CN111144509B - Method, device and computer for classifying system application programs - Google Patents

Abstract

The application relates to the technical field of computers, and discloses a method for classifying system application programs, which comprises the following steps: acquiring operation parameters of an application program; and classifying the application programs according to the operation parameters. The method classifies the application programs through the operation parameters of the application programs, can classify the application programs under the conditions of not detecting the package names of the application program installation packages and not detecting the predefined categories of the application programs, and meanwhile, the classification mode of the application can embody the performance of system parameters when the application programs are operated, is convenient for the system to perform different directional optimization under different application scenes, and particularly can be convenient for the system to optimize the application programs according to the existing optimization scheme after classifying the application programs according to the mode of the application, thereby improving the user experience. The application also discloses a device and a computer for classifying the system application programs.

Description

Method, device and computer for system application classification

Technical field

The present application relates to the field of computer technology, for example, to a method, device and computer for system application classification.

Background technique

For the Android system, new applications are launched every moment. Classification of applications on the Android system can facilitate users to quickly find the applications they need and achieve unified management of applications. However, currently applications can only be classified through The classification is assessed when going online, or manually by the user.

In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in related technologies:

Existing application classifications are classified by the labels of the application itself, such as games, audio and video, social software, etc. However, this classification method is difficult to accurately reflect the operating requirements of the application, and it is difficult to classify according to this method. Get the right optimization strategy.

Contents of the invention

In order to provide a basic understanding of some aspects of the disclosed embodiments, a simplified summary is provided below. This summary is not intended to be a general review, nor is it intended to identify key/important elements or delineate the scope of the embodiments, but is intended to serve as a prelude to the detailed description that follows.

Embodiments of the present disclosure provide a method, device, and computer for classifying system applications to solve the technical problem in the prior art that it is difficult to effectively optimize the system based on application classification.

In some embodiments, the method includes:

Get the running parameters of the application;

Applications are classified based on the operating parameters described.

In some embodiments, the apparatus includes: a processor and a memory storing program instructions, and the processor is configured to perform the above-mentioned method for system application classification when executing the program instructions.

In some embodiments, the computer includes: the device for classifying system applications as described above

The method, device and computer for classifying system applications provided by the embodiments of the present disclosure can achieve the following technical effects: classifying applications through their operating parameters, without detecting the package name of the application installation package, without detecting Applications are classified according to predefined categories of applications. At the same time, the classification method of this application can reflect the performance of system parameters when the application is running, which facilitates the system to perform different directional optimizations in different application scenarios, especially for unpopular applications or Newly launched applications, after being classified according to the method of this application, can facilitate the system to quickly optimize them according to the existing optimization plan, thus improving the user experience.

The above general description and the following description are exemplary and explanatory only and are not intended to limit the application.

Description of the drawings

One or more embodiments are exemplified by corresponding drawings. These exemplary descriptions and drawings do not constitute limitations to the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings are not limited to scale and in which:

Figure 1 is a schematic diagram of a method for system application classification provided by an embodiment of the present disclosure;

Figure 2 is a schematic diagram of another method for system application classification provided by an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a device for application classification provided by an embodiment of the present disclosure.

Detailed ways

In order to understand the characteristics and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. The attached drawings are for reference only and are not intended to limit the embodiments of the present disclosure. In the following technical description, for convenience of explanation, multiple details are provided to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown simplified to simplify the drawings.

The terms "first", "second", etc. in the description and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that data so used are interchangeable under appropriate circumstances for the purposes of the embodiments of the disclosure described herein. Furthermore, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion.

Unless otherwise stated, the term "plurality" means two or more.

In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an "or" relationship. For example, A/B means: A or B.

The term "and/or" is an association relationship describing objects, indicating that three relationships can exist. For example, A and/or B means: A or B, or A and B.

As shown in FIG. 1 , an embodiment of the present disclosure provides a method for classifying system applications, including:

S101, obtain the running parameters of the application program.

S102. Classify applications according to the operating parameters.

Using the method for classifying system applications provided by the embodiments of the present disclosure, the application can be classified according to the running parameters of the application without detecting the package name of the application installation package or detecting the predefined category of the application. Classify applications. At the same time, the classification method of this application can reflect the performance of system parameters when the application is running, and facilitate the system to perform different directional optimizations in different application scenarios, especially for unpopular applications or newly launched applications. Classification according to the method of this application can facilitate the system to quickly optimize it according to the existing optimization plan, thereby improving the user experience.

Optionally, classify applications according to the operating parameters, including:

Obtain sample points corresponding to the application program according to the operating parameters;

Cluster the sample points to obtain a cluster set;

Classify the cluster set.

Optionally, clustering the sample points includes:

Determine core sample points;

The cluster set is determined based on the core sample points.

Optionally, the sample points are clustered through the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm. The DBSCAN clustering algorithm can be used to discover clusters of any shape, and can also find outliers while clustering. Since user operating habits and the running behavior of applications are usually unpredictable, DBSCAN clustering is most suitable.

Optionally, determining core sample points includes:

Set neighborhood parameters ∈;

Determine the ∈-neighborhood of the sample point according to the neighborhood parameter;

The core sample point is determined according to the number of samples in the ∈-neighborhood of the sample point. When the number of samples in the ∈-neighborhood of the sample point is greater than the set threshold MinPts, the sample point is a core sample point, and MinPts is a positive integer.

Optionally, determining a cluster set based on the core sample points includes:

Create a sample cluster for each core sample point, and put all the first objects in the ∈-neighborhood of each core sample point into the corresponding candidate set;

Check the ∈-neighborhood of all first objects in the ∈-neighborhood of each core sample point. When the ∈-neighborhood of the first object contains at least MinPts second objects, then the ∈-neighborhood of the first object All second objects in the domain are added to the candidate set corresponding to the first object; the MinPts is a set threshold, and MinPts is a positive integer;

Iteratively add the first object or the second object in the candidate set that does not belong to any cluster to the corresponding sample cluster until all sample clusters cannot be expanded, that is, until all candidate sets are empty, at which time all samples The clusters are generated and the cluster set is obtained.

Optionally, classifying the cluster set includes:

Randomly select a sample point from each cluster set, classify the selected sample point according to the operation requirements and/or display requirements according to the operating parameters, and obtain the type of the sample point; classify the cluster according to the type of the sample point Classification of class collections. Optionally, determine the type of any sample point, and then mark the cluster set with the type, or mark all sample points in the set and their corresponding applications. In this way, the type of each cluster set only needs to be determined once, that is, the number of determinations is the same as the number of cluster sets. Compared with the existing technology that determines each sample point, it saves a lot of time. The amount of calculation is reduced and the classification efficiency is improved.

Optionally, the operating parameters include:

The application synthesizes the frequency of display caching and the operating frequency of components of the application.

Optionally, the abscissa of the sample point is the frequency of the application's synthetic display cache, and the ordinate of the sample point is the component operation frequency of the application; or,

The abscissa of the sample point is the component operation frequency of the application program, and the ordinate of the sample point is the frequency of the application's synthetic display cache.

In some embodiments, it also includes:

Optimize applications based on the types by which they are classified. Optionally, the application is optimized to migrate the background application from the storage RAM to the memory ROM.

As shown in FIG. 2 , an embodiment of the present disclosure provides a method for classifying system applications, including:

S201, a new application is detected.

S202, determine whether the system has an optimization solution for the new application program; when there is an optimization solution for the new application program, execute step S206; when there is no optimization solution for the new application program, execute step S203.

S203: Obtain the running parameters of the program or the user's usage habits.

S204: Classify the application program through the DBSCAN algorithm according to the program's operating parameters or user usage habits.

S205: Execute the corresponding optimization plan according to the classification result of the application.

S206: Execute the corresponding optimization plan.

This solution uses the DBSCAN clustering algorithm to perform customized clustering of all Android applications according to user operating habits without detecting their package names or predefined categories, so as to intelligently cluster them in different application scenarios. Customize different optimization solutions.

The following scenario is an example. The user downloads a new game application, but the system has never optimized the game. The optimization includes but is not limited to background application management, memory management, display frame rate, etc. If you use a clustering algorithm and pass in the characteristic values of the classification reference, including but not limited to the application memory occupancy, the frequency of synthetic display cache, and the frequency of users operating in-game components, the game category can be subdivided into games with high display requirements and high operation. , low display requirements and low operation games. For the former, targeted optimization can be performed to try to adjust the memory occupied by other background applications to ensure the best foreground application experience. For the latter, the display frame rate can be adjusted without affecting the application experience to reduce device power consumption. For example, a user downloads Game A from the app store, and the system detects whether there is an established targeted optimization plan through the package name. If not, use the DBSCAN algorithm to define the abscissa as the frequency with which the user operates components in game A, and the ordinate as the frequency with which the device synthesizes the display cache to obtain sample points. Cluster the sample points of Game A with the sample points of other applications. After obtaining the cluster set, determine which cluster set Game A belongs to, and then optimize according to the optimization methods of other applications in the set. For example, game B is a game with high operational requirements, or the cluster set in which game B is located is a high-operation game. If game A belongs to the cluster set to which game B belongs, then game A is determined to be a game with high operational requirements. When Game A is a game with high operational requirements, adjust the memory occupied by other background applications while it is running, migrate other applications from the running RAM to the memory ROM or release them directly to ensure the best foreground application experience and reduce the cost of the device. Use power consumption.

As shown in FIG. 3 , an embodiment of the present disclosure provides a device for classifying system applications, including a processor 100 and a memory 101 . Optionally, the device may also include a communication interface (Communication Interface) 102 and a bus 103. Among them, the processor 100, the communication interface 102, and the memory 101 can communicate with each other through the bus 103. Communication interface 102 may be used for information transmission. The processor 100 can call logical instructions in the memory 101 to execute the method for system application classification in the above embodiment.

In addition, the above-mentioned logical instructions in the memory 101 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

As a computer-readable storage medium, the memory 101 can be used to store software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 100 executes program instructions/modules stored in the memory 101 to execute functional applications and data processing, that is, to implement the method for system application classification in the above embodiments.

The memory 101 may include a stored program area and a stored data area, wherein the stored program area may store an operating system and at least one application program required for a function; the stored data area may store data created according to the use of the terminal device, etc. In addition, the memory 101 may include a high-speed random access memory and may also include a non-volatile memory.

An embodiment of the present disclosure provides a product (such as a computer, a mobile phone, etc.) including the above device for classifying system applications.

Embodiments of the present disclosure provide a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are configured to execute the above method for system application classification.

Embodiments of the present disclosure provide a computer program product. The computer program product includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the The computer executes the above method for system application classification.

The above-mentioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which may be a personal computer, a server, or a network equipment, etc.) to perform all or part of the steps of the method described in the embodiments of the present disclosure. The aforementioned storage media can be non-transitory storage media, including: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, etc. A medium that can store program code or a temporary storage medium.

The foregoing description and drawings illustrate embodiments of the disclosure sufficiently to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples represent only possible variations. Unless explicitly required, individual components and features are optional and the order of operations may vary. Portions and features of some embodiments may be included in or substituted for those of other embodiments. Furthermore, the words used in this application are used only to describe the embodiments and not to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. . Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed items. In addition, when used in this application, the term "comprise" and its variations "comprises" and/or "comprising" etc. refer to stated features, integers, steps, operations, elements, and/or The presence of a component does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groupings of these. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method or apparatus including the stated element. In this article, each embodiment may focus on its differences from other embodiments, and the same and similar parts among various embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiment, then the relevant parts can be referred to the description of the method part.

Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The skilled person may use different methods to implement the described functionality for each specific application, but such implementations should not be considered to be beyond the scope of the disclosed embodiments. The skilled person can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be described again here.

In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units may only be a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined. Either it can be integrated into another system, or some features can be ignored, or not implemented. In addition, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.

The flowcharts and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more components for implementing the specified logical function(s). Executable instructions. In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two consecutive blocks may actually execute substantially in parallel, or they may sometimes execute in the reverse order, depending on the functionality involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, operations or steps corresponding to different blocks may also occur in a sequence different from that disclosed in the description, and sometimes there is no specific distinction between different operations or steps. order. For example, two consecutive operations or steps may actually be performed substantially in parallel, or they may sometimes be performed in reverse order, depending on the functionality involved. Each block in the block diagram and/or flowchart illustration, and combinations of blocks in the block diagram and/or flowchart illustration, may be implemented by special purpose hardware-based systems that perform the specified functions or actions, or may be implemented using special purpose hardware implemented in combination with computer instructions.

Claims (8)

1. A method for classifying system applications, characterized by including: Obtain the operating parameters of the application program; the operating parameters include: the frequency of the application program's synthetic display cache and the component operating frequency of the application program; Classify applications based on said operating parameters; Classifying application programs according to the operating parameters includes: obtaining sample points corresponding to the application programs according to the operating parameters; clustering the sample points to obtain a cluster set; The collection is classified; the abscissa of the sample point is the frequency of the application's synthetic display cache, and the ordinate of the sample point is the component operation frequency of the application; or, the abscissa of the sample point is the frequency of the application's component operation. Component operating frequency, the ordinate of the sample point is the frequency of the application's synthetic display cache. 2. The method according to claim 1, characterized in that clustering the sample points includes: Determine core sample points; The cluster set is determined based on the core sample points. 3. The method according to claim 2, characterized in that determining the core sample points includes: Set neighborhood parameters ∈; Determine the ∈-neighborhood of the sample point according to the neighborhood parameter; The core sample point is determined according to the number of samples in the ∈-neighborhood of the sample point. 4. The method according to claim 2, characterized in that determining a cluster set according to the core sample points includes: Create a sample cluster for each core sample point, and put all the first objects in the ∈-neighborhood of each core sample point into the corresponding candidate set; Check the ∈-neighborhood of all first objects in the ∈-neighborhood of each core sample point. When the ∈-neighborhood of the first object contains at least MinPts second objects, then the ∈-neighborhood of the first object All second objects in the domain are added to the candidate set corresponding to the first object; the MinPts is a set threshold, and the MinPts is a positive integer; Iteratively add the first object or the second object in the candidate set that does not belong to any cluster to the corresponding sample cluster until all sample clusters cannot be expanded to obtain a cluster set. 5. The method according to claim 1, characterized in that classifying the cluster set includes: Select a sample point from each cluster set, classify the selected sample point according to the operation requirements and/or display requirements according to the operating parameters, and obtain the type of the sample point; The cluster set is classified according to the type of the sample point. 6. The method of claim 1, further comprising: Optimize applications based on the types by which they are classified. 7. A device for system application classification, comprising a processor and a memory storing program instructions, characterized in that the processor is configured to execute claims 1 to 6 when executing the program instructions. The method for system application classification according to any one of the above. 8. A computer, characterized by comprising the device for classifying system applications as claimed in claim 7.