WO2019242580A1 - 应用程序管理方法、装置及移动终端、存储介质 - Google Patents

应用程序管理方法、装置及移动终端、存储介质 Download PDF

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WO2019242580A1
WO2019242580A1 PCT/CN2019/091523 CN2019091523W WO2019242580A1 WO 2019242580 A1 WO2019242580 A1 WO 2019242580A1 CN 2019091523 W CN2019091523 W CN 2019091523W WO 2019242580 A1 WO2019242580 A1 WO 2019242580A1
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application
application program
started
background
module
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PCT/CN2019/091523
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English (en)
French (fr)
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刘浩
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中兴通讯股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
    • G06F9/5016—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals the resource being the memory
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
    • G06F9/5022—Mechanisms to release resources

Definitions

  • the present disclosure relates to, but is not limited to, the field of communication technologies.
  • mobile terminals can support more and more applications, more and more powerful functions, and develop in a diversified and personalized direction. Users can install a variety of applications, such as game applications, music applications, video applications, office applications, and so on.
  • An application launched by an end user is usually cached in the background so that the application can be launched next time.
  • the system can clean up cached applications on its own.
  • the problem with this approach is that the mobile terminal can only cache applications started by the user to the background. If the applications are not cached to the background, the application startup or related device initialization will be very slow.
  • the application program that the end user needs to start next time may be cleared, which causes the terminal to initialize the application program too frequently and affect the user experience.
  • an application program management method including: obtaining weight parameters obtained through training of a machine learning model; obtaining an application to be started according to the weight parameter, and The application is loaded to run in the background; and in response to receiving a running instruction of the application to be started, the application to be started is switched from running in the background to running in the foreground.
  • an application management apparatus including a first obtaining module, a loading module, and a switching module.
  • the first obtaining module is configured to obtain a weight parameter obtained through training of a machine learning model
  • the loading module is configured to obtain an application to be started according to the weight parameter, and load the application to be started to run in the background.
  • the switching module is configured to switch the to-be-started application program from background running to foreground running in response to receiving a running instruction of the to-be-started application program.
  • a mobile terminal including a memory and a processor, and a computer program is stored on the memory, and when the computer program is executed by the processor, the processor executes Application management method according to the present disclosure.
  • a computer-readable storage medium on which a computer program is stored.
  • the processor executes an application program management method according to the present disclosure.
  • FIG. 1 is a schematic flowchart of an application management method according to an embodiment of the present disclosure
  • FIG. 2 is a schematic structural diagram of an application management apparatus according to an embodiment of the present disclosure
  • FIG. 3 is another schematic structural diagram of an application management apparatus according to an embodiment of the present disclosure.
  • FIG. 4 is a schematic structural diagram of a mobile terminal according to an embodiment of the present disclosure.
  • FIG. 1 is a schematic flowchart of an application management method according to an embodiment of the present disclosure.
  • the application management method may be used for a terminal, and the terminal may be implemented in various forms.
  • the terminals may include mobile terminals such as mobile phones, tablets, laptops, PDAs, PDAs, Portable Media Players (PMPs), navigation devices, wearable devices, smart bracelets, pedometers, etc.
  • fixed terminals such as digital TVs, desktop computers, and more. This disclosure does not limit this.
  • the application management method includes steps S11 to S13.
  • step S11 weight parameters obtained by training the machine learning model are acquired.
  • Machine learning models include neural network-based models.
  • a machine learning model may include one or more convolutional neural network layers, may also include one or more activation function layers, and may also include one or more recurrent neural network layers.
  • the initial model for training can be established based on neural network theory, and the initial number of layers or related parameters can be preset to be used for training based on experience. It should be noted that machine learning models are not limited to this type.
  • step S11 may include: collecting startup data of an application and data of related devices; and obtaining the weight parameters through training of the machine learning model according to the startup data of the application and data of related devices. .
  • the mobile terminal may pass the data of the application programs and related devices that are started multiple times to the machine learning model for training to obtain the weight parameters.
  • Machine learning models can be obtained locally from a server or terminal.
  • the training process of the machine learning model can be performed locally on the terminal or in the server. After training, the training results can be sent directly to the terminal for storage, or stored locally on the server.
  • step S12 the application program to be started is acquired according to the weight parameter, and the application program to be started is loaded to run in the background.
  • the method may further include: obtaining a memory usage situation; and cleaning an application running in the background according to the memory usage situation.
  • step 12 may include: in response to the memory usage exceeding a preset threshold, obtaining an application to be cleaned out of the applications running in the background according to the weight parameter; and cleaning the application to be cleaned up.
  • the memory occupied by the applications can be released and it is easy to load the applications to be started.
  • step S13 in response to receiving a running instruction of the application program to be started, the application program to be started is switched from running in the background to running in the foreground.
  • a run instruction may be an instruction that triggers an application to run in response to an operation by an end user.
  • the applications to be launched through the weight parameter are "WeChat”, “QQ”, and “Qianqianjingting", and “Qianqianjingting” has already been started, so “WeChat” and “QQ” are started in advance, and Transfer the processes of "WeChat” and "QQ” to run in the background.
  • the process of "WeChat” or “QQ” is switched from the background to the foreground to make it obvious to the end user I feel that the application launches very quickly, which leads to a better experience.
  • the method may further include: obtaining the device to be activated according to the weight parameter, and initializing the device to be activated.
  • the "QQ" application is running in the foreground, and the camera of the terminal is obtained through the weight parameter to initialize the camera of the terminal.
  • the "QQ" application starts the camera of the terminal for shooting, the end user can obviously feel that the camera starts up very fast, and thus obtain a better experience.
  • the weight parameters obtained through training of the machine learning model can be used to obtain the application to be started, and the application to be started can be loaded to run in the background, so that the application can be started quickly To improve user experience.
  • FIG. 2 is a schematic structural diagram of an application management apparatus according to an embodiment of the present disclosure.
  • the application management apparatus may include a first obtaining module 21, a loading module 22, and a switching module 23.
  • the first acquisition module 21 is configured to acquire weight parameters obtained through training of a machine learning model.
  • the first obtaining module 21 may be further configured to collect startup data of an application program and modify data of a device, and train the machine learning model according to the startup data of the application program and data of a related device.
  • the weight parameter is obtained.
  • the loading module 22 is configured to obtain an application to be started according to the weight parameter, and load the application to be started to run in the background.
  • the switching module 23 is configured to switch the to-be-started application program from a background operation to a foreground operation in response to receiving a running instruction of the to-be-started application program.
  • FIG. 3 is another schematic structural diagram of an application management apparatus according to an embodiment of the present disclosure.
  • the application management apparatus may further include a second obtaining module 24 and a cleaning module 25.
  • the second acquisition module 24 is configured to acquire a memory usage situation.
  • the cleaning module 25 is configured to clean up applications running in the background according to the memory usage.
  • the cleaning module 25 may be further configured to: in response to the memory usage exceeding a preset threshold, obtain the application to be cleaned from the applications running in the background according to the weight parameter, and clean the application to be cleaned program.
  • the application management apparatus may further include a device initialization module 26.
  • the device initialization module 26 is configured to obtain a device to be activated according to the weight parameter, and initialize the device to be activated.
  • FIG. 4 is a schematic structural diagram of a mobile terminal according to an embodiment of the present disclosure.
  • the mobile terminal includes a memory 31 and a processor 32.
  • a computer program is stored on the memory 31.
  • the processor 32 may execute an application program management method according to various embodiments of the present disclosure.
  • An embodiment of the present disclosure further provides a computer-readable storage medium on which a computer program is stored.
  • the processor may execute the application program management method according to the embodiments of the present disclosure.
  • the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be composed of several physical
  • the components execute cooperatively.
  • Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit .
  • Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media).
  • computer storage medium includes both volatile and nonvolatile implementations in any method or technology used to store information such as computer-readable instructions, data structures, program modules or other data.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or Any other medium used to store desired information and which can be accessed by a computer.

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  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Stored Programmes (AREA)

Abstract

本公开提供了一种应用程序管理方法、应用程序管理装置、移动终端和计算机可读存储介质。所述应用程序管理方法包括：获取通过机器学习模型训练得到的权重参数；根据所述权重参数获取待启动的应用程序，并将所述待启动的应用程序加载到后台运行；以及响应于接收到所述待启动的应用程序的运行指令，将所述待启动的应用程序从后台运行切换到前台运行。

Description

应用程序管理方法、装置及移动终端、存储介质 技术领域
本公开涉及(但不限于)通信技术领域。
背景技术
随着通信技术的不断发展，移动终端能够支持的应用越来越多，功能越来越强大，并向着多样化、个性化的方向发展。用户可自行安装各种各样的应用程序，例如：游戏应用程序、音乐应用程序、视频应用程序、办公应用程序等等。
终端用户启动过的应用程序通常会缓存到后台，以使得下次可以加速启动该应用。当内存不足时，***可自行清理缓存的应用程序。这种方式存在的问题是，移动终端只能将用户启动过的应用程序缓存到后台，若应用程序没有缓存到后台，则应用程序的启动或者相关设备的初始化都将非常缓慢。在内存不足时，可能把终端用户下次需要启动的应用程序清理掉，导致终端过于频繁的初始化启动应用程序，影响用户体验。
发明内容
根据本公开实施例的一个方面，提供的一种应用程序管理方法，包括：获取通过机器学习模型训练得到的权重参数；根据所述权重参数获取待启动的应用程序，并将所述待启动的应用程序加载到后台运行；以及响应于接收到所述待启动的应用程序的运行指令，将所述待启动的应用程序从后台运行切换到前台运行。
根据本公开实施例的另一个方面，提供的一种应用程序管理装置，包括第一获取模块、加载模块以及切换模块。所述第一获取模块构造为获取通过机器学习模型训练得到的权重参数，所述加载模块构造为根据所述权重参数获取待启动的应用程序，并将所述待启动的应用程序加载到后台运行，并且所述切换模块构造为响应于接收到所述待启动的应用程序的运行指令，将所述待启动的应用程序从后台运行 切换到前台运行。
根据本公开实施例的另一个方面，提供的一种移动终端，包括存储器和处理器，在所述存储器上存储有计算机程序，所述计算机程序被所述处理器执行时，所述处理器执行根据本公开的应用程序管理方法。
根据本公开实施例的另一个方面，提供的一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时，所述处理器执行根据本公开的应用程序管理方法。
附图说明
图1为根据本公开实施例的应用程序管理方法的流程示意图；
图2为根据本公开实施例的应用程序管理装置的结构示意图；
图3为根据本公开实施例的应用程序管理装置的另一结构示意图；以及
图4为根据本公开实施例的移动终端结构示意图。
本公开目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
为了使本公开所要解决的技术问题、技术方案及有益效果更加清楚、明白，以下结合附图和实施例，对本公开进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本公开，并不用于限定本公开。
图1为根据本公开实施例的应用程序管理方法的流程示意图。
根据本公开实施例的应用程序管理方法可用于终端，并且终端可以以各种形式来实施。例如，终端可以包括诸如手机、平板电脑、笔记本电脑、掌上电脑、PDA、便捷式媒体播放器(Portable Media Player，PMP)、导航装置、可穿戴设备、智能手环、计步器等移动终端，以及诸如数字电视、台式计算机等固定终端。本公开对此不作限制。
如图1所示，根据本公开实施例的应用程序管理方法包括步骤S11至S13。
在步骤S11，获取通过机器学习模型训练得到的权重参数。
机器学习模型包括基于神经网络的模型，例如，机器学习模型可包括一个或多个卷积神经网络层，还可包括一个或多个激活函数层，也可包括一个或多个循环神经网络层。可基于神经网络理论建立用于训练的初始模型，还可基于经验对网络层数或相关参数进行预先设置用于训练的初始模型。需要说明的是，机器学习模型并不限于此种类型。
在一个实施例中，步骤S11可以包括：收集应用程序的启动数据以及相关设备的数据；以及根据所述应用程序的启动数据以及相关设备的数据，通过所述机器学习模型训练得到所述权重参数。
移动终端可将多次启动的应用程序和相关设备的数据传递给机器学习模型进行训练得到所述权重参数。
机器学习模型可以从服务器或者终端本地获取。机器学习模型的训练过程可以在终端本地进行，也可以在服务器中进行。训练完毕后，可将训练结果直接发送至终端进行存储，或在服务器本地进行存储。
在步骤S12，根据所述权重参数获取待启动的应用程序，并将所述待启动的应用程序加载到后台运行。
在一个实施例中，在步骤12之前还可以包括：获取内存使用情况；以及根据所述内存使用情况清理后台运行的应用程序。
在一个实施例中，步骤12可以包括：响应于内存使用超过预设阈值，根据所述权重参数获取后台运行的应用程序中的待清理的应用程序；以及清理所述待清理的应用程序。
通过将不使用的应用程序进行清理回收，可以释放应用程序所占用的内存，便于对待启动的应用程序进行加载。
在步骤S13，响应于接收到所述待启动的应用程序的运行指令，将所述待启动的应用程序从后台运行切换到前台运行。
运行指令可以为响应于终端用户的操作而触发应用程序运行的 指令。
作为示例，通过权重参数获取待启动的应用程序为“微信”、“QQ”、“千千静听”，而“千千静听”已经启动，则提前启动“微信”与“QQ”，并将“微信”与“QQ”的进程转入后台运行。当接收到用户触控“微信”应用程序的图标或“QQ”应用程序的图标而触发的运行指令时，将“微信”或“QQ”的进程从后台切换至前台运行，从而使终端用户明显感受到应用程序的启动速度非常快，进而获得较好的使用体验。
在一个实施例中，在步骤S13之后还可以包括：根据所述权重参数获取待启用设备，并对所述待启用设备进行初始化。
继续参照上述示例，假设正在前台运行“QQ”应用程序，通过权重参数获取待启用设备为终端的摄像头，从而对终端的摄像头进行初始化。在“QQ”应用程序启动终端的摄像头进行拍摄时，终端用户可明显感受到摄像头的启动速度非常快，进而获得较好的使用体验。
根据本公开实施例的应用程序管理方法，通过机器学习模型训练得到的权重参数可用于获取待启动的应用程序，并可以将待启动的应用程序加载到后台运行，从而使得应用程序可以快速地启动，提升用户体验。
图2为根据本公开实施例的应用程序管理装置的结构示意图。
如图2所示，根据本公开实施例的应用程序管理装置可以包括第一获取模块21、加载模块22以及切换模块23。
第一获取模块21构造为获取通过机器学习模型训练得到的权重参数。
在一个实施例中，第一获取模块21可进一步构造为：收集应用程序的启动数据以及修改设备的数据，并且根据所述应用程序的启动数据以及相关设备的数据，通过所述机器学习模型训练得到所述权重参数。
加载模块22构造为根据所述权重参数获取待启动的应用程序，并将所述待启动的应用程序加载到后台运行。
切换模块23构造为响应于接收到所述待启动的应用程序的运行 指令，将所述待启动的应用程序从后台运行切换到前台运行。
图3为根据本公开实施例的应用程序管理装置的另一结构示意图。
如图3所示，根据本公开实施例的应用程序管理装置还可以包括第二获取模块24和清理模块25。
第二获取模块24构造为获取内存使用情况。
清理模块25构造为根据所述内存使用情况清理后台运行的应用程序。
在一个实施例中，清理模块25可进一步构造为：响应于内存使用超过预设阈值，根据所述权重参数获取后台运行的应用程序中的待清理的应用程序，并且清理所述待清理的应用程序。
如图3所示，根据本公开实施例的应用程序管理装置还可以包括设备初始化模块26。
设备初始化模块26构造为根据所述权重参数获取待启用设备，并对所述待启用设备进行初始化。
图4为根据本公开实施例的移动终端结构示意图。
如图4所示，根据本公开实施例的移动终端包括存储器31和处理器32。存储器31上存储有计算机程序，所述计算机程序被处理器32执行时，处理器32可以执行根据本公开各实施例的应用程序管理方法。
本公开实施例还提供一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时，所述处理器可以执行根据本公开各实施例的应用程序管理方法。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、装置中的功能模块/单元可以被实施为软件、固件、硬件及其适当的组合。
在硬件实施方式中，在以上描述中提及的功能模块/单元之间的划分不一定对应于物理组件的划分；例如，一个物理组件可以具有多个功能，或者一个功能或步骤可以由若干物理组件合作执行。某些物理组件或所有物理组件可以被实施为由处理器，如中央处理器、数字 信号处理器或微处理器执行的软件，或者被实施为硬件，或者被实施为集成电路，如专用集成电路。这样的软件可以分布在计算机可读介质上，计算机可读介质可以包括计算机存储介质(或非暂时性介质)。如本领域普通技术人员公知的，术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。
以上所述，仅为本公开的具体实施方式，但本公开的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本公开揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本公开的保护范围之内。本公开的保护范围应以所述权利要求的保护范围以准。

Claims (12)

  1. 一种应用程序管理方法，包括：
    获取通过机器学习模型训练得到的权重参数；
    根据所述权重参数获取待启动的应用程序，并将所述待启动的应用程序加载到后台运行；以及
    响应于接收到所述待启动的应用程序的运行指令，将所述待启动的应用程序从后台运行切换到前台运行。
  2. 根据权利要求1所述的应用程序管理方法，其中，获取通过机器学习模型训练得到的权重参数的步骤包括：
    收集应用程序的启动数据以及相关设备的数据；以及
    根据所述应用程序的启动数据以及相关设备的数据，通过所述机器学习模型训练得到所述权重参数。
  3. 根据权利要求1所述的应用程序管理方法，其中，在根据所述权重参数获取待启动的应用程序，并将所述待启动的应用程序加载到后台运行的步骤之前，所述方法还包括：
    获取内存使用情况；以及
    根据所述内存使用情况清理后台运行的应用程序。
  4. 根据权利要求3所述的应用程序管理方法，其中，根据所述内存使用情况清理后台运行的应用程序的步骤包括：
    响应于内存使用超过预设阈值，根据所述权重参数获取后台运行的应用程序中的待清理的应用程序；以及
    清理所述待清理的应用程序。
  5. 根据权利要求1所述的应用程序管理方法，其中，在响应于接收到所述待启动的应用程序的运行指令，将所述待启动的应用程序从后台运行切换到前台运行的步骤之后，所述还包括：
    根据所述权重参数获取待启用设备，并对所述待启用设备进行初始化。
  6. 一种应用程序管理装置，包括第一获取模块、加载模块以及切换模块，
    所述第一获取模块构造为获取通过机器学习模型训练得到的权重参数，
    所述加载模块构造为根据所述权重参数获取待启动的应用程序，并将所述待启动的应用程序加载到后台运行，并且
    所述切换模块构造为响应于接收到所述待启动的应用程序的运行指令，将所述待启动的应用程序从后台运行切换到前台运行。
  7. 根据权利要求6所述的应用程序管理装置，其中，所述第一获取模块构造为：
    收集应用程序的启动数据以及相关设备的数据，并且
    根据所述应用程序的启动数据以及相关设备的数据，通过所述机器学习模型训练得到所述权重参数。
  8. 根据权利要求6所述的应用程序管理装置，还包括第二获取模块和清理模块，
    所述第二获取模块构造为获取内存使用情况，并且
    所述清理模块构造为根据所述内存使用情况清理后台运行的应用程序。
  9. 根据权利要求8所述的应用程序管理装置，其中，所述清理模块构造为：
    响应于内存使用超过预设阈值，根据所述权重参数获取后台运行的应用程序中的待清理的应用程序，并且
    清理所述待清理的应用程序。
  10. 根据权利要求6所述的应用程序管理装置，还包括设备初始化模块，
    所述设备初始化模块构造为根据所述权重参数获取待启用设备，并对所述待启用设备进行初始化。
  11. 一种移动终端，包括存储器和处理器，在所述存储器上存储有计算机程序，所述计算机程序被所述处理器执行时，所述处理器执行根据权利要求1至5中任一项所述的应用程序管理方法。
  12. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时，所述处理器执行根据权利要求1至5中任一项所述的应用程序管理方法。
PCT/CN2019/091523 2018-06-22 2019-06-17 应用程序管理方法、装置及移动终端、存储介质 WO2019242580A1 (zh)

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