CN106844047B - Application program optimization method of intelligent terminal - Google Patents
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- 238000012544 monitoring process Methods 0.000 claims abstract description 3
- 238000005457 optimization Methods 0.000 claims description 30
- 230000009471 action Effects 0.000 claims description 19
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- 230000006870 function Effects 0.000 claims description 9
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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
- G06F9/505—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 considering the load
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/329—Power saving characterised by the action undertaken by task scheduling
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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
- G06F9/5055—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 considering software capabilities, i.e. software resources associated or available to the machine
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Abstract
The invention provides an application program optimization method of an intelligent terminal, which comprises the following steps: step S1: starting an application program, and monitoring the behavior of the application program to form one or more behavior nodes when the application program runs; step S2: the intelligent terminal is connected with a remote service terminal, and behavior relation and/or parameter configuration of a behavior node corresponding to the application program are obtained from the remote service terminal; step S3: when the behavior node is triggered, executing the next interactive behavior of the behavior node according to the acquired behavior relation, and/or configuring and adjusting the resource load of the application program by using the intelligent terminal according to the acquired parameters; the behavior relation comprises the execution sequence of other behavior nodes which are topologically connected with the behavior nodes; the parameter configuration comprises CPU configuration, GPU configuration and network resource configuration of the intelligent terminal. By adopting the technical scheme, the operation mode of the application program can be judged more intelligently and quickly, and the power consumption of the system is greatly reduced.
Description
Technical Field
The invention relates to the field of intelligent terminal control, in particular to an application program optimization method of an intelligent terminal.
Background
At present, the system power consumption of the intelligent terminal is optimized, and most of the system power consumption is optimized in a system level by judging the current scene to control system resources such as wakelock, network connection, alarm, screen brightness and the like to realize performance power consumption optimization. For the optimization of the application program, most of the optimization decisions are made on a specific application program under a specific interface or a specific scene by adopting a traditional optimization algorithm, human intuition and the like. The conventional performance power consumption optimization method is difficult to adapt to changes of different application programs in different hardware environments, cannot accurately sense the requirement change of the next load in advance, and cannot realize performance optimization.
The above optimization of the internal interaction behavior of the application program has the following disadvantages:
1. the interaction between a single application program and different people has quite complex nonlinear relation, and the traditional method can only make optimization decision under a specific interface or a specific scene by using a basic engineering formula, human intuition and the like.
2. The interaction behaviors of users under different application programs are different, and a set of self-defined application program power consumption optimization method is difficult to adapt to another application program through self-regulation.
3. The traditional performance power consumption optimization method is difficult to adapt to the change of different application programs in different hardware environments, the power consumption optimization should influence the user experience as little as possible, and different requirements are provided for the power consumption optimization configuration of the application programs by the hardware configuration of different mobile phones.
4. The traditional method cannot accurately know the next specific load requirement in advance, only passively carries out CPU dynamic frequency and voltage modulation after the requirement changes, and cannot realize the optimization of performance experience.
Therefore, an effective optimization method capable of accurately acquiring next resource load requirements in advance, configuring load parameters timely and accurately and realizing performance and power consumption in different hardware environments and at different interactive behavior nodes according to different application programs is urgently needed.
Disclosure of Invention
In order to overcome the technical defects, the invention aims to provide an application program optimization method of an intelligent terminal, which can judge the running mode of an application program more intelligently and quickly and greatly reduce the power consumption of a system.
The invention discloses an application program optimization method of an intelligent terminal, which comprises the following steps:
step S1: starting an application program, and monitoring the behavior of the application program to form one or more behavior nodes when the application program runs;
step S2: the intelligent terminal is connected with a remote service terminal, and the behavior relation and/or parameter configuration of the behavior node corresponding to the application program are obtained from the remote service terminal;
step S3: when the behavior node is triggered, executing the next interactive behavior of the behavior node according to the acquired behavior relation, and/or adjusting the resource load of the application program by using the intelligent terminal according to the acquired parameter configuration; wherein,
the behavior relation comprises an execution sequence of other behavior nodes which are topologically connected with the behavior node; the parameter configuration comprises CPU configuration, GPU configuration and network resource configuration of the intelligent terminal.
Preferably, in the step S1, behavior capture and behavior control are added in the operating system of the smart terminal; the behavior capture determines the behavior node according to the operation of the application program, and the application program occupies the resource load of the intelligent terminal at the behavior node; the behavior control controls the resource load according to the action executed by the behavior node.
Preferably, the interaction action type unit is arranged in the interaction behavior unit; the interactive behavior unit further comprises: and the next node ID unit represents the name of the behavior node of the next jump.
Preferably, the behavior nodes and the occupied resource load are stored in an intelligent terminal database.
Preferably, the step S1 and the step S2 further include: step S1': and when the application program is started for the first time, the intelligent terminal uploads the initial behavior node and/or the initial parameter configuration of the application program.
Preferably, the behavior relationship and/or parameter configuration of the remote service terminal adjusts the initial behavior relationship and initial parameter configuration according to the operation habits of the behavior nodes of the plurality of intelligent terminals to form the behavior relationship and parameter configuration.
Preferably, the behavior node includes: a previous node ID unit indicating the name of a previous action node; a current node ID unit representing a name of a current behavior node; a node type unit which represents the type of the current behavior node; the interactive behavior unit represents all possible executed interactive actions of the current behavior node; an interactive action type unit representing the type of the interactive action; and a resource allocation unit which represents a resource load when the interactive action is executed.
Preferably, the behavior node types include: an Activity action type and a Service type; the interaction types include: clicking on the application and sliding the application.
Preferably, the steps S2 and S3 further include: step S2': and when the remote service terminal does not have the behavior node, the application program executes the interaction action and the resource load, and uploads the behavior relation and the parameter configuration which bear the interaction action and the resource load to the remote service terminal.
Preferably, the parameter configuration further includes: bluetooth connection configuration, screen brightness configuration, GPS positioning configuration, CPU lock holding configuration and application program interface configuration.
After the technical scheme is adopted, compared with the prior art, the method has the following beneficial effects:
1. the customization and customization optimization mode can be performed for different application programs, so that the adaptability is stronger;
2. optimizing contents are deeply inserted into different application program operations, and the occupation of each operation on system resources is optimized extremely;
3. the next work can be predicted, and the system load can be accurately configured in time.
Drawings
FIG. 1 is a schematic diagram of a topological connection of behavior nodes in accordance with a preferred embodiment of the present invention;
FIG. 2 is a flow diagram illustrating behavior node formation in accordance with a preferred embodiment of the present invention;
FIG. 3 is a block diagram of behavior nodes in accordance with a preferred embodiment of the present invention;
fig. 4 is a schematic diagram of establishment of a behavioral topological relation in accordance with a preferred embodiment of the present invention.
Detailed Description
The advantages of the invention are further illustrated in the following description of specific embodiments in conjunction with the accompanying drawings.
The optimization of the application program provided by the invention is mainly realized by the following steps:
step S1: forming behavior nodes
After the user starts the application program, the application program runs under the continuous operation of the user, enters different interfaces, feeds back different information and realizes different functions. After the application program is installed and updated, the functions, buttons and operations of the application program are limited, so that the functions, buttons and operations which can be realized are monitored, and behavior nodes corresponding to the functions, buttons and operations can be formed. For example, for a payment application on the internet, when the application is started, its initial interface will have functional buttons such as payment, code scanning, transfer, setting, etc., or can slide right and left into other interfaces, and the operation of each such functional button and the corresponding interface are monitored to form a behavior node. When such a functional button is clicked, another interface is entered, and in the other interfaces, if the functional button is still provided, a behavior node is continuously formed for each button. Alternatively, such active or passive interactive interfaces may also be monitored to form behavior nodes when an application needs to push an advertisement, a message, or pop an interface box. That is, each button, operation, popup box, and function in the application may have a corresponding behavior node, and the behavior node represents the content of the button, operation, popup box, function, interface display, contextual function, and interface.
Step S2: obtaining behavioral relationships and/or parameter configurations
The intelligent terminal is connected with a remote service terminal, and the remote service terminal is used as a cloud to acquire the behavior relation and/or parameter configuration of the behavior node of the application program. For example, still taking the above-mentioned online payment application as an example, the remote service terminal stores the behavior relationship and the parameter configuration of each behavior node of the online payment application, and the behavior relationship referred to herein refers to fig. 1, which is a schematic diagram of a topological connection relationship of some behavior nodes of the application, because the internal code of the application is fixed, the front and back behavior nodes corresponding to each behavior node are also fixed, for example, the behavior node of the payment button of the online payment application is a behavior node of the payment code interface, when the payment button is clicked, the next node corresponds to a behavior node of the payment code interface, and for example, after the setting button is clicked, the set behavior node of the online payment application jumps to the setting interface. Due to the curability of the application programs, the topological relation of the behavior node of each application program is predictable, and the remote service terminal can learn and configure in advance aiming at the resource configuration of the intelligent terminal of the next existing interface, so that the parameter configuration acquired by the intelligent terminal is the most optimal. This learning process may be formed by sharing the use experience of multiple users, as will be described in more detail below.
Step S3: performing interactive behavior and configuring resource load
When a certain behavior node is triggered, the behavior topological relation of the acquired behavior node can predict which action the behavior node needs to execute to enter the next node, and the resource parameters of the intelligent terminal are configured after the behavior node enters the next node. For example, after the user clicks the code scanning in the online payment application program, since the next action node corresponding to the action node necessarily has a code scanning frame interface and calls the camera resource, the next interaction action can be immediately executed after the click is triggered, and since the camera needs to be called, the processor and the memory resource allocation of the intelligent terminal are also configured in advance, and the configured resource load can be directly called.
The execution sequence and the execution mode of other behavior nodes in the topological connection relation of the behavior nodes are foreseen in advance, and each behavior node comprises parameter configurations such as a CPU, a GPU, network resource configuration, Bluetooth connection configuration, screen brightness configuration, GPS positioning configuration, CPU lock holding configuration and application program interface configuration of the intelligent terminal, so that parameter configuration information of the next action can be obtained in advance, and the resources of the intelligent terminal are reasonably and optimally configured in time, thereby realizing performance and power consumption optimization. Generally, the running of the application program is not to run to any interface and execute any operation to start the optimization of the resource load, but when the application program starts to run, the intelligent terminal already has the resource configuration corresponding to all the operations of the application program, and the resource configuration is most optimized, and whatever type of intelligent terminal and application program have the customized optimization mode.
Referring to fig. 2, which is a schematic flow diagram formed by behavior nodes in a preferred embodiment, after an intelligent terminal is started, behavior capture and behavior control codes are added to an operating system of the intelligent terminal, such as a Framework layer and a Kernel layer of an android system, where the behavior capture obtains an interactive behavior of an application program when the intelligent terminal runs, that is, the aforementioned button click, interface sliding, dialog box popping, and monitors a CPU occupancy, a CPU lock state, interface information, a network connection state, a GPS positioning state, and the like when the aforementioned interactive behavior is executed, and the behavior control controls a resource load according to the interactive behavior executed by the behavior nodes.
On the basis of the behavior capturing and behavior control, the behavior nodes and the resource loads occupied by the behavior nodes are stored in a database of the intelligent terminal. The database can be used as a storage body for behavior nodes and parameter configuration of each application program acquired from the remote service terminal, and also can be used for storing the behavior nodes and parameter configuration of the application program which is operated for the first time, and then the behavior nodes and the parameter configuration are uploaded to the remote service terminal and shared to other users.
As described above, when the application is started for the first time, because the application is not installed or operated before, the behavior relationship and parameter configuration of the application are not obtained from the remote service terminal, or the application is used less frequently, and the remote service terminal does not have the behavior relationship and parameter configuration of the application, then after the application is operated, the smart terminal records the initial behavior node of the application and the initial parameter configuration of the smart terminal during operation according to the operation of the user between steps S1 and S2, and performs step S2' between steps S2 and S3: and uploading the information to a remote service terminal, and storing the initial behavior node and the initial parameter configuration by the remote service terminal.
It can be understood that, when the number of users of the application program increases in the above embodiment, the initial behavior nodes and the initial parameter configuration received by the remote service terminal also increase, and the remote service terminal implements the optimal configuration of the resource load at each behavior node by using various optimization algorithms, such as a neural network algorithm, and the like, together with behavior nodes that affect the user experience and consume too much power to the user through analysis statistics, according to the types of the intelligent terminals and the versions of the application program configured by the initial behavior nodes and the initial parameters. Through the processes of obtaining, learning and stepping, the optimal parameter configuration of each application program is gradually realized.
Referring to fig. 3, a schematic diagram of a behavior node structure according to a preferred embodiment of the present invention is shown, where the behavior node structure is similar to a packet structure. Specifically, it is mainly composed of the following units:
the head of the behavior node is formed by a last node ID unit (LastNodeId), which indicates the name of the last behavior node connected to the behavior node, so that the above of the behavior node can be known, which can help to establish the topological relation of the behavior node;
the latter element is the ID element (CurrentNodeId) of the current activity node, indicating the name of the current activity node, such as "transfer", "set", etc.;
-a Node type element (Node type) representing the type of the current Node, such as an activity type characterizing an action, or a service type characterizing a service;
-an interactive activity unit (Action) representing all possible performed interactive actions of the current activity node, which may be multiple, representing each interactive Action separately. In a preferred embodiment, the interactive action unit comprises an interactive action type unit and further comprises a next node ID unit, which represents the name of the action node to be jumped next and is formed as the head of the interactive action unit.
-a Resource allocation unit (Resource allocation) in which the Resource load when performing the interaction is stored. When the application program runs, the resources of the intelligent terminal are adjusted according to the parameter configuration in the resource allocation unit.
It can be understood that, after the intelligent terminal obtains the behavior relationship and the parameter configuration of the behavior node from the remote service terminal, the above units of each behavior node are updated to replace the original configuration.
Referring to fig. 4, after the configuration is adopted, by executing the application optimization method, a remote service platform can be established between a plurality of intelligent terminals and a remote service terminal, customized interaction behaviors and resource configuration topological relations are established for different application types and different intelligent terminal signals, the intelligent learning optimization method is really realized, the situation that the jamming condition is more serious as the using time of the intelligent terminal is longer is not reproduced, and on the contrary, the intelligent terminal becomes more and more flow along with continuous learning, and the configuration of hardware can also be optimally used.
It should be noted that the embodiments of the present invention have been described in terms of preferred embodiments, and not by way of limitation, and that those skilled in the art can make modifications and variations of the embodiments described above without departing from the spirit of the invention.
Claims (9)
1. An application program optimization method of an intelligent terminal is characterized by comprising the following steps:
step S1: starting an application program, and monitoring the behavior of the application program to form one or more behavior nodes when the application program runs;
step S1': when the application program is started for the first time, the intelligent terminal uploads an initial behavior node and an initial parameter configuration of the application program;
step S2: the intelligent terminal is connected with a remote service terminal, and behavior relation and parameter configuration of the behavior node corresponding to the application program are obtained from the remote service terminal;
step S3: when the behavior node is triggered, executing the next interactive behavior of the behavior node according to the acquired behavior relation, and adjusting the resource load of the application program by using the intelligent terminal according to the acquired parameter configuration; wherein,
the behavior node comprises the operation of the application program to realize the functions, interface display, front and back association functions or interfaces of the application program;
the behavior relation comprises an execution sequence of other behavior nodes which are topologically connected with the behavior node;
the parameter configuration comprises CPU configuration, GPU configuration and network resource configuration of the intelligent terminal.
2. The application optimization method of claim 1,
in step S1, behavior capture and behavior control are added to the operating system of the intelligent terminal;
the behavior capture determines the behavior node according to the operation of the application program, and the application program occupies the resource load of the intelligent terminal at the behavior node;
the behavior control controls the resource load according to the action executed by the behavior node.
3. The application optimization method of claim 2,
and storing the behavior nodes and the occupied resource load into an intelligent terminal database.
4. The application optimization method of claim 3,
and the behavior relation and the parameter configuration of the remote service terminal adjust the initial behavior nodes and the initial parameter configuration according to the operation habits of the behavior nodes of the plurality of intelligent terminals to form the behavior relation and the parameter configuration.
5. The application optimization method of claim 1,
the behavior node comprises:
a previous node ID unit indicating the name of a previous action node;
a current node ID unit representing a name of a current behavior node;
a node type unit which represents the type of the current behavior node;
the interactive behavior unit represents all executed interactive actions of the current behavior node;
an interactive action type unit representing the type of the interactive action;
and a resource allocation unit which represents a resource load when the interactive action is executed.
6. The application optimization method of claim 5,
the types of the behavior nodes include: an Activity action type and a Service type;
the types of interaction include: clicking on the application and sliding the application.
7. The application optimization method of claim 5,
the interactive action type unit is arranged in the interactive behavior unit;
the interactive behavior unit further comprises:
and the next node ID unit represents the name of the behavior node of the next jump.
8. The application optimization method of claim 1,
the steps between S2 and S3 further include:
step S2': and when the remote service terminal does not have the behavior node, the application program executes the interaction action and the resource load, and uploads the behavior relation and the parameter configuration which bear the interaction action and the resource load to the remote service terminal.
9. The application optimization method of claim 1,
the parameter configuration further comprises: bluetooth connection configuration, screen brightness configuration, GPS positioning configuration, CPU lock holding configuration and application program interface configuration.
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| CN109062688B (en) * | 2018-07-16 | 2022-01-14 | 努比亚技术有限公司 | Memory allocation method, server and mobile terminal |
| CN109858019B (en) * | 2018-12-28 | 2023-07-25 | 北京百度网讯科技有限公司 | Dialogue state tracking method and device, storage medium and semantic recognition system |
| CN113377187A (en) * | 2020-03-09 | 2021-09-10 | Oppo广东移动通信有限公司 | Power consumption optimization parameter configuration method and device, storage medium and electronic equipment |
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