CN114595003B - Application starting method, device and storage medium - Google Patents
Application starting method, device and storage medium Download PDFInfo
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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
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
- G06F9/44505—Configuring for program initiating, e.g. using registry, configuration files
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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
- G06F9/50—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
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
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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
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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
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The disclosure relates to an application starting method, an application starting device and a storage medium. The application starting method is applied to the terminal and comprises the following steps: when a target application is started, collecting current CPU load data and memory occupation data of the target terminal; determining a target starting duration of the target application; determining a starting probability corresponding to the completion of starting the target application in the target starting duration according to the CPU load data, the memory occupation data, the target application type of the target application and the target starting duration, and determining a target working frequency of the CPU according to the starting probability; and controlling the CPU to run at the target working frequency within the target starting time. Through the method and the device, the smoothness of the terminal can be improved, and the power consumption and the performance waste of the terminal are reduced.
Description
Technical Field
The disclosure relates to the technical field of terminals, and in particular relates to an application starting method, an application starting device and a storage medium.
Background
The central processing unit (CPU, central Processing Unit) is a computer operation core and control core, and the higher the operation frequency is, the faster the data processing speed is, but the battery consumption is also increased. For the mobile terminal device, in order to ensure the endurance time of the battery of the mobile terminal device, the CPU processing speed generally takes a lower frequency for ensuring the normal operation of the system, but some target application scenarios have a higher requirement on the CPU processing speed, such as when a target application program is started.
In order to enhance the user experience, many terminal manufacturers employ an increase in the higher CPU operating frequency to achieve their quick start-up when the target application is started. The current scheme is to raise the CPU working frequency and keep the CPU working frequency for a fixed period of time when the target application is started. However, for different target application programs, the starting time is different, and the acceleration mode of increasing the working frequency of the CPU and continuously maintaining the fixed time length can cause the waste of CPU acceleration when the starting time length of the target application program is less than the fixed time length, so that unnecessary battery consumption is increased, and the user experience is affected.
Therefore, how to make proper adjustment to the CPU frequency in proper time has great significance in the aspects of smoothness, power consumption, user satisfaction and the like of the terminal.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides an application starting method, apparatus, and storage medium.
According to a first aspect of an embodiment of the present disclosure, there is provided an application starting method, applied to a terminal, including:
When a target application is started, collecting current CPU load data and memory occupation data of the target terminal;
Determining a target starting duration of the target application;
determining a starting probability corresponding to the completion of starting the target application in the target starting duration according to the CPU load data, the memory occupation data, the target application type of the target application and the target starting duration, and determining a target working frequency of the CPU according to the starting probability;
And controlling the CPU to run at the target working frequency within the target starting time.
Optionally, inputting the CPU load data, the memory occupation data, the target application type of the target application and the target starting time length into a preset general model, determining starting probability corresponding to the completion of starting the target application in the target starting time length through the general model, and determining and outputting target working frequency of the CPU according to the starting probability;
The universal model is obtained by training the training sample data which are issued to the target terminal by the server and are uploaded by at least one terminal device, wherein the training sample data comprise a sample application, an application type of the sample application, a starting time of the sample application, CPU sample load data and sample memory occupation data of the corresponding terminal when the sample application is started.
Optionally, the generic model determines a start probability of starting the target application within the target start duration, and determines a target working frequency of the CPU according to the start probability:
wherein F is the target working frequency of CPU, L is the current CPU load data of the target terminal, M is the current memory occupied data of the target terminal, T is the target starting time of the target application, A is the target application type of the target application, E is the event that the target application A is successfully started in the T time under the state of the current CPU load data L and the memory occupied data M of the target terminal, F min is the preset CPU minimum working frequency, and P (E|L, M, T, A) is the starting probability of the target application starting completion in the time T.
Optionally, the method further comprises:
Acquiring a use sample set of the target terminal in a preset time period, wherein each use sample in the use sample set comprises a use sample application, an application type of the use sample application, a starting time of the use sample application, and when the use sample application is started, CPU of the target terminal uses sample load data and use sample memory occupation data;
and sending the use sample set to the server so that the server optimizes the universal model according to the use sample set to obtain a corrected universal model.
Optionally, the method further comprises:
Acquiring the modified universal model issued by the server;
Updating the general model to the corrected general model.
Optionally, the determining the target starting duration of the target application includes:
acquiring a target starting mode selected by a user aiming at the target application, wherein the target starting mode comprises at least one of a quick starting mode, a medium speed starting mode and a slow starting mode;
and obtaining the target starting time length corresponding to the target starting mode according to the starting time length corresponding to the starting mode.
According to a second aspect of the embodiments of the present disclosure, there is provided an application starting apparatus, applied to a terminal, including:
The acquisition module is used for acquiring current CPU load data and memory occupation data of the target terminal in response to the starting of the target application;
the first determining module is used for determining a target starting duration of the target application;
The second determining module is used for determining a starting probability corresponding to the target application which is started and completed in the target starting duration according to the CPU load data, the memory occupation data, the target application type of the target application and the target starting duration, and determining a target working frequency of the CPU according to the starting probability;
And the processing module is used for controlling the CPU to run at the target working frequency within the target starting time.
Optionally, inputting the CPU load data, the memory occupation data, the target application type of the target application and the target starting time length into a preset general model, determining starting probability corresponding to the completion of starting the target application in the target starting time length through the general model, and determining and outputting target working frequency of the CPU according to the starting probability;
The universal model is obtained by training the training sample data which are issued to the target terminal by the server and are uploaded by at least one terminal device, wherein the training sample data comprise a sample application, an application type of the sample application, a starting time of the sample application, CPU sample load data and sample memory occupation data of the corresponding terminal when the sample application is started.
Optionally, the generic model determines a start probability of starting the target application within the target start duration, and determines a target working frequency of the CPU according to the start probability:
wherein F is the target working frequency of CPU, L is the current CPU load data of the target terminal, M is the current memory occupied data of the target terminal, T is the target starting time of the target application, A is the target application type of the target application, E is the event that the target application A is successfully started in the T time under the state of the current CPU load data L and the memory occupied data M of the target terminal, F min is the preset CPU minimum working frequency, and P (E|L, M, T, A) is the starting probability of the target application starting completion in the time T.
Optionally, the acquiring module is further configured to:
Acquiring a use sample set of the target terminal in a preset time period, wherein each use sample in the use sample set comprises a use sample application, an application type of the use sample application, a starting time of the use sample application, and when the use sample application is started, CPU of the target terminal uses sample load data and use sample memory occupation data;
and sending the use sample set to the server so that the server optimizes the universal model according to the use sample set to obtain a corrected universal model.
Optionally, the acquiring module is further configured to:
Acquiring the modified universal model issued by the server;
Updating the general model to the corrected general model.
Optionally, the first determining module determines the target starting duration of the target application by:
acquiring a target starting mode selected by a user aiming at the target application, wherein the target starting mode comprises at least one of a quick starting mode, a medium speed starting mode and a slow starting mode;
and obtaining the target starting time length corresponding to the target starting mode according to the starting time length corresponding to the starting mode.
According to a third aspect of the embodiments of the present disclosure, there is provided an application starting apparatus, including:
A processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: the method for starting the application provided by the first aspect of the present disclosure is implemented.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the application launch method provided by the first aspect of the present disclosure.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: when a target application is started, collecting current CPU load data and memory occupation data of a target terminal; determining a target starting duration of a target application; and then determining the starting probability corresponding to the target application after starting in the target starting time according to the CPU load data, the memory occupation data, the target application type of the target application and the target starting time, and determining the target working frequency of the CPU according to the starting probability. Therefore, the CPU can be controlled to run at the target working frequency within the target starting time, when the application is started, the working frequency of the CPU can be adaptively adjusted according to the target starting time corresponding to the application, so that the CPU can work at the proper working frequency when the application is started by the terminal, the fluency of the terminal is improved, and the power consumption and the performance waste of the terminal are reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flow chart illustrating an application launch method according to an exemplary embodiment.
Fig. 2 is a block diagram illustrating an application launching device, according to an example embodiment.
FIG. 3 is a block diagram illustrating an apparatus for application launch according to an example embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
It should be noted that, all actions of acquiring signals, information or data in the present application are performed under the condition of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
Fig. 1 is a flowchart illustrating an application starting method according to an exemplary embodiment, and as shown in fig. 1, the application starting method is used in a terminal and includes the following steps.
In step S11, current CPU load data and memory occupation data of the target terminal are collected in response to the target application being started.
The target application in the present disclosure may be any application installed in a terminal. In the present disclosure, the CPU load data may be, for example, a CPU load rate, and the memory occupation data may be, for example, a memory occupation rate.
In step S12, a target start-up duration of the target application is determined.
In one embodiment, the target activation time of the target application may be determined, for example, by:
The user may select a target start mode for the target application in advance based on a setting item of the application in the terminal, for example, the target start mode includes a fast start mode, a medium speed start mode, and a slow start mode. Then, in response to the target application being started, a target starting mode selected by a user for the target application can be obtained.
The method and the device can preset the starting time length corresponding to the starting mode, and further obtain the target starting time length corresponding to the target starting mode according to the starting time length corresponding to the starting mode.
In one embodiment, if the user does not set the target starting duration for the target application, a starting mode may be defaulted for the target application, for example, a middle speed starting mode is defaulted, and further, the target starting duration corresponding to the default starting mode is obtained according to the starting duration corresponding to the starting mode.
In step S13, according to the CPU load data, the memory occupation data, the target application type of the target application, and the target startup duration, the startup probability corresponding to the startup completion of the target application within the target startup duration is determined, and according to the startup probability, the target operating frequency of the CPU is determined.
In step S14, the control CPU operates at the target operating frequency for the target startup period.
In one embodiment, the target operating frequency of the CPU corresponding to the starting of the target application may be determined, for example, according to the CPU load data, the memory occupation data, and the target starting duration in the following manner:
Because the Bayesian network can carry out evidence reasoning on the input random variables to obtain a graph mode for representing the connection probability among the variables, the Bayesian network provides a natural method for representing causal information and is used for finding potential relations among the random variables. Wherein the Bayesian network is capable of forming a directed acyclic graph, each random variable representing a node that may influence the inference result, directed edges between nodes representing dependency relationships between different nodes.
Furthermore, in the disclosure, the CPU load data, the memory occupation data, the target application type of the target application, and the target starting time length are input into a preset general model, the starting probability corresponding to the target application being started and completed within the target starting time length is determined through the general model, and the target operating frequency of the CPU is determined and output according to the starting probability.
Wherein the generic model may be a model of a bayesian network structure.
In one embodiment, the generic model determines a start probability of starting the target application within the target start duration by determining a target operating frequency of the CPU according to the start probability:
Wherein F is the target working frequency of CPU, L is the current CPU load data of the target terminal, M is the current memory occupation data of the target terminal, T is the target starting time of the target application, A is the target application type of the target application, E is the event that the target application A is successfully started in the time T under the state of the current CPU load data L and the memory occupation data M. And F min is a preset minimum working frequency of the CPU. P (E|L, M, T, A) is the start probability of the target application starting to complete within a time period T.
For example, the lowest CPU operating frequency F min for the test terminal 1 to start the application a is 0.9GHz. When the application a is started, if the CPU load data L is 30% lower and the memory occupation data M is 40% lower at this time, the test terminal 1 predicts that the starting of the application is completed within 4 seconds T, and the obtained P (e|l, M, T, a) is 0.8 at this time, it can be calculated that the required CPU frequency is 1.125GHz. If the CPU load data L of the test terminal 1 is 90% higher and the M memory occupation data is 80% higher, the system predicts that the start of the application a is completed within 3 seconds, and the bayesian network obtains P (e|l, M, T, a) of 0.5, the required CPU frequency can be calculated to be 1.8GHz.
The universal model is obtained by training a server according to training sample data uploaded by at least one terminal device, wherein the training sample data comprises a sample application, an application type of the sample application, a starting time of the sample application, CPU sample load data and sample memory occupation data of a corresponding terminal when the sample application is started.
For example, before the terminal leaves the factory, a part of applications with highest application store heat is installed in a certain number of test terminals in advance, and the operating system of the test terminals presets CPU working frequency adjustment with enough time length, for example, the CPU fixed frequency is kept for 10 seconds when the applications are started, so as to ensure the stage of starting the covered applications. Through a period of automatic test, the use behavior of a user is simulated, the time consumption of starting the application in a period of time is recorded, and the CPU load data, the memory occupation data, the CPU working frequency and the like of the terminal when the application is started are recorded. The data can be obtained by printing a log in an operating system of the terminal and stored in a file. And then, collecting the acquired training data to obtain training sample data.
In addition, in order to continuously improve the accuracy of the target working frequency of the CPU predicted by the general model, in the present disclosure, more real usage data such as the starting time of the target application, the working frequency of the CPU, and the memory occupation data may be recorded multiple times when the target application is started in the process of using the target terminal by the user. And obtaining a use sample set of the target terminal according to the user use data in the preset time period.
The method comprises the steps that each use sample in a use sample set comprises a use sample application, an application type of the use sample application, a starting time of the use sample application, and when the use sample application is started, a CPU of a target terminal uses sample load data and use sample memory occupation data to send the use sample set to a server, so that the server optimizes a general model according to the use sample set to obtain a corrected general model. Therefore, the accuracy of the universal model prediction can be gradually improved, and the user experience is improved.
After the server obtains the modified universal model, the modified universal model can be issued to the target terminal, and the target terminal can obtain the modified universal model issued by the server and update the universal model into the modified universal model.
In an exemplary embodiment of the present disclosure, current CPU load data and memory footprint data of a target terminal are collected in response to a target application being started; determining a target starting duration of a target application; and then determining the starting probability corresponding to the target application after starting in the target starting time according to the CPU load data, the memory occupation data, the target application type of the target application and the target starting time, and determining the target working frequency of the CPU according to the starting probability. Therefore, the CPU can be controlled to run at the target working frequency within the target starting time, when the application is started, the working frequency of the CPU can be adaptively adjusted according to the target starting time corresponding to the application, so that the CPU can work at the proper working frequency when the application is started by the terminal, the fluency of the terminal is improved, and the power consumption and the performance waste of the terminal are reduced.
Fig. 2 is a block diagram illustrating an application launching device 200, according to an example embodiment. Referring to fig. 2, the apparatus includes:
An acquisition module 201, configured to acquire current CPU load data and memory occupation data of a target terminal in response to a target application being started;
a first determining module 202, configured to determine a target starting duration of the target application;
A second determining module 203, configured to determine, according to the CPU load data, the memory occupation data, a target application type of the target application, and the target startup duration, a startup probability corresponding to the startup completion of the target application within the target startup duration, and determine, according to the startup probability, a target operating frequency of the CPU;
and the processing module 204 is used for controlling the CPU to run at the target working frequency within the target starting duration.
Optionally, inputting the CPU load data, the memory occupation data, the target application type of the target application and the target starting time length into a preset general model, determining starting probability corresponding to the completion of starting the target application in the target starting time length through the general model, and determining and outputting target working frequency of the CPU according to the starting probability;
The universal model is obtained by training the training sample data which are issued to the target terminal by the server and are uploaded by at least one terminal device, wherein the training sample data comprise a sample application, an application type of the sample application, a starting time of the sample application, CPU sample load data and sample memory occupation data of the corresponding terminal when the sample application is started.
Optionally, the generic model determines a start probability of starting the target application within the target start duration, and determines a target working frequency of the CPU according to the start probability:
wherein F is the target working frequency of CPU, L is the current CPU load data of the target terminal, M is the current memory occupied data of the target terminal, T is the target starting time of the target application, A is the target application type of the target application, E is the event that the target application A is successfully started in the T time under the state of the current CPU load data L and the memory occupied data M of the target terminal, F min is the preset CPU minimum working frequency, and P (E|L, M, T, A) is the starting probability of the target application starting completion in the time T.
Optionally, the obtaining module 201 is further configured to:
Acquiring a use sample set of the target terminal in a preset time period, wherein each use sample in the use sample set comprises a use sample application, an application type of the use sample application, a starting time of the use sample application, and when the use sample application is started, CPU of the target terminal uses sample load data and use sample memory occupation data;
and sending the use sample set to the server so that the server optimizes the universal model according to the use sample set to obtain a corrected universal model.
Optionally, the obtaining module 201 is further configured to:
Acquiring the modified universal model issued by the server;
Updating the general model to the corrected general model.
Optionally, the first determining module 202 determines the target starting duration of the target application by:
acquiring a target starting mode selected by a user aiming at the target application, wherein the target starting mode comprises at least one of a quick starting mode, a medium speed starting mode and a slow starting mode;
and obtaining the target starting time length corresponding to the target starting mode according to the starting time length corresponding to the starting mode.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the application launch method provided by the present disclosure.
Fig. 3 is a block diagram illustrating an apparatus 800 for application launching, according to an example embodiment. For example, apparatus 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.
Referring to fig. 3, apparatus 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the apparatus 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the application launch method described above. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any target application or method operating on the device 800, contact data, phonebook data, messages, pictures, video, and the like. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power component 806 provides power to the various components of the device 800. The power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 800.
The multimedia component 808 includes a screen between the device 800 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. The front camera and/or the rear camera may receive external multimedia data when the apparatus 800 is in an operational mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 further includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the apparatus 800. For example, the sensor assembly 814 may detect an on/off state of the device 800, a relative positioning of the components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, an orientation or acceleration/deceleration of the device 800, and a change in temperature of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging target applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communication between the apparatus 800 and other devices, either in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for performing the above application starting methods.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 804 including instructions executable by processor 820 of apparatus 800 to perform the above-described application launch method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned application launch method when executed by the programmable apparatus.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (7)
1. An application starting method is characterized by being applied to a terminal and comprising the following steps:
When a target application is started, collecting current CPU load data and memory occupation data of the target terminal;
Determining a target starting duration of the target application;
determining a starting probability corresponding to the completion of starting the target application in the target starting duration according to the CPU load data, the memory occupation data, the target application type of the target application and the target starting duration, and determining a target working frequency of the CPU according to the starting probability;
Controlling the CPU to run at the target working frequency within the target starting time;
Inputting the CPU load data, the memory occupation data, the target application type of the target application and the target starting time length into a preset general model, determining starting probability corresponding to the completion of starting the target application in the target starting time length through the general model, and determining and outputting target working frequency of the CPU according to the starting probability;
The universal model is obtained by training according to training sample data uploaded by at least one terminal device, wherein the training sample data comprise a sample application, an application type of the sample application, a starting time of the sample application, CPU sample load data and sample memory occupation data of a corresponding terminal when the sample application is started;
The general model determines the starting probability of starting the target application in the target starting time length, and determines the target working frequency of the CPU according to the starting probability:
Wherein F is the target working frequency of CPU, L is the current CPU load data of the target terminal, M is the current memory occupied data of the target terminal, T is the target starting time of the target application, A is the target application type of the target application, E is the event that the target application A is successfully started in the T time under the current CPU load data L and the memory occupied data M of the target terminal, For a preset minimum operating frequency of the CPU,And the starting probability of starting to finish in the duration T is applied to the target application.
2. The method according to claim 1, wherein the method further comprises:
Acquiring a use sample set of the target terminal in a preset time period, wherein each use sample in the use sample set comprises a use sample application, an application type of the use sample application, a starting time of the use sample application, and when the use sample application is started, CPU of the target terminal uses sample load data and use sample memory occupation data;
and sending the use sample set to the server so that the server optimizes the universal model according to the use sample set to obtain a corrected universal model.
3. The method according to claim 2, wherein the method further comprises:
Acquiring the modified universal model issued by the server;
Updating the general model to the corrected general model.
4. The method of claim 1, wherein the determining the target activation time of the target application comprises:
acquiring a target starting mode selected by a user aiming at the target application, wherein the target starting mode comprises at least one of a quick starting mode, a medium speed starting mode and a slow starting mode;
and obtaining the target starting time length corresponding to the target starting mode according to the starting time length corresponding to the starting mode.
5. An application starting apparatus, applied to a terminal, comprising:
The acquisition module is used for acquiring current CPU load data and memory occupation data of the target terminal in response to the starting of the target application;
the first determining module is used for determining a target starting duration of the target application;
The second determining module is used for determining a starting probability corresponding to the target application which is started and completed in the target starting duration according to the CPU load data, the memory occupation data, the target application type of the target application and the target starting duration, and determining a target working frequency of the CPU according to the starting probability;
The processing module is used for controlling the CPU to run at the target working frequency within the target starting time;
Inputting the CPU load data, the memory occupation data, the target application type of the target application and the target starting time length into a preset general model, determining starting probability corresponding to the completion of starting the target application in the target starting time length through the general model, and determining and outputting target working frequency of the CPU according to the starting probability;
The universal model is obtained by training according to training sample data uploaded by at least one terminal device, wherein the training sample data comprise a sample application, an application type of the sample application, a starting time of the sample application, CPU sample load data and sample memory occupation data of a corresponding terminal when the sample application is started;
The general model determines the starting probability of starting the target application in the target starting time length, and determines the target working frequency of the CPU according to the starting probability:
Wherein F is the target working frequency of CPU, L is the current CPU load data of the target terminal, M is the current memory occupied data of the target terminal, T is the target starting time of the target application, A is the target application type of the target application, E is the event that the target application A is successfully started in the T time under the current CPU load data L and the memory occupied data M of the target terminal, For a preset minimum operating frequency of the CPU,And the starting probability of starting to finish in the duration T is applied to the target application.
6. An application starting apparatus, comprising:
A processor;
a memory for storing processor-executable instructions;
Wherein the processor is configured to: the method of any one of claims 1 to 4.
7. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the steps of the method of any of claims 1 to 4.
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| CN110209435A (en) * | 2019-04-28 | 2019-09-06 | 北京蓦然认知科技有限公司 | A kind of application preloading method, apparatus |
| CN110231963A (en) * | 2019-06-12 | 2019-09-13 | Oppo广东移动通信有限公司 | Application control method and relevant apparatus |
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| CN110633192B (en) * | 2019-08-28 | 2023-05-26 | RealMe重庆移动通信有限公司 | Test method, test device, terminal equipment and computer readable storage medium |
| CN112256354B (en) * | 2020-11-25 | 2023-05-16 | Oppo（重庆）智能科技有限公司 | Application starting method and device, storage medium and electronic equipment |
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| CN110209435A (en) * | 2019-04-28 | 2019-09-06 | 北京蓦然认知科技有限公司 | A kind of application preloading method, apparatus |
| CN110231963A (en) * | 2019-06-12 | 2019-09-13 | Oppo广东移动通信有限公司 | Application control method and relevant apparatus |
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