CN111144509B - Method, device and computer for classifying system application programs - Google Patents
Method, device and computer for classifying system application programs Download PDFInfo
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- CN111144509B CN111144509B CN201911411437.3A CN201911411437A CN111144509B CN 111144509 B CN111144509 B CN 111144509B CN 201911411437 A CN201911411437 A CN 201911411437A CN 111144509 B CN111144509 B CN 111144509B
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- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000005457 optimization Methods 0.000 abstract description 15
- 238000003860 storage Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 7
- 238000004891 communication Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
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- 230000002159 abnormal effect Effects 0.000 description 1
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- 239000002131 composite material Substances 0.000 description 1
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
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
Technical Field
The present application relates to the field of computer technology, and for example, to a method, an apparatus, and a computer for classifying system applications.
Background
For the android system, new applications are online every moment, and the application programs on the android system are classified, so that a user can conveniently and quickly find the needed application programs, unified management of the application programs is realized, but the current application classification can only be evaluated and divided by online time, or the user can manually divide the application programs.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art:
the existing application program classification is classified by the labels of the application program, such as game class, video and audio class, social software class and the like, but the classification mode is difficult to accurately reflect the running requirement of the application program, and further, a proper optimization strategy is difficult to obtain according to the classification type of the mode.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview, and is intended to neither identify key/critical elements nor delineate the scope of such embodiments, but is intended as a prelude to the more detailed description that follows.
The embodiment of the disclosure provides a method, a device and a computer for classifying system application programs, which are used for solving the technical problem that the prior art is difficult to effectively optimize the system according to the application program classification.
In some embodiments, the method comprises:
acquiring operation parameters of an application program;
and classifying the application programs according to the operation parameters.
In some embodiments, the apparatus comprises: a processor and a memory storing program instructions, the processor being configured to perform the above-described method for classifying system applications when executing the program instructions.
In some embodiments, the computer comprises: apparatus for classifying system applications
The method, the device and the computer for classifying the system application programs provided by the embodiment of the disclosure can realize the following technical effects: the application program is classified by the operation parameters of the application program, the application program can be classified under the conditions of not detecting the package name of the application program installation package and not detecting the predefined category of the application program, and meanwhile, the classification mode of the application can embody the performance of the system parameters when the application program operates, 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 program according to the existing optimization scheme after classifying the application program according to the mode of the application, thereby improving the user experience.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which like reference numerals refer to similar elements, and in which:
FIG. 1 is a schematic diagram of a method for system application classification provided by an embodiment of the present disclosure;
FIG. 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 an apparatus for classifying applications according to an embodiment of the present disclosure.
Detailed Description
So that the manner in which the features and techniques of the disclosed embodiments can be understood in more detail, a more particular description of the embodiments of the disclosure, briefly summarized below, may be had by reference to the appended drawings, which are not intended to be limiting of the embodiments of the disclosure. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may still be practiced without these details. In other instances, well-known structures and devices may be shown simplified in order to simplify the drawing.
The terms first, second and the like in the description and in the claims of the embodiments of the disclosure and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe embodiments of the present disclosure. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
The term "plurality" means two or more, unless otherwise indicated.
In the embodiment of the present disclosure, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B.
Referring to fig. 1, an embodiment of the present disclosure provides a method for classifying system applications, including:
s101, acquiring operation parameters of an application program.
S102, classifying the application programs according to the operation parameters.
By adopting the method for classifying the application programs of the system, which is provided by the embodiment of the application, the application programs are classified through the operation parameters of the application programs, the application programs can be classified under the conditions of not detecting the package name of the application program installation package and not detecting the predefined category of the application programs, meanwhile, the classification mode of the application can embody the performance of the 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.
Optionally, classifying the application program according to the operation parameters includes:
obtaining sample points corresponding to the application program according to the operation parameters;
clustering the sample points to obtain a clustering set;
and classifying the cluster set.
Optionally, clustering the sample points includes:
determining a core sample point;
and determining a cluster set according to the core sample points.
Optionally, the sample points are clustered by a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm. The clustering clusters with any shapes can be found by adopting the DBSCAN clustering algorithm, abnormal points can be found at the same time of clustering, and the DBSCAN clustering is most suitable because the operation habit of a user and the operation behavior of an application program are generally unpredictable.
Optionally, the determining the core sample point includes:
setting neighborhood parameters E;
determining an epsilon-neighborhood of the sample point according to the neighborhood parameter;
and determining a core sample point according to the number of samples in the epsilon-neighborhood of the sample point. When the number of samples in the epsilon-neighborhood of the sample point is larger than a set threshold value MinPts, the sample point is a core sample point, and MinPts is a positive integer.
Optionally, determining a cluster set according to the core sample points includes:
creating a sample cluster for each core sample point, and putting all first objects in an epsilon-neighborhood of each core sample point into a corresponding candidate set;
checking the E-neighborhood of all first objects in the E-neighborhood of each core sample point, and when the E-neighborhood of the first object at least contains MinPts second objects, adding all the second objects in the E-neighborhood of the first object into a candidate set corresponding to the first object; the MinPts are set thresholds, and the MinPts are positive integers;
and iteratively adding the first object or the second object which does not belong to any cluster in the candidate set into the corresponding sample cluster until all the sample clusters cannot be expanded, namely, until all the candidate sets are empty, and generating all the sample clusters to obtain a cluster set.
Optionally, classifying the cluster set includes:
randomly selecting a sample point from each cluster set, and classifying the selected sample point according to the operation requirement and/or the display requirement according to the operation parameter to obtain the type of the sample point; and classifying the cluster set according to the type of the sample point. Optionally, the type of any sample point is determined, and the cluster set is marked with the type, or all sample points in the set and corresponding application programs are marked. By the method, the type of each cluster set is judged only once, namely the judgment times are the same as the number of the cluster sets, and compared with the mode of judging each sample point in the prior art, the method has the advantages that the calculated amount is greatly saved, and the classification efficiency is improved.
Optionally, the operating parameters include:
the application program synthesizes and displays the frequency of the cache and the component operation frequency of the application program.
Optionally, the abscissa of the sample point is the frequency of the application program synthesized display buffer, and the ordinate of the sample point is the component operation frequency of the application program; or alternatively, the first and second heat exchangers may be,
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 program synthesized display buffer.
In some embodiments, further comprising:
and optimizing the application program according to the type obtained by classifying the application program. Optionally, the optimizing the application program is to migrate the background application program from the operation memory RAM to the memory ROM.
As shown in conjunction with fig. 2, an embodiment of the present disclosure provides a method for classifying system applications, comprising:
s201, detecting a new application program.
S202, judging whether an optimization scheme aiming at the new application program exists in the system or not; when there is an optimization scheme for the new application, step S206 is performed; when there is no optimization scheme for the new application, step S203 is performed.
S203, acquiring the running parameters of the program or the use habit of the user.
S204, classifying the application program through a DBSCAN algorithm according to the operation parameters of the program or the use habit of the user.
S205, executing a corresponding optimization scheme according to the classification result of the application program.
S206, executing a corresponding optimization scheme.
According to the scheme, by utilizing a DBSCAN clustering algorithm, all android applications can be clustered according to user operation habits under the conditions of not detecting package names and not detecting predefined categories, so that different optimization schemes can be customized intelligently under different application scenes.
In the following scenario, the user has downloaded a game application newly, but the system has never optimized the game, including but not limited to background application management, memory management, display frame rate, etc. If a clustering algorithm is utilized, feature values of the incoming classification references, including but not limited to the application memory occupancy, the frequency of the composite display cache, and the frequency of the user operating components within the game, can be used to subdivide the game into a high-display-requirement high-operation-class game and a low-display-requirement low-operation-class game. For the former, directional optimization can be performed, the memory occupied by other applications in the background can be adjusted as much as possible, the best experience of the foreground application is ensured, and for the latter, the display frame rate can be adjusted on the premise of not affecting the application experience, and the use power consumption of the equipment is reduced. For example, the user downloads game A from an application store, and the system detects whether a given directional optimization scheme exists by package name. If not, using DBSCAN algorithm, defining the abscissa as the frequency of user operation of the components in game A, and defining the ordinate as the frequency of equipment composition display buffer, to obtain sample points. And clustering the sample points of the game A with sample points of other application programs to obtain a clustering set, and judging which clustering set the game A belongs to, and optimizing according to the optimizing mode of the other application programs of the clustering set. For example, game B is a high operation requirement game, or, if game a belongs to a cluster set to which game B belongs, game a is determined to be a high operation requirement game. When the game A is a game with high operation requirements, the memory occupied by other applications in the background is adjusted during the running of the game A, and other applications are migrated from the memory RAM to the memory ROM or directly released, so that the optimal experience of the foreground application is ensured, and the use power consumption of equipment is reduced.
As shown in connection with FIG. 3, an embodiment of the present disclosure provides an apparatus for classifying system applications, including a processor (processor) 100 and a memory (memory) 101. Optionally, the apparatus may further comprise a communication interface (Communication Interface) 102 and a bus 103. The processor 100, the communication interface 102, and the memory 101 may communicate with each other via the bus 103. The communication interface 102 may be used for information transfer. Processor 100 may invoke logic instructions in memory 101 to perform the method for system application classification of the above-described embodiments.
Further, the logic instructions in the memory 101 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product.
The memory 101 is a computer readable storage medium that can be used to store a software program, a computer executable program, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 100 executes functional applications and data processing by running program instructions/modules stored in the memory 101, i.e. implements the method for classifying system applications in the above-described embodiments.
The memory 101 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the terminal device, etc. Further, the memory 101 may include a high-speed random access memory, and may also include a nonvolatile memory.
Embodiments of the present disclosure provide an article of manufacture (e.g., a computer, a cell phone, etc.) comprising an apparatus for classifying system applications as described above.
Embodiments of the present disclosure provide a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for classifying system applications.
The disclosed embodiments provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-described method for classification of system applications.
The computer readable storage medium may be a transitory computer readable storage medium or a non-transitory computer readable storage medium.
Embodiments of the present disclosure may be embodied in a software product stored on a storage medium, including one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of a method according to embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium including: a plurality of media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or a transitory storage medium.
The above description and the drawings illustrate embodiments of the disclosure sufficiently to enable those skilled in the art to practice them. Other embodiments may involve structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in, or substituted for, those of others. Moreover, the terminology used in the present application is for the purpose of describing embodiments only and is not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" (the) are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this disclosure is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, when used in the present disclosure, the terms "comprises," "comprising," and/or variations thereof, mean that the recited features, integers, steps, operations, elements, and/or components are present, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements. In this context, each embodiment may be described with emphasis on the differences from the other embodiments, and the same similar parts between the various embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method sections disclosed in the embodiments, the description of the method sections may be referred to for relevance.
Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. The skilled artisan may use different methods for each particular application to achieve the described functionality, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments disclosed herein, the disclosed methods, articles of manufacture (including but not limited to devices, apparatuses, etc.) may be practiced in other ways. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units may be merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to implement the present embodiment. In addition, each functional unit in the embodiments of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in 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, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (8)
1. A method for classifying a system application, comprising:
acquiring operation parameters of an application program; the operating parameters include: the application program synthesizes and displays the frequency of the cache and the component operation frequency of the application program;
classifying the application programs according to the operation parameters;
the classifying the application programs according to the operation parameters comprises the following steps: obtaining sample points corresponding to the application program according to the operation parameters; clustering the sample points to obtain a clustering set; classifying the cluster set; the abscissa of the sample point is the frequency of the application program synthesized display cache, and the ordinate of the sample point is the component operation frequency of the application program; 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 program synthesized display buffer.
2. The method of claim 1, wherein clustering the sample points comprises:
determining a core sample point;
and determining a cluster set according to the core sample points.
3. The method of claim 2, wherein the determining core sample points comprises:
setting neighborhood parameters E;
determining an epsilon-neighborhood of the sample point according to the neighborhood parameter;
and determining a core sample point according to the number of samples in the epsilon-neighborhood of the sample point.
4. The method of claim 2, wherein determining a set of clusters from the core sample points comprises:
creating a sample cluster for each core sample point, and putting all first objects in an epsilon-neighborhood of each core sample point into a corresponding candidate set;
checking the E-neighborhood of all first objects in the E-neighborhood of each core sample point, and when the E-neighborhood of the first object at least contains MinPts second objects, adding all the second objects in the E-neighborhood of the first object into a candidate set corresponding to the first object; the MinPts are set thresholds, and the MinPts are positive integers;
and iteratively adding the first object or the second object which does not belong to any cluster in the candidate set into the corresponding sample cluster until all the sample clusters cannot be expanded, thereby obtaining a cluster set.
5. The method of claim 1, wherein classifying the set of clusters comprises:
selecting a sample point from each cluster set, and classifying the selected sample point according to the operation requirement and/or the display requirement according to the operation parameter to obtain the type of the sample point;
and classifying the cluster set according to the type of the sample point.
6. The method as recited in claim 1, further comprising:
and optimizing the application program according to the type obtained by classifying the application program.
7. An apparatus for system application classification comprising a processor and a memory storing program instructions, wherein the processor is configured, when executing the program instructions, to perform the method for system application classification of any of claims 1 to 6.
8. A computer comprising the apparatus for classifying system applications of claim 7.
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