WO2023245417A1

WO2023245417A1 - Screen brightness adjustment method and apparatus, and electronic device and medium

Info

Publication number: WO2023245417A1
Application number: PCT/CN2022/100137
Authority: WO
Inventors: 李芝珩; 邓嘉俊
Original assignee: 北京小米移动软件有限公司
Priority date: 2022-06-21
Filing date: 2022-06-21
Publication date: 2023-12-28
Also published as: CN117616496A

Abstract

A screen brightness adjustment method and apparatus, and an electronic device and a medium. The screen brightness adjustment method comprises: acquiring target application scenario information; acquiring historical adjustment data, wherein the historical adjustment data is used for representing the correspondence between application scenarios and screen brightness information; generating a target recommended brightness adjustment model according to the historical adjustment data and the target application scenario information; and adjusting the screen brightness of an electronic device to a target recommended automatic brightness value on the basis of the target recommended brightness adjustment model and the target application scenario information. In the screen brightness adjustment method, a target recommended brightness adjustment model is generated on the basis of historical adjustment data and target application scenario information, such that when a user is in a target application scenario in the future, the screen brightness can be automatically adjusted for the user on the basis of the correspondence with the target recommended brightness adjustment model, the requirement of the user is refined, a brightness value desired by the user can be output, and the brightness can better fit the actual brightness requirement of the user, thereby improving the usage experience of the user.

Description

A method, device, electronic equipment and medium for adjusting screen brightness

Technical field

The present disclosure relates to the technical field of electronic equipment, and in particular, to methods, devices, electronic equipment, and media for adjusting screen brightness.

Background technique

With the development of network technology in electronic device technology, users are becoming more and more dependent on electronic devices. Electronic devices can meet users' daily life and business office needs. Due to the portability of electronic devices, users' usage scenarios of electronic devices are not fixed. The scenarios are complex and changeable, and it is difficult to adjust the screen brightness to meet the user's brightness needs.

Contents of the invention

In view of this, the present disclosure provides a method, device, electronic device, and medium for adjusting screen brightness.

According to a first aspect of an embodiment of the present disclosure, a method for adjusting screen brightness is provided, which is applied to electronic devices. The method for adjusting screen brightness includes:

Obtain target application scenario information;

Obtain historical adjustment data, which is used to represent the correspondence between application scene information and screen brightness information;

Determine recommended brightness information based on the historical adjustment data and the target application scene information;

Adjust the screen brightness of the electronic device according to the recommended brightness information.

Optionally, the historical adjustment data includes a target brightness adjustment model, and the screen brightness adjustment method further includes:

Obtain historical screen brightness information corresponding to the same application scene information, and application scene information corresponding to the historical screen brightness information;

Determine target brightness information according to the historical screen brightness information; wherein the target brightness information is a weighted calculation value based on each historical screen brightness information;

According to the target brightness information and corresponding application scene information, a target brightness adjustment model is generated.

Optionally, determining target brightness information based on the historical screen brightness information includes:

Determine the acquisition weight information of each of the historical screen brightness information;

Target brightness information is determined based on the weight information and the historical screen brightness information.

Optionally, the determining of each historical screen brightness information includes:

Obtain the bivariate normal probability distribution of the application scenario information of each historical screen brightness information;

According to each of the binary normal probability distributions, the weight information of each of the historical screen brightness information is determined.

Optionally, determining the weight information of each of the historical screen brightness information according to each of the binary normal probability distributions includes:

Obtain the time information of each historical screen brightness information;

Determine a weight coefficient for each historical screen brightness information according to the time information;

According to the weight coefficient and the binary normal probability distribution, the weight information of each of the historical screen brightness information is determined.

Optionally, the method for adjusting screen brightness also includes:

Search the historical adjustment data in a traversal manner, and if there is no screen brightness information corresponding to the target application scene information, obtain a preset recommended brightness adjustment model;

Adjust the screen brightness based on a preset recommended brightness adjustment model corresponding to the target application scene information.

Optionally, the method for adjusting screen brightness also includes:

Obtain target screen brightness information acting on the electronic device; the target screen brightness information is used to represent the manually adjusted screen brightness information;

Adjust the screen brightness of the electronic device according to the target screen brightness information;

The target screen brightness information and the application scene information corresponding to the target screen brightness information are stored in the historical adjustment data.

Optionally, storing the target screen brightness information and application scene information corresponding to the target screen brightness information into the historical adjustment data includes:

The target screen brightness information and the application scene information corresponding to the target screen brightness information are used as sample data;

In response to the sample data satisfying a condition for triggering the recommended automatic brightness, the sample data is recorded to historical adjustment data.

Optionally, in response to the sample data meeting the conditions for triggering recommended automatic brightness, recording the sample data to historical adjustment data includes:

According to the sample data, determine the distribution state of the target screen brightness information in the sample data;

Analyze the distribution state of the target screen brightness information and determine the correlation between two adjacent sample values in the sample data;

Based on the correlation, in response to the sample data satisfying a condition for triggering recommended automatic brightness, the sample data is recorded to historical adjustment data.

Optionally, the method for adjusting screen brightness also includes:

Get the current screen brightness information and ambient light brightness information;

When the absolute value of the difference between the current screen brightness information and the ambient light brightness information is greater than a preset threshold, warning information is displayed.

Optionally, the application scenario information includes at least two of the following:

Ambient light brightness information, application information, time information, and geographical location information; among which, time information includes usage time and usage date.

According to a second aspect of an embodiment of the present disclosure, a screen brightness adjustment device is provided, which is applied to electronic equipment, including:

The first acquisition module is used to acquire target application scenario information;

The second acquisition module is used to acquire historical adjustment data, where the historical adjustment data is used to represent the correspondence between the application scenario and the screen brightness information;

A determination module configured to determine recommended brightness information based on the historical adjustment data and the target application scene information;

An adjustment module, configured to adjust the screen brightness of the electronic device according to the recommended brightness information.

According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including:

processor;

Memory used to store instructions executable by the processor;

Wherein, the processor is configured as:

Obtain target application scenario information;

Obtain historical adjustment data, which is used to characterize the correspondence between application scenarios and screen brightness information;

According to a fourth aspect of an embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, which when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform an adjustment of screen brightness. Method, the method includes:

Obtain target application scenario information;

The technical solution provided by the embodiments of the present disclosure can include the following beneficial effects: the method in the present disclosure finds the recommended brightness information corresponding to the target application scene information through the correspondence between the application scene and the screen brightness information, and adjusts the screen for the user. Brightness, according to the application scenario, refines the user's needs and outputs the brightness value expected by the user, making it more suitable for the user's actual brightness needs and improving the user experience.

It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and do not limit the present disclosure.

Description of the drawings

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

FIG. 1 is a flowchart of a method for adjusting screen brightness according to an exemplary embodiment.

FIG. 2 is a flowchart of a method for adjusting screen brightness according to an exemplary embodiment.

FIG. 3 is a flowchart of a method for adjusting screen brightness according to an exemplary embodiment.

FIG. 4 is a flowchart of a method for adjusting screen brightness according to an exemplary embodiment.

FIG. 5 is a flowchart of a method for adjusting screen brightness according to an exemplary embodiment.

FIG. 6 is a flowchart of a method for adjusting screen brightness according to an exemplary embodiment.

FIG. 7 is a flowchart of a method for adjusting screen brightness according to an exemplary embodiment.

FIG. 8 is a flowchart of a method for adjusting screen brightness according to an exemplary embodiment.

FIG. 9 is a block diagram of a device for adjusting screen brightness according to an exemplary embodiment.

FIG. 10 is a block diagram of an electronic device according to an exemplary embodiment.

FIG. 11 is a block diagram of a device for adjusting screen brightness according to an exemplary embodiment.

FIG. 12 is a schematic diagram showing the distribution of historical screen brightness information according to an exemplary embodiment.

FIG. 13 is a binary positive probability distribution diagram of historical screen brightness information according to an exemplary embodiment.

Figure 14 is a schematic diagram of a target brightness adjustment model according to an exemplary embodiment.

Figure 15 is a schematic diagram of a target brightness adjustment model according to an exemplary embodiment.

Detailed ways

Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the appended claims.

In some examples, some manufacturers have introduced automatic brightness adjustment design concepts. The screen of an electronic device is equipped with an automatic brightness adjustment function. The automatic brightness adjustment function adjusts the screen brightness based on the ambient light brightness. The two form a relatively simple linear relationship. For example, the stronger the ambient light during the day, the brighter the screen of the electronic device, and the darker the ambient light at night, the darker the screen of the electronic device.

However, since different users have different sensitivity to screen brightness, different users have different needs for screen brightness, and automatically adjusted screen brightness cannot meet the needs of each user. In actual situations, faced with different usage scenarios, users often complain that the automatically adjusted screen brightness cannot meet their needs. For example, if the screen is too bright or too dark when reading at night, the user experience is still uncomfortable; if the screen is too dark when taking photos during the day, etc., the screen brightness still cannot meet the user's needs during the day. The automatic brightness adjustment function completely depends on the ambient light brightness to determine its function. There are too few factors and cannot fully meet the preferences and needs of different users.

The present disclosure proposes a screen brightness adjustment method, which is applied to electronic devices. The screen brightness adjustment method includes obtaining target application scene information; obtaining historical adjustment data, and the historical adjustment data is used to represent the correspondence between the application scene and screen brightness information. relationship; determine the recommended brightness information based on historical adjustment data and target application scene information; adjust the screen brightness of the electronic device based on the recommended brightness information. The method in this disclosure finds the recommended brightness information corresponding to the target application scene information through the correspondence between the application scene and the screen brightness information, adjusts the screen brightness for the user, refines the user's needs according to the application scene, and outputs the user's expectations The brightness value makes it more suitable for the actual brightness needs of users and improves the user experience.

Figure 1 is a flow chart of a method for adjusting screen brightness according to an exemplary embodiment. As shown in Figure 1, the method for adjusting screen brightness is used in an electronic device. The electronic device includes a processor for storing the processor. In the memory of executable instructions, the method of adjusting screen brightness includes the following steps:

S11. Obtain target application scenario information.

In step S11, the processor of the electronic device obtains target application scenario information. When the electronic device detects that the user is using or operating the electronic device, for example, the user is touching a certain area of the screen, and the screen detects operations such as pressing or sliding; or the user is using an application information, such as WeChat , QQ, video browser, game APP, camera mode, etc., the processor obtains the target application scenario information in order to determine the application scenario information currently used by the user. Among them, the application scenario information includes at least two of the following:

The ambient light brightness information is, for example, the ambient light brightness information in different application scenarios.

Application information is, for example, application software in electronic devices. Application software is, for example, communication applications such as WeChat, payment applications such as Alipay, game applications, video applications such as Tencent, etc.

Time information such as time information includes usage time (XX hour XX minute) and usage date (XXXX year XX month XX day). The usage date can be divided into working days and non-working days, or holidays and non-holidays.

For example, geographical location information can use GPS and other technologies to obtain structured address information through longitude and latitude, such as province + city + district + county + town + village + street + house number, etc. The structured address information can be divided into permanent address and emergency address. resident address.

Among them, usage time and ambient light brightness information can be classified as continuous value, and user information, application information, geographical location information and usage date are classified as discrete value. By adding more measurement factors , which can more accurately determine the application scenario information currently used by the user.

It should be noted here that when recording application scenario information, user information can also be recorded to form user-customized personal screen adjustments. User information includes, for example, the user's ID account and password, the user's facial information, the user's fingerprint information, etc.

S12. Obtain historical adjustment data.

In step S12, the processor of the electronic device obtains historical adjustment data. Among them, historical adjustment data is used to represent the correspondence between application scenarios and screen brightness information.

In one example, the screen brightness information may be determined by the screen brightness value when the user manually adjusted it in the past. For example, the processor collects screen brightness information acting on the electronic device. Each screen brightness information has a corresponding screen brightness value during manual adjustment, and the processor stores it in historical adjustment data. Each application scenario information has a corresponding relationship with the screen brightness information. During use, the user will manually adjust the screen brightness to a certain brightness value based on his own preferences and the current application scenario information. , to meet current usage needs. Among them, each application scene information corresponds to a piece of screen brightness information. Users may have different needs in different environments, causing the user to adjust it multiple times, and the application scene information corresponds to one or more screen brightness values when manually adjusted.

When the processor collects the screen brightness value manually adjusted by the user, it can pre-filter the discrete factors, that is, classify them. Each category has a corresponding relationship. The correspondence relationship can be, for example, a correspondence relationship table for easy search, or it can be an independent target brightness adjustment model for intuitive viewing.

For example, the processor collects when user A manually adjusts the screen brightness, such as adjusting it to 50nit (nit), 200nit, etc., obtains the target application scenario information, and determines the category of discrete factors in the target application scenario information. For example, if the usage date in the discrete factor is a working day and user A uses the short video APP when the geographical location information is a certain resident address, the processor collects the current screen brightness information, makes a corresponding relationship, and stores it. based on historical adjustment data.

For example, the processor collects when user B manually adjusts the screen brightness, such as adjusting it to 100nit, 300nit, etc., obtains the target application scenario information, and determines the category of discrete factors in the target application scenario information. If the usage date in the discrete factor is a non-working day, and user B uses the Weibo APP when the geographical location information is a certain resident address, the processor collects the current screen brightness information and makes a corresponding relationship. And stored in historical adjustment data.

In another example, the screen brightness information may be determined by a preset recommended brightness adjustment model, which is set in the processor before leaving the factory. Each application scene information corresponds to preset screen brightness information, and each preset screen brightness information corresponds to a preset screen brightness value. When the processor does not collect the screen brightness information acting on the electronic device, it means that the user has not manually adjusted the screen brightness. The corresponding relationship between the target application scene information and the corresponding screen brightness information can be based on the preset recommended brightness. Adjustment model is determined.

S13. Determine recommended brightness information based on historical adjustment data and target application scene information.

In step S13, the processor determines recommended brightness information based on historical adjustment data and application scene information. When the processor obtains the target application scenario information, it searches the historical adjustment data in a traversal manner to find the screen brightness information corresponding to the target application scenario information. The processor processes and analyzes the screen brightness information to determine the recommended brightness information. .

In one example, when the processor collects the screen brightness value when the user manually adjusted it, it means that the user has manually adjusted the screen, and the processor determines the screen brightness information based on the screen brightness value that the user has manually adjusted in the past.

Among them, the recommended brightness information is, for example, the screen brightness value that the user is most likely to expect. The screen brightness value that the user is most likely to expect can be, for example, any one of the screen brightness values that the user has manually adjusted in the past, or it can be based on The screen brightness value when the user manually adjusted it in the past was weighed to arrive at a more reasonable screen brightness value.

Due to the uncertainty and variability of user preferences, the probability algorithm of density clustering can be used to calculate the screen brightness value that the user is most likely to expect, or a deep learning neural network can be used to simulate more complex nonlinear relationships to determine The screen brightness value that the user is most likely to expect.

In another example, when the processor does not collect the screen brightness value when the user manually adjusts it, it means that the user has not manually adjusted the screen. The processor can determine the screen brightness corresponding to the target application scenario information based on the preset recommended brightness adjustment model. Information, the processor processes and analyzes the screen brightness information to determine the recommended brightness information. Among them, in order to simplify the processing and analysis process and quickly determine the recommended brightness information, the screen brightness information can correspond to the recommended brightness information, and the screen brightness information is used as the recommended brightness information.

S14. Adjust the screen brightness of the electronic device according to the recommended brightness information.

In step S14, the processor adjusts the screen brightness of the electronic device according to the recommended brightness information so that the screen brightness of the electronic device meets the user's needs. The recommended brightness information is, for example, a recommended screen brightness value. The recommended screen brightness value may be, for example, but is not limited to 10nit, 100nit, or 500nit.

The method in this embodiment can make targeted recommended brightness adjustments for each user and determine the recommended brightness information based on the correspondence between the application scene information and the screen brightness information to meet the user's needs. Through the application scene information and the user's previous manual adjustment of the screen brightness value, the recommended screen brightness value that meets the user's needs is weighed, which is more in line with each user's personal needs and improves the user's experience.

Figure 2 is a flow chart of a method for adjusting screen brightness according to an exemplary embodiment. As shown in Figure 2, the method for adjusting screen brightness is used in an electronic device. The electronic device includes a processor for storing the processor. Memory for executable instructions. Among them, the historical adjustment data includes the target brightness adjustment model, and the screen brightness adjustment method includes the following steps:

S21. Obtain historical screen brightness information corresponding to the same application scene information.

In step S21, the processor obtains historical screen brightness information located in the same application scene information from historical adjustment data. Among them, based on the classification of target application scenario information, the same application information is used during the same usage period. Since the user's preferences and needs change at any time, the user may adjust the screen brightness multiple times, generating multiple screen brightness information, forming a history. For screen brightness information, the processor can obtain all historical screen brightness information located in the same application scene information.

S22. Determine target brightness information based on historical screen brightness information.

In step S22, the processor determines target brightness information based on all historical screen brightness information. The target brightness information is a weighted operation value based on each historical screen brightness information.

Multiple historical screen brightness information can represent the user's usual expected brightness value of the screen, and the weighted operation value between multiple historical screen brightness information can be calculated, which is the weighted average value between multiple historical screen brightness information, that is, Calculate the user's demand for the screen brightness of the electronic device and determine the target brightness information. For example, if multiple screen brightness information are 35nit, 40nit, and 45nit respectively, then the target brightness information is 40nit.

In an example, referring to Figure 3, in step S22, generating a target brightness adjustment model based on the target brightness information also includes the following steps:

S221. Determine the weight information of each historical screen brightness information.

In step S221, the processor determines the weight information of each historical screen brightness information. Among them, the weight information can be obtained through calculation. The weight information is, for example, the binary normal probability distribution of the application scenario information that can be obtained for each historical screen brightness information. According to each binary normal probability distribution, the weight information of each historical screen brightness information is determined. For example, the binary normal probability distribution of the application scene information of each historical screen brightness information may be determined based on the ambient light information and time information in the application scene information.

For example, when determining the application scenario information of historical screen brightness information, a bivariate normal probability distribution is calculated. The bivariate normal probability distribution is the weight information, and the formula of the bivariate normal probability distribution is:

Among them, x is amb level (ambient light level, that is, ambient light information), y can be understood as time (time information), δX and δY are the variances of X and Y, μX and μY are the mean values of X and Y, and ρ is the two The correlation coefficient of variables X and Y; the calculation method of ρ is:

The calculation method based on probability weight can better avoid the influence of outlier factors. Outliers are also called escape values. They refer to one or several values in the sample data that are significantly different from other values. , if the probability that a value deviates from the mean value of the sample data is less than or equal to 1/(2n), the sample data should be discarded (n is the number of sample data examples, and the probability can be estimated based on the distribution of the sample data). Probabilistic weighting can give more weight to closer values, improving the algorithm's ability to combat the uncertainty of manual adjustment of brightness data and achieving more accurate predictions. For example, corresponding to the screen brightness information during manual adjustment in Figure 12, the new target brightness adjustment model is the recommended brightness curve shown in Figure 14 (view from z-axis) and Figure 15. In Figure 14 and Figure 15, The X-axis represents ambient light brightness information (ambientlight), the y-axis represents time information (minscale), the time information is from 0h to 24h, for example, the unit is min, and the z-axis represents the target screen brightness information (brightness) after manual adjustment. In the figure Different color depths represent different target screen brightness information. For specific values, please refer to the histograms on the right side of Figure 14 and Figure 15. It should be noted that in manually adjusted areas with dense screen brightness information, such as between 20:00 and 24:00 time, the target brightness adjustment model is adjusted to the brightness value of the screen brightness information that the user has manually adjusted in the past. Among them, as shown in Figure 15, it can be seen that the new target brightness adjustment model does not follow a linear relationship. For the historical adjustment data of isolated manually adjusted screen brightness information (for example, 15 o'clock and 17 o'clock), because the conditions for stimulating recommended automatic brightness are not met (detailed records will be described in the embodiments described later), the target brightness adjustment model does not have manual adjustment. The area of the screen brightness information during adjustment still maintains the default preset recommended brightness adjustment model (details will be described in the embodiments described later).

Since the user's preference for screen brightness changes over time, the algorithm for weight information can have the ability to self-update. 4, in step S221, the weight information of each historical screen brightness information is determined according to each binary normal probability distribution, which can be determined through the following steps:

S2211. Obtain the time information of each historical screen brightness information.

In step S2211, the processor searches historical adjustment data in a traversal manner, and determines the time information of each historical screen brightness information based on the historical adjustment data. The time point at which each historical screen brightness information is stored is determined based on the time information, and arranged in sequence to determine early historical screen brightness information and recent historical screen brightness information.

S2212. Determine the weight coefficient of each historical screen brightness information according to the time information.

In step S2212, the processor determines the weight coefficient of each historical screen brightness information based on the time information. The weight information of the early historical screen brightness information is given a lower weight, and the weight information of the recent historical screen brightness information is given a higher weight. Among them, the weight coefficient is, for example, 0.7, 0.5, 0.3, 0, etc.

S2213. Determine the weight information of each historical screen brightness information based on the weight coefficient and the binary normal probability distribution.

In step S2213, the processor determines the weight information of each historical screen brightness information through calculation according to the weight coefficient and the binary normal probability distribution.

In one example, based on the time point of each historical screen brightness information, the recent to early weight information is defined as the first binary normal probability, the second binary normal probability, the third binary normal probability, and the fourth binary normal probability. Bivariate normal probability and fifth bivariate normal probability. Among them, the weight coefficient assigned to the first binary normal probability is 0.7, that is, the first binary normal probability is multiplied by 0.7 to obtain the updated weight information. The weight coefficient assigned to the second binary normal probability is 0.5, that is, the second binary normal probability is multiplied by 0.5 to obtain the updated weight information. The weight coefficient assigned to the third binary normal probability is 0.3, that is, the third binary normal probability is multiplied by 0.3 to obtain the updated weight information. The fourth binary normal probability and the fifth binary normal probability belong to early manually adjusted sample data, and the later time point is more in line with the user's preferences, and the earlier the date, the more out of touch with the user's current preferences and needs. Giving a weighting coefficient can forget the early screen brightness information, realize the ability of automatic updating, meet the user's personal needs, and adjust as the user's preference for screen brightness changes, improving the user experience.

S222. Determine the target brightness information based on the weight information and historical screen brightness information.

In step S222, the processor determines target brightness information based on the weight information and historical screen brightness information. Among them, the average value between the product of each weight information and the corresponding historical screen brightness information is the determined target brightness information.

S23. Generate a target brightness adjustment model based on the target brightness information and corresponding application scene information.

In step S23, the processor generates a target brightness adjustment model based on the target brightness information. Among them, the target brightness information can be directly defined as the target brightness value, and a target brightness adjustment model can be constructed to make the determination method more intuitive and improve the efficiency of screen brightness adjustment. Alternatively, the target brightness information can be given a weight to convert it into a target brightness value, and a new target brightness adjustment model can be constructed.

The numerical values in the target brightness adjustment model can be expressed as recommended brightness information. Among them, the recommended brightness information satisfies the following formula:

The historical screen brightness information is all manually adjusted screen brightness values within the same usage period, where i=1 and n is the number of manually adjusted screen brightness values within the usage period. The binary normal probability is the sum of the probabilities corresponding to all the times the screen brightness is adjusted during the same usage period.

In the method in this embodiment, when the processor searches historical adjustment data in a traversal manner, and when it queries the historical screen brightness information corresponding to the same application scenario information in the historical adjustment data, it means that the user has manually adjusted the screen brightness, and the processor Based on the historical screen brightness information, the target brightness information is determined, and the target brightness adjustment model is generated from the target brightness information and the corresponding application scene information to meet the user's needs. Among them, since the user's preferences will change over time, different weight coefficients are assigned to the weight information on different dates to update the weight information and adjust it at any time according to the user's preferences to improve the user experience and meet the user's needs. And the weight information is determined by binary normal probability, which can better avoid the influence of outlier factors, improve the algorithm's ability to resist the uncertainty of manual adjustment of brightness data, and improve the accuracy of recommendations. Assign different coefficients to the weight information at different time points, forget the premature historical screen brightness information, and further realize the ability of self-updating.

Figure 5 is a flow chart of a method for adjusting screen brightness according to an exemplary embodiment. As shown in Figure 5, the method for adjusting screen brightness is used in an electronic device. The electronic device includes a processor for storing the processor. In the memory of executable instructions, the method of adjusting screen brightness includes the following steps:

S31. Obtain target application scenario information.

S32. Obtain historical adjustment data.

S33. Search historical adjustment data in a traversal manner.

In step S33, the processor searches historical adjustment data in a traversal manner.

S34. Determine whether screen brightness information exists in the historical adjustment data.

In step S34, during the processor query process, if there is screen brightness information corresponding to the target application scene information, steps S37 to S39 are executed; if there is no screen brightness information corresponding to the target application scene information, step S35 is executed. .

S35. Obtain the preset recommended brightness adjustment model.

In step S35, a preset recommended brightness adjustment model is obtained. The preset recommended brightness adjustment model is stored in the processor before leaving the factory to provide a basis for adjusting the screen brightness. For example, the preset recommended brightness adjustment model can be preset to have an independent adjustment model for each application scenario. Each preset recommended brightness value in the preset recommended brightness adjustment model has a corresponding relationship with the application scenario information.

S36. Adjust the screen brightness based on the preset recommended brightness adjustment model corresponding to the target application scene information.

In step S36, the processor adjusts the brightness of the screen using a preset recommended brightness adjustment model corresponding to the target application scene information. When the processor detects that the user is in the target application scene information, it searches for an adjustment model corresponding to the target application scene information according to the preset recommended brightness adjustment model. Based on the adjustment model, it determines the preset recommended automatic brightness value to use the preset recommended automatic brightness value. Adjust the screen brightness of electronic devices.

Among them, the preset recommended brightness adjustment model can formulate corresponding adjustment curves based on ambient light information, or it can collect the preferences of different users and use them as sample data, conduct modeling analysis on them, and calculate the corresponding adjustment curves. Specifically, based on actual The design shall prevail and shall not be further limited here. For example, the preferences of different users can be collected based on different user information. During the application process, each user can upload user information and application scenario information to the cloud based on his or her personal wishes to provide analysis support for the data.

S37. Obtain the screen brightness information corresponding to the target application scene information in the historical adjustment data.

S38. Determine the target brightness information according to the screen brightness information.

S39. Determine the recommended automatic brightness information based on the target brightness information.

The methods implemented in steps S37 to S39 are the same as the methods implemented in steps S33 to S35, and will not be repeated here.

The method in this embodiment uses the processor to search historical adjustment data to determine whether there is screen brightness information in the historical adjustment data, and then determine whether the user has manually adjusted the screen brightness. If there is no manual adjustment of the screen brightness, it means that the user has not manually adjusted the screen brightness. The preset recommended brightness adjustment model is quite satisfactory. The processor adjusts the screen brightness of the electronic device based on the preset recommended brightness adjustment model so that the current screen brightness meets the user's eye needs for the target application scene information and improves the user's visual effect.

Figure 6 is a flow chart of a method for adjusting screen brightness according to an exemplary embodiment. As shown in Figure 6, the method for adjusting screen brightness is used in an electronic device. The electronic device includes a processor for storing the processor. In the memory of executable instructions, the method of adjusting screen brightness includes the following steps:

S41. Obtain the target screen brightness information acting on the electronic device.

In step S41, the processor obtains target screen brightness information acting on the electronic device. The target screen brightness information is the screen brightness value manually adjusted by the user under the current application scene information.

In one example, a sensor is provided in the screen. When the user touches the screen and wants to adjust the screen brightness value, the user operates the brightness adjustment bar. The processor collects that the user has called up the brightness adjustment interface and adjusted the screen brightness value. The processor obtains the screen brightness value. For example, the screen brightness value is adjusted to 100nit, or other screen brightness values that meet the user's needs.

Among them, the brightness adjustment interface can be set in the drop-down notification bar of the electronic device, or can be set at any position on the screen display interface, or the brightness adjustment interface can be brought up through preset gestures to display the brightness adjustment bar to the user to facilitate the user. It adjusts.

When the user completes manual adjustment, he can slide the notification bar up and down to hide the brightness adjustment bar. Or, if the user clicks a position other than the brightness adjustment bar, the processor hides the brightness adjustment bar. Or, if the processor does not obtain any operation of the brightness adjustment bar within the preset time period, the processor hides the brightness adjustment bar.

The brightness adjustment bar may include a slide bar to facilitate the user to slide and adjust its brightness. The brightness adjustment bar can also include "+" and "-" keys, so that the user can click the "+" and "-" keys to accurately adjust the brightness. The brightness adjustment bar also includes a close key so that users can quickly turn off the automatic recommended brightness adjustment function. Among them, the close key can be expressed in graphic form, text form, or symbolic form, which shall be subject to the actual design.

S42. Adjust the screen brightness of the electronic device according to the target screen brightness information.

In step S42, the processor adjusts the screen brightness of the electronic device according to the target screen brightness information. When the processor collects the screen brightness value, it sends a control instruction to the screen to control the screen brightness of the electronic device to display the target screen brightness information.

S43. Store the target screen brightness information and the application scene information corresponding to the target screen brightness information into historical adjustment data.

In step S43, the processor stores the target screen brightness information and the application scene information corresponding to the target screen brightness information into historical adjustment data. Among them, if the user manually adjusts the screen brightness under the target application scenario information, it means that the preset recommended automatic brightness value set by the preset recommended brightness adjustment model cannot meet the user's current needs. The processor can combine the target screen brightness information and the current The corresponding application scenario information is stored in the historical adjustment data so as to be used as sample data to provide data support for generating the target brightness adjustment model.

The method in this embodiment obtains the target screen brightness information that the user acts on the electronic device. The target screen brightness information is the screen brightness information value when the user currently adjusts manually, and the screen displays the screen brightness value when the user currently adjusts manually. , to meet the current needs of users. And the screen brightness information and the corresponding application scenario information are synchronously stored in the historical adjustment data, so as to provide data support for the target brightness adjustment model of future applications, realize the storage of targeted information, and formulate the user's personalized screen brightness design to meet the needs of the user. Meet the needs of different users and improve the user experience.

Referring to FIG. 7 , in step S43 , the target screen brightness information and the application scene information corresponding to the target screen brightness information are stored in the historical adjustment data. Also includes the following steps:

S431. Use the target screen brightness information and the application scene information corresponding to the target screen brightness information as sample data.

In step S431, the processor uses the current screen brightness information for screen brightness adjustment and the application scene information corresponding to the target screen brightness information as sample data. The sample data may include a plurality of target screen brightness information for screen brightness adjustment. For example, when user A manually adjusted the screen brightness when using WeChat at 22:00 on multiple days, the screen brightness information for each target of the screen brightness adjustment and the corresponding application scenario information were used as sample data.

S432. Analyze the sample data to determine whether the sample data meets the conditions for triggering the recommended automatic brightness.

In step S432, the processor analyzes the sample data to determine whether the sample data meets the conditions for triggering the recommended automatic brightness. Since there is a possibility of accidental and mis-adjustment when the user manually adjusts the screen brightness, you can set the conditions that trigger the recommended automatic brightness to take effect, so as to determine whether the sample data is valid data.

Among them, in step S432, the sample data is analyzed to determine whether the sample data meets the conditions for triggering recommended automatic brightness, including the following steps:

S4321. According to the sample data, determine the distribution status of the target screen brightness information in the sample data.

In step S4321, the processor determines the distribution state of the target screen brightness information in the sample data based on the sample data. Among them, the distribution status of the target screen brightness information in the sample data can be determined by calculating the bivariate Gaussian Distribution of each target screen brightness information using the density-based clustering method. The formula for the bivariate normal probability distribution is:

Among them, x is amb level (ambient light level, that is, ambient light brightness information), y can be understood as time (time), δX and δY are the variances of X and Y, μX and μY are the mean values of X and Y, and ρ is The correlation coefficient of two variables X and Y; ρ is calculated as:

For example, the category that the user is currently in is a short video APP. The processor collects through the sensor that the current ambient light brightness is 12lux and the usage time is 22 o'clock. The processor obtains that the screen brightness has been adjusted several times and generates multiple sample data. Among them, adjusting several times means that during the adjustment process, users often cannot adjust to the most satisfactory state in one go. When they feel that the adjusted screen brightness is inappropriate, they need to adjust it multiple times until they reach the most satisfactory screen brightness. When calculated through the binary normal probability distribution, it can be determined that the probability of the binary normal probability f is relatively large during the current usage time, and the user is very likely to adjust the screen brightness during this usage time.

There may also be accidental touches during the adjustment process. For example, the category that the user is currently in is a short video APP. The processor collects through the sensor that the current ambient light brightness is 1ux and the usage time is 2 o'clock. The processor obtains and adjusts the screen brightness once. When calculated through the binary normal probability distribution, it can be determined that the probability of the binary normal probability f is small during the current use time, and the user is highly likely to accidentally touch it during this use time.

S4322. Analyze the distribution status of the target screen brightness information and determine the correlation between two adjacent sample values in the sample data.

In step S4332, the processor analyzes the distribution state of the target screen brightness information and determines the correlation between two adjacent sample values in the sample data. Among them, the correlation is represented by the possibility of the user manually adjusting the screen brightness under a specific ambient light brightness and a specific usage time. If the correlation is stronger, it means that the user is more likely to manually adjust the screen brightness instead of accidentally touching it. If the correlation is weaker, it means that the user is more likely to accidentally touch it.

In one example, in a certain category, the processor collects multiple target screen brightness information, as shown in Figure 12. The X-axis in Figure 12 represents ambient light brightness information (ambientlight), and the y-axis represents time information (minscale). , the time information is from 0h to 24h, for example, the unit is min, and the z-axis represents the target screen brightness information (brightness) after manual adjustment. Different color depths in Figure 12 represent different target screen brightness information, and their values correspond to the right side of Figure 12 Bar chart. In order to confirm the correlation between two adjacent sample values in the sample data, the density-based clustering method is used to calculate the bivariate Gaussian Distribution of the application scenario information of each target screen brightness information. ), determine the correlation between two sample values based on the binary normal probability distribution. For example, when the ambient light brightness information is 12lux and the usage time is 22 o'clock, the binary normal probability distribution of this point is shown in Figure 13. In Figure 13, the X-axis represents amb level (ambient light level, that is, ambient light brightness) information), the Y-axis represents time (time information), and the bar chart represents the correlation between different coordinate points (such as the correlation coefficient, which represents the probability of the correlation degree of the corresponding coordinate point). If the second point is closer to this point, the greater the probability, which means the stronger the correlation between the two points. On the contrary, if the distance between the two points is farther, the probability is smaller and the correlation is weaker.

For example, if the probabilities formed by two target screen brightness information are f ₁ =0.6 and f ₂ =0.65 respectively, then both probabilities are very large and both are greater than 0.5, indicating that the correlation between the two sample values is strong.

S4323. Based on the correlation, determine whether the sample data meets the conditions for stimulating automatic brightness recommendation.

In step S4323, the processor determines whether the sample data satisfies the conditions for triggering the recommended automatic brightness according to the correlation. The sample data with a stronger correlation, that is, it satisfies the conditions for stimulating the automatic brightness recommendation. In response to the sample data meeting the conditions for stimulating the automatic brightness recommendation, step S433 is executed. If the sample data has a weaker correlation, it does not satisfy the conditions for stimulating the automatic brightness recommendation. condition, in response to the sample data not meeting the conditions for activating the recommended automatic brightness, step S434 is executed.

Wherein, when in the sample data, the probability distribution of the application scenario information of at least one target screen brightness information is located at a position greater than 0.5, or the probability distribution of the application scenario information of at least two target screen brightness information is located at a position greater than 0.625, Then the conditions for triggering recommended automatic brightness are met.

S433. In response to the sample data meeting the conditions for triggering the recommended automatic brightness, record the sample data to historical adjustment data.

In step S433, the processor records the sample data that satisfies the conditions for triggering the recommended automatic brightness into historical adjustment data in preparation for generating a target brightness adjustment model.

S434. Discard the sample data.

According to the method in this embodiment, when in the sample data, the probability distribution of at least one screen brightness is located at a position greater than 0.5, or at least two screen brightness probability distributions are located at a position greater than 0.625, then the conditions for triggering recommended automatic brightness are met. conditions, effectively preventing misadjustment from having a greater impact on the target brightness adjustment model, improving the accuracy of recommended automatic adjustment, and improving the user experience.

It can be understood that all the above target screen brightness information, when stored in historical data, will form historical screen brightness information, that is, serve as historical screen brightness information to facilitate subsequent calculation and use.

Figure 8 is a flow chart of a method for adjusting screen brightness according to an exemplary embodiment. As shown in Figure 8, the method for adjusting screen brightness is used in an electronic device. The electronic device includes a processor and a sensor for storing The processor is a memory that can execute instructions. The sensor is used to obtain ambient light brightness information and screen brightness information. The method of adjusting screen brightness includes the following steps:

S61. Obtain current screen brightness information and ambient light brightness information.

In step S61, the sensor collects the current screen brightness information and the ambient light brightness information respectively, and the processor is communicatively connected with the sensor so that the processor can obtain the current screen brightness information and the ambient light brightness information.

S62. When the absolute value of the difference between the current screen brightness information and the ambient light brightness information is greater than the preset threshold, a warning message is displayed.

In step S62, the processor compares the current screen brightness information and the ambient light brightness information to determine the difference between the current screen brightness information and the ambient light brightness information. When the absolute value of the difference between the current screen brightness information and the ambient light brightness information is greater than the preset threshold, a warning message is displayed. Among them, the display warning information may be displayed in the display interface of the screen in the form of a pop-up window, prompting the user that the current screen brightness information does not match the ambient light brightness information in the current environment, and the screen brightness information should be adjusted. For example, the warning information may be displayed directly in the form of sound to remind the user. When the current screen brightness information does not match the ambient light brightness information in the current environment, a prompt voice may be issued. The prompt voice may be, for example, unsafe or the screen brightness is not sufficient. Standards and other warning words.

When a warning message is displayed, the processor can issue instructions to adjust the screen brightness so that the current screen brightness information matches the ambient light brightness information to protect the user's eyes.

The method in this embodiment determines whether the user's current usage is inappropriate by calculating the difference between the current screen brightness information and the ambient light brightness information. If used improperly, a warning message will be displayed to remind the user to change the current screen brightness information to prevent users from developing bad usage habits and protect human eyes. For example, when the user stares at the mobile phone for a long time, the ambient light brightness information is 10lux and the current environment is relatively dim. The user adjusts the screen brightness information to 100nit to make the screen brighter, which will irritate the human eyes and cause damage. When the user stares at the phone for a long time, the ambient light brightness information is 100lux. The current environment is relatively bright. The user adjusts the screen brightness information to 10nit, making the screen brightness darker. Long-term gaze will cause eye fatigue and even irreversible damage. . In this disclosure, warning information is displayed to try to guide the user's screen brightness to remain within an appropriate brightness range to protect human health.

Here, it should be noted that the method in the present disclosure is not limited to monitoring screen brightness information, but can also monitor the user's usage time. If the user's usage time is too long, a warning message can also be displayed and the screen can be adjusted to warm colors. Keep its screen brightness at the best state to protect human eyes. And/or, the method in the present disclosure can also monitor the display content of the screen display interface and determine the spectrum of the displayed content. Among them, the blue spectrum is the most harmful to human eyes and will affect the user's sleep quality. If the user watches content with blue spectrum for a long time, a warning message should be displayed to remind the user and guide the user to protect their eyes. The monitoring method in this disclosure can set specific conditions and values for human eye health by recommending automatic brightness model settings to correct the user's bad usage habits and protect the user's health.

The screen brightness adjustment method proposed in this disclosure uses the probability algorithm of density clustering or the deep learning neural network to simulate more complex nonlinear relationships, introduces application scenario information, and classifies it, and details it according to the application scenario information. Customize the needs of users and make targeted adjustments and designs for each user. The application scenario information includes at least two of the following: ambient light brightness information, application program information, time information, and geographical location information; where the time information includes usage time and usage date.

The target brightness adjustment model is automatically calculated based on multiple factors in the target application scenario information and the historical screen brightness information when the user manually adjusts the historical adjustment data, so as to output the screen brightness expected by the user in the same application scenario information in the future. value, based on the target brightness adjustment model, determine the screen brightness information corresponding to the same application scene information, and adjust the screen brightness of the electronic device to meet the needs of different users.

Due to the uncertainty and variability of user preferences, using the probability algorithm of density clustering to calculate the screen brightness value that the user is most likely to expect can better avoid the influence of outlier factors and improve the algorithm's resistance to manual adjustment of brightness data. The ability to reduce uncertainty and achieve more accurate predictions. It also calculates the screen brightness for each manual adjustment in a probabilistic manner, determines the correlation between screen brightness information, reduces the impact of uncertain factors, and better predicts user needs.

Deep learning neural networks can also be used to replace density clustering algorithms to simulate more complex nonlinear relationships, so as to generate target brightness adjustment models and improve accuracy.

This disclosure is provided with a human eye protection mechanism to promptly remind users of bad usage habits to avoid damage to human health and achieve a more humane design.

FIG. 9 is a block diagram of a device for adjusting screen brightness according to an exemplary embodiment. Referring to FIG. 9 , the device includes a first acquisition module 700 , a second acquisition module 710 , a determination module 720 and an adjustment module 730 .

The first acquisition module 700 is configured to acquire target application scenario information. The second acquisition module 710 is configured to acquire historical adjustment data, where the historical adjustment data is used to represent the correspondence between the application scene and the screen brightness information. The determination module 720 is configured to determine recommended brightness information based on historical adjustment data and target application scene information. The adjustment module 730 is configured to adjust the screen brightness of the electronic device according to the recommended brightness information.

Regarding the devices in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method, and will not be described in detail here.

FIG. 10 is a block diagram of an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 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, or the like.

Referring to FIG. 10 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power supply component 806 , a multimedia component 808 , an audio component 810 , an input/output (I/O) interface 812 , and a sensor component 814 , and communication component 816.

Processing component 802 generally controls the overall operations of electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. Additionally, processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

Memory 804 is configured to store various types of data to support operations at device 800 . Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 may 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 (EEPROM), Programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

Power supply component 806 provides power to various components of electronic device 800 . Power supply components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800 .

Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 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 touches, swipes, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide action. In some embodiments, multimedia component 808 includes a front-facing camera and/or a rear-facing camera. When the device 800 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-facing camera and rear-facing camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

Audio component 810 is configured to output and/or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in operating modes, such as call mode, recording mode, and voice recognition mode. The received audio signal may be further stored in memory 804 or sent via communication component 816 . In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, a click wheel, a button, etc. These buttons may include, but are not limited to: Home button, Volume buttons, Start button, and Lock button.

Sensor component 814 includes one or more sensors for providing various aspects of status assessment for electronic device 800 . For example, the sensor component 814 can detect the open/closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor component 814 can also detect the electronic device 800 or a component of the electronic device 800. changes in position, the presence or absence of user contact with the electronic device 800 , the orientation or acceleration/deceleration of the electronic device 800 and changes in the temperature of the electronic device 800 . Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communications component 816 also includes a near field communications (NFC) module to facilitate short-range communications. 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.

In an exemplary embodiment, electronic device 800 may be configured by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A programmable gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation is used to perform the above method.

In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions, such as a memory 804 including instructions, which can be executed by the processor 820 of the electronic device 800 to complete the above method is also provided. For example, the non-transitory computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

A non-transitory computer-readable storage medium that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform a method for adjusting screen brightness, the method comprising:

Obtain target application scenario information. Obtain historical adjustment data, which is used to represent the correspondence between application scenarios and screen brightness information. Determine recommended brightness information based on historical adjustment data and target application scenario information. Adjust the screen brightness of the electronic device based on recommended brightness information.

FIG. 11 is a block diagram of a device 900 for adjusting screen brightness according to an exemplary embodiment. For example, device 900 may be provided as a server. 11, apparatus 900 includes a processing component 922, which further includes one or more processors, and memory resources represented by memory 932 for storing instructions, such as application programs, executable by processing component 922. The application program stored in memory 932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 922 is configured to execute instructions to perform the above-described method.

Device 900 may also include a power supply component 926 configured to perform power management of device 900, a wired or wireless network interface 950 configured to connect device 900 to a network, and an input-output (I/O) interface 958. Device 900 may operate based on an operating system stored in memory 932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

Other embodiments of the invention will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary technical means in the technical field that are not disclosed in the present disclosure. . It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

It is to be understood that the present invention is not limited to the precise construction described above and illustrated in the accompanying drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Industrial applicability

The method in this disclosure finds the recommended brightness information corresponding to the target application scene information through the correspondence between the application scene and the screen brightness information, adjusts the screen brightness for the user, refines the user's needs according to the application scene, and outputs the user's expectations The brightness value makes it more suitable for the actual brightness needs of users and improves the user experience.

Claims

A method for adjusting screen brightness, wherein the method for adjusting screen brightness includes:

Obtain target application scenario information;

Obtain historical adjustment data, which is used to represent the correspondence between application scene information and screen brightness information;

Determine recommended brightness information based on the historical adjustment data and the target application scene information;

Adjust the screen brightness of the electronic device according to the recommended brightness information.
The method for adjusting screen brightness according to claim 1, wherein the historical adjustment data includes a target brightness adjustment model, and the method for adjusting screen brightness further includes:

Obtain historical screen brightness information corresponding to the same application scene information;

Determine target brightness information according to the historical screen brightness information; wherein the target brightness information is a weighted calculation value based on each historical screen brightness information;

According to the target brightness information and corresponding application scene information, a target brightness adjustment model is generated.
The method for adjusting screen brightness according to claim 2, wherein determining the target brightness information based on the historical screen brightness information includes:

Determine the weight information of each historical screen brightness information;

Target brightness information is determined based on the weight information and the historical screen brightness information.
The method for adjusting screen brightness according to claim 3, wherein the determining the weight information of each historical screen brightness information includes:

Obtain the binary normal probability distribution of the application scenario information of each historical screen brightness information;

According to each of the binary normal probability distributions, the weight information of each of the historical screen brightness information is determined.
The method for adjusting screen brightness according to claim 4, wherein determining the weight information of each historical screen brightness information according to each binary normal probability distribution includes:

Obtain the time information of each historical screen brightness information;

Determine a weight coefficient for each historical screen brightness information according to the time information;

According to the weight coefficient and the binary normal probability distribution, the weight information of each of the historical screen brightness information is determined.
The method for adjusting screen brightness according to claim 1, wherein the method for adjusting screen brightness further includes:

Search the historical adjustment data in a traversal manner, and if there is no screen brightness information corresponding to the target application scene information, obtain a preset recommended brightness adjustment model;

Adjust the screen brightness based on a preset recommended brightness adjustment model corresponding to the target application scene information.
The method for adjusting screen brightness according to claim 1, wherein the method for adjusting screen brightness further includes:

Obtain target screen brightness information acting on the electronic device; the target screen brightness information is used to represent the manually adjusted screen brightness information;

Adjust the screen brightness of the electronic device according to the target screen brightness information;

The target screen brightness information and the application scene information corresponding to the target screen brightness information are stored in the historical adjustment data.
The method for adjusting screen brightness according to claim 7, wherein the storing the target screen brightness information and the application scene information corresponding to the target screen brightness information into the historical adjustment data includes:

Use the target screen brightness information and the application scene information corresponding to the target screen brightness information as sample data;

In response to the sample data satisfying a condition for triggering the recommended automatic brightness, the sample data is recorded to historical adjustment data.
The method for adjusting screen brightness according to claim 8, wherein recording the sample data to historical adjustment data in response to the sample data meeting conditions for stimulating recommended automatic brightness includes:

According to the sample data, determine the distribution state of the target screen brightness information in the sample data;

Analyze the distribution state of the target screen brightness information and determine the correlation between two adjacent sample values in the sample data;

Based on the correlation, in response to the sample data satisfying a condition for triggering recommended automatic brightness, the sample data is recorded to historical adjustment data.
The method for adjusting screen brightness according to claim 1, wherein the method for adjusting screen brightness further includes:

Get the current screen brightness information and ambient light brightness information;

When the absolute value of the difference between the current screen brightness information and the ambient light brightness information is greater than a preset threshold, warning information is displayed.
The method for adjusting screen brightness according to claim 1, wherein the application scene information includes at least two of the following:

Ambient light brightness information, application information, time information, and geographical location information; among which, time information includes usage time and usage date.
A screen brightness adjustment device, applied to electronic equipment, including:

The first acquisition module is used to acquire target application scenario information;

The second acquisition module is used to acquire historical adjustment data, where the historical adjustment data is used to represent the correspondence between the application scenario and the screen brightness information;

A determination module configured to determine recommended brightness information based on the historical adjustment data and the target application scene information;

An adjustment module, configured to adjust the screen brightness of the electronic device according to the recommended brightness information.
An electronic device, including:

processor;

Memory used to store instructions executable by the processor;

Wherein, the processor is configured as:

Obtain target application scenario information;

Obtain historical adjustment data, which is used to represent the correspondence between the application scene and the screen brightness information;

Determine recommended brightness information based on the historical adjustment data and the target application scene information;

Adjust the screen brightness of the electronic device according to the recommended brightness information.
A non-transitory computer-readable storage medium that, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform a method for adjusting screen brightness, the method comprising:

Obtain target application scenario information;

Obtain historical adjustment data, which is used to characterize the correspondence between application scenarios and screen brightness information;

Determine recommended brightness information based on the historical adjustment data and the target application scene information;

Adjust the screen brightness of the electronic device according to the recommended brightness information.