters, the target sample may be input into the algorithm model for prediction, and the preset application program is controlled according to a prediction result of the algorithm model. Referring to fig. 8, fig. 8 is a schematic flowchart illustrating an application control method according to an embodiment of the present application. Specifically, the application control method comprises the following steps:
in step 301, a target sample is determined according to a preset application.
The preset application can refer to the above contents, and details are not described herein. The target sample may be determined by the above feature extraction method, and specific reference may be made to the above contents, which are not described herein again.
In step 302, the target sample is input into a preset algorithm model for prediction, and a prediction result is obtained.
The predetermined algorithm model may be any classification algorithm, such as a decision tree algorithm, a proximity algorithm (KNN), a logistic regression algorithm, or a support vector machine algorithm (SVM). The decision tree algorithm may include, for example, an ID3 algorithm, a C4.5 algorithm, or a Random Forest (Random Forest) algorithm, among others.
It should be noted that the preset algorithm model may also be a bayesian classifier, a gaussian mixture model, a markov model, a mixed neural network, or the like.
Such as: and inputting the target sample into a Gaussian mixture model, wherein the Gaussian mixture model obtains a prediction result according to the characteristic parameters of the input target sample.
It should be noted that the preset algorithm model in the embodiment of the present application may be a model obtained by direct training on the electronic device, may also be obtained by training from a server, and may also be a part of training based on the server and a part of training based on the electronic device. According to the embodiment of the application, whether the training is carried out at the electronic equipment end or the server end is selected according to the running capacity of the electronic equipment and the training amount of the preset algorithm model in the training process. It should be further noted that the preset algorithm model adopted in the embodiment of the present application may also be a trained model, and is directly applied to the present application.
The predicted result may be a probability value, such as a usage probability, a closing probability, etc. corresponding to a certain application.
In step 303, the default application is managed according to the prediction result.
The preset application program may be various application programs, and the details may be referred to above. After the electronic equipment obtains the prediction result from the preset algorithm model, the electronic equipment can judge that the corresponding application program algorithm needs to be cleaned or closed according to the prediction result. Such as: and according to the calculated use probability corresponding to each application program, cleaning or closing the background application programs with the use probabilities meeting certain conditions so as to reduce the occupation of the application programs on the resources of the electronic equipment.
Therefore, the application control method provided by the embodiment can reduce the occupation of the terminal resources of the electronic equipment, improve the operation smoothness of the electronic equipment, and reduce the power consumption of the electronic equipment.
In order to better implement the application control method provided in the embodiments of the present application, an application control device is also provided in the embodiments of the present application. The meaning of the noun is the same as that of the application control method, and specific implementation details can refer to the description in the method embodiment.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an application management and control apparatus according to an embodiment of the present application. The application management apparatus 300 includes an extraction module 310, a prediction module 320, and a management module 330.
The extraction module 310 is configured to determine a target sample according to a preset application.
The preset application can refer to the above contents, and details are not described herein. The target sample may be determined by the above feature extraction method, and specific reference may be made to the above contents, which are not described herein again.
The prediction module 320 is configured to input the target sample into a preset algorithm model for prediction, and obtain a prediction result.
The preset algorithm model can refer to the above contents, and is not described herein again.
The management and control module 330 is configured to manage and control the preset application according to the prediction result.
The preset application program may be various application programs, and the details may be referred to above. After the electronic equipment obtains the prediction result from the preset algorithm model, the electronic equipment can judge that the corresponding application program algorithm needs to be cleaned or closed according to the prediction result. Such as: and according to the calculated use probability corresponding to each application program, cleaning or closing the background application programs with the use probabilities meeting certain conditions so as to reduce the occupation of the application programs on the resources of the electronic equipment.
Therefore, the application control device provided by the embodiment of the application control device can reduce the occupation of the terminal resources of the electronic equipment, improve the operation smoothness of the electronic equipment and reduce the power consumption of the electronic equipment.
The embodiment of the application also provides the electronic equipment. Referring to fig. 10, 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 whole electronic device by using various interfaces and lines, and executes various functions of the electronic device 400 and processes data by running or loading a computer program stored in the memory 402 and calling data stored in the memory 402, thereby performing overall monitoring of the electronic device 400.
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 some embodiments, the processor 401 in the electronic device 400 may load instructions corresponding to one or more processes of the computer program into the memory 402, and the processor 401 may execute the computer program stored in the memory 402, so as to implement the following steps:
obtaining a first sample of a preset application program, wherein the first sample comprises at least one characteristic parameter;
judging whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold value or not;
if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold value, determining the first sample as a target sample;
and if the parameter value of the characteristic parameter in the first sample is different from the first preset threshold value, acquiring a second sample of the preset application program, wherein the second sample comprises at least one characteristic parameter, and determining the first sample and the second sample as a target sample together.
In some embodiments, the processor 401 is further configured to perform the steps of:
if the parameter value of the characteristic parameter in the first sample is different from the first preset threshold value, acquiring a second sample of the preset application program, wherein the second sample comprises at least one characteristic parameter;
judging whether the parameter value of the characteristic parameter in the second sample is the same as a second preset threshold value or not;
and if the parameter value of the characteristic parameter in the second sample is the same as a second preset threshold value, determining the first sample and the second sample as a target sample together.
In some embodiments, the processor 401 is further configured to perform the steps of:
and if the parameter value of the characteristic parameter in the second sample is different from a second preset threshold value, acquiring a third sample of the preset application program, wherein the third sample comprises at least one characteristic parameter, and determining the first sample, the second sample and the third sample as target samples together.
In some embodiments, the processor 401 is further configured to perform the steps of:
judging whether the parameter value of the characteristic parameter in the third sample is the same as a third preset threshold value or not;
and if the parameter value of the characteristic parameter in the third sample is the same as a third preset threshold value, determining the first sample, the second sample and the third sample as target samples together.
In some embodiments, the processor 401 is further configured to perform the steps of:
determining a target sample according to a preset application program, wherein the target sample is the target sample in any one of the above items;
inputting the target sample into a preset algorithm model for prediction, and obtaining a prediction result;
and controlling the preset application program according to the prediction result.
As can be seen from the above, in the electronic device provided in the embodiment of the present application, the target sample is determined according to the parameter value of the characteristic parameter in the first sample, and then the target sample is input into the preset algorithm model for prediction, so as to obtain a prediction result; according to the prediction result, the preset application program is managed and controlled, so that the extraction amount in the feature extraction process can be saved, the calculation amount is saved, the accuracy of prediction of the preset application program can be improved, and the intelligence and the accuracy of management and control of the application program entering the background are improved.
Referring to fig. 11, 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, among other things, to display information entered by or provided to a 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 configured to transmit and receive rf signals, so as to establish wireless communication with a network device or other electronic devices through wireless communication, and transmit and receive signals with the network device or other electronic devices.
The audio circuit 405 may be used to provide an audio interface between a user and an electronic device through a speaker or a microphone, among other things.
The power source 406 may be used to power various components of the electronic device 400, among other things. In some embodiments, power supply 406 may be logically coupled to processor 401 via a power management system, such that functions to manage charging, discharging, and power consumption management are performed via the power management system.
Although not shown in fig. 11, 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 medium, where a computer program is stored, and when the computer program runs on a computer, the computer is caused to execute the feature extraction method or the application management and control method in any one of the above embodiments, such as: obtaining a first sample of a preset application program, wherein the first sample comprises at least one characteristic parameter; judging whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold value or not; if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold value, determining the first sample as a target sample; and if the parameter value of the characteristic parameter in the first sample is different from the first preset threshold value, acquiring a second sample of the preset application program, wherein the second sample comprises at least one characteristic parameter, and determining the first sample and the second sample as the target sample together.
In some embodiments, the 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 feature extraction method and the application control method of the embodiments of the present application, it can be understood by a person skilled in the art that all or part of processes for implementing the feature extraction method and the application control method of the embodiments 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 medium, such as a memory of an electronic device, and executed by at least one processor in the electronic device, and during the execution process, the processes of the embodiments of the feature extraction method and the application control method can be included.
For the feature extraction device and the application management and control device in the embodiments 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 as a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable medium such as a read-only memory, a magnetic or optical disk, or the like.
A feature extraction method, an application management and control method, an apparatus, a medium, and an electronic device provided in the embodiments of the present application are described in detail above, and a specific example is applied in the description to explain the principle and the implementation of the present application, and the description of the embodiments is only used to help understand the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims (10)

1. A feature extraction method is applied to electronic equipment, and is characterized by comprising the following steps:
obtaining a first sample of a preset application program, wherein the first sample comprises at least one characteristic parameter;
judging whether the parameter value of the characteristic parameter in the first sample is the same as a first preset threshold value, wherein the first preset threshold value is the last state of the characteristic parameter in the first sample;
if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold value, determining the first sample as a target sample;
if the parameter value of the feature parameter in the first sample is different from the first preset threshold, obtaining a second sample of the preset application program, wherein the second sample comprises at least one feature parameter, the first sample and the second sample have different priorities, the priority of the first sample is higher than that of the second sample, and the priority of the feature parameter included in the first sample is higher than that of the feature parameter included in the second sample;
judging whether the parameter value of the characteristic parameter in the second sample is the same as a second preset threshold value, wherein the second preset threshold value is the last state of the characteristic parameter in the second sample;
if the parameter value of the characteristic parameter in the second sample is the same as a second preset threshold value, determining the first sample and the second sample as a target sample together;
if the parameter value of the feature parameter in the second sample is different from a second preset threshold, obtaining a third sample of the preset application program, wherein the third sample comprises at least one feature parameter, the priority of the second sample is higher than the priority of the third sample, and the priority of the feature parameter included in the second sample is higher than the priority of the feature parameter included in the third sample;
judging whether the parameter value of the characteristic parameter in the third sample is the same as a third preset threshold value, wherein the third preset threshold value is the last state of the characteristic parameter in the third sample;
and if the parameter value of the characteristic parameter in the third sample is the same as a third preset threshold value, determining the first sample, the second sample and the third sample as target samples together.
2. The feature extraction method according to claim 1, wherein the first sample includes an operation feature parameter of the preset application program and a state feature parameter of the electronic device.
3. A feature extraction device applied to electronic equipment is characterized by comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a first sample of a preset application program, and the first sample comprises at least one characteristic parameter;
a first judging module, configured to judge whether a parameter value of a feature parameter in the first sample is the same as a first preset threshold, where the first preset threshold is a last state of the feature parameter in the first sample;
the determining module is used for determining the first sample as a target sample if the parameter value of the characteristic parameter in the first sample is the same as the first preset threshold;
a second obtaining module, configured to obtain a second sample of the preset application program if a parameter value of a feature parameter in the first sample is different from the first preset threshold, where the second sample includes at least one feature parameter;
the determining module is further configured to determine whether a parameter value of the characteristic parameter in the second sample is the same as a second preset threshold, where the second preset threshold is a last state of the characteristic parameter in the second sample; if the parameter value of the characteristic parameter in the second sample is the same as a second preset threshold, determining the first sample and the second sample as a target sample together, wherein the first sample and the second sample have different priorities, the priority of the first sample is higher than that of the second sample, and the priority of the characteristic parameter included in the first sample is higher than that of the characteristic parameter included in the second sample; if the parameter value of the feature parameter in the second sample is different from a second preset threshold, obtaining a third sample of the preset application program, wherein the third sample comprises at least one feature parameter, the priority of the second sample is higher than the priority of the third sample, and the priority of the feature parameter included in the second sample is higher than the priority of the feature parameter included in the third sample; judging whether the parameter value of the characteristic parameter in the third sample is the same as a third preset threshold value, wherein the third preset threshold value is the last state of the characteristic parameter in the third sample; and if the parameter value of the characteristic parameter in the third sample is the same as a third preset threshold value, determining the first sample, the second sample and the third sample as target samples together.
4. The feature extraction apparatus according to claim 3, wherein the first sample includes an operation feature parameter of the preset application program and a status feature parameter of the electronic device.
5. An application management and control method is applied to electronic equipment, and is characterized by comprising the following steps:
determining a target sample according to a preset application, wherein the target sample is the target sample according to any one of claims 1 to 2;
inputting the target sample into a preset algorithm model for prediction, and obtaining a prediction result;
and controlling the preset application program according to the prediction result.
6. The utility model provides an use management and control device, is applied to among the electronic equipment, its characterized in that includes:
an extraction module, configured to determine a target sample according to a preset application, where the target sample is the target sample according to any one of claims 1 to 2;
the prediction module is used for inputting the target sample into a preset algorithm model for prediction and obtaining a prediction result;
and the control module is used for controlling the preset application program according to the prediction result.
7. A medium, characterized in that the medium comprises a computer program which, when run on a computer, causes the computer to perform the feature extraction method according to any one of claims 1 to 2.
8. A medium characterized by comprising a computer program that, when run on a computer, causes the computer to execute the application management method according to claim 5.
9. An electronic device, comprising a processor and a memory electrically connected to each other, the memory storing a computer program, the processor being configured to execute the feature extraction method according to any one of claims 1 to 2 by calling the computer program.
10. An electronic device, comprising a processor and a memory electrically connected to each other, wherein the memory stores a computer program, and the processor executes the application management and control method according to claim 5 by calling the computer program.
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