WO2019242580A1

WO2019242580A1 - Method and apparatus for application program management, mobile terminal, and storage medium

Info

Publication number: WO2019242580A1
Application number: PCT/CN2019/091523
Authority: WO
Inventors: 刘浩
Original assignee: 中兴通讯股份有限公司
Priority date: 2018-06-22
Filing date: 2019-06-17
Publication date: 2019-12-26
Also published as: CN110633134A

Abstract

Provided are a method for application program management, an apparatus for application program management, a mobile terminal and a computer-readable storage medium. The method for application program management comprises: acquiring a weight parameter obtained by means of training a machine learning model; acquiring, according to the weight parameter, an application program to be started, and loading said application program for background running; and in response to receiving a running instruction for the application program to be started, switching said application program from background running to foreground running.

Description

Application program management method, device, mobile terminal and storage medium

Technical field

The present disclosure relates to, but is not limited to, the field of communication technologies.

Background technique

With the continuous development of communication technology, 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. When memory is low, 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. When the memory is insufficient, 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.

Summary of the Invention

According to an aspect of the embodiments of the present disclosure, an application program management method is provided, 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.

According to another aspect of the embodiments of the present disclosure, an application management apparatus is provided, 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, and 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. And 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.

According to another aspect of the embodiments of the present disclosure, there is provided 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.

According to another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the processor executes an application program management method according to the present disclosure. .

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic flowchart of an application management method according to an embodiment of the present disclosure;

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; and

FIG. 4 is a schematic structural diagram of a mobile terminal according to an embodiment of the present disclosure.

The realization, functional characteristics and advantages of the purpose of the present disclosure will be further described with reference to the embodiments and the drawings.

detailed description

In order to make the technical problems, technical solutions and beneficial effects to be more clearly understood by the present disclosure, the present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not intended to limit 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 according to an embodiment of the present disclosure may be used for a terminal, and the terminal may be implemented in various forms. For example, 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. And fixed terminals such as digital TVs, desktop computers, and more. This disclosure does not limit this.

As shown in FIG. 1, the application management method according to an embodiment of the present disclosure includes steps S11 to S13.

In step S11, weight parameters obtained by training the machine learning model are acquired.

Machine learning models include neural network-based models. For example, 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.

In one embodiment, 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.

In 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.

In an embodiment, before step 12, the method may further include: obtaining a memory usage situation; and cleaning an application running in the background according to the memory usage situation.

In one embodiment, 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.

By cleaning up and recycling the unused applications, the memory occupied by the applications can be released and it is easy to load the applications to be started.

In 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.

As an example, 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. When receiving a running instruction triggered by the user touching the icon of the "WeChat" application or the icon of the "QQ" application, 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.

In one embodiment, after step S13, the method may further include: obtaining the device to be activated according to the weight parameter, and initializing the device to be activated.

Continuing to refer to the above example, it is assumed that 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. When 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.

According to the application management method in the embodiment of the present disclosure, 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.

As shown in FIG. 2, the application management apparatus according to an embodiment of the present disclosure 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.

In one embodiment, 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.

As shown in FIG. 3, the application management apparatus according to an embodiment of the present disclosure 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.

In one embodiment, 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.

As shown in FIG. 3, the application management apparatus according to an embodiment of the present disclosure 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.

As shown in FIG. 4, the mobile terminal according to an embodiment of the present disclosure includes a memory 31 and a processor 32. A computer program is stored on the memory 31. When the computer program is executed by the processor 32, 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. When the computer program is executed by a processor, the processor may execute the application program management method according to the embodiments of the present disclosure.

Those of ordinary skill in the art can understand that all or some steps in the methods disclosed above, and functional modules / units in the device can be implemented as software, firmware, hardware, and appropriate combinations thereof.

In a hardware implementation, 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). As is known to those of ordinary skill in the art, the term 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. Removable, removable and non-removable media. 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.

The above are only specific implementations of the present disclosure, but the scope of protection of the present disclosure is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present disclosure. It should be covered by the protection scope of this disclosure. The protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

An application management method includes:

Obtaining weight parameters obtained through machine learning model training;

Obtaining an application to be started according to the weight parameter, and loading the application to be started to run in the background; and

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.
The method for managing an application program according to claim 1, wherein the step of obtaining weight parameters obtained through training of a machine learning model comprises:

Collect application launch data and related device data; and

The weight parameters are obtained by training the machine learning model according to the startup data of the application and data of related equipment.
The method for managing an application program according to claim 1, wherein before the step of obtaining an application program to be started according to the weight parameter and loading the application program to be started to run in the background, the method further comprises:

Get memory usage; and

Clean up applications running in the background according to the memory usage.
The method for managing an application program according to claim 3, wherein the step of cleaning an application running in the background according to the memory usage comprises:

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

Clean up the application to be cleaned.
The method for managing an application program according to claim 1, wherein, in response to receiving a running instruction of the application program to be started, switching the application program to be started from a background operation to a foreground operation, It also includes:

Acquiring the device to be activated according to the weight parameter, and initializing the device to be activated.
An application management device includes a first acquisition module, a loading module, and a switching module.

The first acquisition module is configured to acquire weight parameters 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, and

The switching module 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.
The application management device according to claim 6, wherein the first acquisition module is configured as:

Collect application launch data and related device data, and

The weight parameters are obtained by training the machine learning model according to the startup data of the application and data of related equipment.
The application management apparatus according to claim 6, further comprising a second acquisition module and a cleaning module,

The second obtaining module is configured to obtain a memory usage situation, and

The cleaning module is configured to clean up an application running in the background according to the memory usage.
The application management device according to claim 8, wherein the cleaning module is configured as:

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

Clean up the application to be cleaned.
The apparatus for managing an application program according to claim 6, further comprising a device initialization module,

The device initialization module is configured to obtain a device to be activated according to the weight parameter, and initialize the device to be activated.
A mobile terminal includes a memory and a processor, and a computer program is stored on the memory. When the computer program is executed by the processor, the processor executes the method according to any one of claims 1 to 5. Application management method.
A computer-readable storage medium having stored thereon a computer program. When the computer program is executed by a processor, the processor executes the application program management method according to any one of claims 1 to 5.