CN111124632B - Optimization method and device of mobile terminal, terminal equipment and storage medium - Google Patents
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- 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
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- G06F9/4806—Task transfer initiation or dispatching
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- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
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Abstract
The application provides an optimization method, device, terminal equipment and storage medium of a mobile terminal, wherein historical behavior data of a user are obtained, and a first application program to be used by the user is predicted according to the historical behavior data, so that an optimization strategy for optimizing the mobile terminal is determined according to the first application program, the mobile terminal is optimized according to the optimization strategy, the personalized optimization strategy which meets the characteristics of the user and meets the use requirements of the user is formulated according to the application program to be used by the user, and the memory of the mobile terminal is automatically optimized according to the optimization strategy, so that the purpose of intelligent optimization is achieved, the individuation and pertinence of the optimization are improved, and the user experience is improved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and apparatus for optimizing a mobile terminal, a terminal device, and a storage medium.
Background
Along with the continuous improvement of the Android operating system and the continuous increase of the number of Android users, various applications on the mobile terminal running the Android system also show explosive growth. Due to the design reasons of the Android system, personal behavior habits of users and the characteristics of various applications, the problems of long starting speed of a mobile phone, slow application running, poor battery endurance and the like are caused, so that the mobile terminal needs to be optimized.
In the prior art, the optimization of the mobile terminal is mainly realized through application programs, such as 360, mobile phone households and the like, specifically, a user enters a corresponding application program client, and the optimization of the mobile terminal is realized through interactive operation with the application program.
Although the optimization method in the prior art can improve part of the performance of the mobile terminal, the optimization method is excessively dependent on autonomous setting and autonomous optimization of a user, increases the interaction burden of the user and an application program, and cannot realize true intellectualization.
Disclosure of Invention
The application provides an optimization method and device of a mobile terminal, terminal equipment and a storage medium, which are used for solving the problem that the optimization method in the prior art is not high in intelligence.
In a first aspect, the present application provides a method for optimizing a mobile terminal, including:
acquiring historical behavior data of a user, wherein the historical behavior data is used for reflecting the use condition of the user on an application program in a mobile terminal;
predicting a first application program to be used by the user according to the historical behavior data;
determining an optimization strategy for optimizing the mobile terminal according to the first application program, wherein the optimization strategy is used for releasing the memory of the mobile terminal so as to improve the operation speed;
And optimizing the mobile terminal according to the optimization strategy.
Optionally, predicting, according to the historical behavior data, a first application program to be used by the user includes:
and determining the first application program to be used by the user according to the historical behavior data, the opening time of a second application program and a Markov chain algorithm, wherein the second application program comprises an application program which is opened currently or an application program which is closed at the latest time interval at the current moment.
Optionally, the historical behavior data includes an opening time, a name and process information of the application program;
the determining the first application program to be used by the user according to the historical behavior data, the opening time of the second application program and a Markov chain algorithm comprises the following steps:
determining the second application program;
determining a time segment to which the opening time of the second application program belongs according to a time segment function;
determining a transition probability matrix according to the opening time, the name and the process information of the application program in the historical behavior data, wherein the transition probability matrix comprises the probability that each application program transits to the next application program through at least one step;
Determining the probability of transferring from the second application program to other application programs according to the time segment and the transfer probability matrix;
and determining the application program corresponding to the maximum value of the probability as the first application program.
Optionally, before determining, according to the first application program, an optimization policy for optimizing the mobile terminal, the method further includes:
determining the user type of the user according to the historical behavior data;
the determining, according to the first application program, an optimization policy for optimizing the mobile terminal includes:
determining an optimization strategy for optimizing the mobile terminal according to the user type and the first application program
Optionally, the determining the user type of the user according to the historical behavior data includes:
determining whether the time of continuously using the same application program in one day of the user is greater than a first threshold value and whether the average time of using the mobile terminal by the user exceeds a second threshold value according to the opening time, the name and the process information of the application program in the historical behavior data;
if the time of the user continuously using the same application program in one day is greater than a first threshold value and the average time of the user using the mobile terminal exceeds a second threshold value, determining that the user is a heavily dependent user, otherwise, determining that the user is a non-heavily dependent user; wherein the user type includes a heavily dependent user or a non-heavily dependent user.
Optionally, the determining, according to the user type and the first application program, an optimization policy for optimizing the mobile terminal includes:
if the user is a heavily dependent user, determining the optimization strategy as a first optimization strategy; the first optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes, and forcedly starting the graphics processor to render;
if the user is a non-heavily dependent user, determining the optimization strategy as a second optimization strategy; the second optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes.
Optionally, the method further comprises:
establishing a self-starting application list, wherein the self-starting application list comprises the names of application programs with self-starting functions;
And prohibiting the self-starting function of the application program with the self-starting function in the mobile terminal according to the self-starting application list.
In a second aspect, the present application provides an optimizing apparatus for a mobile terminal, including:
the mobile terminal comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring historical behavior data of a user, and the historical behavior data is used for reflecting the use condition of the user on an application program in the mobile terminal;
the processing module is used for predicting a first application program to be used by the user according to the historical behavior data, and determining an optimization strategy for optimizing the mobile terminal according to the first application program, wherein the optimization strategy is used for releasing the memory of the mobile terminal so as to improve the processing efficiency;
and the optimizing module is used for optimizing the mobile terminal according to the optimizing strategy.
In a third aspect, the present application provides a terminal device, including: a memory and a processor; the memory is used for storing a computer program, and the processor executes the computer program to realize the optimization method of the mobile terminal.
In a fourth aspect, the present application provides a storage medium for storing a computer program for implementing the above provided method for optimizing a mobile terminal.
According to the optimization method, the device, the terminal equipment and the storage medium of the mobile terminal, historical behavior data of a user are obtained, wherein the historical behavior data are used for reflecting the use condition of the user on application programs in the mobile terminal, and a first application program to be used by the user is predicted according to the historical behavior data, so that an optimization strategy for optimizing the mobile terminal is determined according to the first application program, the optimization strategy is used for releasing a memory of the mobile terminal to improve the operation speed, the mobile terminal is optimized according to the optimization strategy, the individuation optimization strategy which meets the characteristics of the user and meets the use requirements of the user is formulated according to the application programs to be used by the user, the memory of the mobile terminal is automatically optimized according to the optimization strategy, the purpose of intelligent optimization is achieved, the individuation and pertinence of optimization are improved, the operation smoothness of an operation system is improved, the problems of the application program such as blocking and slow starting are solved, and the use experience of the user is further improved.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description of the embodiments or the drawings used in the description of the prior art will be presented in brief, it being obvious that the drawings in the following description are some embodiments of the invention and that other drawings can be obtained from them without inventive faculty for a person skilled in the art
Fig. 1 is a schematic flow chart of an embodiment one of an optimization method of a mobile terminal provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of a second embodiment of an optimization method of a mobile terminal according to the embodiment of the present application;
fig. 3 is a schematic flow chart of a third embodiment of an optimization method of a mobile terminal according to the embodiment of the present application;
fig. 4 is a flow chart of a fourth embodiment of an optimization method of a mobile terminal according to the embodiment of the present application;
fig. 5 is a schematic flow chart of a fifth embodiment of an optimization method of a mobile terminal provided in the embodiment of the present application;
fig. 6 is a schematic structural diagram of an embodiment of an optimizing apparatus of a mobile terminal according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an embodiment of a terminal device provided in an embodiment of the present application.
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 application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
In the prior art, a user enters mobile optimization software, such as 360 and a mobile phone manager, and performs interaction with the optimization software to realize the optimization of the mobile terminal, while the performance of a part of the mobile terminal can be improved, the method in the prior art is low in intelligent degree, the mobile terminal is usually passively optimized according to a user operation instruction, and on the other hand, the mobile terminal is often optimized in a one-tool-cut mode, so that the pertinence is low, and therefore, the problems of stuck operation, slow starting and poor user experience still occur in the mobile terminal by adopting the method in the prior art.
The optimization method of the mobile terminal provided by the embodiment of the application is mainly performed in three stages, and comprises the following steps: a pre-preparation stage, in which, for example, setting of a preset threshold value of the memory occupancy rate, setting of a filtering rule of a process, classification of the process, filtering of an application self-starting function and the like are performed; the optimizing strategy determining stage is used for mainly determining the type of the user and predicting the application program to be used by the user and making an optimizing strategy conforming to the personality of the user; and in the intelligent optimization stage, the mobile terminal is optimized according to the determined optimization strategy. Through the combined application of the three stages, the personalized optimization strategy which meets the user characteristics and meets the user use requirements is formulated according to different user types and application programs to be used by the user, the memory of the mobile terminal is automatically optimized according to the optimization strategy, the purpose of intelligent optimization is achieved, the individuation and pertinence of optimization are improved, the running smoothness of an operating system is improved, the problems of application program operation blocking, slow starting and the like are solved, and the use experience of the user is further improved.
It should be noted that, the method, the device, the terminal device and the storage medium for optimizing the mobile terminal provided by the embodiment of the application are suitable for optimizing the performance of the mobile terminal using the Android operating system.
Fig. 1 is a flowchart of an embodiment one of an optimization method of a mobile terminal according to an embodiment of the present application, where the optimization method of the mobile terminal according to the embodiment may be executed by an operating system in the mobile terminal, as shown in fig. 1, and the method of the embodiment includes:
s101, acquiring historical behavior data of a user.
In this step, the historical behavior data of the user reflects the frequency of the use of the mobile terminal by the user, the viscosity of the mobile terminal, the specific application program in the mobile terminal used by the user, and the like, and the method of this embodiment is based on analyzing the historical behavior data of the user, so that the historical behavior data of the user needs to be acquired before the following steps are executed.
In one possible implementation, the historical behavior data includes the application's open time, name, and process information.
In one possible implementation, obtaining historical behavior data of the user includes:
and taking the opening time, the name and the process information of the application program as keywords, intercepting through the keyword field, and acquiring the historical behavior data of the user from the system log.
In this step, only the historical behavior data of the user using the mobile terminal in one period (for example, one week) may be obtained, or all the historical behavior data of the user using the mobile terminal may be obtained, so that in order to reflect the latest situation of the user, the user behavior data in a period of time before the current system time is usually obtained as the historical behavior data, and analysis is performed through the historical behavior data to provide an optimization strategy suitable for the recent situation of the user.
The opening time of the application program is used for reflecting the specific moment when the user opens different application programs, for example, the opening time of a first application program is 9:10, and the opening time of a second application program is 10:00;
the names of the application programs are used for reflecting the names of various applications opened by a user, such as that the first application program is WeChat and the second application program is Taobao;
the process information of the application program is used to reflect state information, attribute information, and the like of the process.
It will be appreciated that the historical behavior data may also include other content as desired, such as the service type of the application, for reflecting whether the application is a game, chat, office, or other type.
S103, predicting a first application program to be used by a user according to the historical behavior data.
In this step, after S101, according to the obtained historical behavior data of the user, a prediction algorithm is used to predict a first application program to be used by the user, where the first application program is an application program to be used by the user in a skip mode.
In one possible implementation, S103 includes:
the first application to be used by the user is determined based on the historical behavior data, the open time of the second application, and a Markov chain algorithm.
The second application program comprises an application program which is opened currently or an application program which is closed at the closest time interval to the current time, and the opening time of the second application program refers to the time of a corresponding system when the second application program is opened.
Markov chains (Markov chain), also known as discrete-time Markov chains (discrete-time Markov chain), are random processes in the state space that undergo a transition from one state to another, due to the Russian math Andery Markov name. This process requires "memoryless" properties: the probability distribution of the next state can only be determined by the current state, and the events preceding it in the time series are independent of it. At each step of the Markov chain, the system may change from one state to another or may maintain the current state according to a probability distribution. The change of state is called transition and the probabilities associated with the different state changes are called transition probabilities.
In this step, the historical behavior data is used to construct a transition probability matrix for the Markov chain.
S104, determining an optimization strategy for optimizing the mobile terminal according to the first application program, wherein the optimization strategy is used for releasing the memory of the mobile terminal so as to improve the processing efficiency.
In this step, in order to avoid that "one cut" adopts the same optimization strategy for all users and all application programs, the optimization strategy is improved more specifically, pertinently, intelligently and individually, and based on the first application program to be used by the user determined in S103, different optimization strategies for optimizing the memory of the mobile terminal are determined, and the optimization strategies are used for releasing the memory of the mobile terminal, so that the running speed of operations such as system, application program and startup is improved.
S105, optimizing the mobile terminal according to the optimization strategy.
In this step, according to the optimization strategy determined in S104, the mobile terminal is optimized by corresponding measures, so as to release the memory of the mobile terminal, improve the running speed, and further improve the user experience.
In this embodiment, the historical behavior data is used to reflect the use condition of the user on the application program in the mobile terminal, and predict the first application program to be used by the user according to the historical behavior data, so as to determine an optimization strategy for optimizing the mobile terminal according to the first application program, where the optimization strategy is used to release the memory of the mobile terminal to increase the running speed, optimize the mobile terminal according to the optimization strategy, and implement an individualized optimization strategy which meets the user characteristics and meets the user use requirement according to the application program to be used by the user, and automatically optimize the memory of the mobile terminal according to the optimization strategy, thereby achieving the purpose of intelligent optimization, improving the individuation and pertinence of optimization, so as to improve the running smoothness of the operating system, solve the problems of application program running blocking and slow startup, and further improve the use experience of the user.
Fig. 2 is a flow chart of a second embodiment of an optimization method of a mobile terminal according to the embodiment of the present application, and in this embodiment, as shown in fig. 2, based on the embodiment shown in fig. 1, a first application program to be used by a user is determined according to historical behavior data, an opening time of a second application program, and a markov chain algorithm, including:
s201, determining a second application program.
In this step, since the markov chain algorithm is a machine learning algorithm that determines probability distribution of a next state according to a current state, when a first application program to be used by a user is predicted by applying the markov chain algorithm and a time piecewise function, it is necessary to determine an application program currently opened on the mobile terminal or an application program closest to a current time of closing a time interval, that is, a second application program.
In this step, the second application program may be determined according to the process information in the historical behavior data, specifically, an application program corresponding to the last process information in the process information is used as the second application program, or the operating system may directly obtain the information of the application program currently running in the system to determine the second application program.
S202, determining a time segment to which the opening time of the second application program belongs according to the time segment function.
In this step, since the time when the user opens the application is random, even if the same application is used, if the user opens in a different period of time, the application to be used by the user at the next moment may be different, for example, after the user opens the WeChat 7:00 in the morning, the user needs to go out, so that the user opens the application for checking weather conditions next, and after the user opens the WeChat 12:00 in the noon, the user directly opens the application for ordering take-out next because the lunch time is up. Therefore, in order to accurately determine the application program to be used by the user, the time needs to be redefined, the time is segmented, a time segmentation function is defined, the time segment to which the opening time of the second application program belongs is determined according to the time segmentation function, and the first application program is predicted by combining with the Markov chain algorithm, so that the accuracy of the determined first application program is improved.
It is understood that, when the second application program is determined in S201, the open time of the second application program may be acquired together.
In one possible implementation, the time-slicing function is determined by equation (1-1):
t 0 ＝[time] (1-1)
wherein t is 0 Segment value, t, representing time 0 E {0,1,2 …,23}, time represents the open time of the second application, [ time ]]The maximum integer that does not exceed the time is represented, so that the time segment to which the open time of the second application belongs can be determined by a time segment function.
S203, determining a transition probability matrix according to the opening time, the name and the process information of the application program in the historical behavior data.
In this step, a transition probability matrix of the markov chain is determined according to the opening time, the name and the process information of the application program in the historical behavior data, where the transition probability matrix includes a probability that each application program transitions to the next application program through at least one step, that is, the transition probability matrix includes at least one step transition probability matrix, and may further include a multi-step transition probability matrix.
The one-step transition probability matrix is determined by equation (1-2):
wherein p is ij The probability of transitioning from an application with sequence number i to an application with sequence number j is indicated.
According to the one-step transition probability matrix, an n-step transition probability matrix can be obtained, and the formula of the n-step transition probability matrix is as follows:
The value of n can be set according to actual conditions.
S204, according to the time segmentation and the transition probability matrix, probabilities of transition from the second application program to other application programs are respectively determined.
In this step, probabilities of transitioning from the second application to other applications are determined based on the time segment of the second application determined in S202 and the transition probability matrix determined in S203.
In one possible implementation, the probability of transitioning from the second application to the other applications is determined by equation (1-4):
P(k)＝w 1 ∧ 1 t 0 +w 2 ∧ 2 t 0 +…+w n ∧ n t 0 (1-4)
where k is the serial number of the application program installation, t 0 For the time segment to which the open time of the second application belongs, w i Is the weight, w 1 +w 2 +…+w i ＝1，∧ n Is a multi-step probability transition matrix of a Markov chain, P (k) is the probability that the user transitions from the second application to the kth application.
S205, determining an application program corresponding to the maximum value of the probability as a first application program.
In this step, the application corresponding to the maximum value of P (k) is determined as the first application by sorting the values of P (k) obtained in S204.
In this embodiment, by determining the second application program, determining the time segment to which the open time of the second application program belongs according to the time segment function, and determining the transition probability matrix according to the open time, the name and the process information of the application program in the historical behavior data, where the transition probability matrix includes the probability that each application program transitions to the next application program through at least one step, determining the probability that each application program transitions from the second application program to other application programs according to the time segment and the transition probability matrix, determining the application program corresponding to the maximum value of the probability as the first application program, so as to implement prediction of the first application program to be used by the user, and predicting the first application program based on the time segment to which the open time of the application program currently opened or the application program closest to the current time of closing time interval belongs.
Fig. 3 is a schematic flow chart of a third embodiment of an optimization method of a mobile terminal according to the embodiment of the present application, and in this embodiment, as shown in fig. 3, before S104, the method of this embodiment further includes:
s102, determining the user type of the user according to the historical behavior data.
In this step, after S101, the user type of the user is determined according to the obtained historical behavior data of the user, so as to reflect which type of user the user belongs to.
In one possible implementation, the user type includes a heavily dependent user or a non-heavily dependent user, and determining the user type of the user based on the historical behavior data includes:
determining whether the time of continuously using the same application program in one day of a user is greater than a first threshold value and whether the average time of using the mobile terminal by the user exceeds a second threshold value according to the opening time, the name and the process information of the application program in the historical behavior data;
if the time of the user continuously using the same application program is greater than a first threshold value and the average daily time of the user using the mobile terminal exceeds a second threshold value, determining that the user is a heavy dependent user, otherwise, determining that the user is a non-heavy dependent user.
The heavy-dependency user refers to a user who uses the mobile terminal for a long time on average every day and continuously uses a certain application program for a long time in a day, and the non-heavy-dependency user refers to a user who does not satisfy the conditions of the heavy-dependency user.
The first threshold and the second threshold are lengths of time defining a time for which the heavily dependent user and the non-heavily dependent user continuously use the same application program in one day and a daily average time for which the user uses the mobile terminal, and the first threshold is 1 hour, the second threshold is 8 hours, and if it is determined that a time for which a user continuously uses the WeChat in one day is 1.2 hours and a daily average time for which the user uses the mobile terminal is 10 hours according to historical behavior data of the user, the user can be determined to be a heavily dependent user.
The determining whether the time of the user continuously using the same application program in one day is greater than a first threshold may be understood as determining whether there is an application program with a continuous use time greater than the first threshold in the application programs used by the user in one day, for example, if the obtained historical behavior data is behavior data in the last week of the user, whether there is an application program with a continuous use time greater than the first threshold in each day may be determined respectively, and if there is at least one application program with a continuous use time greater than the first threshold in each day, it is determined that the time of the user continuously using the same application program in one day is greater than the first threshold.
It will be appreciated that the applications that use a continuous use time greater than the first threshold value for each day may be different or the same.
In one possible implementation manner, the daily average time of using the mobile terminal by the user may be determined according to the total accumulated time length of using the mobile terminal in one period and the total number of days in one period, that is, the accumulated total time length of using the mobile terminal in one period and the total number of days in one period are divided.
It can be understood that S102 and S103 are parallel steps, and there is no obvious sequence of the steps when executing.
After determining the user type of the user, in this embodiment, S104 includes:
s1041, determining an optimization strategy for optimizing the mobile terminal according to the user type and the first application program.
In this step, in order to further improve the user pertinence of the determined optimization policy, meet the personalized requirement of the user, and further improve the satisfaction of the user, an optimization policy for optimizing the mobile terminal is determined according to the user type determined in S102 and the first application program predicted in S103.
In this embodiment, the user type of the user is determined according to the historical behavior data, and the optimization strategy for optimizing the mobile terminal is determined according to the user type and the first application program, so that the user pertinence of the determined optimization strategy is improved, the personalized requirements of the user are met, and the satisfaction degree of the user is further improved.
Fig. 4 is a flow chart of a fourth embodiment of an optimization method of a mobile terminal provided in the embodiment of the present application, and in this embodiment, determining, based on a user type and a first application program, an optimization policy for optimizing the mobile terminal includes:
s301, if the user is a heavily dependent user, determining that the optimization strategy is a first optimization strategy; and if the user is not heavily dependent, determining the optimization strategy as a second optimization strategy.
In this step, after determining the user type of the user, determining a corresponding optimization policy according to whether the user type of the user is a heavily dependent user, specifically, if the user is a heavily dependent user, determining that the optimization policy is a first optimization policy, where the first optimization policy includes: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes, and forcedly starting the graphics processor to render; the second optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes.
The method comprises the steps that a preset threshold value user judges whether the memory occupancy rate of the mobile terminal reaches the condition of executing subsequent optimizing operation or not, namely, the time for executing the optimizing operation is determined, and it is understood that before the time, an operating system needs to periodically acquire the current memory occupancy rate of the mobile terminal, compares the acquired memory occupancy rate with the preset threshold value, and when the memory occupancy rate of the mobile terminal reaches the preset threshold value, the operating system is triggered to execute buffer cleaning and process ending operation on processes of other application programs except for a first application program according to the filtering rule of the processes and/or the classification of the processes, and the graphics processor is forcedly started for rendering.
The preset threshold value can be preset according to actual conditions, but in order to optimize mobile movement in time, the preset threshold value is not set too large, and when the system is not blocked or runs slowly, the mobile terminal is automatically optimized, so that the mobile terminal keeps better performance, and the optimal use experience of a user is ensured.
In one possible implementation, the preset threshold is set in a range of 70% ± 5%.
The filtering rules are used to determine which processes can filter and which processes cannot. Because some special processes in the operating system have the function of improving the running smoothness of the mobile terminal or are essential processes of the mobile terminal, if the processes are ended, the operating system cannot be normally used or the running speed of the mobile terminal is slower, therefore, the filtering rules of the processes need to be formulated in advance, some special processes are put in a white list, and when the processes are executed to finish operation or buffer cleaning, the processes in the white list are filtered out, so that the reliability of optimization is ensured.
In one possible implementation, the filtering rule is to determine whether the process is a process of the application program of the mobile terminal, and when the process is finished or the cache is cleaned, the process of the application program of the mobile terminal is put into the white list.
The classification of the process refers to the category to which the process belongs, and the classification can be performed according to the characteristics of the process, for example, the classification of the process includes the following six types:
(1) Foreground process: a running program in a mobile phone screen;
(2) The visible process is as follows: processes that are not in the foreground, but that can be seen by the user, such as input methods, widgets, etc.;
(3) Secondary processes: secondary processes are called hidden processes, and some currently running service programs, such as Gmail internal storage, contact internal storage and the like;
(4) Background process: the background process refers to a process running in the foreground and returns to the Launcher by pressing a home key, and the program is converted into the background process;
(5) Content supply node: without process entities, some application programs can access the content therein through the interfaces provided by the application programs, so that the multiplexing effect is achieved;
(6) And (3) an empty process: there is no content running in the memory, and some processes can reside an empty process in the memory after the process exits in order to be able to start faster next time.
Therefore, when the operations of cache cleaning and process ending are executed according to the classification of the process, the operations of cache cleaning and process ending can be executed according to the classification of the process, for example, the operations of cache cleaning and process ending are executed for the foreground process, the operations of cache cleaning and process ending are executed for the visible process, and so on, the operations of cache cleaning and process ending are executed for all processes except the first application program, so that the completeness of optimization is ensured.
Optionally, before S301, the method of this embodiment further includes:
s300, classifying the processes of the application programs according to the characteristics of the processes.
In this step, the processes of the application programs are classified according to the characteristics of the processes, so as to obtain the classification of the processes, so that the operating system can execute the operations of cache cleaning and process ending on the processes of the application programs except the first application program according to the classification of the processes.
Since the first application is an application to be used by the user, no matter whether the user is a heavily-dependent user or a non-heavily-dependent user, when optimization is performed, only the processes of other applications except the first application are subjected to cache cleaning and process ending operations.
When the user is a heavily-dependent user, when the user is optimized, the buffer cleaning and process ending operations are required to be executed on the processes of other application programs except the first application program, the rendering of the graphic processor is also forced to be started, and the graphic processor bears the graphic function, so that the burden of the central processing unit is lightened, the display capacity and the display speed are improved, and the smoothness of the system operation is ensured.
In this embodiment, optimizing the mobile terminal according to the optimization policy includes:
s302, optimizing the mobile terminal according to the first optimization strategy or the second optimization strategy.
In this embodiment, if the user is a heavily dependent user, the optimization policy is determined to be a first optimization policy, where the first optimization policy includes: when the memory occupancy rate of the mobile terminal reaches a preset threshold, according to the filtering rule of the process and/or the classification of the process, executing cache cleaning and process ending operations on the processes of other application programs except the first application program, and forcefully starting the graphics processor for rendering, if the user is not a heavily dependent user, determining the optimization strategy as a second optimization strategy, wherein the second optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, according to the filtering rules of the process and/or the classification of the process, the processes of other application programs except the first application program are subjected to cache cleaning and process ending operation, and according to the first optimization strategy or the second optimization strategy, the mobile terminal is optimized, different optimizations are carried out on the mobile terminal according to the user type of the user and the first application program, when the user type of the user is different and the predicted specific situation of the first application program is determined, the optimization strategy meeting the use requirement of the user is determined, the purpose of intelligent optimization is achieved, the individuation and pertinence of optimization are improved, the running smoothness of an operating system is improved, the problems of blocking of the running of the application program, slow starting and the like are solved, and the use experience of the user is further improved.
Fig. 5 is a schematic flow chart of a fifth embodiment of an optimization method of a mobile terminal according to the embodiment of the present application, where on the basis of the foregoing embodiments, the method in this embodiment further includes:
s401, a self-starting application list is established.
In this step, in order to further improve the smoothness of the operation of the mobile terminal and improve the optimization efficiency, before optimizing the mobile terminal according to the optimization strategy, the self-starting function of the application program with the self-starting function is forbidden.
The self-starting application list comprises names of application programs with self-starting functions.
In one possible implementation, when a user performs an application uninstallation/installation operation, updating a self-starting application list by using a corresponding broadcast sent by the system, and if the installed application has a self-starting function, updating the list; if the uninstalled application is in the list, it is deleted from the list.
S402, prohibiting the self-starting function of an application program with the self-starting function in the mobile terminal according to the self-starting application list.
In this step, according to the self-starting application list, the self-starting function of the application program in the self-starting application list is prohibited.
In one possible implementation, the auto-launch function close key of the application in the auto-launch application list is turned on.
In this embodiment, by establishing the self-starting application list, according to the self-starting application list, the self-starting function of the application program with the self-starting function in the mobile terminal is prohibited, which is conducive to improving the optimization efficiency and providing better optimization service for the user, thereby improving the running smoothness of the mobile terminal and improving the user experience.
Optionally, in an embodiment of the present application, before optimizing the mobile terminal according to the optimization policy, the method further includes: and pre-cleaning the cached garbage data, and cleaning the residual garbage after the last optimization is finished.
Fig. 6 is a schematic structural diagram of an embodiment of an optimizing apparatus of a mobile terminal according to an embodiment of the present application. As shown in fig. 6, the optimizing apparatus 10 of the mobile terminal in the embodiment of the present application includes:
an acquisition module 11, a processing module 12 and an optimization module 13.
The acquiring module 11 is configured to acquire historical behavior data of a user, where the historical behavior data is used to reflect a use condition of the user on an application program in a mobile terminal
In one possible implementation, the historical behavior data includes the application's open time, name, and process information;
the processing module 12 is configured to predict a first application program to be used by a user according to the historical behavior data, and determine an optimization policy for optimizing the mobile terminal according to the first application program, where the optimization policy is used to release a memory of the mobile terminal to improve processing efficiency;
and the optimizing module 13 is used for optimizing the mobile terminal according to the optimizing strategy.
Optionally, the processing module 12 is specifically configured to:
and determining a first application program to be used by the user according to the historical behavior data, the opening time of a second application program and a Markov chain algorithm, wherein the second application program comprises the application program which is currently opened or the application program which is closest to the current moment in the closing time interval.
Optionally, the processing module 12 is specifically configured to:
determining the second application program;
determining a time segment to which the opening time of the second application program belongs according to a time segment function;
determining a transition probability matrix according to the opening time, the name and the process information of the application program in the historical behavior data, wherein the transition probability matrix comprises the probability that each application program transits to the next application program through at least one step;
Determining the probability of transferring from the second application program to other application programs according to the time segment and the transfer probability matrix;
and determining the application program corresponding to the maximum value of the probability as the first application program.
Optionally, the processing module 12 is further configured to:
determining the user type of the user according to the historical behavior data;
the processing module 12 is specifically configured to:
and determining an optimization strategy for optimizing the mobile terminal according to the user type and the first application program.
Optionally, the processing module 12 is specifically configured to:
determining whether the time of continuously using the same application program in one day of the user is greater than a first threshold value and whether the average time of using the mobile terminal by the user exceeds a second threshold value according to the opening time, the name and the process information of the application program in the historical behavior data;
if the time of the user continuously using the same application program in one day is greater than a first threshold value and the average time of the user using the mobile terminal exceeds a second threshold value, determining that the user is a heavily dependent user, otherwise, determining that the user is a non-heavily dependent user; wherein the user type includes a heavily dependent user or a non-heavily dependent user.
Optionally, the processing module 12 is specifically configured to:
if the user is a heavily dependent user, determining the optimization strategy as a first optimization strategy; the first optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes, and forcedly starting the graphics processor to render;
if the user is a non-heavily dependent user, determining the optimization strategy as a second optimization strategy; the second optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes.
Optionally, the processing module 12 is further configured to:
and classifying the processes of the application programs according to the characteristics of the processes.
Optionally, the processing module 12 is further configured to:
establishing a self-starting application list, wherein the self-starting application list comprises the names of application programs with self-starting functions;
And prohibiting the self-starting function of the application program with the self-starting function in the mobile terminal according to the self-starting application list.
Optionally, the processing module 12 is also used for
And pre-cleaning the cached garbage data to clean the residual garbage after the last optimization is finished.
In this embodiment, the historical behavior data of the user is obtained through the obtaining module 11, the historical behavior data includes the opening time, the name and the process information of the application program, the processing module 12 determines the user type of the user and predicts the first application program to be used by the user according to the historical behavior data, determines the optimizing strategy for optimizing the mobile terminal according to the user type and the first application program, the optimizing strategy is used for releasing the memory of the mobile terminal to improve the processing efficiency, and the optimizing module 13 is used for optimizing the mobile terminal according to the optimizing strategy, so that the personalized optimizing strategy which meets the user characteristics and meets the user use requirements according to different user types and application programs to be used by the user is achieved, the memory of the mobile terminal is automatically optimized according to the optimizing strategy, the intelligent optimizing purpose is achieved, the optimizing individuation and pertinence are improved, the running fluency of the operating system is improved, the problems of the application program such as blocking and slow starting are solved, and the use experience of the user is further improved.
Fig. 7 is a schematic structural diagram of an embodiment of a terminal device provided in the embodiment of the present application, as shown in fig. 6, a terminal device 20 in the embodiment includes: a memory 21 and a processor 22, the memory 21 being for storing a computer program, the processor 22 executing the computer program to implement the method of optimizing the mobile terminal in any of the method embodiments described above.
The application also provides a storage medium for storing a computer program for implementing the method for optimizing the mobile terminal provided by any one of the foregoing method embodiments.
All or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a readable memory. The program, when executed, performs steps including the method embodiments described above; and the aforementioned memory (storage medium) includes: read-only memory, random access memory, flash memory, hard disk, solid state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (8)
1. An optimization method for a mobile terminal, comprising:
acquiring historical behavior data of a user, wherein the historical behavior data is used for reflecting the use condition of the user on an application program in a mobile terminal;
predicting a first application program to be used by the user according to the historical behavior data;
determining the user type of the user according to the historical behavior data; wherein the user type comprises a heavily dependent user or a non-heavily dependent user;
according to the first application program, determining an optimization strategy for optimizing the mobile terminal comprises the following steps: determining an optimization strategy for optimizing the mobile terminal according to the user type and the first application program; the optimization strategy is used for releasing the memory of the mobile terminal so as to improve the running speed;
the determining an optimization strategy for optimizing the mobile terminal according to the user type and the first application program comprises the following steps: if the user is a heavily dependent user, determining the optimization strategy as a first optimization strategy; the first optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes, and forcedly starting the graphics processor to render; if the user is a non-heavily dependent user, determining the optimization strategy as a second optimization strategy; the second optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes;
And optimizing the mobile terminal according to the optimization strategy.
2. The method of claim 1, wherein predicting a first application to be used by the user based on the historical behavioral data comprises:
and determining the first application program to be used by the user according to the historical behavior data, the opening time of a second application program and a Markov chain algorithm, wherein the second application program comprises an application program which is opened currently or an application program which is closed at the latest time interval at the current moment.
3. The method of claim 2, wherein the historical behavior data includes opening time, name, and process information of an application;
the determining the first application program to be used by the user according to the historical behavior data, the opening time of the second application program and a Markov chain algorithm comprises the following steps:
determining the second application program;
determining a time segment to which the opening time of the second application program belongs according to a time segment function;
determining a transition probability matrix according to the opening time, the name and the process information of the application program in the historical behavior data, wherein the transition probability matrix comprises the probability that each application program transits to the next application program through at least one step;
Determining the probability of transferring from the second application program to other application programs according to the time segment and the transfer probability matrix;
and determining the application program corresponding to the maximum value of the probability as the first application program.
4. A method according to claim 3, wherein said determining the user type of the user from the historical behavior data comprises:
determining whether the time of continuously using the same application program in one day of the user is greater than a first threshold value and whether the average time of using the mobile terminal by the user exceeds a second threshold value according to the opening time, the name and the process information of the application program in the historical behavior data;
and if the time of the user continuously using the same application program in one day is greater than a first threshold value and the average time of the user using the mobile terminal exceeds a second threshold value, determining that the user is a heavily dependent user, otherwise, determining that the user is a non-heavily dependent user.
5. The method according to any one of claims 1-4, further comprising:
establishing a self-starting application list, wherein the self-starting application list comprises the names of application programs with self-starting functions;
And prohibiting the self-starting function of the application program with the self-starting function in the mobile terminal according to the self-starting application list.
6. An optimizing apparatus for a mobile terminal, comprising:
the mobile terminal comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring historical behavior data of a user, and the historical behavior data is used for reflecting the use condition of the user on an application program in the mobile terminal;
the processing module is used for predicting a first application program to be used by the user according to the historical behavior data; determining the user type of the user according to the historical behavior data; wherein the user type comprises a heavily dependent user or a non-heavily dependent user; and determining an optimization strategy for optimizing the mobile terminal according to the first application program, wherein the optimization strategy comprises the following steps: determining an optimization strategy for optimizing the mobile terminal according to the user type and the first application program; the optimization strategy is used for releasing the memory of the mobile terminal so as to improve the processing efficiency; the determining an optimization strategy for optimizing the mobile terminal according to the user type and the first application program comprises the following steps: if the user is a heavily dependent user, determining the optimization strategy as a first optimization strategy; the first optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes, and forcedly starting the graphics processor to render; if the user is a non-heavily dependent user, determining the optimization strategy as a second optimization strategy; the second optimization strategy comprises: when the memory occupancy rate of the mobile terminal reaches a preset threshold value, executing cache cleaning and process ending operations on the processes of other application programs except the first application program according to the filtering rules of the processes and/or the classification of the processes;
And the optimizing module is used for optimizing the mobile terminal according to the optimizing strategy.
7. A terminal device, comprising: a memory and a processor; the memory is for storing a computer program, the processor executing the computer program to implement the method of optimizing a mobile terminal according to any of claims 1-5.
8. A storage medium for storing a computer program for implementing the optimization method of a mobile terminal provided in any one of claims 1 to 5.
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