CN114974056A

CN114974056A - Method and device for adjusting screen refresh rate

Info

Publication number: CN114974056A
Application number: CN202110208663.2A
Authority: CN
Inventors: 罗子豪; 张木军; 陈建国; 郭津
Original assignee: Samsung Guangzhou Mobile R&D Center; Samsung Electronics Co Ltd
Current assignee: Samsung Guangzhou Mobile R&D Center; Samsung Electronics Co Ltd
Priority date: 2021-02-24
Filing date: 2021-02-24
Publication date: 2022-08-30
Also published as: WO2022182046A1

Abstract

A method and apparatus for adjusting a screen refresh rate are provided. The adjusting method comprises the following steps: detecting a detection parameter related to a screen refresh rate of the electronic device; determining an adjustment strategy for the current screen refresh rate based on the detection parameter when the detection parameter changes; adjusting a current screen refresh rate of the electronic device based on the adjustment policy. The present disclosure can intelligently adjust the screen refresh rate and effectively reduce power consumption and device heating.

Description

Method and device for adjusting screen refresh rate

Technical Field

The present disclosure relates to. More particularly, the present disclosure relates to a method and an apparatus for adjusting a screen refresh rate.

Background

At present, it is a popular trend to use a high screen refresh rate in an intelligent mobile terminal to obtain a better picture display effect. However, a high screen refresh rate increases work per unit time, providing better display effects, but also increases power consumption and heat generation of the terminal device.

Disclosure of Invention

Exemplary embodiments according to the present disclosure provide a method for adjusting a screen refresh rate and a device for adjusting a screen refresh rate to solve at least the above-mentioned problems.

According to an exemplary embodiment of the present disclosure, there is provided an adjustment method of a screen refresh rate, which may include: detecting a detection parameter related to a screen refresh rate of the electronic device; determining an adjustment strategy for the current screen refresh rate based on the detection parameter when the detection parameter changes; adjusting a current screen refresh rate of the electronic device based on the adjustment policy.

Optionally, the detection parameter may include at least one of a screen refresh rate, a display content frame rate, and a temperature of the electronic device.

Optionally, the adjustment policy may include one of adjusting up the preset screen refresh rate, adjusting down the preset screen refresh rate, and maintaining the screen refresh rate.

Optionally, the step of determining an adjustment policy for the current screen refresh rate may comprise: determining the adjustment strategy using an artificial intelligence model based on the detection parameters.

Optionally, the artificial intelligence model may be trained based on: taking the detection parameters as the input of the artificial intelligence model; calculating the maximum return value of the detection parameter under which adjustment strategy is obtained by utilizing a Q-Learning formula; and taking the adjustment strategy which obtains the maximum return as the output of the artificial intelligence model.

Optionally, the step of determining the adjustment strategy based on the detection parameters by using an artificial intelligence model may comprise: inputting the detection parameters to the artificial intelligence model and outputting an adjustment strategy by the artificial intelligence model.

Optionally, the artificial intelligence model may be a table structure model, wherein the table structure model may be obtained by: calculating a maximum return value of a sample detection parameter related to the screen refresh rate under which adjustment strategy is obtained by using a Q-Learning formula; and storing the sample detection parameters and the corresponding adjustment strategy for obtaining the maximum return in the table structure model.

Optionally, the step of determining the adjustment strategy using an artificial intelligence model based on the detection parameters may comprise: selecting a maximum reward adjustment strategy corresponding to the detection parameters by matching the detection parameters with parameters stored in the table structure model.

Optionally, the adjusting method is performed when the following conditions are satisfied: the ambient light brightness of the electronic device is greater than or equal to a first threshold; and/or the electronic equipment is in a non-information screen constant display state.

According to another exemplary embodiment of the present disclosure, there is provided an adjusting apparatus of a screen refresh rate, which may include: a detection module configured to detect a detection parameter of the electronic device related to a screen refresh rate; and a processing module configured to: determining an adjustment strategy for the current screen refresh rate based on the detection parameter when the detection parameter changes; adjusting a current screen refresh rate of the electronic device based on the adjustment policy.

Optionally, the processing module may be configured to determine the adjustment strategy using an artificial intelligence model based on the detection parameters.

Optionally, the artificial intelligence model may be trained based on: taking the detection parameters as the input of the artificial intelligence model; calculating the maximum return value of the detection parameter under which adjustment strategy by using a Q-Learning formula; and taking the adjustment strategy which obtains the maximum return as the output of the artificial intelligence model.

Optionally, the processing module may be configured to input the detection parameters to the artificial intelligence model and output an adjustment strategy by the artificial intelligence model.

Optionally, the artificial intelligence model may be a table structure model, wherein the table structure model is obtained by: calculating a maximum return value of a sample detection parameter related to the screen refresh rate under which adjustment strategy by using a Q-Learning formula; and storing the sample detection parameters and the corresponding adjustment strategy for obtaining the maximum return in the table structure model.

Optionally, the processing module may be configured to select the adjustment strategy with the largest return corresponding to the detection parameters by matching the detection parameters with parameters stored in the table structure model.

Optionally, the detection module may be configured to detect ambient brightness, resolution, and display state of the electronic device, wherein the processing module may determine to adjust a current screen refresh rate of the electronic device when the following conditions are satisfied: the ambient light brightness of the electronic device is greater than or equal to a first threshold; and/or the electronic equipment is in a non-information screen constant display state.

According to an exemplary embodiment of the present disclosure, there is provided a computer-readable storage medium having stored thereon instructions which, when executed by a processor, implement a method of adjusting a screen refresh rate according to an exemplary embodiment of the present disclosure.

According to an exemplary embodiment of the present disclosure, there is provided a computing apparatus including: a processor; a memory storing instructions that, when executed by the processor, implement a method of adjusting a screen refresh rate according to an exemplary embodiment of the present disclosure.

According to an exemplary embodiment of the present disclosure, a computer program product is provided, in which instructions are executed by at least one processor in an electronic device to perform the method of adjusting a screen refresh rate as described above.

According to the method and the device, the screen refresh rate of the electronic equipment is intelligently adjusted by detecting the state parameters (such as the current display content frame rate, the current temperature and the current screen refresh rate) of the electronic equipment and outputting the screen refresh rate which is most matched with the current scene by using the artificial intelligence model so as to use different application scenes, thereby achieving the balance of screen refresh rate performance, power consumption and heating and improving the user experience.

Additional aspects and/or advantages of the present general inventive concept will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the general inventive concept.

Drawings

These and/or other aspects and advantages of the present disclosure will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 illustrates a flowchart of an adjustment method of a screen refresh rate according to an exemplary embodiment of the present disclosure;

FIG. 2 illustrates a flowchart of a method of adjusting a screen refresh rate according to another exemplary embodiment of the present disclosure;

fig. 3 illustrates a flowchart of a method of adjusting a screen refresh rate according to an exemplary embodiment of the present disclosure;

fig. 4 illustrates a block diagram of an apparatus for adjusting a screen refresh rate according to an exemplary embodiment of the present disclosure;

FIG. 5 shows a schematic diagram of an electronic device according to an exemplary embodiment of the present disclosure;

fig. 6 shows a schematic diagram of a computing device according to an exemplary embodiment of the present disclosure.

Detailed Description

The following detailed description is provided to assist the reader in obtaining a thorough understanding of the methods, devices, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and/or systems described herein will be apparent to those skilled in the art upon reading the disclosure of the present application. For example, the order of operations described herein is merely an example, and is not limited to those set forth herein, but may be changed as will become apparent after understanding the disclosure of the present application, except to the extent that operations must occur in a particular order. Moreover, descriptions of features known in the art may be omitted for clarity and conciseness.

The features described herein may be embodied in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, devices, and/or systems described herein, which will be apparent after understanding the disclosure of the present application.

The terminology used herein is for the purpose of describing various examples only and is not intended to be limiting of the disclosure. The singular is also intended to include the plural unless the context clearly indicates otherwise. The terms "comprises," "comprising," and "having" specify the presence of stated features, quantities, operations, elements, components, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and/or combinations thereof.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in general dictionaries) should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal sense.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

Further, in the description of the examples, when it is considered that detailed description of well-known related structures or functions will cause a vague explanation of the present disclosure, such detailed description will be omitted.

In the related art, some smart terminals provide a screen refresh rate of a fixed gear in a display menu for a user to select, for example, 60Hz, 90Hz, and 120Hz gears. The user can manually select one of the gears, and after the user selects one gear, the terminal screen can work according to the refresh rate set by the user and cannot be changed. The disadvantage of this solution is that the terminal screen always operates at the set screen refresh rate regardless of whether the frame rate of the display content of the terminal is low or high. Therefore, power consumption is wasted when low frame rate content is displayed, and display performance cannot be achieved when high frame rate content is displayed, which results in poor user experience.

In addition, a part of intelligent terminals provide dynamic refresh rates in a display menu for users to select, and after the users select, the terminals can adapt to different screen refresh rates according to scenes. According to researches, the scheme is that dynamic adaptation of the screen refresh rate is achieved according to a black-and-white list and a fixed event drive, for example, the screen refresh rate is 60Hz when the mobile phone is set to be unchanged, the screen refresh rate is increased to 120Hz when a screen is touched or slid and lasts for a period of time (for example, 3 seconds), the mobile phone judges when an application is started, and adapts to different screen refresh rates by detecting the packet name of the application and then searching the black-and-white list preset in the mobile phone, for example, a video application is entered, the screen refresh rate is forcibly set to be 60Hz, a game application is entered, and the screen refresh rate is forcibly set to be 120 Hz. However, many applications have different content frame rates in different scenes, and the black-and-white list scheme cannot adjust the screen refresh rate in real time according to the frame rate of the displayed content.

In addition, a part of intelligent terminals dynamically match a screen refresh rate through a frame rate of display contents, display is carried out at 144Hz when a screen is slid so as to ensure smoothness, and the static pictures are instantly switched to 50 Hz. The disadvantage of this solution is that when the temperature of the terminal device is too high, the high frame rate content is running to increase the heat, and the instantaneous switch from 144Hz to 50Hz is not smooth enough, and the jump of the screen refresh rate will bring bad user experience, and there is no learning user habit, and it cannot match the balance point between the screen refresh rate and the temperature of each user.

The present disclosure provides a method for intelligently adjusting a screen refresh rate to balance performance, power consumption, and heat generation of a terminal of a high refresh rate screen, thereby improving user experience.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the embodiments may be implemented in various forms and are not limited to the examples described herein.

Fig. 1 illustrates a flowchart of an adjustment method of a screen refresh rate according to an exemplary embodiment of the present disclosure.

The adjusting method shown in fig. 1 may be executed by an electronic device. The electronic device may be any electronic apparatus such as, but not limited to, a portable communication device (e.g., a smartphone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, and the like. According to the embodiments of the present disclosure, the electronic device is not limited to the above.

Referring to fig. 1, in step S101, a detection parameter of an electronic device related to a screen refresh rate is detected. In the process of using the electronic device, different application programs, different use modes, different environmental temperatures and the like have different screen refresh rates, display content frame rates and temperatures, and therefore, the parameters can be detected to know whether the use scene of the current user is changed.

The detection parameter according to the present disclosure may include at least one of a screen refresh rate, a display content frame rate, and a temperature of the electronic device. Here, the temperature may be a temperature parameter such as an AP temperature, a device surface temperature, or a battery temperature, which may be variously defined according to device hardware and design. However, the above examples are merely exemplary, and the present disclosure is not limited thereto.

As an example, the current screen refresh rate of the electronic device may be obtained through an LCD interface of the electronic device, the current display content frame rate may be obtained through a Surface flicker interface, and the current temperature may be obtained through a temperature sensor (such as using a Surface temperature sensor to obtain the temperature of the Surface of the electronic device). However, the above examples are merely exemplary, and the present disclosure is not limited thereto.

In addition, the parameters of the electronic device may be recorded and collected while the above parameters are detected.

In step S102, in the case where the detection parameter changes, an adjustment policy for the current screen refresh rate is determined based on the detection parameter.

According to an embodiment of the present disclosure, the adjustment policy may be to adjust up the preset screen refresh rate, adjust down the preset screen refresh rate, or maintain the screen refresh rate.

As an example, an artificial intelligence model may be utilized to determine an adjustment strategy based on the detected parameters. For example, the artificial intelligence model may be implemented using a Q-Learning model. According to embodiments of the present disclosure, the artificial intelligence model may be trained online or may be trained offline. The artificial intelligence model may be trained based on: taking the detection parameters as the input of an artificial intelligence model; calculating the maximum return value of the detection parameter under which adjustment strategy by using a Q-Learning formula; and taking the adjustment strategy which obtains the maximum return as the output of the artificial intelligence model.

Specifically, the artificial intelligence model may be initialized first. For example, a four-dimensional table is defined, and coordinates are Temp (for temperature), FPS (for display content frame rate), SRR (for screen refresh rate), and Action (for adjustment policy), respectively. The threshold temperature T is set according to the experimental data, and one maximum reward value is set at initialization for each cell corresponding to the threshold temperature T (i.e., the display content frame rate, the screen refresh rate, and the adjustment policy at which the maximum reward value is obtained) as shown below.

Temp

FPS

SRR

Action

For example, in the case where the threshold temperature is 36 degrees, the display content frame rate is 60Hz and the screen refresh rate is 96Hz, and the policy is adjusted to maintain the current screen refresh rate, at which time the maximum reward value is obtained. As another example, the adjustment strategy at the threshold temperature T to maintain the current screen refresh rate is initialized to the maximum reward value, while the remaining elements in the table are all null values, and the reward value is calculated by Q-learning equation.

When a change in a parameter of the electronic device is detected, the detected parameter can be used as an input parameter of the artificial intelligence model, and a reward value of the current input parameter is calculated according to Q-learning equation (1) as shown below:

Q(S,A)＝(1-α)Q(S,A)+α[R(S,a)+γMaxQ(S',a)] (1)

where Q (S, a) represents a historical reported value, R (S, a) represents a current reported value, MaxQ (S ', a) represents an empirical reported value, S is a current state, a is a selected action, S' is a state after performing the selected action, α is a learning rate, and γ is a discount factor. In the present disclosure, a is set to 0.5 and will be set to 0.8 by empirical and/or experimental data. However, the above examples are merely exemplary, and the present disclosure is not limited thereto.

For example, assuming that the current Temp is 36 degrees, the FPS is changed from 60Hz to 96Hz, and learning is performed using equation (1) and the adjacent states according to the above-described initialization result. Through learning calculation, under the condition that Temp is 36 degrees and FPS is 96Hz, the screen refresh rate is 120Hz, and the maximum report value exists, so that the next action can select to excessively increase the screen refresh rate to 120Hz, and if the current SRR is lower than 120, the SRR gear can be increased, for example, the SRR is increased from 96Hz to 120 Hz; if the current SRR is equal to 120, then the current SRR range may be maintained, e.g., SRR is maintained at 120 Hz.

After calculating a reward value according to the sample data by using equation (1), the detection parameters and the corresponding reward value are filled in the initialized four-dimensional table. Therefore, the training artificial intelligence model can be updated according to the calculation result (the return value).

As another example, the artificial intelligence model can be a table structure model, wherein the table structure model can be derived by: calculating a maximum return value of a sample detection parameter related to the screen refresh rate under which adjustment strategy by using a Q-Learning formula; and storing the sample detection parameters and the corresponding adjustment strategy for obtaining the maximum return in the table structure model.

After the artificial intelligence model is initialized, sample data (including sample SRR data, sample FPS data and sample Temp data) can be used as input parameters of the artificial intelligence model to train the artificial intelligence model. The training process may obtain a trained table structure model as described above using equation (1) to calculate the maximum return value. After the artificial intelligence model is trained, the adjustment strategy of the maximum return corresponding to the detection parameters can be selected by matching the detection parameters with the parameters stored in the table structure model.

It is possible to search from the artificial intelligence model which adjustment strategy (such as up-adjustment of the screen refresh rate, maintenance of the screen refresh rate, or down-adjustment of the screen refresh rate) is to be adopted in a state where the temperature is 36 degrees, the display content frame rate is 96Hz, and the screen refresh rate is 96Hz, and then the artificial intelligence model outputs the adjustment strategy that can obtain the maximum reward value. For example, according to the method for adjusting the screen refresh rate up by one gear under the condition that the temperature is 36 degrees, the frame rate of the display content is 96Hz, and the screen refresh rate is 96Hz, which is obtained from the artificial intelligence model table lookup, the return value of the screen refresh rate up-adjusted by one gear is larger than the return value of the current screen refresh rate and is also larger than the return value of the screen refresh rate down-adjusted by one gear, therefore, the artificial intelligence model can output the adjustment strategy of up-adjusted by one gear. However, the above examples are merely exemplary, and the present disclosure is not limited thereto.

In step S103, the current screen refresh rate of the electronic device is adjusted based on the adjustment policy. Generally, screen refresh rates of existing electronic devices are set with different gears, for example, 36Hz, 60Hz, 96Hz, and 120 Hz. However, the above examples are merely exemplary, and the present disclosure is not limited thereto. Assuming that the current screen refresh rate is 60Hz, the screen refresh rate may be adjusted up from 60 to 96Hz when the adjustment strategy is to be adjusted up by one gear, and may be adjusted up to 120Hz when the adjustment strategy is to be adjusted up by two gears. However, the above examples are merely exemplary, and the present disclosure is not limited thereto.

The electronic equipment changes the current screen refresh rate according to the output of the artificial intelligence model, thereby achieving the purpose of dynamically and intelligently adjusting the screen refresh rate.

Further, according to an embodiment of the present disclosure, the above-described adjustment method of the screen refresh rate may be performed when the following conditions are satisfied: the ambient light brightness of the electronic device is greater than or equal to a first threshold; and/or the electronic equipment is in a non-information screen constant display state. In other words, if the ambient light brightness of the electronic device is lower than the first threshold, the above adjustment method may not be performed. If the electronic device is in the off-screen always-on state AOD, the above adjustment method may not be performed. This is because the refresh rate in the AOD scene is low, and therefore the lowest refresh rate may be used. Furthermore, if the highest resolution does not support the high refresh rate mode, the above adjustment method may not be performed. However, the above conditions are merely exemplary and may be differently set according to user settings or electronic device configurations.

And adjusting the screen refresh rate according to an adjusting strategy output by the artificial intelligence model to realize the balance among the display performance, the equipment heating and the equipment power consumption.

Fig. 2 illustrates a flowchart of an adjustment method of a screen refresh rate according to another exemplary embodiment of the present disclosure. Fig. 3 illustrates a flowchart of a method for adjusting a screen refresh rate according to an exemplary embodiment of the present disclosure.

Referring to fig. 2 and 3, in step S201, state parameters of the electronic device, such as a screen refresh rate, a display content frame rate, and a temperature, are detected.

In step S202, it is determined whether the detected state parameter has changed. If at least one of the detected state parameters changes, step S203 is entered, otherwise, the state parameters are continuously detected.

In step S203, the detected state parameters may be preprocessed. For example, the detected state parameters are recorded and collected.

According to an embodiment of the present disclosure, the output of the artificial intelligence model (AI model) may be obtained while training the AI model on line (e.g., step S204 and step S206). Alternatively, the detected state parameters may be input to a trained artificial intelligence model to output an adjustment strategy (e.g., steps S205 and S207).

In step S204, the artificial intelligence model is trained using the detected state parameters as input parameters of the artificial intelligence model. The artificial intelligence model according to the present disclosure is a Q-Learning model. For example, equation (1) above is used to calculate under which adjustment strategy the detected state parameter achieves the maximum reward value. Thereafter, at step S206, the artificial intelligence model is updated by populating the artificial model with these state parameters and corresponding reward values.

In step S205, prediction is performed using an artificial intelligence model based on the detected state parameters. An artificial intelligence model according to the present disclosure is a table structure model. Each parameter of the sample detection parameter, under which adjustment strategy the maximum return value is obtained, is stored in the table structure model. In step S207, the detected state parameters are matched with the parameters stored in the table structure model to output an appropriate adjustment strategy.

In step S208, the electronic device obtains an output of the artificial intelligence model, i.e., the adjustment strategy.

In step S209, it is determined whether the current screen refresh rate needs to be changed according to the adjustment policy. If the current screen refresh rate needs to be changed, step S210 is performed, a new screen refresh rate is set according to the adjustment policy, and if not, the current screen refresh rate is maintained, and the state parameters of the electronic device are continuously detected.

According to an embodiment of the present disclosure, in the early stage, the prediction process and the training process of the artificial intelligence model may be performed simultaneously. After the artificial intelligence model training is mature in the later period, only the prediction process of the artificial intelligence model can be carried out, and the training process is not carried out.

Fig. 4 illustrates a block diagram of an apparatus for adjusting a screen refresh rate according to an exemplary embodiment of the present disclosure. Referring to fig. 4, the adjusting apparatus 400 may include a detection module 401 and a processing module 402. Each module in the adjusting apparatus 400 may be implemented by one or more modules, and names of the corresponding modules may vary according to types of the modules. In various embodiments, some modules in the adjustment apparatus 400 may be omitted, or additional modules may also be included. Furthermore, modules/elements according to various embodiments of the present disclosure may be combined to form a single entity, and thus the functions of the respective modules/elements may be equivalently performed prior to the combination.

The detection module 401 may be configured to detect a detection parameter of the electronic device related to a screen refresh rate. Here, the detection parameter may include at least one of a screen refresh rate, a display content frame rate, and a temperature of the electronic device.

The processing module 402 may be configured to determine an adjustment policy for the current screen refresh rate based on the detection parameter if the detection parameter changes, and adjust the current screen refresh rate of the electronic device based on the adjustment policy. Here, the adjustment policy may include one of up-adjusting the preset screen refresh rate, down-adjusting the preset screen refresh rate, and maintaining the screen refresh rate.

The processing module 402 may be configured to determine an adjustment strategy using an artificial intelligence model based on the detected parameters. Here, the artificial intelligence model may be a Q-Learning model. For example, the processing module 402 may input detection parameters to the artificial intelligence model and output adjustment policies by the artificial intelligence model.

Optionally, the artificial intelligence model may be trained based on: taking the detection parameters as the input of an artificial intelligence model; calculating the maximum return value of the detection parameter under which adjustment strategy by using a Q-Learning formula; and taking the adjustment strategy which obtains the maximum return as the output of the artificial intelligence model.

Optionally, the artificial intelligence model may be a table structure model, wherein the table structure model is obtained by: calculating a maximum return value of a sample detection parameter related to the screen refresh rate under which adjustment strategy is obtained by using a Q-Learning formula; and storing the sample detection parameters and the corresponding adjustment strategy for obtaining the maximum return in the table structure model.

Where the artificial intelligence model is a table structure model, the processing module 402 may be configured to select the adjustment strategy that corresponds to the greatest return on the detected parameters by matching the detected parameters to parameters stored in the table structure model.

Alternatively, the detection module 401 may be configured to detect the ambient brightness, resolution, and display state of the electronic device. The processing module 402 may determine to adjust the current screen refresh rate of the electronic device when the following conditions are met: the ambient light brightness of the electronic device is greater than or equal to a first threshold; and/or the electronic equipment is in a non-information screen constant display state. However, the above examples are merely exemplary, and the present disclosure is not limited thereto.

At least one of the plurality of modules may be implemented by an AI model. The functions associated with the AI may be performed by the non-volatile memory, the volatile memory, and the processor.

The processor may include one or more processors. At this time, the one or more processors may be general-purpose processors such as a Central Processing Unit (CPU), an Application Processor (AP), etc., graphics-only processors such as a Graphics Processor (GPU), a Visual Processor (VPU), and/or an AI-specific processor such as a Neural Processing Unit (NPU).

The one or more processors control the processing of the input data according to predefined operating rules or Artificial Intelligence (AI) models stored in the non-volatile memory and the volatile memory. Predefined operating rules or artificial intelligence models may be provided through training or learning. Here, the provision by learning means that a predefined operation rule or AI model having a desired characteristic is formed by applying a learning algorithm to a plurality of learning data. The learning may be performed in the device itself performing the AI according to the embodiment, and/or may be implemented by a separate server/device/system.

As an example, the artificial intelligence model may be composed of multiple neural network layers. Each layer has a plurality of weight values, and a layer operation is performed by calculation of a previous layer and operation of the plurality of weight values. Examples of neural networks include, but are not limited to, Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), generative countermeasure networks (GANs), and deep Q networks.

A learning algorithm is a method of training a predetermined target device (e.g., a robot) using a plurality of learning data to cause, allow, or control the target device to make a determination or prediction. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.

As shown in fig. 5, the electronic device 500 may include: processing component 501, communication bus 502, network interface 503, input output interface 504, memory 505, and power components 506, and sensors (not shown). Wherein a communication bus 502 is used to enable connective communication between these components. The input-output interface 504 may include a video display (such as a liquid crystal display), a microphone and speakers, and a user-interaction interface (such as a keyboard, mouse, touch-input device, etc.), and optionally, the input-output interface 504 may also include a standard wired interface, a wireless interface. The network interface 503 may optionally include a standard wired interface, a wireless interface (e.g., a wireless fidelity interface). The memory 505 may be a high speed random access memory or may be a stable non-volatile memory. The memory 505 may alternatively be a storage device separate from the processing component 501 described previously. The sensors are used to sense, for example, the surface temperature of the electronic device, the display content frame rate, the screen refresh rate, and the like. The above examples are merely illustrative, and the present disclosure is not limited thereto.

Those skilled in the art will appreciate that the configuration shown in fig. 5 does not constitute a limitation of the electronic device 500, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.

As shown in fig. 5, the memory 505, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, a screen refresh rate adjustment program, and a database.

In the electronic device 500 shown in fig. 5, the network interface 503 is mainly used for data communication with an external device/terminal; the input/output interface 504 is mainly used for data interaction with a user; the processing component 501 and the memory 505 in the electronic device 500 may be disposed in the electronic device 500, and the electronic device 500 executes the method for adjusting the screen refresh rate provided by the embodiment of the disclosure by calling the program for adjusting the screen refresh rate stored in the memory 505 through the processing component 501.

The processing component 501 may include at least one processor, and the memory 505 stores a computer executable instruction set, when the computer executable instruction set is executed by the at least one processor, the method for adjusting the screen refresh rate according to the embodiment of the present disclosure is performed. Further, the processing component 501 may perform encoding operations and decoding operations, among others. However, the above examples are merely exemplary, and the present disclosure is not limited thereto.

The sensor may detect a detection parameter of the electronic device related to a screen refresh rate, such as at least one of a screen refresh rate, a display content frame rate, and a temperature of the electronic device.

In the event that a change in the detection parameter occurs, processing component 501 may determine an adjustment policy for the current screen refresh rate based on the detected detection parameter. For example, the processing component 501 may adjust the preset screen refresh rate upward, adjust the preset screen refresh rate downward, or maintain the screen refresh rate.

As an alternative embodiment, processing component 501 may utilize an artificial intelligence model to determine an adjustment strategy for the screen refresh rate based on the detected detection parameters.

The processing component 501 may train the artificial intelligence model or the electronic device 500 may receive the trained artificial intelligence model from an external electronic device, and the processing component 501 may use the trained artificial intelligence model to obtain the adjustment strategy.

As an alternative embodiment, the processing component 501 may input the detection parameters detected by the sensors to the artificial intelligence model and output the adjustment strategy by the artificial intelligence model.

As an alternative embodiment, the processing component 501 may use the detection parameters detected by the sensors as the input of the artificial intelligence model, calculate, by using the Q-Learning formula, the adjustment strategy under which the detection parameters obtain the maximum reward value, and then use the adjustment strategy that obtains the maximum reward as the output of the artificial intelligence model.

As an alternative embodiment, the artificial intelligence model is a table structure model, wherein the table structure model is obtained by: calculating a maximum return value of a sample detection parameter related to the screen refresh rate under which adjustment strategy by using a Q-Learning formula; and storing the sample detection parameters and the corresponding adjustment strategy for obtaining the maximum return in the table structure model.

The processing component 501 may select the adjustment strategy with the largest return corresponding to the detected parameters by matching the detected parameters with the parameters stored in the table structure model.

As an alternative embodiment, processing component 501 may determine to adjust the current screen refresh rate of the electronic device when the following conditions are met: the ambient light brightness of the electronic device is greater than or equal to a first threshold; and/or the electronic equipment is in an off-screen normally-displayed state, otherwise, even if the detection parameters are changed, the adjustment of the screen refresh rate can not be executed.

By way of example, the electronic device 500 may be a PC computer, tablet device, personal digital assistant, smartphone, or other device capable of executing the set of instructions described above. Here, the electronic device 500 need not be a single electronic device, but can be any collection of devices or circuits that can execute the above instructions (or sets of instructions) individually or in combination. The electronic device 500 may also be part of an integrated control system or system manager, or may be configured as a portable electronic device that interfaces with local or remote (e.g., via wireless transmission).

In the electronic device 500, the processing component 501 may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a programmable logic device, a special-purpose processor system, a microcontroller, or a microprocessor. By way of example, and not limitation, processing component 501 may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, and the like.

The processing component 501 may execute instructions or code stored in a memory, wherein the memory 505 may also store data. Instructions and data may also be sent and received over a network via the network interface 503, where the network interface 503 may employ any known transmission protocol.

The memory 505 may be integral to the processor, e.g., having RAM or flash memory disposed within an integrated circuit microprocessor or the like. Further, memory 505 may comprise a stand-alone device, such as an external disk drive, storage array, or any other storage device that may be used by a database system. The memory and the processor may be operatively coupled or may communicate with each other, e.g., through an I/O port, a network connection, etc., such that the processor can read files stored in the memory.

Referring to fig. 6, a computing apparatus 600 according to an exemplary embodiment of the present disclosure includes a memory 601 and a processor 602, the memory 601 having stored thereon a computer program that, when executed by the processor 602, implements a method of adjusting a screen refresh rate according to an exemplary embodiment of the present disclosure.

As an example, the computer program, when executed by the processor 602, may implement the steps of: detecting a detection parameter related to a screen refresh rate of the electronic device; determining an adjustment strategy for the current screen refresh rate based on the detection parameter when the detection parameter changes; adjusting a current screen refresh rate of the electronic device based on the adjustment policy.

The computing devices in the embodiments of the present disclosure may include, but are not limited to, devices such as mobile phones, notebook computers, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, and the like. The computing device shown in fig. 6 is only one example and should not bring any limitations to the functionality or scope of use of the embodiments of the present disclosure.

As used herein, the term "module" may include units implemented in hardware, software, or firmware, and may be used interchangeably with other terms (e.g., "logic," "logic block," "portion," or "circuitry"). A module may be a single integrated component adapted to perform one or more functions or a minimal unit or portion of the single integrated component. For example, according to an embodiment, the modules may be implemented in the form of Application Specific Integrated Circuits (ASICs).

The various embodiments set forth herein may be implemented as software including one or more instructions stored in a storage medium readable by a machine (e.g., a mobile device). For example, under control of a processor, the processor of the machine may invoke and execute at least one of the one or more instructions stored in the storage medium with or without the use of one or more other components. This enables the machine to be operable to perform at least one function in accordance with the invoked at least one instruction. The one or more instructions may include code generated by a compiler or code capable of being executed by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Where the term "non-transitory" simply means that the storage medium is a tangible device and does not include a signal (e.g., an electromagnetic wave), the term does not distinguish between data being semi-permanently stored in the storage medium and data being temporarily stored in the storage medium.

According to embodiments, methods according to various embodiments of the present disclosure may be included and provided in a computer program product. The computer program product may be used as a product for conducting a transaction between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium, such as a compact disc read only memory (CD-ROM), or may be distributed (e.g., downloaded or uploaded) online via an application store (e.g., a Play store), or may be distributed (e.g., downloaded or uploaded) directly between two user devices (e.g., smartphones). At least part of the computer program product may be temporarily generated if it is published online, or at least part of the computer program product may be at least temporarily stored in a machine readable storage medium, such as a memory of a manufacturer's server, a server of an application store, or a forwarding server.

According to various embodiments, each of the above components (e.g., modules or programs) may comprise a single entity or multiple entities (e.g., in fig. 6, memory 601 may comprise one or more memories and processor 602 may comprise one or more processors). According to various embodiments, one or more of the above-described components may be omitted, or one or more other components may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into a single component. In such a case, according to various embodiments, the integrated component may still perform one or more functions of each of the plurality of components in the same or similar manner as the corresponding one of the plurality of components performed the one or more functions prior to integration. Operations performed by a module, program, or another component may be performed sequentially, in parallel, repeatedly, or in a heuristic manner, or one or more of the operations may be performed in a different order or omitted, or one or more other operations may be added, in accordance with various embodiments.

According to the embodiment of the disclosure, by detecting the state parameters (such as the current display content frame rate, the current temperature and the current screen refresh rate) of the electronic device, the screen refresh rate which is most matched with the current scene is output by using the artificial intelligence model to intelligently adjust the screen refresh rate of the electronic device so as to use different application scenes, thereby achieving the balance of screen refresh rate performance, power consumption and heating, and improving user experience.

While the present disclosure has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the following claims.

Claims

1. A method for adjusting a screen refresh rate, the method comprising:

detecting a detection parameter related to a screen refresh rate of the electronic device;

determining an adjustment strategy for the current screen refresh rate based on the detection parameter when the detection parameter changes;

adjusting a current screen refresh rate of the electronic device based on the adjustment policy.

2. The adjustment method according to claim 1, wherein the detection parameter includes at least one of a screen refresh rate, a display content frame rate, and a temperature of the electronic device.

3. The adjustment method of claim 1, wherein the adjustment strategy comprises one of up-adjusting the preset screen refresh rate, down-adjusting the preset screen refresh rate, and maintaining the screen refresh rate.

4. The adjustment method of claim 1, wherein the step of determining an adjustment policy for the current screen refresh rate comprises:

determining the adjustment strategy using an artificial intelligence model based on the detection parameters.

5. The adaptation method of claim 4, wherein the artificial intelligence model is trained based on:

taking the detection parameters as the input of the artificial intelligence model;

calculating the maximum return value of the detection parameter under which adjustment strategy by using a Q-Learning formula;

and taking the adjustment strategy which obtains the maximum return as the output of the artificial intelligence model.

6. The adaptation method according to claim 4 or 5, wherein the step of determining the adaptation strategy using an artificial intelligence model based on the detection parameters comprises:

inputting the detection parameters to the artificial intelligence model and outputting an adjustment strategy by the artificial intelligence model.

7. The adaptation method of claim 4, wherein the artificial intelligence model is a table structure model,

wherein the table structure model is obtained by:

calculating a maximum return value of a sample detection parameter related to the screen refresh rate under which adjustment strategy by using a Q-Learning formula;

and storing the sample detection parameters and the corresponding adjustment strategy for obtaining the maximum return in the table structure model.

8. The adaptation method according to claim 4 or 7, wherein the step of determining the adaptation strategy using an artificial intelligence model based on the detection parameters comprises:

selecting a maximum reward adjustment strategy corresponding to the detection parameters by matching the detection parameters with parameters stored in the table structure model.

9. The adjustment method according to claim 1, characterized in that the adjustment method is performed when the following conditions are satisfied:

the ambient light brightness of the electronic device is greater than or equal to a first threshold; and/or

The electronic equipment is in the non-information screen constant display state.

10. An apparatus for adjusting a screen refresh rate, the apparatus comprising:

a detection module configured to detect a detection parameter of the electronic device related to a screen refresh rate;

a processing module configured to:

11. The adjustment apparatus of claim 10, wherein the detection parameter comprises at least one of a screen refresh rate, a display content frame rate, and a temperature of the electronic device.

12. The adjustment apparatus of claim 10, wherein the adjustment strategy comprises one of adjusting up a preset screen refresh rate, adjusting down a preset screen refresh rate, and maintaining a screen refresh rate.

13. The adjustment apparatus of claim 10, wherein the processing module is configured to determine the adjustment strategy using an artificial intelligence model based on the detected parameters.

14. The adjustment apparatus of claim 13, wherein the artificial intelligence model is trained based on:

15. The adaptation device of claim 13 or 14, wherein a processing module is configured to input the detection parameters to the artificial intelligence model and to output an adaptation strategy by the artificial intelligence model.

16. The tuning rig in accordance with claim 13, wherein the artificial intelligence model is a table structure model,

wherein the table structure model is obtained by:

calculating a maximum return value of a sample detection parameter related to the screen refresh rate under which adjustment strategy is obtained by using a Q-Learning formula;

17. The adaptation device according to claim 13 or 16, wherein the processing module is configured to select the adaptation strategy with the largest reward corresponding to the detection parameters by matching the detection parameters with parameters stored in the table structure model.

18. The adjustment apparatus of claim 10, wherein the detection module is configured to detect an ambient brightness, a resolution, and a display state of the electronic device,

wherein the processing module determines to adjust a current screen refresh rate of the electronic device when the following conditions are satisfied:

19. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method of adjusting the screen refresh rate of any one of claims 1 to 9.

20. A computing device, comprising:

a processor;

a memory storing a computer program which, when executed by the processor, implements the screen refresh rate adjustment method of any one of claims 1 to 9.