A processing method and an electronic device

Abstract

The present application discloses a processing method and an electronic device. If a running instruction of a target application program is obtained, a first running environment is configured for the target application program based on a first configuration parameter. In response to the running instruction, 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, the actual frame rate of the running process of the target task of the target application program is obtained. The actual frame rate is timing data within the time period of the 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.

Description

Technical Field

[0001] This application relates to the field of data processing, and particularly to a processing method and an electronic device. Background Art

[0002] For some applications, such as large-scale mobile games, during operation, due to different operations of different users, there will be different performance requirements for different users. If the same set of configuration strategies is adopted for all users, it may lead to an increase in power consumption or frame stuttering of the screen. Summary of the Invention

[0003] In view of this, this application provides a processing method and an electronic device, and the specific solutions are as follows:

[0004] A processing method, the method includes:

[0005] If a running instruction of a target application is obtained, configure a first running environment for the target application based on first configuration parameters;

[0006] In response to the running instruction, the target application is in a running state based on the first running environment;

[0007] When the target application is in a running state based on the first running environment, obtain an actual frame rate of a target task running process of the target application, where the actual frame rate is timing data within a time period of the target task;

[0008] Determine second configuration parameters based on the actual frame rate, where the second configuration parameters are different from the first configuration parameters.

[0009] Further, the determining the second configuration parameters based on the actual frame rate includes:

[0010] Process the actual frame rate based on a machine learning classification model to obtain a data label of the actual frame rate, where the data label is used to characterize the intensity within a time period of the target task;

[0011] Determine the second configuration parameters based on the data label of the actual frame rate.

[0012] Further, the obtaining the actual frame rate of the target task running process of the target application includes:

[0013] When the target task running of the target application is within a monitoring period, obtain the actual frame rate of the target task running process of the target application;

[0014] Wherein, the monitoring period includes the actual frame rate obtained each time the target task running process of the target application.

[0015] Further, determining the second configuration parameter based on the actual frame rate includes:

[0016] Determining the second configuration parameter based on multiple data tags of the target application within the monitoring period.

[0017] Further, the method further includes:

[0018] Obtaining the usage duration of the target application each time it is in the running state based on the monitoring period; or,

[0019] Recording the target application each time it is in the running state based on the monitoring period.

[0020] Further, determining the second configuration parameter based on the actual frame rate includes:

[0021] Determining the second configuration parameter based on multiple data tags, multiple usage durations, and usage times of the target application within the monitoring period.

[0022] Further, determining the second configuration parameter based on the actual frame rate includes:

[0023] Determining a user profile based on multiple data tags, multiple usage durations, and usage times of the target application within the monitoring period;

[0024] Determining the second configuration parameter based on the user profile, where different user profiles correspond to different second configuration parameters.

[0025] Further, the method further includes:

[0026] When the target application configures a second operating environment based on the second configuration parameter and is in the running state, the curve of the actual frame rate of the target task running process of the target application is smoother than the curve of the actual frame rate of the target task running process of the target application when it is in the running state based on the first operating environment.

[0027] An electronic device, the electronic device includes:

[0028] A display output component;

[0029] A processor, which is configured to, if a running instruction of a target application is obtained, configure a first running environment for the target application based on a first configuration parameter; in response to the running instruction, the target application is in a running state based on the first running environment; when the target application is in a running state based on the first running environment, obtain an actual frame rate of the running process of the target task of the target application, where the actual frame rate is timing data within a time period of the target task; determine a second configuration parameter based on the actual frame rate, and the second configuration parameter is different from the first configuration parameter.

[0030] Further, the processor is configured to determine the second configuration parameter based on the actual frame rate, including:

[0031] The processor is configured to process the actual frame rate based on a machine learning classification model to obtain a data label of the actual frame rate, where the data label is used to characterize the intensity within a time period of the target task; determine the second configuration parameter based on the data label of the actual frame rate;

[0032] Wherein, the machine learning classification model includes:

[0033] An eigenvalue acquisition module, configured to obtain an eigenvalue in the actual frame rate;

[0034] A data processing module, configured to perform normalization processing on the eigenvalue;

[0035] A classification module, configured to determine a data label based on the eigenvalue after the normalization processing.

[0036] As can be seen from the above technical solutions, for the processing method and electronic device disclosed in this application, if a running instruction of a target application is obtained, a first running environment is configured for the target application based on a first configuration parameter, in response to the running instruction, the target application is in a running state based on the first running environment, when the target application is in a running state based on the first running environment, an actual frame rate of the running process of the target task of the target application is obtained, the actual frame rate is timing data within a time period of the target task, and 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 this solution, during the running process of the target application, the configuration parameters of the target application 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 during the running process, to ensure that the target application runs with configuration parameters adapted to the actual frame rate during the running process, so that the configuration parameters can be adapted to the user's actual operations, and to avoid the phenomenon of increased power consumption or frame stuttering caused by the mismatch between the configuration parameters and the actual frame rate. Description of the Drawings

[0037] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0038] Figure 1 Flow chart of a processing method disclosed in an embodiment of the present application;

[0039] Figure 2 Flow chart of a processing method disclosed in an embodiment of the present application;

[0040] Figure 3 Schematic diagram showing the intensity represented by the frame rate curve in an embodiment of the present application;

[0041] Figure 4 Flow chart of a processing method disclosed in an embodiment of the present application;

[0042] Figure 5 Flow chart of a processing method disclosed in an embodiment of the present application;

[0043] Figure 6 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. Detailed implementation manners

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0045] The present application discloses a processing method, and its flow chart is as Figure 1 shown, including:

[0046] Step S11: If a running instruction of the target application program is obtained, configure a first running environment for the target application program based on the first configuration parameter;

[0047] Step S12: In response to the running instruction, the target application program is in a running state based on the first running environment;

[0048] Step S13: When the target application program is in a running state based on the first running environment, obtain the actual frame rate of the running process of the target task of the target application program. The actual frame rate is the timing data within the time period of the target task;

[0049] Step S14: Determine a second configuration parameter based on the actual frame rate, where the second configuration parameter is different from the first configuration parameter.

[0050] Taking the target application as a game as an example, since different users have different operation methods, different users will have different performance requirements for the electronic device. If the same set of configuration strategies is adopted regardless of which user controls the operation of the target application, if the user has a low performance requirement, using a unified configuration strategy will result in high system power consumption and unnecessary waste; if the user has a high performance requirement, using a unified configuration strategy will result in insufficient power to support the user's operations, thus causing the phenomenon of frame stuttering.

[0051] To solve the above problems, in this solution, when the target application is in the running state, the configuration parameters of the target application are adjusted based on the actual frame rate during the running process of the target application, so that the configuration parameters during the running of the target application are related to its actual frame rate, and the actual frame rate is related to the user's operation method, thereby realizing the configuration of different configuration parameters based on different users and avoiding the problems of increased power consumption or frame stuttering that may occur when using the same set of configuration strategies for all users.

[0052] When obtaining the running instruction of the target application, control the running of the target application, that is, when the user starts the target application, control the running of the target application. Among them, the target application runs in the first running environment, and the first running environment is configured based on the first configuration parameter, and moreover, the first configuration parameter is only used to configure the running environment of the target application.

[0053] For other applications, the running environment is configured with the configuration parameters corresponding to the application, and the application is made to run in the configured running environment, that is, different applications run in different running environments, and each running environment is configured based on the configuration parameters corresponding to the application.

[0054] Among them, the first configuration parameter can be a default configuration parameter, and the default configuration parameter can be the default configuration parameter of the target application, that is, as long as the target application is started, when the target application is just started, the same running environment will be configured based on the same configuration parameter, and the target application will be started and run in the same running environment regardless of which start. Only during the running process will the configuration parameters be adjusted based on the actual frame rate, and then the running environment will be adjusted.

[0055] Alternatively, the first configuration parameter may also be a configuration parameter that has been set in the history of the target application, wherein the first configuration parameter may be a configuration parameter set when the target application is run for the shortest time interval with the current acquisition of the running instruction, such as: when the target application is run for the first time, the second configuration parameter determined based on the actual frame rate is configuration parameter A; when the target application is started for the second time, the configuration of the first running environment for the target application is based on configuration parameter A, and the second configuration parameter determined based on the actual frame rate this time is configuration parameter B; when the target application is started for the third time, the configuration of the first running environment for the target application is based on configuration parameter B. That is, the configuration parameters used in the previous running process are used as the configuration parameters set when the target application is started next time;

[0056] In addition, the first configuration parameter may also be an average configuration parameter in historical data, that is, all previous configuration parameters in the historical records are averaged to obtain an average configuration parameter, and the average configuration parameter is used as the configuration parameter for configuring the first operating environment when the target application is started.

[0057] The configuration parameters configure the resources during system operation, such as CPU frequency increase or frequency limit, GPU frequency increase or frequency limit, CPU resource allocation, GPU resource allocation, and policy parameters for certain specific scenarios. Among them, the allocation of CPU resources, such as the scheduling of each CPU core; policy parameters for certain specific scenarios, such as how long the frame rate must be below a certain value to be recognized as a freeze, in which case the policy parameters need to be configured.

[0058] After configuring the first operating environment for the target application based on the first configuration parameter, the operation of the target application is controlled so that the target application runs in the first operating environment.

[0059] During the operation of the target application, the actual frame rate of the target task of the target application is obtained. The actual frame rate is the time series data within the time period of the target task. Among them, the target task of the target application refers to the execution of a target task during the operation of the target application, such as a game. The actual frame rate of the target task operation means that the corresponding actual frame rate is obtained at each time point within the time consumed by the target task operation. Then, the actual frame rate is the time series data within a time period, which is a set of data, not a single data. The time period is the time required for the target task to run. For example, during a game, the actual frame rate is obtained at each time point in the operation of this game.

[0060] Among them, the actual frame rate is the number of frames actually displayed on the screen per second during the execution of the target task by the target application. Among them, a target frame rate is set for the target application in advance. This target frame rate may be related to the hardware of the electronic device itself, may be related to the target application, or may be preset for the user. The target frame rate is set so that the target application outputs a picture at the target frame rate during operation. However, since different users operate the electronic device differently during the operation of the target application, the actual number of frames will be different. Then, there may be a difference between the actually displayed number of frames and the target frame rate. That is, although the target frame rate is the same, the actual frame rate is related to the user's operation.

[0061] Therefore, obtaining the actual frame rate during the operation of the target task actually determines the operation habit or label of the user who controls the operation of the target task.

[0062] After determining the second configuration parameter based on the target frame rate, since the second configuration parameter is determined based on the target frame rate, and the target frame rate is related to the user's operation habit or label, the determined second configuration parameter is actually related to the user's operation habit or label. That is, it realizes the adjustment of the configuration parameter of the target application based on the user's operation, so that the configuration parameter of the target application is related to the user's operation, thereby avoiding the phenomenon of increased power consumption or frame stuttering caused by the mismatch between the configuration parameter and the actual frame rate.

[0063] Furthermore, after determining the second configuration parameter, configure the second operating environment based on the second configuration parameter. Then, control the target application to run in the second operating environment so that the operation of the target application can better conform to the user's operation, thereby making the frame rate curve of the actual frame rate smoother.

[0064] Among them, each actual frame rate is a set of timing data. The timing data is displayed in the form of a frame rate curve. After adjusting the configuration parameter based on the actual frame rate, when the target application runs in the second operating environment configured with the adjusted configuration parameter, the curve formed by the actual frame rate obtained by executing the target task is smoother than the curve formed by the actual frame rate obtained by executing the target task before adjusting the configuration parameter.

[0065] Since the second operating environment is implemented by configuring the configuration parameter adjusted based on the actual frame rate, then, when the target application runs in the second operating environment, as long as the user's operation habit does not change, then, after adjusting the configuration parameter and then detecting the actual frame rate, it will be smoother than the actual frame rate before adjusting the configuration parameter, that is, the change is less. When the frame rate curve jitters up and down, or the jitter degree is large, the picture of the user operating the electronic device to execute the target task will be unsmooth. If the frame rate curve is smooth, the picture during the execution of the target task will be smoother. Therefore, it is necessary to adjust the configuration parameter based on the actual frame rate.

[0066] Among them, the adjustment of the configuration parameters can be as follows: If the average frame rate in the first configuration parameter drops below 60 within 2 seconds, it is considered a stutter. After adjusting based on the actual frame rate, the second configuration parameter can be: setting the drop below 60 within 3 seconds as one stutter. Compared with the configuration parameters before adjustment, the adjusted configuration parameters reduce the situation of screen stuttering.

[0067] For each execution of the target task, the finally formed frame rate curve is formed based on the actual frame rate obtained after adjusting the configuration parameters according to the actual frame rate.

[0068] The processing method disclosed in this embodiment, if a running instruction of a target application is obtained, configure a first running environment for the target application based on the first configuration parameter. In response to the running instruction, the target application is in a running state based on the first running environment. When the target application is in a running state based on the first running environment, obtain the actual frame rate of the running process of the target task of the target application. The actual frame rate is the timing data within the time period of the target task. Determine a second configuration parameter based on the actual frame rate. The second configuration parameter is different from the first configuration parameter. In this solution, during the running process of the target application, adjust the configuration parameters of the target application based on the actual frame rate of the target task, so that the adjusted configuration parameters are related to the actual frame rate during the running process, to ensure that the target application runs with configuration parameters adapted to the actual frame rate during the running process, so that the configuration parameters can be adapted to the user's actual operations, and avoid the phenomenon of increased power consumption or screen stuttering caused by the mismatch between the configuration parameters and the actual frame rate.

[0069] This embodiment discloses a processing method, and its flowchart is as Figure 2 shown, including:

[0070] Step S21, if a running instruction of a target application is obtained, configure a first running environment for the target application based on the first configuration parameter;

[0071] Step S22, in response to the running instruction, the target application is in a running state based on the first running environment;

[0072] Step S23, when the target application is in a running state based on the first running environment, obtain the actual frame rate of the running process of the target task of the target application. The actual frame rate is the timing data within the time period of the target task;

[0073] Step S24, process the actual frame rate based on a machine learning classification model to obtain a data label of the actual frame rate. The data label is used to characterize the intensity within the time period of the target task;

[0074] Step S25: determining a second configuration parameter based on the data tag of the actual frame rate, where the second configuration parameter is different from the first configuration parameter.

[0075] When the actual frame rate is obtained and the second configuration parameter is determined based on the actual frame rate, the actual frame rate can be classified by machine learning. Specifically, the actual frame rate can be input into a machine learning classification model, and an output result is obtained through the model. The output result is a data label of the actual frame rate, and the second configuration parameter is further determined based on the data label.

[0076] Among them, the data label is used to characterize the intensity of the target task within the time period, and can characterize the user's operating habits when performing the target task, that is, during the execution of a target task, the actual frame rate is used to indicate the frame rate corresponding to each time point, that is, the actual frame rate is actually a time series data within a time period. Based on the time series data, the intensity of the user's operation in the process of completing the target task can be determined, wherein the time series data can be displayed in the form of a frame rate curve.

[0077] Since the actual frame rate is a set of continuous frame rate data, the average value or variance of this set of continuous frame rate data can indicate the intensity of the user's operation. The larger the characteristic value, the higher the intensity, and the smaller the characteristic value, the lower the intensity. For example, in the game, if you hide in the bushes and do not move, it indicates a low intensity; if you rush to the opponent's object to fight, it indicates a high intensity.

[0078] The frame rate curve based on the actual frame rate shows the intensity of the user operation when executing the target task. A flat frame rate curve indicates a low intensity, and a large frame rate curve indicates a high intensity.

[0079] like Figure 3 The actual frame rate performance of the six target task execution processes collected in one monitoring cycle in the example shown, that is, the actual frame rate curves of the target task execution processes of three target applications with a target frame rate of 90 frames, Figure 3 Group A Figure 6 The curve is generally flat, but it will drop frames at the beginning or end, indicating that the user calls the setting interface at the beginning or end, because the target frame rate of the setting interface is lower than the target frame rate of the interface during the target task execution process; Figure 3 In the graph of group b in the target task, there is a significant frame rate drop during the target task execution, indicating that the intensity is high. If the target application is a game, then Figure 3 The large frame rate drop in the b group of pictures should be the situation of team battles. The team battles are very intense and the rapid changes in the picture lead to large frame rate drops. Figure 3 The large frame rate drop phenomenon in group c is less thanFigure 3 Group B pictures in [reference], but the overall frame rate curve jitters significantly and there are multiple sudden frame drops, indicating that a relatively intense operation state is maintained throughout the execution of the target task.

[0080] Among them, for the eigenvalue, it can be extracted from the actual frame rate. A relatively large number of eigenvalues can be extracted from each data in this continuous time-series data, such as: mean, median, variance, autoregressive coefficient, etc. Effective eigenvalues are selected from all eigenvalues for classifying the actual frame rate based on the effective eigenvalues.

[0081] Among them, the effective eigenvalue is the eigenvalue effective for classifying user operation habits and can characterize the change state of the frame rate curve of the time-series data within a time period. The screening process can be: screening according to the meaning of the eigenvalue, such as: analysis parameters in the time domain and frequency domain; determining the eigenvalues irrelevant to the classification result based on historical data, such as: similar data, or individual data with large differences in all data, etc.

[0082] Among them, effective eigenvalues can be screened out from each data in the time-series data within this time period, that is, the effective eigenvalues with the same meaning for different data. For example: a game lasts for 10 minutes and there is a frame rate value for each second, so there are 600 frame rate values in the time period corresponding to this game. Eigenvalues are extracted from each frame rate value respectively and effective eigenvalues are screened respectively.

[0083] After the effective eigenvalues are screened out, the effective eigenvalues are standardized to obtain structured data. Different standardization processes are performed for different target application programs. Among them, the data standardization algorithms include min-max standardization and z-score standardization, etc.

[0084] Performing different standardization processes for different target application programs can be: performing different standardization processes on different eigenvalues based on the time period of the target task and the target frame rate of the target application program. The target tasks of different target application programs have different time periods, and moreover, the target frame rates of different target application programs are different, which makes the standardization processes performed for different target application programs different.

[0085] For example: when standardizing the eigenvalue related to the frame rate in a game, it can be: dividing the sum of the detected frame rate values by the target frame rate to achieve standardization, and then calculating the eigenvalue related to the frame rate; if standardizing the eigenvalue related to the game duration, then the detected game duration can be divided by the standard duration to achieve standardization, and then calculating the eigenvalue related to the duration.

[0086] Input the structured data obtained after standardization into a pre-trained machine learning classification model to obtain the labels of the data within the time period of the target task, that is, the data labels for each actual frame rate.

[0087] Among them, when training the machine learning classification model, feature extraction and screening are performed based on historical data and / or internal user data. After filtering out the effective features, standardization processing is performed to obtain structured data, and the structured data is subjected to machine learning clustering analysis. An unsupervised Kmeans clustering model is used. This clustering model clusters the given sample set according to the distance between samples, so that the points within each cluster are closely connected, and the distance between clusters is as large as possible. The clustered structured data is labeled according to categories to form a labeled and standardized structured data set; the labeled structured data set is input into the machine learning classification model for training. Among them, the classification model can adopt a supervised KNN classification model.

[0088] After the training is completed, the trained machine learning classification model can be used to process the actual frame rate to obtain data labels, and further determine the corresponding configuration parameters. Determining the data labels through model training and further determining the second configuration parameters can improve the accuracy of the data labels, thereby improving the accuracy of the second configuration parameters.

[0089] Different data labels correspond to different configuration parameters. A correspondence table between data labels and configuration parameters is established in advance. If it is determined that the data label of the currently detected actual frame rate is the first label, then determine the configuration parameter corresponding to the first label as the second configuration parameter, and reconfigure the operating environment of the target application; if it is determined that the data label of the currently monitored actual frame rate is the second label, then determine the configuration parameter corresponding to the second label as the second configuration parameter, and reconfigure the operating environment of the target application.

[0090] The processing method disclosed in this embodiment, if a running instruction of a target application is obtained, configures a first running environment for the target application based on a first configuration parameter, responds to the running instruction, and the target application is in a running state based on the first running environment. When the target application is in a running state based on the first running environment, the actual frame rate of the running process of the target task of the target application is obtained. The actual frame rate is the timing data within the time period of the target task. The actual frame rate is processed based on a machine learning classification model to obtain a data label of the actual frame rate. The data label is used to characterize the intensity within the time period of the target task. The second configuration parameter is determined based on the data label of the actual frame rate. In this solution, after the actual frame rate is obtained, the actual frame rate is input into the machine learning classification model to obtain the data label of the actual frame rate, and the second configuration parameter is determined based on the data label of the actual frame rate. That is, it realizes adjusting the configuration parameter in a manner based on the user's operation and machine learning, improves the accuracy of adjusting the configuration parameter based on the actual frame rate, and avoids the phenomenon of increased power consumption or frame stuttering caused by the mismatch between the configuration parameter and the actual frame rate.

[0091] This embodiment discloses a processing method, and its flowchart is as Figure 4 shown, including:

[0092] Step S41, if a running instruction of a target application is obtained, configure a first running environment for the target application based on a first configuration parameter;

[0093] Step S42, respond to the running instruction, and the target application is in a running state based on the first running environment;

[0094] Step S43, when the target application is in a running state based on the first running environment and the target task running of the target application is within a monitoring period, obtain the actual frame rate of the running process of the target task of the target application. The actual frame rate is the timing data within the time period of the target task, and the monitoring period includes the actual frame rate obtained for each running process of the target task of the target application;

[0095] Step S44, determine a second configuration parameter based on the actual frame rate, and the second configuration parameter is different from the first configuration parameter.

[0096] Set a monitoring period for the target application, run the target application within the monitoring period, and execute the target task. Among them, a monitoring period is usually longer than the time period of a target task. That is, within a monitoring period, the target application can be run only once, or can be run multiple times, or multiple target tasks can be run.

[0097] For example, the time period of a target task of a target application is 10 minutes, that is, one run of the target task can be completed in 10 minutes, and a monitoring period can be 1 day or 1 week. Then, within 1 day or 1 week, the target task in the target application can run only once, can run multiple times, or may not run at all. This is related to the frequency or number of times the user runs the target task of the target application.

[0098] Then, within a monitoring period, one actual frame rate can be obtained, or multiple actual frame rates can be obtained. Within this monitoring period, as long as the target task runs once, an actual frame rate will be obtained. When the target task runs multiple times, multiple actual frame rates will be obtained, and each actual frame rate represents a set of timing data within the time period of the target task. Then, when the target task runs multiple times within a monitoring period and multiple actual frame rates are obtained, what is actually obtained is multiple sets of timing data.

[0099] When determining the second configuration parameter based on the obtained multiple actual frame rates, first, each actual frame rate is respectively input into a pre-trained machine learning classification model, and the output of the machine learning classification model is the data label of the actual frame rate. When multiple actual frame rates are obtained within a monitoring period and each actual frame rate is respectively input into the machine learning classification model, each actual frame rate will obtain a corresponding data label, and finally multiple data labels are obtained. The second configuration parameter is determined based on the obtained multiple data labels.

[0100] Since each data label corresponds to a set of configuration parameters respectively, after determining the multiple data labels within the monitoring period, it is necessary to count the multiple data labels, and finally determine one label as the data label representing the intensity of the target task running within the monitoring period. The second configuration parameter is determined through the finally determined label, that is, the configuration parameter corresponding to the finally determined label is selected as the second configuration parameter.

[0101] Finally, one label is determined from the multiple data labels to represent the intensity of the target task running within the monitoring period. The determination method of this label can be: determined in a proportion-based manner, that is, determine the ratio of the number of each type of data label among the multiple data labels obtained within the monitoring period to the number of types of all data labels obtained within the monitoring period, and select the data label with the highest proportion to represent the intensity of the target task running within the monitoring period.

[0102] For example, 5 actual frame rates are obtained within a monitoring period. Among them, the data label corresponding to the first actual frame rate is the first label, the data label corresponding to the second actual frame rate is the second label, the data label corresponding to the third actual frame rate is the second label, the data label corresponding to the fourth actual frame rate is the second label, and the data label corresponding to the fifth actual frame rate is the first label. Then, there are 2 first labels and 3 second labels. Since 2 / 5 is less than 3 / 5, the second label is determined as the label that can characterize the intensity of the target task running within the monitoring period.

[0103] Alternatively, only when the proportion reaches a certain preset value will the corresponding data label be selected. If the proportions of all data labels do not reach the preset value, the monitoring period can be extended to increase the number of data labels.

[0104] Alternatively, it can also be: Determine multiple actual frame rates obtained within the monitoring period, respectively extract the corresponding feature values from each actual frame rate, calculate the average value of each feature value among the multiple actual frame rates to obtain each average feature value, determine a corresponding data label based on all the average feature values, and characterize the intensity of the target task running within the monitoring period through this data label, and then determine the second configuration parameter, so that after the configuration parameter configures the second running environment, the target application can be run based on the reconfigured second running environment to achieve the matching of the configuration parameter and the user's operation habit.

[0105] The processing method disclosed in this embodiment, if a running instruction of the target application is obtained, configure the first running environment for the target application based on the first configuration parameter. In response to the running instruction, the target application is in a running state based on the first running environment. When the target application is in a running state based on the first running environment, obtain the actual frame rate of the running process of the target task of the target application. The actual frame rate is the time-series data within the time period of the target task. Determine the second configuration parameter based on the actual frame rate. The second configuration parameter is different from the first configuration parameter. In this solution, during the running process of the target application, adjust the configuration parameter of the target application based on the actual frame rate of the target task, so that the adjusted configuration parameter is related to the actual frame rate during the running process, to ensure that the target application runs with the configuration parameter adapted to the actual frame rate during the running process, so that the configuration parameter can be adapted to the user's actual operation, and avoid the phenomenon of increased power consumption or screen freezing caused by the mismatch between the configuration parameter and the actual frame rate.

[0106] This embodiment discloses a processing method, and its flowchart is as Figure 5 shown, including:

[0107] Step S51, if a running instruction of the target application is obtained, configure the first running environment for the target application based on the first configuration parameter;

[0108] Step S52: In response to the running instruction, the target application is in a running state based on the first running environment;

[0109] Step S53: When the target application is in a running state based on the first running environment and the target task of the target application is running within the monitoring period, obtain the actual frame rate of the running process of the target task of the target application. The actual frame rate is the timing data within the time period of the target task, and the monitoring period includes the actual frame rate obtained for each running process of the target task of the target application;

[0110] Step S54: Determine the second configuration parameter based on multiple data tags, multiple usage durations, and usage times of the target application within the monitoring period. The second configuration parameter is different from the first configuration parameter.

[0111] The target task can be run multiple times within a monitoring period. Running the target task multiple times can be achieved based on running the target application once, or can be achieved based on running the target application multiple times.

[0112] When the target task is run multiple times within a monitoring period, an actual frame rate can be obtained each time the target task is run. Running the target task multiple times can obtain multiple actual frame rates. Among them, when the target task is running, in addition to the actual frame rate, the system can at least also obtain the duration of running the target task, and can also record the running state of the target task.

[0113] Recording the running state of the target task means that each time the target task runs, it will be recorded, so as to obtain the usage times of the target task within the monitoring period; the system obtains the usage duration each time the target task runs, so as to obtain the usage duration and usage times of the target task each time it runs within the monitoring period.

[0114] After determining the usage times, usage durations, and actual frame rates of each run of the target task within the monitoring period, determine a data tag based on each actual frame rate, and jointly determine the second configuration parameter based on multiple data tags, multiple usage durations, and usage times.

[0115] If the usage duration of each run of the target task is relatively long, the impact of the power consumption of running the target task for a long time on the device's battery life needs to be considered when configuring parameters; if the number of runs of the target task within the monitoring period is relatively large, the impact of the power consumption of running the target task a relatively large number of times on the device's battery life also needs to be considered when configuring parameters; and if the usage duration of each run of the target task within the monitoring period is relatively short and the number of runs is relatively small, even if the power consumption is relatively high, it will not have an obvious impact on the device's battery life.

[0116] Therefore, when determining the second configuration parameter, it is necessary to jointly determine it based not only on the data tags that can characterize the intensity when running the target task, but also on the usage duration and usage times within the monitoring period, so as to ensure that when the target task runs in the second running environment configured based on the finally generated second configuration parameter, there will be no phenomenon of high power consumption or frame freezing due to the mismatch between the configuration parameter and the actual frame rate.

[0117] Furthermore, determining the second configuration parameter based on the actual frame rate includes: determining a user profile based on multiple data tags, multiple usage durations, and usage times of the target application within the monitoring period, and determining the second configuration parameter based on the user profile. Different user profiles correspond to different second configuration parameters.

[0118] Configure different configuration parameters for different user profiles, and the user profile is jointly determined based on multiple data tags, multiple usage durations, and usage times within the monitoring period. Among them, the user profile characterizes the characteristics of different users when running the target task, and these characteristics include both whether it is intense and whether they run the target task for a long time or run the target task multiple times.

[0119] For example: The target task is a game, and the monitoring period is 1 week. Determine the total duration of playing the game within a monitoring period and determine it as attribute A; determine the average game duration of each game within a monitoring period and determine it as attribute B; determine the daily game frequency according to the number of times of playing the game within a monitoring period and determine it as attribute C; determine the data tags of the game for a preset number of times within a monitoring period and determine it as attribute D.

[0120] When the first user plays the game, the game frequency is high, that is, attribute C is high, and the game duration is long, that is, attribute A is long. Moreover, the data tags of each game show that the game frame rate jitters greatly and the game is intense. Then it can be considered that the first user is a first type of player, where the first type of player is a player who often plays games and likes intense gameplay in each game;

[0121] When the second user plays the game, the game frequency is high, that is, attribute C is high, and the game duration is long, that is, attribute A is long. However, the data tags of each game show that the game frame rate is smooth and the game is not intense. Then it can be considered that the second user is a second type of player, where the second type of player is a player who often plays games, but likes to win by strategy and does not like intense gameplay;

[0122] When the third user plays the game, the game frequency is low, that is, attribute C is low, and the game duration is short, that is, attribute A is short. However, the data tags of each game show that the game frame rate jitters greatly and the game is intense. Then it can be considered that the third user is a third type of player, where the third type of player is a player who does not often play games, but likes intense gameplay in each game;

[0123] If the fourth user plays games with a low game frequency, i.e., attribute C is low, and a short game time, i.e., attribute A is short, and the data label of each game shows a smooth game frame rate and the game is not intense, then the fourth user can be considered as a fourth type of player. Among them, the fourth type of player is a player who does not often play games and does not like intense gameplay, belonging to a relatively Buddhist player.

[0124] For the processing method disclosed in this embodiment, if a running instruction of a target application is obtained, a first running environment is configured for the target application based on a first configuration parameter. In response to the running instruction, the target application is in a running state based on the first running environment. When the target application is in a running state based on the first running environment, the actual frame rate of the target task running process of the target application is obtained. The actual frame rate is the timing data within the time period of the 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 this solution, during the running process of the target application, the configuration parameters of the target application 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 during the running process, to ensure that the target application runs with configuration parameters adapted to the actual frame rate during the running process, so that the configuration parameters can be adapted to the actual operations of the user, and to avoid the phenomenon of increased power consumption or frame stuttering caused by the mismatch between the configuration parameters and the actual frame rate.

[0125] This embodiment discloses an electronic device, and its structural schematic diagram is as Figure 6 shown, including:

[0126] A display output component 61 and a processor 62.

[0127] Among them, the processor 62 is used to configure a first running environment for the target application based on the first configuration parameter when obtaining a running instruction of the target application; in response to the running instruction, the target application is in a running state based on the first running environment; when the target application is in a running state based on the first running environment, the actual frame rate of the target task running process of the target application is obtained. The actual frame rate is the timing data within the time period of the 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.

[0128] Further, the processor determines the second configuration parameter based on the actual frame rate, including: the processor processes the actual frame rate based on a machine learning classification model to obtain a data label of the actual frame rate, and the data label is used to characterize the intensity within the time period of the target task, and the second configuration parameter is determined based on the data label of the actual frame rate.

[0129] Among them, the machine learning classification model includes:

[0130] An eigenvalue acquisition module, configured to obtain eigenvalues in the actual frame rate;

[0131] A data processing module, configured to perform normalization processing on the eigenvalues;

[0132] A classification module, configured to determine data labels based on the eigenvalues after normalization processing.

[0133] Among them, the eigenvalue acquisition module includes a time series mining and analysis module. The time series mining and analysis module extracts eigenvalues from the real-time frame rate curve collected for each game, that is, Figure 3 The real-time situation of any one of the actual frame rates in any one of group a, group b, and group c is reflected. The actual frame rate during the operation of the target task is a set of data of real-time frame rates that are continuous in time (for example, the frame rate curve of a game). The eigenvalue acquisition module further includes an eigenvalue screening module. The eigenvalue screening module screens out eigenvalues from the above-mentioned set of data. These eigenvalues can effectively describe the change state of the frame rate curve and thus reflect the intensity of the user's operation. Among these eigenvalues, some focus on the continuous trough in the frame rate curve (most likely corresponding to the intense fighting scene in the game), and some focus on the overall fluctuation of the frame rate curve (corresponding to whether the interactive operations such as clicking on the screen in the game are frequent). That is, the extracted eigenvalues can reflect the user's operation habits to the greatest extent,

[0134] Among them, the data processing module performs normalization processing on the screened eigenvalues. Different from these conventional data normalization processing methods, this patent performs different data normalization processing on different eigenvalue data according to information such as game duration information and target frame rate. For example, data such as mean, maximum value, and minimum value need to be normalized in combination with the target frame rate, while data such as longest_strike and number_of_peaks_n need to be normalized in combination with the game duration and output a structured data set

[0135] Among them, the classification module includes a clustering analysis module. The clustering analysis module performs machine learning clustering analysis on the structured data of the normalized eigenvalues and outputs a structured data set with labels. The classification module further includes a classification model training module. The classification model training module receives the structured data set with labels and thus outputs classification labels. For example, the frame rate curve of a game obtains a classification label.

[0136] Further, the processor obtains the actual frame rate during the operation of the target task of the target application program, including:

[0137] The target task of the target application runs within the monitoring period, and the processor obtains the actual frame rate of the running process of the target task of the target application; wherein, within the monitoring period, it includes the actual frame rate obtained for each running process of the target task of the target application.

[0138] Further, the processor determines the second configuration parameter based on the actual frame rate, including:

[0139] The processor determines the second configuration parameter based on multiple data tags of the target application within the monitoring period.

[0140] Further, the processor is also used for:

[0141] Obtaining the usage duration of each running state of the target application based on the monitoring period; or, recording each running state of the target application based on the monitoring period.

[0142] Further, the processor determines the second configuration parameter based on the actual frame rate, including:

[0143] The processor determines the second configuration parameter based on multiple data tags, multiple usage durations, and usage times of the target application within the monitoring period.

[0144] Further, the processor determines the second configuration parameter based on the actual frame rate, including:

[0145] The processor determines a user profile based on multiple data tags, multiple usage durations, and usage times of the target application within the monitoring period; and determines the second configuration parameter based on the user profile, where different user profiles correspond to different second configuration parameters.

[0146] Further, the processor is also used for:

[0147] When the target application configures the second running environment based on the second configuration parameter and is in the running state, the curve of the actual frame rate of the running process of the target task of the target application is smoother than the curve of the actual frame rate of the running process of the target task of the target application when the target application is in the running state based on the first running environment.

[0148] The electronic device disclosed in this embodiment is implemented based on the processing method disclosed in the above embodiment, and details are not described herein again.

[0149] For the electronic device disclosed in this embodiment, if a running instruction of a target application is obtained, a first running environment is configured for the target application based on a first configuration parameter. In response to the running instruction, the target application is in a running state based on the first running environment. When the target application is in a running state based on the first running environment, the actual frame rate of the running process of the target task of the target application is obtained. The actual frame rate is timing data within the 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. In this solution, during the running process of the target application, the configuration parameter of the target application is adjusted based on the actual frame rate of the target task, so that the adjusted configuration parameter is related to the actual frame rate during the running process, to ensure that the target application runs with a configuration parameter adapted to the actual frame rate during the running process, so that the configuration parameter can be adapted to the actual operation of the user, and avoid the phenomenon of increased power consumption or frame freezing caused by the mismatch between the configuration parameter and the actual frame rate.

[0150] An embodiment of the present application further provides a readable storage medium, on which a computer program is stored. The computer program is loaded and executed by a processor to implement the steps of the above-mentioned processing method. The specific implementation process can refer to the description of the corresponding part of the above embodiment, and this embodiment will not be elaborated.

[0151] The present application also proposes a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are 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 methods provided in various optional implementation manners of the above-mentioned processing method aspect or processing system aspect. The specific implementation process can refer to the description of the corresponding embodiment above and will not be elaborated.

[0152] The various embodiments in this specification are described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the various embodiments can be referred 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 parts can be referred to the description of the method part.

[0153] Those skilled in the art may further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0154] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field.

[0155] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A processing method, the method comprising: If a running instruction of a target application is obtained, configuring a first running environment for the target application based on a first configuration parameter; In response to the running instruction, the target application is in a running state based on the first running environment; When the target application is in a running state based on the first running environment, obtaining an actual frame rate of a target task running process of the target application, generating a data label corresponding to the actual frame rate, the data label being one of a plurality of data labels for determining a user profile within a monitoring period, the actual frame rate being time-series data within a time period of the target task, and the actual frame rate being related to a user's operation mode; Determining the user profile of the user based on a plurality of data labels of the target application within the monitoring period for characterizing the intensity within a time period of the target task; Determining a second configuration parameter based on the user profile to implement adjusting the configuration parameter of the target application based on the user's operation, so that the configuration parameter of the target application is related to the user's operation, and the second configuration parameter is different from the first configuration parameter.

2. The method according to claim 1, further comprising: Processing the actual frame rate based on a machine learning classification model to obtain a data label of the actual frame rate, the data label being used to characterize the intensity within a time period of the target task; Determining the second configuration parameter based on the data label of the actual frame rate.

3. The method according to claim 2, wherein, The obtaining the actual frame rate of the target task running process of the target application includes: When the target task running of the target application is within a monitoring period, obtaining the actual frame rate of the target task running process of the target application; Wherein, within the monitoring period, it includes the actual frame rate obtained for each target task running process of the target application.

4. The method according to claim 3, further comprising: Determining the second configuration parameter based on a plurality of data labels of the target application within the monitoring period.

5. The method according to claim 3, wherein The method further comprises: Obtaining the usage duration of each time the target application is in a running state based on the monitoring period; or, Recording each time the target application is in a running state based on the monitoring period.

6. The method according to claim 5, further comprising: Determining the second configuration parameter based on a plurality of data labels, a plurality of usage durations, and usage times of the target application within the monitoring period.

7. The method according to claim 1, wherein, The method further comprises: When the target application configures a second running environment based on the second configuration parameter and is in a running state, obtaining that the curve of the actual frame rate of the target task running process of the target application is smoother than the curve of the actual frame rate of the target task running process of the target application when the target application is in a running state based on the first running environment.

8. An electronic device, the electronic device comprising: A display output component; A processor, the processor being configured to, if a running instruction of a target application is obtained, configure a first running environment for the target application based on a first configuration parameter; In response to the running instruction, the target application is in a running state based on the first running environment; When the target application is in a running state based on the first running environment, obtain the actual frame rate of the target task running process of the target application, generate a data label corresponding to the actual frame rate, the data label is one of multiple data labels used to determine the user profile within the monitoring period, the actual frame rate is the time-series data within the time period of the target task, and the actual frame rate is related to the user's operation method; determine the user profile of the user based on multiple data labels of the target application within the monitoring period that characterize the intensity within the time period of the target task; Determine the second configuration parameter based on the user profile; To achieve adjusting the configuration parameter of the target application based on the user's operation, so that the configuration parameter of the target application is related to the user's operation, and the second configuration parameter is different from the first configuration parameter.

9. The apparatus according to claim 8, wherein, The processor is used to determine the second configuration parameter based on the actual frame rate, and further includes: The processor is used to process the actual frame rate based on a machine learning classification model, obtain the data label of the actual frame rate, the data label is used to characterize the intensity within the time period of the target task; determine the second configuration parameter based on the data label of the actual frame rate; Wherein, the machine learning classification model includes: An eigenvalue acquisition module for obtaining the eigenvalues in the actual frame rate; A data processing module for performing normalization processing on the eigenvalues; A classification module for determining a data label based on the eigenvalues after normalization processing.

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