CN114706626B - A processing method and electronic device - Google Patents
A processing method and electronic deviceInfo
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- CN114706626B CN114706626B CN202210334634.5A CN202210334634A CN114706626B CN 114706626 B CN114706626 B CN 114706626B CN 202210334634 A CN202210334634 A CN 202210334634A CN 114706626 B CN114706626 B CN 114706626B
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- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
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- G06F9/44505—Configuring for program initiating, e.g. using registry, configuration files
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Abstract
本申请公开了一种处理方法及电子设备，如果获得目标应用程序的运行指令，基于第一配置参数为目标应用程序配置第一运行环境，响应运行指令，目标应用程序基于第一运行环境处于运行状态，在目标应用程序基于第一运行环境处于运行状态，获得目标应用程序的目标任务运行过程的实际帧率，实际帧率为目标任务的时间周期内的时序数据，基于实际帧率确定第二配置参数，第二配置参数与第一配置参数不同。
The present application discloses a processing method and an electronic device. If an operating instruction of a target application is obtained, a first operating environment is configured for the target application based on a first configuration parameter. In response to the operating instruction, the target application is in an operating state based on the first operating environment. When the target application is in an operating state based on the first operating environment, an actual frame rate of a target task operating process of the target application is obtained. The actual frame rate is timing data within a time period of the target task. A second configuration parameter is determined based on the actual frame rate. The second configuration parameter is different from the first configuration parameter.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a processing method and an electronic device.
Background
For some application programs, such as a general mobile terminal game, during the running process, different performance requirements for different users can be caused due to different operations of different users, and if the same set of configuration strategies are adopted for all users, power consumption can be increased or a picture can be blocked.
Disclosure of Invention
In view of this, the present application provides a processing method and an electronic device, and the specific scheme is as follows:
A method of processing, the method comprising:
If an operation instruction of a target application program is obtained, configuring a first operation environment for the target application program based on a first configuration parameter;
responding to the running instruction, wherein the target application program is in a running state based on the first running environment;
when the target application program is in a running state based on the first running environment, acquiring the actual frame rate of a target task running process of the target application program, wherein the actual frame rate is time sequence data in a time period of the target task;
the second configuration parameter is determined based on the actual frame rate, the second configuration parameter being different from the first configuration parameter.
Further, the determining the second configuration parameter based on the actual frame rate includes:
processing the actual frame rate based on a machine learning classification model to obtain a data tag of the actual frame rate, wherein the data tag is used for representing the severity of the target task in a time period;
the second configuration parameter is determined based on the data tag of the actual frame rate.
Further, the obtaining the actual frame rate of the target task running process of the target application program includes:
the target task operation of the target application program is positioned in a monitoring period, and the actual frame rate of the target task operation process of the target application program is obtained;
The monitoring period comprises an actual frame rate obtained in the target task operation process of the target application program every time.
Further, the determining the second configuration parameter based on the actual frame rate includes:
The second configuration parameter is determined based on a plurality of data tags of the target application within the monitoring period.
Further, the method further comprises:
The use duration of the target application program in the running state each time is obtained based on the monitoring period, or,
And recording that the target application program is in a running state each time based on the monitoring period.
Further, the determining a second configuration parameter based on the actual frame rate includes:
the second configuration parameter is determined based on a plurality of data tags, a plurality of use durations, and a number of uses of the target application within the monitoring period.
Further, the determining a second configuration parameter based on the actual frame rate includes:
Determining a user representation based on a plurality of data tags, a plurality of use durations, and a number of uses of the target application within the monitoring period;
And determining the second configuration parameters based on the user portraits, wherein different user portraits correspond to different second configuration parameters.
Further, the method further comprises:
And configuring a second running environment to be in a running state based on the second configuration parameter by the target application program, wherein a curve for obtaining the actual frame rate of the target task running process of the target application program is smoother than a curve for obtaining the actual frame rate of the target task running process of the target application program when the target application program is in the running state based on the first running environment.
An electronic device, the electronic device comprising:
a display output section;
the processor is used for configuring a first operation environment for a target application program based on a first configuration parameter if an operation instruction of the target application program is obtained, responding to the operation instruction, enabling the target application program to be in an operation state based on the first operation environment, obtaining an actual frame rate of a target task operation process of the target application program when the target application program is in the operation state based on the first operation environment, wherein the actual frame rate is time sequence data in a time period of the target task, and determining a second configuration parameter based on the actual frame rate, wherein the second configuration parameter is different from the first configuration parameter.
Further, the processor is configured to determine the second configuration parameter based on the actual frame rate, including:
The processor is used for processing the actual frame rate based on a machine learning classification model to obtain a data tag of the actual frame rate, wherein the data tag is used for representing the severity of the target task in a time period;
wherein the machine learning classification model comprises:
the characteristic value acquisition module is used for acquiring a characteristic value in the actual frame rate;
The data processing module is used for carrying out standardized processing on the characteristic values;
and the classification module is used for determining the data tag based on the characteristic value after the normalization processing.
From the above technical solution, it can be seen that, in the processing method and the electronic device disclosed in the present application, if an operation instruction of a target application program is obtained, a first operation environment is configured for the target application program based on a first configuration parameter, the target application program is in an operation state based on the first operation environment in response to the operation instruction, an actual frame rate of a target task operation process of the target application program is obtained when the target application program is in the operation state based on the first operation environment, the actual frame rate is time sequence data in a time period of a target task, a second configuration parameter is determined based on the actual frame rate, and the second configuration parameter is different from the first configuration parameter. In the scheme, in the running process of the target application program, the configuration parameters of the target application program are adjusted based on the actual frame rate of the target task, so that the adjusted configuration parameters are related to the actual frame rate in the running process, the configuration parameters which are matched with the actual frame rate in the running process of the target application program are ensured to run, the configuration parameters can be matched with the actual operation of a user, and the phenomenon of power consumption increase or picture blocking caused by mismatching of the configuration parameters and the actual frame rate is avoided.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a processing method disclosed in an embodiment of the present application;
FIG. 2 is a flow chart of a processing method disclosed in an embodiment of the present application;
FIG. 3 is a schematic diagram of a frame rate curve illustrating severity of a frame rate curve according to an embodiment of the present application;
FIG. 4 is a flow chart of a processing method disclosed in an embodiment of the present application;
FIG. 5 is a flow chart of a processing method disclosed in an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application discloses a processing method, the flow chart of which is shown in figure 1, comprising the following steps:
Step S11, if an operation instruction of a target application program is obtained, configuring a first operation environment for the target application program based on a first configuration parameter;
step S12, responding to the operation instruction, wherein the target application program is in an operation state based on the first operation environment;
Step S13, obtaining the actual frame rate of the target task operation process of the target application program when the target application program is in an operation state based on the first operation environment, wherein the actual frame rate is time sequence data in the time period of the target task;
Step S14, determining a second configuration parameter based on the actual frame rate, wherein the second configuration parameter is different from the first configuration parameter.
Taking a target application program as an example for game, the operation modes of different users can cause different performance requirements of different users on the electronic equipment, if any user controls the operation of the target application program, the same set of configuration strategies are adopted, if the user has lower performance requirements, the system power consumption is higher and unnecessary waste is caused by adopting the uniform configuration strategies, and if the user has higher performance requirements, the power consumption is insufficient to support the operation of the user by adopting the uniform configuration strategies, so that the phenomenon of picture blocking is caused.
In order to solve the above problems, in the present solution, when a target application is in a running state, a configuration parameter of the target application is adjusted based on an actual frame rate in a running process of the target application, so that the configuration parameter of the target application in running is related to the actual frame rate thereof, and the actual frame rate is related to an operation mode of a user, thereby implementing different configuration parameters based on different user configurations, and avoiding a problem of power consumption increase or picture blocking that may occur when the same configuration policy is adopted for all users.
And when the operation instruction of the target application program is obtained, controlling the operation of the target application program, namely, when the user starts the target application program, controlling the operation of the target application program. Wherein the target application is run in a first run environment, the first run environment being configured based on a first configuration parameter, and the first configuration parameter being used only to configure the run environment of the target application.
For other application programs, the configuration parameters corresponding to the application program are adopted to configure the running environment, and the application program is enabled to run in the configured running environment, namely different application programs are run in different running environments, and each running environment is configured based on the configuration parameters corresponding to the application program.
The first configuration parameter may be a default configuration parameter, and the default configuration parameter may be a default configuration parameter of the target application program, that is, when the target application program is just started, the same operating environment is configured based on the same configuration parameter, and the target application program is started and operated in the same operating environment no matter which time the target application program is started, and the configuration parameter is adjusted based on the actual frame rate only in the operation process, so as to adjust the operating environment.
Or the first configuration parameter may be a configuration parameter which is set by the history of the target application program, where the first configuration parameter may be a configuration parameter set when the target application program is operated once with the shortest interval time between the first time of obtaining the operation instruction, for example, when the target application program is operated for the first time, the second configuration parameter determined based on the actual frame rate is the configuration parameter a, when the target application program is started for the second time, the first operation environment is configured for the target application program based on the configuration parameter a, and when the target application program is started for the third time, the second configuration parameter determined based on the actual frame rate is the configuration parameter B, and when the target application program is started for the third time, the first operation environment is configured for the target application program based on the configuration parameter B. I.e. the configuration parameters used in the last running process are used as the configuration parameters set when the next target application program is started;
In addition, the first configuration parameter may be an average configuration parameter in the history data, that is, an average configuration parameter obtained by averaging the historical configuration parameters in the history data, and the average configuration parameter is used as a configuration parameter for configuring the first running environment when the target application program is started.
The configuration parameters are configured by resources during system operation, such as frequency raising or limiting of a CPU, frequency raising or limiting of a GPU, allocation of CPU resources, allocation of GPU resources, policy parameters for certain specific scenes and the like, wherein the allocation of the CPU resources comprises scheduling of each core of the CPU, and the configuration of the policy parameters is needed if the frame rate of the CPU is less than a certain frame rate and is identified as one time of the card for card detection for the policy parameters for certain specific scenes.
After the first running environment is configured for the target application program based on the first configuration parameters, the running of the target application program is controlled so that the target application program runs in the first running environment.
In the running process of the target application program, the actual frame rate of the running process of the target task of the target application program is obtained, wherein the actual frame rate is time sequence data in the time period of the target task. The target task of the target application program, namely, the target task is executed once in the running process of the target application program, such as a game. The actual frame rate of the target task operation process, that is, in the duration consumed by the target task operation process, each time point obtains a corresponding actual frame rate, and then the actual frame rate is time sequence data in a time period, which is a group of data instead of a single data, and the time period is the duration required to be consumed by the target task operation. For example, the actual frame rate is obtained once at each point in time during the play of a game.
The actual frame rate is the number of frames actually displayed by the screen per second in the process of executing the target task by the target application program. The target frame number may be related to hardware of the electronic device itself, or may be related to the target application, or may be preset by a user, so that the target frame number outputs a picture in the running process of the target application, however, since different users operate the electronic device in the running process of the target application, the actual frame number may be different, and the actually displayed frame number may be different from the target frame number, that is, although the target frame number is the same, the actual frame number is related to the user operation.
Thus, the actual number of frames of the target task running process is obtained, and the actual determination is made of the operation habit or label of the user who controls the target task running process.
After the second configuration parameter is determined based on the target frame number, and the target frame number is related to the operation habit or the label of the user, so that the configuration parameter of the target application program is adjusted based on the operation of the user, and the configuration parameter of the target application program is related to the operation of the user, thereby avoiding the phenomenon of power consumption increase or picture blocking caused by mismatching of the configuration parameter and the actual frame rate.
Further, after the second configuration parameters are determined, the second operation environment is configured based on the second configuration parameters, and then the target application program is controlled to operate in the second operation environment, so that the operation of the target application program can be more in line with the operation of a user, and the frame rate curve of the actual frame rate is smoother.
Each actual frame rate is a set of time sequence data, the time sequence data is displayed in a frame rate curve form, and when the configuration parameters are adjusted based on the actual frame rates, the target application program runs in a second running environment in which configuration of the configuration parameters after adjustment is completed, a curve formed by the actual frame rate obtained by executing the target task is smoother than a curve formed by the actual frame rate obtained by executing the target task before adjustment of the configuration parameters.
Since the second running environment is configured based on the configuration parameters adjusted by the actual frame rate, when the target application runs in the second running environment, as long as the operation habit of the user does not change, the actual frame rate is detected after the configuration parameters are adjusted, and the actual frame rate is smoother, i.e. less change, than the actual frame rate before the configuration parameters are adjusted. When the frame rate curve shakes up and down or the shake degree is large, the picture of the user operating the electronic device to execute the target task is not smooth, and if the frame rate curve is smooth, the picture when the target task is executed is smoother, so that the configuration parameters need to be adjusted based on the actual frame rate.
The adjusting of the configuration parameters may be that if the average frame rate of 2 seconds falls below 60 in the first configuration parameters to be regarded as a stuck state, the second configuration parameters after the adjustment based on the actual frame rate may be that the situation that the picture is stuck is reduced by the configuration parameters after the adjustment relative to the configuration parameters before the adjustment by setting the configuration parameters after the adjustment to be one stuck state after the 3 seconds falls below 60.
And for each target task execution, the finally formed frame rate curve is formed based on the actual frame rate obtained after the configuration parameters are adjusted based on the actual frame rate.
In the processing method disclosed in this embodiment, if an operation instruction of a target application program is obtained, a first operation environment is configured for the target application program based on a first configuration parameter, the target application program is in an operation state based on the first operation environment in response to the operation instruction, an actual frame rate of a target task operation process of the target application program is obtained when the target application program is in the operation state based on the first operation environment, the actual frame rate is time sequence data in a time period of a target task, a second configuration parameter is determined based on the actual frame rate, and the second configuration parameter is different from the first configuration parameter. In the scheme, in the running process of the target application program, the configuration parameters of the target application program are adjusted based on the actual frame rate of the target task, so that the adjusted configuration parameters are related to the actual frame rate in the running process, the configuration parameters which are matched with the actual frame rate in the running process of the target application program are ensured to run, the configuration parameters can be matched with the actual operation of a user, and the phenomenon of power consumption increase or picture blocking caused by mismatching of the configuration parameters and the actual frame rate is avoided.
The embodiment discloses a processing method, a flow chart of which is shown in fig. 2, comprising:
Step S21, if an operation instruction of a target application program is obtained, configuring a first operation environment for the target application program based on a first configuration parameter;
step S22, responding to the operation instruction, wherein the target application program is in an operation state based on the first operation environment;
Step S23, obtaining an actual frame rate of a target task operation process of the target application program when the target application program is in an operation state based on the first operation environment, wherein the actual frame rate is time sequence data in a time period of the target task;
Step S24, processing the actual frame rate based on the machine learning classification model to obtain a data tag of the actual frame rate, wherein the data tag is used for representing the severity of a target task in a time period;
Step S25, determining a second configuration parameter based on the data tag of the actual frame rate, wherein the second configuration parameter is different from the first configuration parameter.
When the actual frame rate is obtained and the second configuration parameter is determined based on the actual frame rate, the actual frame rate may be classified by means of machine learning, specifically, the actual frame rate may be input into a machine learning classification model, an output result is obtained through the model, the output result is a data tag of the actual frame rate, and the second configuration parameter is further determined according to the data tag.
The data tag is used for representing the severity of a target task in a time period, and can represent the operation habit of a user when the target task is executed, namely, in the execution process of one target task, the actual frame rate is used for indicating the frame rate corresponding to each time point, namely, the actual frame rate is time sequence data in one time period, and the severity of the user operation in the process of completing the target task can be determined based on the time sequence data, wherein the time sequence data can be displayed in the form of a frame rate curve.
Since the actual frame rate is a set of continuous frame rate data, the severity of the user operation can be indicated by the characteristic value such as the average value or variance of the set of continuous frame rate data. The larger these eigenvalues indicate a higher severity and the smaller eigenvalues indicate a lower severity. For example, in a game, a play method that is not moved in a grass is adopted, and the play method indicates that the intensity is low, and when the game is played around the opponent, the play method indicates that the intensity is high.
Based on the display of the intensity of the user operation when the target task is executed through the frame rate curve of the actual frame rate, the frame rate curve is gentle, the intensity is lower, and the amplitude of the frame rate curve is larger, the intensity is higher.
As shown in the example of fig. 3, the actual frame rate of the 6 target task execution processes acquired in one monitoring period shows that three actual frame rate curves of the target task execution processes for the target application program with the target frame rate of 90 frames are in overall smooth curve in fig. 3, but frame dropping occurs at the beginning or the end, which indicates that the user invokes the setting interface at the beginning or the end, the frame dropping phenomenon of the frame rate of the b group graph in fig. 3 is large in the target task execution process due to the fact that the target frame rate of the setting interface is lower than the target frame rate of the interface in the target task execution process, and indicates that the severity is high, if the target application program is a game, the severity is high in the event that the frame dropping position of the large frame rate in the b group graph in fig. 3 is in the battle, the frame dropping phenomenon is rapid, the large frame rate in the c group graph in fig. 3 is less than the b group graph in the figure 3, but the overall frame dropping phenomenon is high in the overall frame rate curve, which indicates that the severity is high in the target task execution process is maintained for a plurality of times.
For the feature values, the feature values can be extracted from the actual frame rate, and more feature values, such as a mean value, a median value, a variance value, an autoregressive coefficient, and the like, can be extracted from each data in the continuous time sequence data, and effective feature values are selected from all feature values so as to classify the actual frame rate based on the effective feature values.
The effective characteristic value is a characteristic value effective for classifying user operation habits, and can represent the change state of a frame rate curve of time sequence data in a time period. The screening process can be to screen according to the meaning of the characteristic values, such as analysis parameters of time domain and frequency domain, determine characteristic values irrelevant to classification results based on historical data, such as similar data, or individuals with larger differences in all data, and the like.
The effective characteristic values can be screened out from each data in the time sequence data in the time period, namely, the effective characteristic values with the same meaning for different data, for example, one game has 10 minutes and one frame value for each second, 600 frame values are all available in the time period corresponding to the game, the characteristic values are respectively extracted from each frame value, and the effective characteristic values are respectively screened.
And after screening the effective characteristic values, carrying out standardization processing on the effective characteristic values to obtain the structured data. Different normalization processes are performed for different target applications. The data normalization processing algorithm comprises min-max normalization processing, z-score normalization processing and the like.
Different normalization processes are performed for different target applications, which may be that different normalization processes are performed for different feature values based on time periods and target frame rates of target tasks of the target applications, the target tasks of the different target applications have different time periods, and the target frame rates of the different target applications are different, which makes the normalization processes performed for the different target applications different.
The method comprises the steps of dividing the sum of detected frame rate values by a target frame rate when the feature values related to the frame rate in a game are subjected to standardization, calculating the feature values related to the frame rate after the standardization is achieved, dividing the detected game duration by the standard duration when the feature values related to the game duration are subjected to standardization, and calculating the feature values related to the duration after the standardization is achieved.
And inputting the structured data obtained after the standardization processing into a machine learning classification model which is trained in advance so as to obtain the data labels in the time period of the target task, namely the data labels of each actual frame rate.
When the machine learning classification model is subjected to model training, feature extraction and screening are performed based on historical data and/or inside user data, standardized processing is performed after effective features are screened out to obtain structured data, the structured data is subjected to machine learning cluster analysis, an unsupervised Kmeans cluster model is adopted, a given sample set is clustered according to the distance between samples by the cluster model, so that points in each cluster are closely connected together, the distance between clusters is as large as possible, the clustered structured data is labeled according to categories to form a standardized structured data set with labels, and the labeled structured data set is input into the machine learning classification model for training, wherein the classification model can adopt a supervised KNN classification model.
After training is completed, the actual frame rate can be processed by using the trained machine learning classification model to obtain a data tag, and further determine corresponding configuration parameters, and the data tag is determined by a model training mode, and further determine a second configuration parameter, so that the accuracy of the data tag can be improved, and the accuracy of the second configuration parameter is improved.
Different data labels correspond to different configuration parameters, a corresponding relation table of the data labels and the configuration parameters is established in advance, if the data label of the current detected actual frame rate is determined to be a first label, the configuration parameter corresponding to the first label is determined to be a second configuration parameter, and the running environment of the target application program is reconfigured; if the data tag of the current monitored actual frame rate is determined to be the second tag, determining the configuration parameter corresponding to the second tag as the second configuration parameter, and reconfiguring the running environment of the target application program.
According to the processing method disclosed by the embodiment, if the operation instruction of the target application program is obtained, the first operation environment is configured for the target application program based on the first configuration parameter, the target application program is in an operation state based on the first operation environment, the actual frame rate of the target task operation process of the target application program is obtained when the target application program is in the operation state based on the first operation environment, the actual frame rate is time sequence data in the time period of the target task, the actual frame rate is processed based on the machine learning classification model, the data tag of the actual frame rate is obtained, the severity of the target task in the time period is represented by the data tag, and the second configuration parameter is determined based on the data tag of the actual frame rate. In the scheme, after the actual frame rate is obtained, the actual frame rate is input into the machine learning classification model to obtain the data tag of the actual frame rate, and the second configuration parameter is determined based on the data tag of the actual frame rate, namely, the configuration parameter is determined to be adjusted in a mode based on the operation of a user and the machine learning, the accuracy of adjusting the configuration parameter based on the actual frame rate is improved, and the phenomenon of power consumption increase or picture blocking caused by mismatching of the configuration parameter and the actual frame rate is avoided.
The embodiment discloses a processing method, a flow chart of which is shown in fig. 4, comprising:
Step S41, if an operation instruction of a target application program is obtained, configuring a first operation environment for the target application program based on the first configuration parameter;
Step S42, responding to the operation instruction, wherein the target application program is in an operation state based on the first operation environment;
Step S43, when the target application program is in an operation state based on the first operation environment, the target task operation of the target application program is in a monitoring period, the actual frame rate of the target task operation process of the target application program is obtained, the actual frame rate is time sequence data in the time period of the target task, and the monitoring period comprises the actual frame rate obtained in the target task operation process of each target application program;
Step S44, determining a second configuration parameter based on the actual frame rate, wherein the second configuration parameter is different from the first configuration parameter.
And setting a monitoring period for the target application program, running the target application program in the monitoring period, and executing the target task. In this case, a monitoring period is generally longer than a time period of a target task, i.e., a target application may be run only once, multiple target applications may be run, or multiple target tasks may be run in a monitoring period.
If the time period of one target task of one target application program is 10 minutes, that is, 10 minutes, the operation of one target task can be completed, and one monitoring period can be 1 day, or 1 week, then the target task in the target application program can be operated for only 1 time, or can be operated for multiple times, or can be operated for no time 1, which is related to the frequency or the number of times that the target task of the target application program is operated by a user.
Then, an actual frame rate may be obtained in a monitoring period, and also a plurality of actual frame rates may be obtained, in the monitoring period, an actual frame rate may be obtained as long as the target task is operated once, when the target task is operated a plurality of times, a plurality of actual frame rates may be obtained, and each actual frame rate represents a set of time sequence data in a time period of the target task, and when the target task is operated a plurality of times in a monitoring period, a plurality of sets of time sequence data are actually obtained.
When determining the second configuration parameters based on the obtained actual frame rates, each actual frame rate is firstly input into a machine learning classification model which is trained in advance, and the output of the machine learning classification model is a data tag of the actual frame rate. When a plurality of actual frame rates are obtained in a monitoring period, each actual frame rate is respectively input into the machine learning classification model, then each actual frame rate can obtain a corresponding data tag, a plurality of data tags are finally obtained, and a second configuration parameter is determined based on the obtained plurality of data tags.
Because each data tag corresponds to a group of configuration parameters respectively, after a plurality of data tags in a monitoring period are determined, the data tags are required to be counted, one tag is finally determined as the data tag representing the operation severity of the target task in the monitoring period, and a second configuration parameter is determined through the finally determined tag, namely, the configuration parameter corresponding to the finally determined tag is selected as the second configuration parameter.
And finally determining the severity of the target task operation in the monitoring period by one label from the plurality of data labels, wherein the determining mode of the label can be that the label is determined in a duty ratio mode, namely determining the ratio of the number of each data label in the plurality of data labels obtained in the monitoring period to the number of all the types of the data labels obtained in the monitoring period, and selecting the data label with the highest duty ratio from the number of the data labels to represent the severity of the target task operation in the monitoring period.
For example, 5 actual frame rates are obtained in the monitoring period, wherein the data label corresponding to the 1 st actual frame rate is the 1 st label, the data label corresponding to the 2 nd actual frame rate is the 2 nd label, the data label corresponding to the 3 rd actual frame rate is the 2 nd label, the data label corresponding to the 4 th actual frame rate is the 2 nd label, the data label corresponding to the 5 th actual frame rate is the 1 st label, 2 labels are all 1 st, 3 labels are all 2 nd, and 2/5 is less than 3/5, and the 2 nd label is determined to be the label capable of representing the operation intensity of the target task in the monitoring period.
Or only when the duty ratio reaches a certain preset value, the corresponding data labels are selected, and if the duty ratio of all the data labels does not reach the preset value, the monitoring period can be prolonged so as to increase the number of the data labels.
Or determining a plurality of actual frame rates obtained in the monitoring period, respectively extracting corresponding characteristic values from each actual frame rate, calculating an average value of each characteristic value in the actual frame rates, thus obtaining each average characteristic value, determining a corresponding data tag based on all the average characteristic values, characterizing the severity of the operation target task in the monitoring period through the data tag, and further determining a second configuration parameter, so that after the configuration parameter is configured to complete the second operation environment, the operation target application program can be operated based on the reconfigured second operation environment, and matching of the configuration parameter and the user operation habit can be realized.
In the processing method disclosed in this embodiment, if an operation instruction of a target application program is obtained, a first operation environment is configured for the target application program based on a first configuration parameter, the target application program is in an operation state based on the first operation environment in response to the operation instruction, an actual frame rate of a target task operation process of the target application program is obtained when the target application program is in the operation state based on the first operation environment, the actual frame rate is time sequence data in a time period of a target task, a second configuration parameter is determined based on the actual frame rate, and the second configuration parameter is different from the first configuration parameter. In the scheme, in the running process of the target application program, the configuration parameters of the target application program are adjusted based on the actual frame rate of the target task, so that the adjusted configuration parameters are related to the actual frame rate in the running process, the configuration parameters which are matched with the actual frame rate in the running process of the target application program are ensured to run, the configuration parameters can be matched with the actual operation of a user, and the phenomenon of power consumption increase or picture blocking caused by mismatching of the configuration parameters and the actual frame rate is avoided.
The embodiment discloses a processing method, a flow chart of which is shown in fig. 5, comprising:
step S51, if an operation instruction of a target application program is obtained, configuring a first operation environment for the target application program based on the first configuration parameter;
step S52, responding to the operation instruction, wherein the target application program is in an operation state based on the first operation environment;
Step S53, when the target application program is in an operation state based on the first operation environment, the target task operation of the target application program is in a monitoring period, the actual frame rate of the target task operation process of the target application program is obtained, the actual frame rate is time sequence data in the time period of the target task, and the monitoring period comprises the actual frame rate obtained in each target task operation process of the target application program;
Step S54, determining a second configuration parameter based on the plurality of data labels, the plurality of usage periods and the number of uses of the target application program in the monitoring period, wherein the second configuration parameter is different from the first configuration parameter.
Multiple target tasks can be executed within one monitoring period, and the multiple target tasks can be executed on the basis of one target application program or on the basis of multiple target application programs.
When the target task is operated for a plurality of times in one monitoring period, the actual frame rate can be obtained once for each operation of the target task, and the actual frame rate can be obtained for a plurality of times. When the target task is operated, the system can acquire at least the time length for operating the target task besides the actual frame rate, and can also record the operation state of the target task.
Recording the running state of the target task, namely recording each time the target task runs, so as to obtain the using times of the target task in the monitoring period; the system obtains the use duration of each time the target task runs so as to obtain the use duration and the use times of each time the target task runs in the monitoring period.
And after determining the use times, the use time length and the actual frame rate of each operation of the target task in the monitoring period, determining a data tag based on each actual frame rate, and determining a second configuration parameter based on the plurality of data tags, the plurality of use time lengths and the use times.
If the service time of each time of running the target task is longer, the influence of the power consumption of the longer-time running target task on the equipment endurance capacity needs to be considered when the parameter configuration is carried out; if the number of times of running the target task in the monitoring period is more, the influence of the power consumption of running the target task for more times on the equipment endurance capacity is considered when the parameter configuration is carried out, and if the using time of running the target task for each time in the monitoring period is shorter and the number of times is less, the power consumption is higher, the equipment endurance capacity is not obviously influenced.
Therefore, when the second configuration parameters are determined, the method not only needs to be based on the data labels capable of representing the severity of the running target task, but also needs to be determined together based on the use duration and the use times in the monitoring period, so that the phenomenon that the power consumption is high or the picture is blocked due to the fact that the configuration parameters are not matched with the actual frame rate when the target task runs in the second running environment configured based on the finally generated second configuration parameters is avoided.
Further, determining the second configuration parameters based on the actual frame rate includes determining a user representation based on a plurality of data tags, a plurality of use durations, and a number of uses of the target application within the monitoring period, determining the second configuration parameters based on the user representation, the different user representations corresponding to different second configuration parameters.
Different configuration parameters are configured for different user portraits, and the user portraits are jointly determined based on a plurality of data labels, a plurality of using time periods and using times in a monitoring period, wherein the user portraits characterize characteristics of different users when the target tasks are operated, and the characteristics comprise whether the target tasks are fierce, whether the target tasks are operated for a long time or not, or whether the target tasks are operated for a plurality of times.
For example, the target task is a game, the monitoring period is 1 week, the total duration of the game played in one monitoring period is determined to be the attribute A, the average game duration of each game played in one monitoring period is determined to be the attribute B, the frequency of the game played every day is determined to be the attribute C according to the number of the game played in one monitoring period, and the data label of the game played in the preset number of times in one monitoring period is determined to be the attribute D.
If the first user plays the game, the game frequency is high, namely the attribute C is high, the game duration is long, namely the attribute A is long, and the data label of each game shows that the frame rate of the game is high in jitter, the game is fierce, the first user can be considered to be a first type player, wherein the first type player is a player who plays the game frequently and enjoys fierce playing method in each game;
if the second user plays the game, the game frequency is high, namely the attribute C is high, the game duration is long, namely the attribute A is long, but the data label of each game shows the game frame rate to be gentle, the game is not drastic, the second user can be considered to be a second type player, wherein the second type player is a player who plays the game frequently, but likes the strategy to win and dislikes drastic playing;
If the third user plays the game, the game frequency is low, namely the attribute C is low, the game time is short, namely the attribute A is short, but the data label of each game shows that the frame rate of the game is high in jitter, and the game is intense, the third user can be considered to be a third type player, wherein the third type player is a player who plays the game infrequently, but enjoys intense play in each game;
if the fourth user plays the game, the game frequency is low, namely, the attribute C is low, the game time is long, namely, the attribute a is short, and the data tag of each game shows that the game frame rate is gentle, the game is not intense, the fourth user can be considered as a fourth type player, wherein the fourth type player is a player who plays the game infrequently and does not like intense play, and belongs to a comparative Buddha system.
In the processing method disclosed in this embodiment, if an operation instruction of a target application program is obtained, a first operation environment is configured for the target application program based on a first configuration parameter, the target application program is in an operation state based on the first operation environment in response to the operation instruction, an actual frame rate of a target task operation process of the target application program is obtained when the target application program is in the operation state based on the first operation environment, the actual frame rate is time sequence data in a time period of a target task, a second configuration parameter is determined based on the actual frame rate, and the second configuration parameter is different from the first configuration parameter. In the scheme, in the running process of the target application program, the configuration parameters of the target application program are adjusted based on the actual frame rate of the target task, so that the adjusted configuration parameters are related to the actual frame rate in the running process, the configuration parameters which are matched with the actual frame rate in the running process of the target application program are ensured to run, the configuration parameters can be matched with the actual operation of a user, and the phenomenon of power consumption increase or picture blocking caused by mismatching of the configuration parameters and the actual frame rate is avoided.
The embodiment discloses an electronic device, a schematic structural diagram of which is shown in fig. 6, including:
An output section 61 and a processor 62 are displayed.
The processor 62 is configured to, when obtaining a running instruction of a target application program, configure a first running environment for the target application program based on a first configuration parameter, respond to the running instruction, obtain an actual frame rate of a target task running process of the target application program when the target application program is in a running state based on the first running environment, the actual frame rate being time-series data in a time period of a target task, and determine a second configuration parameter based on the actual frame rate, the second configuration parameter being different from the first configuration parameter.
Further, the processor determining the second configuration parameter based on the actual frame rate includes the processor processing the actual frame rate based on the machine learning classification model to obtain a data tag for the actual frame rate, the data tag being used to characterize the severity of the target task over a period of time, the second configuration parameter being determined based on the data tag for the actual frame rate.
Wherein the machine learning classification model comprises:
The characteristic value acquisition module is used for acquiring a characteristic value in the actual frame rate;
The data processing module is used for carrying out standardized processing on the characteristic values;
and the classification module is used for determining the data tag based on the characteristic value after the normalization processing.
The feature value acquisition module comprises a time sequence mining analysis module, and feature value extraction is performed on a real-time frame rate curve collected by each game through the time sequence mining analysis module, namely, the real-time condition of any one of the real frame rates in the group a, the group b and the group c in fig. 3 is shown, and the real frame rate of the target task operation process is a set of data of a real frame rate which is continuous in time (for example, a frame rate curve of a game). The characteristic value acquisition module further comprises a characteristic value screening module, and the characteristic value screening module is used for screening the characteristic values of the group of data, so that the characteristic values can effectively describe the change state of the frame rate curve, and the severity of the operation of a user is reflected. Some of these feature values are focused on a continuous low valley in the frame rate curve (a large probability corresponds to a strong fight scene in a game), and some are focused on a fluctuation condition of the entire frame rate curve (corresponds to whether or not interactive operations such as clicking a screen in a game are frequent). That is, the extracted characteristic values can reflect the operation habit of the user to the greatest extent,
And the data processing module performs standardization processing on the screened characteristic values. Different from the conventional data standardization processing methods, the patent performs different data standardization processing on different characteristic value data according to the information such as the game duration information and the target frame rate. Data such as mean, maximum, minimum are normalized with respect to the target frame rate, while data such as longest _struke, number_of_ peaks _n are normalized with respect to the game duration and output a structured dataset
Wherein the classification module comprises a cluster analysis module. The cluster analysis module performs machine learning cluster analysis on the normalized feature value structured data and outputs a labeled structured dataset. The classification module further comprises a classification model training module. The classification model training module receives the tagged structured dataset to output classification tags, e.g., a frame rate curve for a game, to derive classification tags.
Further, the processor obtains an actual frame rate of a target task running process of the target application program, including:
The target task operation of the target application program is located in a monitoring period, and the processor obtains the actual frame rate of the target task operation process of the target application program, wherein the monitoring period comprises the actual frame rate obtained by the target task operation process of the target application program every time.
Further, the processor determines a second configuration parameter based on the actual frame rate, comprising:
The processor determines a second configuration parameter based on a plurality of data tags of the target application within the monitoring period.
Further, the processor is further configured to:
and obtaining the using time length of the target application program in the running state each time based on the monitoring period, or recording the running state of the target application program each time based on the monitoring period.
Further, the processor determines a second configuration parameter based on the actual frame rate, comprising:
The processor determines a second configuration parameter based on the plurality of data tags, the plurality of usage durations, and the number of uses of the target application within the monitoring period.
Further, the processor determines a second configuration parameter based on the actual frame rate, comprising:
the processor determines a user profile based on the plurality of data tags, the plurality of use durations, and the number of uses of the target application within the monitoring period, determines a second configuration parameter based on the user profile, and the different user profiles correspond to different second configuration parameters.
Further, the processor is further configured to:
And when the target application program is in the running state based on the first running environment, the curve of the actual frame rate of the target task running process of the target application program is smoother than the curve of the actual frame rate of the target task running process of the target application program.
The electronic device disclosed in the present embodiment is implemented on the basis of the processing method disclosed in the foregoing embodiment, and will not be described herein.
If an operation instruction of a target application program is obtained, the electronic device configures a first operation environment for the target application program based on a first configuration parameter, responds to the operation instruction, and obtains an actual frame rate of a target task operation process of the target application program when the target application program is in an operation state based on the first operation environment, wherein the actual frame rate is time sequence data in a time period of a target task, and determines a second configuration parameter based on the actual frame rate, and the second configuration parameter is different from the first configuration parameter. In the scheme, in the running process of the target application program, the configuration parameters of the target application program are adjusted based on the actual frame rate of the target task, so that the adjusted configuration parameters are related to the actual frame rate in the running process, the configuration parameters which are matched with the actual frame rate in the running process of the target application program are ensured to run, the configuration parameters can be matched with the actual operation of a user, and the phenomenon of power consumption increase or picture blocking caused by mismatching of the configuration parameters and the actual frame rate is avoided.
The embodiment of the present application further provides a readable storage medium, on which a computer program is stored, where the computer program is loaded and executed by a processor, to implement each step of the above processing method, and a specific implementation process may refer to descriptions of corresponding parts of the foregoing embodiment, which is not repeated in this embodiment.
The application also proposes a computer program product or a computer program comprising computer instructions stored in a computer readable storage medium. The processor of the electronic device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the method provided in the various optional implementation manners of the processing method aspect or the processing system aspect, and the specific implementation process may refer to the description of the corresponding embodiment and is not repeated.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
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| CN109104638A (en) * | 2018-08-03 | 2018-12-28 | Oppo广东移动通信有限公司 | frame rate optimization method, device, terminal and storage medium |
| CN109343997A (en) * | 2018-10-31 | 2019-02-15 | Oppo广东移动通信有限公司 | Caton detection method, device, terminal and storage medium |
| CN111176758A (en) * | 2019-12-31 | 2020-05-19 | 腾讯科技（深圳）有限公司 | Configuration parameter recommendation method and device, terminal and storage medium |
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| CN110941411A (en) * | 2019-11-19 | 2020-03-31 | 深圳传音控股股份有限公司 | Frame rate control method and device and computer storage medium |
| CN113641488A (en) * | 2021-07-07 | 2021-11-12 | 荣耀终端有限公司 | Method and device for optimizing resources based on user use scene |
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| CN109104638A (en) * | 2018-08-03 | 2018-12-28 | Oppo广东移动通信有限公司 | frame rate optimization method, device, terminal and storage medium |
| CN109343997A (en) * | 2018-10-31 | 2019-02-15 | Oppo广东移动通信有限公司 | Caton detection method, device, terminal and storage medium |
| CN111176758A (en) * | 2019-12-31 | 2020-05-19 | 腾讯科技（深圳）有限公司 | Configuration parameter recommendation method and device, terminal and storage medium |
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