Display method and device, electronic equipment and medium

Abstract

The invention relates to a display method and device, electronic equipment and a medium, in the use process of the electronic equipment, the brightness of a screen of the electronic equipment is automatically adjusted based on a preset adjustment model pre-stored in the electronic equipment, and multiple groups of brightness adjustment parameter sets are collected; when the multiple groups of brightness adjustment parameter sets meet a first condition, training a brightness adjustment model of a screen of the electronic equipment based on the multiple groups of brightness adjustment parameter sets; and adjusting the brightness of the screen based on the trained brightness adjustment model. Since the brightness adjustment parameter set is the historical use data of the user, the process of automatically adjusting the brightness of the screen by using the trained brightness adjustment model is more in line with the personalized demand of the user, the adjusted brightness of the screen conforms to the use habit of the user, and the visual experience of the user is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of electronic devices, and in particular to a display method, device, electronic device and medium. Background Art

[0002] With the rapid development of electronic technology, electronic devices with display functions such as smart phones, tablet computers, and laptops are widely used in daily life. At present, most electronic devices have a screen brightness adjustment function to automatically adjust the brightness of the display screen according to environmental changes to avoid the display screen brightness not matching the ambient light, causing user eye discomfort.

[0003] At present, the brightness of the screen is mostly adjusted according to the brightness curve pre-stored in the electronic device. The brightness curve represents the mapping relationship between the illuminance value and the brightness value of the ambient light. The illuminance value and the brightness value of the ambient light are both monotonically increasing. That is, the illuminance value of the ambient light is collected by the light sensor, and the brightness value corresponding to the collected illuminance value of the ambient light is found based on the brightness curve, and then the display brightness of the display is adjusted according to the brightness value.

[0004] Although this method is simple and easy, due to individual differences among users, under the same ambient light, when the screen brightness is adjusted according to the same brightness curve, some users may feel that the brightness is too dim, while some users may feel that the brightness is too bright. This does not conform to the user's preferences, cannot meet the user's personalized visual needs, and affects the user's usage experience. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the present disclosure provides a display method, device, electronic device and medium.

[0006] According to a first aspect of an embodiment of the present disclosure, a display method is provided, which is applied to an electronic device, and the display method includes:

[0007] During use of the electronic device, the brightness of the screen of the electronic device is automatically adjusted based on a preset adjustment model pre-stored in the electronic device, and multiple sets of brightness adjustment parameter sets are collected, wherein each set of the brightness adjustment parameter sets includes a brightness setting value, an environmental feature, and a preset feature of the electronic device;

[0008] When the multiple sets of brightness adjustment parameter sets meet a first condition, training a brightness adjustment model of the screen of the electronic device based on the multiple sets of brightness adjustment parameter sets;

[0009] Based on the trained brightness adjustment model, the brightness of the screen is adjusted.

[0010] In some exemplary embodiments of the present disclosure, the step of training a brightness adjustment model for the screen of the electronic device based on the multiple sets of brightness adjustment parameter sets includes:

[0011] Selecting part of the brightness adjustment parameter sets from the multiple groups of brightness adjustment parameter sets as a first sample set, and training a first prediction model and a second prediction model based on the first sample set respectively;

[0012] Selecting part of the brightness adjustment parameter sets from the multiple groups of brightness adjustment parameter sets as second sample sets, and training the fusion model based on the second sample sets, the first predicted brightness output by the trained first prediction model, and the second predicted brightness output by the trained second prediction model;

[0013] Based on the trained fusion model, a first weight parameter of the first prediction model and a second weight parameter of the second prediction model are determined.

[0014] In some exemplary embodiments of the present disclosure, automatically adjusting the brightness of the screen based on the trained brightness adjustment model includes:

[0015] Acquire target environment characteristics and target preset characteristics in the current state of the electronic device;

[0016] The target environment feature and the target preset feature are used as input parameters, respectively input into the first prediction model and the second prediction model, to obtain the first predicted brightness and the second predicted brightness in the current state;

[0017] Obtaining a target brightness prediction value based on the first predicted brightness, the second predicted brightness, the first weight parameter, and the second weight parameter;

[0018] Based on the target brightness prediction value, the brightness of the screen in the current state is adjusted.

[0019] In some exemplary embodiments of the present disclosure, the environmental features include ambient light and ambient light intervals; the preset features include a first brightness, a first brightness interval, a first application name and display mode, a first screen display parameter, and a first application code, wherein the display mode in the first application name and display mode is that when the screen brightness value is set, the application displayed on the screen is displayed in landscape or portrait mode; the first application code is used to indicate the application being displayed on the screen;

[0020] and / or,

[0021] The target environment characteristics include target ambient light and target ambient light range; the target preset characteristics include a second brightness, a second brightness range, a second application name and display mode, a second screen display parameter and a second application code, wherein the display mode in the second application name and display mode is whether the application displayed on the screen in the current state is horizontal or vertical display; the second application code is used to indicate the application being displayed on the screen in the current state.

[0022] In some exemplary embodiments of the present disclosure, the display method further includes:

[0023] Testing the preset adjustment model and the trained brightness adjustment model respectively;

[0024] When the error value of the first test result output by the brightness adjustment model is smaller than the error value of the second test result output by the preset adjustment model, disabling the preset adjustment model and enabling the brightness adjustment model;

[0025] When the error value of the first test result output by the brightness adjustment model is greater than or equal to the error value of the second test result output by the preset adjustment model, the brightness adjustment model is disabled.

[0026] In some exemplary embodiments of the present disclosure, the screen is turned off for a period of time greater than or equal to a preset period of time, and the illuminance value of the ambient light in the environment where the electronic device is located after the screen is turned on is not within a preset illuminance range, and the brightness adjustment model is enabled, wherein the preset illuminance range is related to the illuminance value of the ambient light in the environment where the electronic device is located before the screen is turned off.

[0027] In some exemplary embodiments of the present disclosure, the display method further includes:

[0028] If the brightness adjustment model is in an enabled state, based on an instruction to set the brightness of the screen, the brightness adjustment model is disabled.

[0029] In some exemplary embodiments of the present disclosure, the display method further includes:

[0030] If the brightness adjustment model has not been trained, based on the setting instruction for the brightness of the screen, the brightness setting value indicated by the setting instruction, the environmental characteristics when the setting instruction is received, and the preset characteristics are collected as the brightness adjustment parameter set and stored.

[0031] In some exemplary embodiments of the present disclosure, the display method further includes:

[0032] If the brightness adjustment model is in a disabled state, the brightness adjustment model is retrained based on an instruction to set the brightness of the screen.

[0033] In some exemplary embodiments of the present disclosure, the display method further includes:

[0034] Based on a reset instruction of the automatic brightness adjustment function, the brightness adjustment model is disabled.

[0035] According to a second aspect of an embodiment of the present disclosure, a display device is provided, which is applied to an electronic device, and the display device includes:

[0036] A collection module, used for automatically adjusting the brightness of the screen of the electronic device based on a preset adjustment model pre-stored in the electronic device during use of the electronic device, and collecting a plurality of sets of brightness adjustment parameter sets, wherein the brightness adjustment parameter sets include brightness setting values, environmental characteristics, and preset characteristics of the electronic device;

[0037] A training module, configured to train a brightness adjustment model of the screen of the electronic device based on the multiple sets of brightness adjustment parameter sets when the multiple sets of brightness adjustment parameter sets meet a first condition;

[0038] The adjustment module is used to adjust the brightness of the screen based on the trained brightness adjustment model.

[0039] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, the electronic device comprising:

[0040] Screen;

[0041] processor;

[0042] a memory for storing processor-executable instructions;

[0043] Among them, the processor is configured to execute executable instructions in the memory to implement the display method provided in the first aspect of the present disclosure.

[0044] According to a fourth aspect of an embodiment of the present disclosure, a non-temporary computer-readable storage medium is provided, on which executable instructions are stored. When the executable instructions are executed by a processor, the display method provided by the first aspect of the present disclosure is implemented.

[0045] The above method disclosed in the present invention has the following beneficial effects: the electronic device automatically adjusts the brightness of the screen of the electronic device based on a pre-stored preset adjustment model, and collects multiple sets of brightness adjustment parameter sets. Since the brightness adjustment parameter sets are all historical usage data of the user, the process of automatically adjusting the brightness of the screen using the trained brightness adjustment model is more in line with the user's personalized needs, and the adjusted screen brightness is in line with the user's usage habits, thereby improving the user's visual experience.

[0046] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0048] Figure 1 The figure is a flowchart of a display method according to an exemplary embodiment.

[0049] Figure 2 The figure is a flowchart of a display method according to an exemplary embodiment.

[0050] Figure 3 The figure is a flowchart of a display method according to an exemplary embodiment.

[0051] Figure 4 It is a schematic diagram of an application scenario according to an exemplary embodiment.

[0052] Figure 5 is a block diagram of a display device according to an exemplary embodiment.

[0053] Figure 6 is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0054] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0055] The present disclosure provides a display method, in which an electronic device automatically adjusts the brightness of a screen of the electronic device based on a pre-stored preset adjustment model, and in the process of a user using the electronic device, when a user applies an instruction to adjust the brightness of the screen on the electronic device, multiple preset features involved and brightness values ​​set by the user are collected to form multiple groups of brightness adjustment parameter sets. Since the brightness adjustment parameter sets are all historical usage data of the user, the brightness adjustment model trained based on the brightness adjustment parameter set can meet individual differences. The brightness of the screen is automatically adjusted based on the trained brightness adjustment model, and the user does not need to manually adjust the brightness, thereby realizing intelligent brightness adjustment, meeting the user's personalized visual needs, and improving the user's usage experience.

[0056] The exemplary embodiments of the present disclosure provide a display method, which is applied to an electronic device, which may be a smart electronic device such as a mobile phone, a tablet computer, a smart wearable device, etc., which has a screen brightness automatic adjustment function. Figure 1 As shown, the display method shown in the present disclosure includes:

[0057] S101, automatically adjusting the brightness of the screen of the electronic device based on a preset adjustment model pre-stored in the electronic device, and collecting multiple sets of brightness adjustment parameter sets;

[0058] S102: When the plurality of sets of brightness adjustment parameter sets meet the first condition, training a brightness adjustment model for the screen of the electronic device based on the plurality of sets of brightness adjustment parameter sets;

[0059] S103: Adjust the brightness of the screen based on the trained brightness adjustment model.

[0060] In step S101, the electronic device is pre-stored with a preset adjustment model when it leaves the factory. The preset adjustment model has a built-in correspondence between the illumination value of the ambient light (unit: lux) and the brightness of the screen (unit: nit). Among them, the preset adjustment model is trained based on a large amount of data from different users as samples. Due to the large number of samples and the wide source of samples, it does not have a personalized adjustment effect. When the preset adjustment model is used to automatically adjust the screen brightness, it may not meet the usage needs of users of the current electronic device. When the preset adjustment model is used to automatically adjust the brightness of the screen, the ambient light collected by the ambient light sensor is input into the preset adjustment model. The preset adjustment model outputs a screen brightness value that matches the ambient light illumination value based on the ambient light illumination value in the current environment, thereby realizing automatic adjustment of the screen brightness.

[0061] The electronic device obtains the illuminance value of the ambient light in the environment in which the electronic device is located through a light sensor, wherein the light sensor can collect the illuminance value of the ambient light in real time, or collect the illuminance value of the ambient light at certain intervals. Therefore, during use, the electronic device can automatically adjust the brightness of the screen corresponding to the illuminance value of the ambient light based on a preset adjustment model. If the brightness of the screen adjusted based on the preset adjustment model cannot meet the user's personalized visual experience, the user can increase or decrease the brightness of the screen by manually adjusting the brightness bar based on personal visual habits, so the screen will adjust the brightness and display according to the brightness setting value set by the user.

[0062] In order to make the adjusted brightness more in line with the personal visual habits of the user who uses the electronic device during the automatic brightness adjustment process of the screen, when the user adjusts the screen brightness by manually adjusting the brightness bar based on his personal visual habits, the electronic device collects various characteristic parameters of the user when adjusting the brightness bar to form a brightness adjustment parameter set, so as to form a brightness adjustment model that meets the user's personal visual needs in subsequent steps based on the collected groups of brightness adjustment parameter sets.

[0063] Among them, the brightness adjustment parameter set is composed of brightness setting values, environmental characteristics and preset characteristics. The electronic device can upload the collected brightness adjustment parameter set to the cloud server through the application and store it in the cloud server, so that it can be downloaded from the cloud server when the brightness adjustment model needs to be trained later, thereby avoiding occupying the storage space of the electronic device. Of course, if the storage space of the electronic device is large enough, the information corresponding to each feature contained in the brightness adjustment parameter set collected by the user in the process of adjusting the brightness of the screen according to his own usage habits can also be stored in the electronic device for use in the subsequent training process of the brightness adjustment model.

[0064] It should be noted that the various features selected during the training process and the weights of the various features can be set at the factory, so that when the samples are collected later, the relationship between the brightness value of the screen set by the user and the state of the user using the electronic device can be more comprehensively reflected. Of course, multiple feature values ​​can also be set on the application, and the user can select the features and the importance of the features that he wants to pay attention to through the application to select the features that can better reflect the personalized needs of the user for collection as training samples. However, all sets of brightness adjustment parameter sets used for training are historical data during the user's use of the electronic device, wherein the environmental features are related to the brightness of the screen, and the environmental features may include the illumination value of the ambient light in the environment where the electronic device is located when the brightness of the screen is adjusted, the specific time when the adjustment is made, etc. The preset features are also related to the brightness of the screen, and the preset features may include the name of the application running on the electronic device when the brightness of the screen is adjusted, the grayscale of the screen, the color temperature of the screen, etc. Since the more features included in the brightness adjustment parameter set, the more accurate the brightness adjustment model training is. Therefore, during the training data collection process of the brightness adjustment model, the number and type of features in the brightness adjustment parameter set can be increased, so that when the brightness adjustment parameter set is subsequently collected, more number and type of features can be obtained at the same time, thereby improving the training accuracy.

[0065] For example, environmental characteristics include the illuminance value of the ambient light in the environment where the electronic device is located when the screen brightness is adjusted, and preset characteristics include the name of the application running on the electronic device and the color temperature of the screen when the adjustment is made, that is, the brightness adjustment parameter set consists of the brightness setting value, the illuminance value of the ambient light, and the name of the application.

[0066] In one example, if a user is running an album to view images, the electronic device obtains an ambient light illumination value of 1000 lux through a light sensor, and automatically adjusts the screen brightness to 150 nit for display based on a preset adjustment model. However, the user thinks the brightness is too dim and cannot see the details in the image, so he manually adjusts the brightness to 200 nit. Therefore, the brightness setting value is 200 nit. Therefore, the set of brightness adjustment parameters collected by the electronic device is [200 nit, 1000 lux, album].

[0067] In another example, if the user is running a browser to read a novel, the electronic device obtains the ambient light illumination value of 2000 lux through the light sensor, and automatically adjusts the screen brightness to 200 nit for display based on the preset adjustment model. However, the user thinks that the brightness is too bright and makes the eyes feel tired, so he manually adjusts the brightness to 150 nit. Therefore, the brightness setting value is 150 nit. Therefore, the set of brightness adjustment parameters collected by the electronic device is [150 nit, 2000 lux, browser].

[0068] In step S102, the first condition is that the number of brightness adjustment parameter sets exceeds the preset number. Since model training requires a large amount of data support to ensure that the trained model can output accurate results, when the number of brightness adjustment parameter sets collected by the electronic device is greater than or equal to the preset number, the brightness adjustment model is trained using multiple sets of brightness adjustment parameter sets. The electronic device inputs the multiple sets of brightness adjustment parameter sets into the brightness adjustment model, and the brightness adjustment model learns according to the brightness setting value, environmental characteristics and preset characteristics in the brightness adjustment model set, so that the brightness adjustment model can output a target brightness prediction value close to the brightness device value according to the environmental characteristics of the input electronic device's environment and the preset characteristics of the electronic device.

[0069] It should be noted that the training process of the brightness adjustment model can be completed locally in the electronic device without connecting to the server. The brightness adjustment model after training is reset and stored in the electronic device for the brightness adjustment model pre-stored in the electronic device for subsequent use by the electronic device to adjust the brightness of the screen. Of course, it is understandable that when the processor of the electronic device does not have sufficient memory and processing power to train the brightness adjustment model, after triggering the brightness adjustment model training, the collected multiple sets of brightness adjustment parameter sets can be uploaded to the cloud server for training through the application, and the trained brightness adjustment model that meets the user's personalized visual needs can be downloaded through the application, and in the subsequent use of the electronic device, the trained brightness adjustment model is used to replace the preset adjustment model to automatically adjust the brightness of the screen.

[0070] In step S103, the trained brightness adjustment model is stored in the electronic device. When the environmental characteristics or preset characteristics of the electronic device change, the brightness adjustment model can be used to output a brightness value corresponding to the changed characteristics to adjust the brightness of the screen to the brightness value.

[0071] In the present disclosure, since the brightness adjustment parameter sets are all historical usage data of users, the brightness of the screen is adjusted using a trained brightness adjustment model, which can meet the user's personalized visual needs. The user does not need to manually adjust the brightness, and the electronic device can display the brightness the user wants, thereby improving the user's usage experience.

[0072] According to an exemplary embodiment, Figure 2 As shown, the display method in this embodiment includes:

[0073] S201, automatically adjusting the brightness of the screen of the electronic device based on a preset adjustment model pre-stored in the electronic device, and collecting multiple sets of brightness adjustment parameter sets;

[0074] S202, determining whether the plurality of sets of brightness adjustment parameter sets satisfy a first condition, if the plurality of sets of brightness adjustment parameter sets satisfy the first condition, executing step S203; if the plurality of sets of brightness adjustment parameter sets do not satisfy the first condition, executing step S201;

[0075] S203, selecting a part of brightness adjustment parameter sets from the multiple groups of brightness adjustment parameter sets as a first sample set, and training a first prediction model and a second prediction model based on the first sample set respectively;

[0076] S204, selecting a portion of brightness adjustment parameter sets from the multiple sets of brightness adjustment parameter sets as a second sample set, and training the fusion model based on the second sample set, a first predicted brightness output by the trained first prediction model, and a second predicted brightness output by the trained second prediction model;

[0077] S205: Determine a first weight parameter of the first prediction model and a second weight parameter of the second prediction model based on the trained fusion model;

[0078] S206, obtaining target environment characteristics and target preset characteristics in the current state of the electronic device;

[0079] S207, taking the target environment characteristics and the target preset characteristics as input parameters, inputting them into the first prediction model and the second prediction model respectively, to obtain the first prediction brightness and the second prediction brightness in the current state;

[0080] S208, obtaining a target brightness prediction value based on the first predicted brightness, the second predicted brightness, the first weight parameter, and the second weight parameter;

[0081] S209: Adjust the brightness of the screen in the current state based on the target brightness prediction value.

[0082] Among them, step S201 is the same as the implementation in the above embodiment and will not be repeated here.

[0083] In some embodiments, the brightness adjustment parameter set includes environmental characteristics and preset characteristics during the sample collection process, wherein the environmental characteristics include ambient light and ambient light interval, the ambient light is the illuminance value of the ambient light in the environment when the user sets the screen brightness value, and the ambient light interval is the illuminance value interval of the ambient light in the environment when the user sets the screen brightness value; the preset characteristics include a first brightness, a first brightness interval, a first application name and display mode, a first screen display parameter, and a first application code, wherein the first brightness is the brightness value of the screen before the user sets the screen brightness value; the first brightness interval is the brightness value interval of the screen before the user sets the screen brightness value; the first application name and display mode is the package name and screen status of the application displayed on the screen when the user sets the screen brightness value; the first screen display parameter is used to characterize the grayscale of the screen when the user sets the screen brightness value; the first application code is the coding serial number of the application being displayed on the screen when the user sets the screen brightness value.

[0084] Among them, the illumination value interval can be set by the electronic device when it leaves the factory, or it can be divided and set by the user according to personal visual habits, for example, 0-1000 lux is set as the first ambient light interval, 1001-5000 lux is set as the second ambient light interval, 5000-10000 lux is set as the third ambient light interval, etc. The brightness interval can be set by the electronic device when it leaves the factory, or it can be divided and set by the user according to personal visual habits, for example, 0-300 nit is set as the A brightness interval, 301-500 nit is set as the B brightness interval, 501-800 nit is set as the C brightness interval, etc. The first application name can be the specific name of the application program, or it can be the package name of the application program. The display mode represents the direction of the screen when the application program is displayed when the user uses the electronic device, such as horizontal or vertical (that is, horizontal screen display or vertical screen display). Grayscale represents the brightness of the screen, that is, the RGB image displayed on the screen is converted into a grayscale image, and the image processing method is used to count the proportion of all pixels between 0 pixels and 255 pixels. After weighted calculation, the grayscale is obtained. If the screen is displayed in full black, the calculated grayscale is 1, and if the screen is displayed in full white, the calculated grayscale is 0. According to the specific data between 0 and 1, the relationship between the screen display content and the brightness can be determined. The code and package name of the application are saved in the electronic device when the electronic device downloads the application. The code and package name of each application are different. For example, the user downloaded three commonly used applications, WeChat, Alipay, and Meituan Takeout, through the application store. Among them, the code of WeChat is 0x10, and the package name is com.tencent.mm; the code of Alipay is 0x11, and the package name is com.eg.android.AlipayGphone; the code of Meituan Takeout is 0x12, and the package name is com.sankuai.meituan.takeoutnew.

[0085] In order to make the output results of the trained brightness adjustment model more accurate and cover every scene in the process of users using electronic devices, the electronic device can add as many feature values ​​in the brightness adjustment parameter set as possible when leaving the factory. It should be noted that after the electronic device leaves the factory, the user cannot change the feature values ​​in the brightness adjustment parameter set, and the electronic device will collect information for each feature value. Since the illumination of ambient light has a greater impact on the brightness of the screen, the illumination value of ambient light is a necessary feature value.

[0086] For example, when a user is using an electronic device, the user manually adjusts the brightness of the screen through the brightness adjustment bar. At this time, data from the adjustment process is collected. The collected data includes that the brightness is set to 300nit, the illumination of the ambient light in the environment where the electronic device is located before setting the screen brightness value is 3000lux, which belongs to the first ambient light interval, and before setting the screen brightness value, the user uses Youku to watch videos horizontally at a brightness of 250nit, which belongs to the A brightness interval. The application code of Youku is 0xx1, the package name is com.youku.phone, and the grayscale is 156. Therefore, each of the above collected data is stored as a set of brightness adjustment parameter sets, that is, the set of brightness adjustment parameter sets is [300nit, 3000lux, first ambient light interval, 250nit, A brightness interval, com.youku.phone, horizontal display, 0xx1]. Every time the user adjusts the brightness according to personal usage needs, data is collected based on environmental characteristics and preset characteristics to form a set of brightness adjustment parameters for storage.

[0087] In step S202, if the number of the plurality of sets of brightness adjustment parameter sets exceeds the preset number, that is, the first condition is met, then step S203 and other subsequent steps are executed to train the brightness adjustment model using the plurality of sets of brightness adjustment parameter sets. If the number of the brightness adjustment parameter sets is less than the preset number, it indicates that the brightness adjustment parameter sets collected by the electronic device are not enough to complete the training of the brightness adjustment parameter sets, so the process returns to step S201 to continue collecting and saving the brightness adjustment parameter sets.

[0088] In step S203, the electronic device selects part of the brightness adjustment parameter sets from the multiple sets of brightness adjustment parameter sets as the first sample set, and uses the first sample set to train the first prediction model and the second prediction model respectively, so that the first prediction model and the second prediction model output the first predicted brightness and the second predicted brightness respectively. For example, the electronic device collects 10,000 sets of brightness adjustment parameter sets in total, selects 7,000 sets of brightness adjustment parameter sets as the first sample set, and uses the 7,000 sets of brightness adjustment parameter sets to train the first prediction model and the second prediction model respectively. During the training process, the first prediction model outputs 7,000 first predicted brightnesses, and the second prediction model outputs 7,000 second predicted brightnesses.

[0089] In one example, the first prediction model is a Bayesian model, which is a probabilistic model and a traditional machine learning model. The Bayesian model can be trained with a small number of samples and output a better prediction result. The second prediction model is a deep neural network model. The deep neural network model requires a large number of samples for training to output a better prediction result. A small number of samples cannot support the training of the deep neural network model. Therefore, in the process of training the brightness adjustment model, the electronic device can train the Bayesian model after collecting a small number of brightness adjustment parameter sets, while the deep neural network model needs to collect more than a preset number of brightness adjustment parameter sets in the electronic device before it can start training, but the deep neural network model has a higher accuracy. Therefore, in the process of training the brightness adjustment model, the Bayesian model and the deep neural network model are trained at the same time, and the advantages of the two models can be utilized to reduce the training time and improve the prediction accuracy.

[0090] In step S204, the brightness adjustment model is composed of a fusion model, a first prediction model, and a second prediction model, wherein the fusion model is used to determine a weight parameter of a first predicted brightness output by the first prediction model and a second predicted brightness output by the second prediction model. The electronic device selects a portion of the brightness adjustment parameter sets from the plurality of brightness adjustment parameter sets as a second sample set, and the electronic device trains the fusion model using the second sample set, the first predicted brightness output by the trained first prediction model, and the second predicted brightness output by the trained second prediction model.

[0091] In one example, the first sample set and the second sample set may also be composed of the same set of brightness adjustment parameters, that is, the first sample set and the second sample set both include the same number of sets of brightness adjustment parameters, and each set of brightness adjustment parameters in the first sample set and the second sample set has the same characteristic value. When the first sample set and the second sample set are exactly the same, the trained fusion model determines that the first weight parameter and the second weight parameter are 0.5 respectively, which can improve the speed of model training.

[0092] In another example, the first sample set and the second sample set may also be composed of partially identical sets of brightness adjustment parameters, that is, the first sample set and the second sample set may include the same number of sets of brightness adjustment parameters, but only some of the sets of brightness adjustment parameters have the same characteristic values, or the first sample set and the second sample set include different numbers of sets of brightness adjustment parameters, but some of the sets of brightness adjustment parameters are the same. When some sample data are the same in the first sample set and the second sample set, the weight values ​​corresponding to the first prediction model and the second prediction model may be further adjusted, so as to further improve the accuracy of the model.

[0093] In another example, in order to improve the reliability and accuracy of the trained model, the first sample set and the second sample set are different. For example, the electronic device collects a total of 10,000 sets of brightness adjustment parameter sets, selects the remaining 3,000 sets of brightness adjustment parameter sets as the second sample set, and uses the 3,000 sets of brightness adjustment parameter sets to train the fusion model. The 3,000 sets of brightness adjustment parameter sets are respectively input into the trained first prediction model and the second prediction model. The first prediction model outputs 3,000 first prediction brightnesses and 3,000 second prediction brightnesses respectively. The electronic device uses 3,000 sets of brightness adjustment parameter sets, 3,000 first prediction brightnesses, and 3,000 second prediction brightnesses to train the fusion model. Using the same sample set to train the first prediction model and the second prediction model can ensure the accuracy of the prediction results output by the final two prediction models. At the same time, using a sample set that is completely different from the training prediction model to train the fusion model can avoid using the same sample set to cause a single training sample, and the training results may be interfered. Using a completely different sample set to train the fusion model can further improve the accuracy of the entire brightness adjustment model.

[0094] In step S205, the fusion model determines the first weight parameter of the first prediction model and the second weight parameter of the second prediction model by comparing the errors between the first predicted brightness output by the first prediction model and the second predicted brightness output by the second prediction model and the brightness setting value when the same set of brightness adjustment parameters are input, wherein the smaller the error between the predicted brightness and the brightness setting value, the larger the weight parameter of the corresponding prediction model determined by the fusion model. For example, the weight parameter can be determined by the mean absolute error (MAE), that is, the absolute value of the difference between the brightness setting value and the predicted brightness is calculated, then the sum is divided by the number of samples for averaging, and the mean absolute error of the first prediction model is calculated to be 0.2, and the mean absolute error of the second prediction model is 0.4. Since the mean absolute error of the first prediction model is less than the mean absolute error of the second prediction model, the fusion model determines that the first weight parameter of the first prediction model is 0.6, and the second weight parameter of the second prediction model is 0.4.

[0095] In step S206, the electronic device obtains the target environment characteristics and target preset characteristics in the current state of the electronic device. It should be noted that the target environment characteristics and target preset characteristics are similar to the environmental characteristics and preset characteristics, except that the environmental characteristics are data collected in the sample collection stage and are used to train the brightness adjustment model, while the target environment characteristics and target preset characteristics are data collected in the current state when the brightness adjustment model has been trained and is in the activation stage, when the environmental state of the electronic device or the state of the user using the electronic device changes. The collection methods are the same, except that the user distinguishes between parameters collected in different time periods.

[0096] Among them, the target environment characteristics include target ambient light and target ambient light interval. The target ambient light is the illumination value of the ambient light in the environment where the electronic device is located in the current state, and the target ambient light interval is the illumination value interval of the ambient light in the environment where the electronic device is located in the current state.

[0097] The target preset features include a second brightness, a second brightness range, a second application name and display mode, a second screen display parameter and a second application code, wherein the second brightness is the brightness value of the screen in the current state; the second brightness range is the brightness value range in which the brightness value of the screen is in the current state; the second application name and display mode are the package name and screen state of the application displayed on the screen in the current state; the second screen display parameter is used to characterize the grayscale of the screen in the current state; the second application code is the coding serial number of the application being displayed on the screen in the current state.

[0098] In step S207, the electronic device uses the target environment characteristics and the target preset characteristics as input parameters, and inputs them into the trained first prediction model and the trained second prediction model respectively, to obtain the first predicted brightness and the second predicted brightness in the current state.

[0099] For example, when the brightness adjustment model is enabled, the electronic device obtains the target environment characteristics and target preset characteristics in the current state. The illumination of the ambient light in the environment where the electronic device is located in the current state is 6000 lux, which belongs to the second ambient light interval. The user is using UC browser to read novels vertically at a brightness of 300 nit, which belongs to the A brightness interval. The application code of UC browser is 0xx2, the package name is com.UCMobile, and the grayscale is 180. Therefore, the target ambient light is 6000 lux, the target ambient light interval is the second ambient light interval, the second brightness is 300 nit, the second brightness interval is the A brightness interval, the package name of the second application is com.UCMobile, the display mode is vertical display, the second screen display parameter is 180, and the second application code is 0xx2. The electronic device uses the above-mentioned collected data as input parameters, and inputs them into the first prediction model that has been trained and the second prediction model that has been trained respectively. The first prediction model and the second prediction model will output the first predicted brightness and the second predicted brightness in the current state based on each input data.

[0100] In step S208, since the trained fusion model determines the first weight parameter and the second weight parameter, the electronic device can calculate the target brightness prediction value using the first predicted brightness, the second predicted brightness, the first weight parameter and the second weight parameter. There is no limitation on the calculation method of the target brightness prediction value. In one example, the target brightness prediction value can be the sum of the product of the first predicted brightness and the first weight parameter and the product of the second predicted brightness and the second weight parameter. For example, the first predicted brightness is 300nit, the first weight parameter is 0.6, the second predicted brightness is 400nit, and the first weight parameter is 0.4, so the target brightness prediction value is 300×0.6+400×0.4=340nit. In another example, the target brightness prediction value can also be the first predicted brightness, the second predicted brightness, the product of the first predicted brightness and the first weight parameter, and the product of the second predicted brightness and the second weight parameter, and the average value of the sum of the three.

[0101] In step S209, the target brightness prediction value is the output value of the brightness adjustment model. The electronic device uses the brightness adjustment model to adjust the brightness of the screen in the current state to the target brightness prediction value.

[0102] In the present disclosure, the electronic device uses multiple sets of brightness adjustment parameter sets exceeding a preset number to train the first prediction model, the second prediction model, and the fusion model. The model fusion method makes the trained brightness adjustment model robust and the output target brightness prediction value has a higher confidence level.

[0103] According to an exemplary embodiment, Figure 3 As shown, the display method in this embodiment includes:

[0104] S301, receiving a reset instruction of the automatic brightness adjustment function;

[0105] S302, disabling the brightness adjustment model;

[0106] S303, receiving an instruction to set the brightness of the screen;

[0107] S304, determining whether the brightness adjustment model is in a disabled state, if the brightness adjustment model is in a disabled state, executing step S311; if the brightness adjustment model is not in a disabled state (ie, in an enabled state), executing step S305;

[0108] S305, determining whether the brightness adjustment model has been trained, if the brightness adjustment model has been trained, executing step S302; if the brightness adjustment model has not been trained, executing step S306;

[0109] S306, based on the setting instruction for the brightness of the screen, collecting the brightness setting value indicated by the setting instruction, the environmental characteristics when the setting instruction is received, and the preset characteristics as a set of brightness adjustment parameters and storing them;

[0110] S307: When the multiple sets of brightness adjustment parameter sets meet the first condition, training a brightness adjustment model for the screen of the electronic device based on the multiple sets of brightness adjustment parameter sets;

[0111] S308, testing the preset adjustment model and the trained brightness adjustment model respectively;

[0112] S309, determining whether the error value of the first test result output by the brightness adjustment model is less than the error value of the second test result output by the preset adjustment model, if the error value of the first test result output by the brightness adjustment model is less than the error value of the second test result output by the preset adjustment model, executing step S310; if the error value of the first test result output by the brightness adjustment model is greater than or equal to the error value of the second test result output by the preset adjustment model, executing step S302;

[0113] S310, disabling the preset adjustment model and enabling the brightness adjustment model;

[0114] S311. Retrain the brightness adjustment model.

[0115] In step S301, the reset instruction may be an instruction input by the user through the screen of the electronic device, an instruction input by the user through voice, or an instruction input by selecting the automatic brightness adjustment function option displayed on the screen through touch. Figure 4As shown, the user can trigger a reset instruction through the virtual button 40 of automatic brightness adjustment displayed on the touch screen, and the reset instruction is used to instruct to turn off the automatic brightness adjustment function or to turn on the brightness adjustment function again after turning it off. If the electronic device receives a reset instruction of the automatic brightness adjustment function, that is, the user is not satisfied with the current automatic brightness adjustment process of the screen, and hopes to change the adjustment logic of the automatic brightness adjustment function, that is, to change the brightness adjustment model currently in use, and subsequently continue to use the preset adjustment model pre-stored in the electronic device to adjust the brightness of the screen, that is, the electronic device will determine the brightness based on the illuminance value of the ambient light in the environment, and the screen display will be the same as the illuminance value of the ambient light, and the brightness adjustment model will be disabled in step S302.

[0116] In step S303, the setting instruction may be an instruction for the user to input a brightness value on the screen of the electronic device, or an instruction for the user to slide on the brightness bar displayed on the screen to select the brightness by touch, or an instruction for the user to input a brightness value by voice. Figure 4 As shown, the user can trigger the setting instruction through the brightness bar 41 displayed on the touch screen, and the user can change the displayed brightness by sliding the brightness bar 41 left and right on the screen. If the electronic device receives the setting instruction for the brightness of the screen, that is, the user is not satisfied with the brightness of the current screen and the user has decided the brightness to be displayed. If it is determined in step S304 that the brightness adjustment model has been disabled, the electronic device can retrain the brightness adjustment model in step S311 using the brightness adjustment parameter set containing the brightness that the user wants to display, so that the frequently retrained brightness adjustment model can output a target brightness preset value that is closer to the user's visual habits. In the process of retraining, the electronic device can train the first prediction model or the second prediction model using the brightness adjustment parameter set, or train both the first prediction model and the second prediction model using the brightness adjustment parameter set. Since the fusion model is completed based on the first predicted brightness output by the first prediction model and the second predicted brightness output by the second prediction model, the fusion model must be retrained in the retraining process.

[0117] For example, the first prediction model is a Bayesian model, and the second prediction model is a deep neural network model. Since the number of brightness adjustment parameter sets obtained in the retraining process is limited and has little impact on the Bayesian model, only the deep neural network model is retrained, and then the first predicted brightness output by the Bayesian model and the second predicted brightness output by the retrained deep neural network model are used to retrain the fusion model to determine the first weight parameters of the Bayesian model and the second weight parameters of the deep neural network model.

[0118] For another example, the first prediction model is a Bayesian model and the second prediction model is a deep neural network model. Since more sets of brightness adjustment parameters are obtained, in order to improve the accuracy of the final brightness adjustment model output, only the Bayesian model and the deep neural network model are retrained, and then the first predicted brightness output by the retrained Bayesian model and the second predicted brightness output by the retrained deep neural network model are used to retrain the fusion model to determine the first weight parameters of the Bayesian model and the second weight parameters of the deep neural network model.

[0119] If it is determined in step S304 that the brightness adjustment model has been enabled, it is necessary to determine in step S305 whether the electronic device has trained the brightness adjustment model. If the brightness adjustment model has been trained, that is, the electronic device can already use the brightness adjustment model to adjust the brightness, then the trained brightness adjustment model needs to be disabled, because in step S303 the electronic device receives an instruction to set the brightness of the screen, that is, the target brightness preset value output by the enabled trained brightness adjustment model cannot meet the user's visual needs, so step S302 is executed to disable the brightness adjustment model.

[0120] If it is determined in step S305 that the electronic device has not trained the brightness adjustment model, step S306 is executed. Based on the setting instruction for the brightness of the screen, the electronic device collects the brightness setting value indicated by the setting instruction, the environmental characteristics when the setting instruction is received, and the preset characteristics as a set of brightness adjustment parameters. The electronic device normalizes the set of brightness adjustment parameters, removes invalid data, and other data processing before storing it for subsequent training of the brightness adjustment model. When the number of the collected brightness adjustment parameter sets exceeds the preset number, that is, the first condition is met, multiple sets of brightness adjustment parameter sets can be used in step S307 to train the brightness adjustment model of the screen of the electronic device.

[0121] In step S308, the error value represents the mean absolute error (MAE), that is, the absolute value of the difference between the brightness setting value and the predicted value is calculated and then summed and averaged. The electronic device tests the preset adjustment model and the trained brightness adjustment model respectively, calculates the error value output by the brightness adjustment model by calculating the absolute value of the difference between the first test result output by the brightness adjustment model each time and the brightness setting value, and calculates the error value output by the preset adjustment model by calculating the difference between the second test result output by the preset adjustment model and the brightness setting value.

[0122] For example, during the training process, the brightness setting value is 200nit, and the brightness adjustment model outputs three first test results of 210nit, 205nit, and 197nit, respectively. The absolute values ​​of the differences between the brightness setting value and each first test result are calculated to be 10, 5, and 3, and the error value between the first test result and the brightness setting value is calculated to be (10+5+3) / 3=6. The preset adjustment model outputs three second test results of 200nit, 220nit, and 195nit, respectively. The absolute values ​​of the differences between the brightness setting value and each first test result are calculated to be 0, 20, and 5, and the error value between the first test result and the brightness setting value is calculated to be (0+20+5) / 3=8.33.

[0123] If step S309 determines that the error value output by the brightness adjustment model is smaller than the error value output by the preset adjustment model, it means that the first test result output by the brightness adjustment model is closer to the brightness setting value that conforms to the user's visual habits than the second test result output by the preset adjustment model. Therefore, in step S310, the preset adjustment model is disabled and the brightness adjustment model is enabled, so that the electronic device can use the brightness adjustment model to adjust the brightness of the screen.

[0124] If step S309 determines that the error value output by the brightness adjustment model is greater than or equal to the error value output by the preset adjustment model, it means that the second test result output by the preset adjustment model is closer to the brightness setting value that conforms to the user's visual habits than the first test result output by the brightness adjustment model. Therefore, step S302 is executed to disable the pre-brightness adjustment model, and the electronic device uses the preset adjustment model to adjust the brightness of the screen, or adjusts the brightness of the screen according to the received instruction to set the brightness of the screen.

[0125] In some embodiments, the screen is off for a period of time greater than or equal to a preset period of time, and the illuminance value of the ambient light in the environment in which the electronic device is located after the screen is turned on is not within a preset illuminance range, and a brightness adjustment model is enabled, wherein the preset illuminance range is related to the illuminance value of the ambient light in the environment in which the electronic device is located before the screen is turned off.

[0126] In order to ensure that the brightness of the screen will not be suddenly adjusted based on the trained brightness adjustment model, which will affect the user's visual experience, it is necessary to reset the brightness adjustment model and enable the trained brightness adjustment model after the screen is turned off for a preset period of time and the ambient light in the environment where the electronic device is located changes dramatically. The preset duration can be set by the user based on personal visual habits, or it can be set by the electronic device at the factory based on its performance, for example, to 3 minutes, 5 minutes, etc. The condition for judging that the ambient light has changed dramatically is that the illuminance value of the ambient light in the environment where the electronic device is located after the screen is turned on is not within the preset illuminance range, wherein the preset illuminance range is related to the illuminance value of the ambient light in the environment where the electronic device is located before the screen is turned off.

[0127] In one example, when the electronic device leaves the factory, it is set that when the illuminance value of the ambient light in the environment in which the electronic device is located after the screen is turned on exceeds ±0.6 times the illuminance value of the ambient light in the environment in which the electronic device is located before the screen is turned off, the ambient light is considered to have changed drastically. If the illuminance value of the ambient light in the environment in which the electronic device is located before the screen is turned off is 10,000 lux, the two extreme values ​​of the preset illuminance range are 10,000×(1-0.6)=4,000, and 10,000×(1+0.6)=16,000, that is, the preset illuminance range is [4,000, 16,000]. If the illuminance value of the ambient light in the environment in which the electronic device is located after the screen is turned on is not within the preset illuminance range, then the ambient light is considered to have changed drastically. For example, if the illuminance value of the ambient light in the environment where the electronic device is located is 20,000 lux after the screen is turned on, it is considered that the ambient light has changed drastically, the brightness adjustment model is reset, and the brightness adjustment model after training is enabled; if the illuminance value of the ambient light in the environment where the electronic device is located is 12,000 lux after the screen is turned on, it is considered that the ambient light has not changed drastically, and the preset adjustment model or the brightness adjustment model before reset is still used to adjust the screen brightness.

[0128] In the present disclosure, the electronic device can adjust the brightness based on the user's setting instructions for the screen brightness, or can adjust the brightness based on the reset instructions of the automatic brightness adjustment function, or can automatically adjust the brightness based on a brightness adjustment model, thereby providing the user with more brightness adjustment methods, and retraining the brightness adjustment model when the user sets the screen brightness, thereby improving the accuracy of the target brightness prediction value output by the brightness adjustment model, so that the reset brightness adjustment model can conform to the user's visual habits and enhance the user's usage experience.

[0129] The exemplary embodiment of the present disclosure provides a display device, which is applied to electronic equipment. Figure 5 As shown, a block diagram of a display device shown in the present disclosure.

[0130] The block diagram includes: an acquisition module 51, a training module 52, and an adjustment module 53. The acquisition module 51 is used to automatically adjust the brightness of the screen of the electronic device based on a preset adjustment model pre-stored in the electronic device during use of the electronic device, and to collect multiple sets of brightness adjustment parameter sets, wherein the brightness adjustment parameter sets include brightness setting values, environmental characteristics, and preset characteristics of the electronic device; the training module 52 is used to train the brightness adjustment model of the screen of the electronic device based on the multiple sets of brightness adjustment parameter sets when the multiple sets of brightness adjustment parameter sets meet the first condition; the adjustment module 53 is used to adjust the brightness of the screen based on the trained brightness adjustment model.

[0131] In an exemplary embodiment of the present disclosure, the training module 52 is also used to: select a part of the brightness adjustment parameter sets from multiple groups of brightness adjustment parameter sets as a first sample set, and train the first prediction model and the second prediction model based on the first sample set; select a part of the brightness adjustment parameter sets from multiple groups of brightness adjustment parameter sets as a second sample set, and train the fusion model based on the second sample set, the first predicted brightness output by the trained first prediction model, and the second predicted brightness output by the trained second prediction model; determine the first weight parameter of the first prediction model and the second weight parameter of the second prediction model based on the trained fusion model.

[0132] In an exemplary embodiment of the present disclosure, the adjustment module 53 is also used to: obtain target environment characteristics and target preset characteristics in the current state of the electronic device; input the target environment characteristics and target preset characteristics as input parameters into the first prediction model and the second prediction model respectively to obtain the first predicted brightness and the second predicted brightness in the current state; obtain the target brightness prediction value based on the first predicted brightness, the second predicted brightness, the first weight parameter and the second weight parameter; and adjust the brightness of the screen in the current state based on the target brightness prediction value.

[0133] In an exemplary embodiment of the present disclosure, the environmental features include ambient light and ambient light intervals; the preset features include a first brightness, a first brightness interval, a first application name and display mode, a first screen display parameter, and a first application code, wherein the display mode in the first application name and display mode is that when the screen brightness value is set, the application displayed on the screen is displayed in landscape or portrait mode; the first application code is used to indicate the application being displayed on the screen;

[0134] And / or, the target environment characteristics include target ambient light and target ambient light range; the target preset characteristics include a second brightness, a second brightness range, a second application name and display mode, a second screen display parameter and a second application code, wherein the display mode in the second application name and display mode is whether the application displayed on the screen in the current state is horizontal or vertical display; the second application code is used to indicate the application being displayed on the screen in the current state.

[0135] In an exemplary embodiment of the present disclosure, the adjustment module 53 is also used to: test the preset adjustment model and the trained brightness adjustment model respectively; when the error value of the first test result output by the brightness adjustment model is less than the error value of the second test result output by the preset adjustment model, disable the preset adjustment model and enable the brightness adjustment model; when the error value of the first test result output by the brightness adjustment model is greater than or equal to the error value of the second test result output by the preset adjustment model, disable the brightness adjustment model.

[0136] In an exemplary embodiment of the present disclosure, the adjustment module 53 is also used to: when the screen off time is greater than or equal to a preset time length, and the illuminance value of the ambient light in the environment in which the electronic device is located after the screen is turned on is not within a preset illuminance range, enable the brightness adjustment model, wherein the preset illuminance range is related to the illuminance value of the ambient light in the environment in which the electronic device is located before the screen is turned off.

[0137] In an exemplary embodiment of the present disclosure, the adjustment module 53 is further configured to: if the brightness adjustment model is in an enabled state, disable the brightness adjustment model based on an instruction to set the brightness of the screen.

[0138] In an exemplary embodiment of the present disclosure, the acquisition module 51 is also used for: if the brightness adjustment model has not been trained, based on the setting instruction for the brightness of the screen, collecting the brightness setting value indicated by the setting instruction, the environmental characteristics when the setting instruction is received, and the preset characteristics as a set of brightness adjustment parameters and storing them.

[0139] In an exemplary embodiment of the present disclosure, the training module 52 is further used to: if the brightness adjustment model is in a disabled state, retrain the brightness adjustment model based on an instruction to set the brightness of the screen.

[0140] In an exemplary embodiment of the present disclosure, the adjustment module 53 is further configured to: disable the brightness adjustment model based on a reset instruction of the automatic brightness adjustment function.

[0141] Regarding the display device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0142] Figure 6 is a block diagram of an electronic device 600 according to an exemplary embodiment. For example, the electronic device 600 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0143] Reference Figure 6 , the electronic device 600 may include one or more of the following components: a processing component 602 , a memory 604 , a power component 606 , a multimedia component 608 , an audio component 610 , an input / output (I / O) interface 612 , a sensor component 614 , and a communication component 616 .

[0144] The processing component 602 generally controls the overall operation of the electronic device 600, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 602 may include one or more modules to facilitate the interaction between the processing component 602 and other components. For example, the processing component 602 may include a multimedia module to facilitate the interaction between the multimedia component 608 and the processing component 602.

[0145] The memory 604 is configured to store various types of data to support operations on the electronic device 600. Examples of such data include instructions for any application or method operating on the electronic device 600, contact data, phone book data, messages, pictures, videos, etc. The memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0146] The power supply component 606 provides power to the various components of the electronic device 600. The power supply component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 600.

[0147] The multimedia component 608 includes a screen that provides an output interface between the electronic device 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 includes a front camera and / or a rear camera. When the electronic device 600 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0148] The audio component 610 is configured to output and / or input audio signals. For example, the audio component 610 includes a microphone (MIC), and when the electronic device 600 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 604 or sent via the communication component 616. In some embodiments, the audio component 610 also includes a speaker for outputting audio signals.

[0149] I / O interface 612 provides an interface between processing component 602 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0150] The sensor assembly 614 includes one or more sensors for providing various aspects of status assessment for the electronic device 600. For example, the sensor assembly 614 can detect the open / closed state of the electronic device 600, the relative positioning of components, such as the display and keypad of the electronic device 600, and the sensor assembly 614 can also detect the position change of the electronic device 600 or a component of the electronic device 600, the presence or absence of user contact with the electronic device 600, the orientation or acceleration / deceleration of the electronic device 600, and the temperature change of the electronic device 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 614 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0151] The communication component 616 is configured to facilitate wired or wireless communication between the electronic device 600 and other devices. The electronic device 600 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 616 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0152] In an exemplary embodiment, the electronic device 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0153] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 604 including instructions, and the instructions can be executed by a processor 620 of an electronic device 600 to perform the above display method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0154] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a processor of an electronic device, enables a processing device of the electronic device to execute a display method provided by an exemplary embodiment of the present disclosure.

[0155] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0156] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A display method, characterized in that: Applied to electronic equipment, the display method comprises: During use of the electronic device, the brightness of the screen of the electronic device is automatically adjusted based on a preset adjustment model pre-stored in the electronic device, and multiple sets of brightness adjustment parameter sets are collected, wherein each set of the brightness adjustment parameter sets includes a brightness setting value, an environmental feature, and a preset feature of the electronic device; When the multiple sets of brightness adjustment parameter sets meet a first condition, training a brightness adjustment model of the screen of the electronic device based on the multiple sets of brightness adjustment parameter sets; Based on the trained brightness adjustment model, the brightness of the screen is adjusted.

2. The display method according to claim 1, characterized in that: The step of training a brightness adjustment model for the screen of the electronic device based on the multiple sets of brightness adjustment parameter sets includes: Selecting part of the brightness adjustment parameter sets from the multiple groups of brightness adjustment parameter sets as a first sample set, and training a first prediction model and a second prediction model based on the first sample set respectively; Selecting part of the brightness adjustment parameter sets from the multiple groups of brightness adjustment parameter sets as second sample sets, and training the fusion model based on the second sample sets, the first predicted brightness output by the trained first prediction model, and the second predicted brightness output by the trained second prediction model; Based on the trained fusion model, a first weight parameter of the first prediction model and a second weight parameter of the second prediction model are determined.

3. The display method according to claim 2, characterized in that: The automatically adjusting the brightness of the screen based on the trained brightness adjustment model includes: Acquire target environment characteristics and target preset characteristics in the current state of the electronic device; The target environment feature and the target preset feature are used as input parameters, respectively input into the first prediction model and the second prediction model, to obtain the first predicted brightness and the second predicted brightness in the current state; Obtaining a target brightness prediction value based on the first predicted brightness, the second predicted brightness, the first weight parameter, and the second weight parameter; Based on the target brightness prediction value, the brightness of the screen in the current state is adjusted.

4. The display method according to claim 3, characterized in that: The environmental features include ambient light and ambient light intervals; the preset features include a first brightness, a first brightness interval, a first application name and display mode, a first screen display parameter, and a first application code, wherein the display mode in the first application name and display mode is that when the screen brightness value is set, the application displayed on the screen is displayed in landscape or portrait mode; the first application code is used to indicate the application being displayed on the screen; and / or, The target environment characteristics include target ambient light and target ambient light range; the target preset characteristics include a second brightness, a second brightness range, a second application name and display mode, a second screen display parameter and a second application code, wherein the display mode in the second application name and display mode is whether the application displayed on the screen in the current state is horizontal or vertical display; the second application code is used to indicate the application being displayed on the screen in the current state.

5. The display method according to any one of claims 1 to 4, characterized in that: The display method further includes: Testing the preset adjustment model and the trained brightness adjustment model respectively; When the error value of the first test result output by the brightness adjustment model is smaller than the error value of the second test result output by the preset adjustment model, disabling the preset adjustment model and enabling the brightness adjustment model; When the error value of the first test result output by the brightness adjustment model is greater than or equal to the error value of the second test result output by the preset adjustment model, the brightness adjustment model is disabled.

6. The display method according to claim 5, characterized in that: The screen off time is greater than or equal to a preset time length, and the illuminance value of the ambient light in the environment where the electronic device is located after the screen is turned on is not within a preset illuminance range, the brightness adjustment model is enabled, wherein the preset illuminance range is related to the illuminance value of the ambient light in the environment where the electronic device is located before the screen is turned off.

7. The display method according to any one of claims 1 to 4, characterized in that: The display method further includes: If the brightness adjustment model is in an enabled state, based on an instruction to set the brightness of the screen, the brightness adjustment model is disabled.

8. The display method according to any one of claims 1 to 4, characterized in that: The display method further includes: If the brightness adjustment model has not been trained, based on the setting instruction for the brightness of the screen, the brightness setting value indicated by the setting instruction, the environmental characteristics when the setting instruction is received, and the preset characteristics are collected as the brightness adjustment parameter set and stored.

9. The display method according to any one of claims 1 to 4, characterized in that: The display method further includes: If the brightness adjustment model is in a disabled state, the brightness adjustment model is retrained based on an instruction to set the brightness of the screen.

10. The display method according to any one of claims 1 to 4, characterized in that: The display method further includes: Based on a reset instruction of the automatic brightness adjustment function, the brightness adjustment model is disabled.

11. A display device, characterized in that: Applied to electronic equipment, the display device comprises: A collection module, used for automatically adjusting the brightness of the screen of the electronic device based on a preset adjustment model pre-stored in the electronic device during use of the electronic device, and collecting multiple sets of brightness adjustment parameter sets, wherein each set of the brightness adjustment parameter sets includes a brightness setting value, an environmental feature, and a preset feature of the electronic device; A training module, configured to train a brightness adjustment model of the screen of the electronic device based on the multiple sets of brightness adjustment parameter sets when the multiple sets of brightness adjustment parameter sets meet a first condition; The adjustment module is used to adjust the brightness of the screen based on the trained brightness adjustment model.

12. An electronic device, characterized in that: The electronic device comprises: Screen; processor; a memory for storing processor-executable instructions; The processor is configured to execute executable instructions in the memory to implement the display method according to any one of claims 1 to 10.

13. A non-transitory computer-readable storage medium having executable instructions stored thereon, characterized in that: When the executable instruction is executed by a processor, the display method according to any one of claims 1 to 10 is implemented.

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