CN117616496A

CN117616496A - Screen brightness adjusting method and device, electronic equipment and medium

Info

Publication number: CN117616496A
Application number: CN202280004340.4A
Authority: CN
Inventors: 李芝珩; 邓嘉俊
Original assignee: Beijing Xiaomi Mobile Software Co Ltd
Current assignee: Beijing Xiaomi Mobile Software Co Ltd
Priority date: 2022-06-21
Filing date: 2022-06-21
Publication date: 2024-02-27
Also published as: WO2023245417A1

Abstract

The invention relates to a method, a device, electronic equipment and a medium for adjusting screen brightness, wherein the method for adjusting the screen brightness comprises the steps of obtaining target application scene information; acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between an application scene and screen brightness information; generating a target recommended brightness adjustment model according to the historical adjustment data and the target application scene information; and adjusting the screen brightness of the electronic equipment to a target recommended automatic brightness value based on the target recommended brightness adjustment model and the target application scene information. According to the method, the target recommended brightness adjustment model is generated through the historical adjustment data and the target application scene information, when a user is in the target application scene information in the future, the screen brightness can be automatically adjusted for the user based on the corresponding relation with the target recommended brightness adjustment model, the requirements of the user are refined, the brightness value expected by the user is output, the brightness value is enabled to be more fit with the actual brightness requirement of the user, and the use experience of the user is improved.

Description

Screen brightness adjusting method and device, electronic equipment and medium

Technical Field

The disclosure relates to the technical field of electronic equipment, and in particular relates to a method and a device for adjusting screen brightness, electronic equipment and a medium.

Background

With the development of network technology of electronic device technology, users are increasingly dependent on electronic devices. The electronic equipment can meet the requirements of daily life, business offices and the like of users, and due to portability of the electronic equipment, the use scene of the electronic equipment is not fixed by the users, the scene is complex and changeable, and the brightness of the screen is adjusted to be difficult to fit the brightness requirement of the users.

Disclosure of Invention

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

According to a first aspect of embodiments of the present disclosure, there is provided a method for adjusting screen brightness, applied to an electronic device, the method for adjusting screen brightness including:

acquiring target application scene information;

acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between application scene information and screen brightness information;

determining recommended brightness information according to the history adjustment data and the target application scene information;

and adjusting the screen brightness of the electronic equipment according to the recommended brightness information.

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

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

determining target brightness information according to the historical screen brightness information; the target brightness information is a weighted operation value based on the brightness information of each historical screen;

and generating a target brightness adjustment model according to the target brightness information and the corresponding application scene information.

Optionally, the determining target brightness information according to the historical screen brightness information includes:

determining acquisition weight information of each historical screen brightness information;

and determining target brightness information according to the weight information and the historical screen brightness information.

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

acquiring binary normal probability distribution of application scene information of each historical screen brightness information;

and determining weight information of each historical screen brightness information according to each binary normal probability distribution.

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

Acquiring time information of each historical screen brightness information;

determining a weight coefficient of each historical screen brightness information according to the time information;

and determining the weight information of each historical screen brightness information according to the weight coefficient and the binary normal probability distribution.

Optionally, the method for adjusting the brightness of the screen further includes:

searching the historical adjustment data in a traversing mode, and acquiring a preset recommended brightness adjustment model if screen brightness information corresponding to the target application scene information does not exist;

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

Optionally, the method for adjusting the brightness of the screen further comprises:

acquiring target screen brightness information acting on the electronic equipment; the target screen brightness information is used for representing screen brightness information after manual adjustment;

adjusting the screen brightness of the electronic equipment according to the target screen brightness information;

and storing the target screen brightness information and application scene information corresponding to the target screen brightness information into the history adjustment data.

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

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

and responsive to the sample data meeting a condition that motivates recommended automatic brightness, recording the sample data to historical adjustment data.

Optionally, the recording the sample data to the history adjustment data in response to the sample data meeting a condition that motivates recommended automatic brightness includes:

determining the distribution state of the target screen brightness information in the sample data according to the sample data;

analyzing the distribution state of the brightness information of the target screen, and determining the relevance of two adjacent sample values in the sample data;

based on the correlation, the sample data is recorded to historical adjustment data in response to the sample data meeting a condition that motivates recommended automatic brightness.

acquiring current screen brightness information and ambient light brightness information;

and displaying warning information when the absolute value of the difference value between the current screen brightness information and the environment brightness information is larger than a preset threshold value.

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

Ambient light information, application information, time information, and geographic location information; wherein the time information includes a use time and a use date.

According to a second aspect of embodiments of the present disclosure, there is provided a device for adjusting screen brightness, applied to an electronic apparatus, including:

the first acquisition module is used for acquiring the target application scene information;

the second acquisition module is used for acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between the application scene and the screen brightness information;

the determining module is used for determining recommended brightness information according to the historical adjustment data and the target application scene information;

and the adjusting module is used for adjusting the screen brightness of the electronic equipment according to the recommended brightness information.

According to a third aspect of embodiments of the present disclosure, there is provided an electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to:

acquiring target application scene information;

acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between an application scene and screen brightness information;

According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform a method of adjusting screen brightness, the method comprising:

acquiring target application scene information;

The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects: according to the method, the recommended brightness information corresponding to the target application scene information is found through the corresponding relation between the application scene and the screen brightness information, the screen brightness is adjusted for the user, the user demand is refined according to the application scene, the brightness value expected by the user is output, the actual brightness demand of the user is more met, and the use experience of the user is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

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 illustrating a method of adjusting screen brightness according to an exemplary embodiment.

Fig. 2 is a flowchart illustrating a method of adjusting screen brightness according to an exemplary embodiment.

Fig. 3 is a flowchart illustrating a method of adjusting screen brightness according to an exemplary embodiment.

Fig. 4 is a flowchart illustrating a method of adjusting screen brightness according to an exemplary embodiment.

Fig. 5 is a flowchart illustrating a method of adjusting screen brightness according to an exemplary embodiment.

Fig. 6 is a flowchart illustrating a method of adjusting screen brightness according to an exemplary embodiment.

Fig. 7 is a flowchart illustrating a method of adjusting screen brightness according to an exemplary embodiment.

Fig. 8 is a flowchart illustrating a method of adjusting screen brightness according to an exemplary embodiment.

Fig. 9 is a block diagram illustrating a screen brightness adjusting apparatus according to an exemplary embodiment.

Fig. 10 is a block diagram of an electronic device, according to an example embodiment.

Fig. 11 is a block diagram illustrating a screen brightness adjusting apparatus according to an exemplary embodiment.

Fig. 12 is a diagram showing a 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 example embodiment.

Fig. 14 is a schematic diagram of a target brightness adjustment model, according to an example embodiment.

Fig. 15 is a schematic diagram of a target brightness adjustment model, according to an example embodiment.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples 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 accompanying claims.

In some examples, some manufacturers offer automatic brightness adjustment design concepts. The screen of the electronic equipment is provided with the automatic brightness adjusting function, the automatic brightness adjusting function adjusts the screen brightness based on the ambient light brightness, and the automatic brightness adjusting function and the ambient light brightness adjusting function form a simpler linear relation. For example, the higher the ambient light level during the day, the brighter the screen of the electronic device, and the darker the ambient light level during the night, the darker the screen of the electronic device.

However, since different users have different sensitivity to the screen brightness, the screen brightness needs of different users are different, and the automatically adjusted screen brightness cannot meet the needs of each user. In practice, users often complain that the automatically adjusted screen brightness is not satisfactory for different usage scenarios. For example, when reading books at night, the screen is too bright or too dark, the user experience is still uncomfortable, when photographing in daytime, the screen is too dark, the screen brightness still cannot meet the requirements of the user in the daytime, the brightness automatic adjusting function completely depends on the brightness of the environment, and the factors for determining the functions of the brightness automatic adjusting function are too few and cannot completely meet the favorites and requirements of different users.

The invention provides a screen brightness adjusting method which is applied to electronic equipment, and the screen brightness adjusting method comprises the steps of obtaining target application scene information; acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between an application scene and screen brightness information; determining recommended brightness information according to the historical adjustment data and the target application scene information; and adjusting the screen brightness of the electronic equipment according to the recommended brightness information. According to the method, the recommended brightness information corresponding to the target application scene information is found through the corresponding relation between the application scene and the screen brightness information, the screen brightness is adjusted for the user, the user demand is refined according to the application scene, the brightness value expected by the user is output, the actual brightness demand of the user is more met, and the use experience of the user is improved.

Fig. 1 is a flowchart illustrating a method for adjusting screen brightness according to an exemplary embodiment, and as shown in fig. 1, the method for adjusting screen brightness is used in an electronic device, the electronic device includes a processor, a memory for storing instructions executable by the processor, and the method for adjusting screen brightness includes the steps of:

s11, acquiring target application scene information.

In step S11, the processor of the electronic device acquires target application scenario information. When the electronic device detects that the user is using or operating the electronic device, if the user is touching a certain area of the screen, the screen detects operations such as pressing or sliding; or the user is using certain application program information, such as WeChat, QQ, video browser, game APP, photographing mode, and the like, and the processor acquires target application scene information so as to determine the application scene information currently used by the user. Wherein the application scenario information includes at least two of:

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

The application information is, for example, application software in the electronic device, and the application software is, for example, a communication application such as WeChat, a payment application such as payment treasures, a game application, a video application such as Tech, etc.

The time information such as time information includes a use time (XX time XX minutes) and a use date (XXXX year XX month XX day), which may be divided into working days and non-working days, or holidays and non-holidays, and the like.

The geographic location information may be, for example, GPS, etc. by using longitude and latitude to obtain structural address information, such as province+city+county+town+country+street+house number, etc., where the structural address information may be divided into a resident address and an extraordinary resident address.

The usage time and the ambient light brightness information may be classified as continuous values (continuous values), and the user information, the application information, the geographic location information, and the usage date may be classified as discrete values (discrete values), so that the application scenario information currently used by the user may be more accurately determined by adding more measurement factors.

It should be noted here that, when recording application scene information, user information may also be recorded, resulting in a customized personal screen adjustment. The user information is, for example, an ID account and password of the user, face information of the user, fingerprint information of the user, or the like.

S12, acquiring history adjustment data.

In step S12, the processor of the electronic device acquires history adjustment data. The historical adjustment data are used for representing the corresponding relation between the application scene and the screen brightness information.

In one example, the screen brightness information may be determined, for example, by a screen brightness value at the time of a previous manual adjustment by the user. For example, the processor collects screen brightness information for the electronic device, each screen brightness information having a corresponding screen brightness value at the time of manual adjustment, and stores it in the history adjustment data. Each application scene information has a corresponding relation with the screen brightness information, and a user can manually adjust the screen brightness according to own preference and the current application scene information in the use process so as to enable the screen brightness to be at a certain brightness value, thereby meeting the current use requirement. Each application scene information corresponds to one screen brightness information, and users may have different requirements under different environments, so that the users adjust the screen brightness information for multiple times, and the application scene information corresponds to one or more screen brightness values during manual adjustment.

When the processor collects the screen brightness value under the manual adjustment of the user, the processor can perform pre-filtering treatment (pre-filtering) on the discrete factors, namely, classify the discrete factors. Each category has a correspondence. The corresponding relation can be a corresponding relation table, is convenient to search, and can be an independent target brightness adjusting model for visual check.

For example, when the processor collects that the user a manually adjusts the screen brightness, for example, to 50nit (nit), 200nit, etc., the processor obtains the target application scene information, and determines the category of the discrete factor in the target application scene information. If the use date in the discrete factors is a working day, when the geographic position information of the user A is a resident address, the processor collects the current screen brightness information, makes the current screen brightness information into a corresponding relation and stores the corresponding relation in the history adjustment data.

For example, when the processor collects that the screen brightness is manually adjusted by the user B, for example, to 100nit, 300nit, etc., the processor obtains the target application scene information, and determines the category of the discrete factor in the target application scene information. If the use date in the discrete factors is a non-working day and the user B uses the microblog APP when the geographic position information is a resident address, the processor collects the current screen brightness information, makes the current screen brightness information into a corresponding relation and stores the corresponding relation in the history adjustment data.

In another example, the screen brightness information may be determined, for example, by a preset recommended brightness adjustment model that is set in the processor before shipment. And under each application scene information, corresponding 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 equipment, the processor indicates that the user does not manually adjust the screen brightness, and the corresponding relation between the target application scene information and the corresponding screen brightness information can be determined according to a preset recommended brightness adjustment model.

S13, determining recommended brightness information according to the historical adjustment data and the target application scene information.

In step S13, the processor determines recommended luminance information based on the history adjustment data and the application scene information. When the processor acquires the target application scene information, searching the historical adjustment data in a traversing mode to find screen brightness information corresponding to the target application scene information, and processing and analyzing the screen brightness information by the processor to determine recommended brightness information.

In one example, when the processor collects the screen brightness value manually adjusted by the user, the processor indicates that the user manually adjusts the screen, and the processor determines the screen brightness information according to the screen brightness value manually adjusted by the user.

The recommended luminance information is, for example, a screen luminance value that is most likely to be expected by the user, and the screen luminance value that is most likely to be expected by the user may be, for example, any one of screen luminance values when the user manually adjusts the screen luminance value in the past, or may be, based on the screen luminance value when the user manually adjusts the screen luminance value in the past, a more reasonable screen luminance value may be weighted.

Because of uncertainty and variability in user preferences, probability algorithms of density clustering may be employed to calculate the most likely desired screen brightness values for the user, or deep learning neural networks may be employed to model more complex nonlinear relationships to determine the most likely desired screen brightness values for the user.

In another example, when the processor does not collect the screen brightness value when the user manually adjusts, the processor indicates that the user does not manually adjust the screen, the processor can determine the screen brightness information corresponding to the target application scene information according to the preset recommended brightness adjustment model, and the processor processes and analyzes the screen brightness information to determine the recommended brightness information. In order to simplify the processing and analysis process and rapidly judge the recommended brightness information, the screen brightness information may correspond to the recommended brightness information, and the screen brightness information may be used as the recommended brightness information.

S14, adjusting the screen brightness of the electronic equipment 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 requirement. The recommended luminance information is, for example, a recommended screen luminance value, which may be, for example, but not limited to, 10nit, 100nit, 500nit.

According to the method, the recommended brightness information can be determined by performing targeted recommended brightness adjustment on each user based on the corresponding relation between the application scene information and the screen brightness information, so that the requirements of the users are met. The recommended screen brightness value meeting the user requirement is weighed out by applying the scene information and the screen brightness value of the user during the previous manual adjustment, so that the personal requirement of each user is more met, and the use experience of the user is improved.

Fig. 2 is a flowchart illustrating a method for adjusting screen brightness according to an exemplary embodiment, and as shown in fig. 2, the method for adjusting screen brightness is used in an electronic device, which includes a processor, and a memory for storing instructions executable by the processor. The historical adjustment data comprises a target brightness adjustment model, and the screen brightness adjustment method comprises the following steps of:

s21, acquiring historical screen brightness information corresponding to the same application scene information.

In step S21, the processor acquires the historical screen brightness information located in the same application scene information from the historical adjustment data. The processor can acquire all the historical screen brightness information located in the same application scene information.

S22, determining target brightness information according to the historical screen brightness information.

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

The plurality of historical screen brightness information can represent expected brightness values of a user on a screen at ordinary times, and a weighted operation value among the plurality of historical screen brightness information, namely a weighted average among the plurality of historical screen brightness information, can be used for calculating the requirement of the user on the screen brightness of the electronic equipment, and determining target brightness information. The plurality of screen luminance information are, for example, 35nit, 40nit, 45nit, respectively, and the target luminance information is 40nit.

In one example, referring to fig. 3, in step S22, a target brightness adjustment model is generated according to the target brightness information, and further includes the steps of:

s221, determining weight information of each historical screen brightness information.

In step S221, the processor determines weight information for each of the history screen brightness information. Wherein the weight information may be obtained by calculation. The weight information may, for example, obtain a binary normal probability distribution of application scene information for each of the historical screen brightness information. And determining the weight information of each historical screen brightness information according to each binary normal probability distribution. For example, a binary normal probability distribution of application scene information for each historical screen brightness information may be determined based on ambient light information and time information in the application scene information.

For example, when determining application scene information of historical screen brightness information, calculating a binary normal probability distribution, where the binary normal probability distribution is weight information, and a formula of the binary normal probability distribution is:

where X is amblevel (ambient light level, i.e., 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 of X and Y, ρ is the correlation coefficient of two variables X and Y; the calculation mode of ρ is:

the calculation method based on the probability weight can better avoid the influence of an outlier (outlier) factor, wherein the outlier is also called an escape value, which means that one or more values in the sample data have larger difference compared with other values, and if the probability that one value deviates from the average value of the sample data by less than or equal to 1/(2 n), the sample data should be discarded (wherein n is the number of samples of the sample data, and the probability can be estimated according to the distribution of the sample data). The probability weight can give more weight to the closer value, so that the capability of the algorithm for resisting uncertainty of manually adjusting brightness data is improved, and more accurate prediction is realized. For example, corresponding to the screen brightness information at the time of manual adjustment of fig. 12, a new target brightness adjustment model is shown in fig. 14 (view from z-axis) and the recommended brightness curve shown in fig. 15, as in fig. 14 and 15, the X-axis represents ambient light brightness information (ambientlight), the y-axis represents time information (minscale) such as from 0h to 24h, the unit is min, the z-axis represents target screen brightness information (bright) after manual adjustment, and different color depths in the figure represent different target screen brightness information, and specific values thereof can refer to the right bar chart corresponding to fig. 14 and 15; it should be noted that the target brightness adjustment model adjusts to the value of the screen brightness information brightness that the user has manually adjusted between the time of 20 hours and 24 hours in the area where the manually adjusted screen brightness information is dense. Therein, as shown in fig. 15, it can be seen that the new target brightness adjustment model does not follow a linear relationship. For the history adjustment data of the screen brightness information of the isolated manual adjustment (for example, at 15 hours and 17 hours), since the condition for exciting the recommended automatic brightness is not satisfied (detailed in the embodiment described later), the target brightness adjustment model remains the default preset recommended brightness adjustment model as in the area of the screen brightness information when the manual adjustment is not performed (detailed in the embodiment described later).

Since the user's preference for screen brightness may change over time, the algorithm for the weight information may have a self-updating capability. In step S221, the weight information of each historical screen brightness information is determined according to each binary normal probability distribution, as shown in fig. 4, and the determination may be performed by the following steps:

s2211, time information of each history screen brightness information is acquired.

In step S2211, the processor searches the history adjustment data in a traversal manner, and determines time information of each of the history screen brightness information according to the history adjustment data. The time point for storing the brightness information of each historical screen is determined by the time information, and the historical screen brightness information is arranged according to the sequence, so that early historical screen brightness information and recent historical screen brightness information are determined.

S2212, determining a weight coefficient of each historical screen brightness information according to the time information.

In step S2212, the processor determines a weight coefficient of each of the history screen brightness information according to 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. The weight coefficient is, for example, 0.7, 0.5, 0.3, 0, or the like.

S2213, determining the weight information of each historical screen brightness information according to the weight coefficient and the binary normal probability distribution.

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

In one example, the near-term to early weight information is defined as a first binary normal probability, a second binary normal probability, a third binary normal probability, a fourth binary normal probability, and a fifth binary normal probability according to a point in time of each of the historical screen brightness information. The weight coefficient given by the first binary normal probability is 0.7, that is, the first binary normal probability is multiplied by 0.7, so that updated weight information is obtained. The weight coefficient given by the second binary normal probability is 0.5, namely the second binary normal probability is multiplied by 0.5, so that updated weight information is obtained. The weight coefficient given by the third binary normal probability is 0.3, namely the third binary normal probability is multiplied by 0.3, and updated weight information is further obtained. The fourth binary normal probability and the fifth binary normal probability belong to sample data which are manually adjusted in an early stage, the later time point can accord with user preference, the earlier time point is separated from the current preference and demand of the user, the early time point can forget early screen brightness information through giving weight coefficients, the automatic updating capability is realized, the personal demand of the user is met, the user can adjust the screen brightness preference along with the change of the user, and the use experience of the user is improved.

S222, determining target brightness information according to the weight information and the historical screen brightness information.

In step S222, the processor determines target luminance information from the weight information and the historical screen luminance information. The average value between the products of each weight information and the corresponding historical screen brightness information is the determined target brightness information.

S23, generating a target brightness adjustment model according to the target brightness information and the corresponding application scene information.

In step S23, the processor generates a target brightness adjustment model from the target brightness information. The target brightness information can be directly defined as a target brightness value, and a target brightness adjustment model is constructed and formed, so that the determination mode is more visual, and the efficiency of screen brightness adjustment is improved. Alternatively, the target luminance information may be weighted so as to be converted into a target luminance value, and a new target luminance adjustment model may be constructed and formed.

The values in the target brightness adjustment model can be expressed as recommended brightness information. Wherein the recommended luminance information satisfies the following formula, for example:

the historical screen brightness information is all manually adjusted screen brightness values in the same use time, wherein i=1 and n is the number of times of all manually adjusted screen brightness in the use time. The binary normal probability is the sum of probabilities corresponding to the times of adjusting the brightness of the screen in the same use time.

According to the method, when the processor searches the historical adjustment data in a traversing mode, when the historical screen brightness information corresponding to the same application scene information in the historical adjustment data is queried, the fact that the screen brightness is manually adjusted by a user is indicated, the processor determines target brightness information based on the historical screen brightness information, and a target brightness adjustment model is generated by the target brightness information and the corresponding application scene information, so that the requirement of the user is met. Because the preference of the user changes along with time, different weight coefficients are given to the weight information of different dates so as to update the weight information, the preference of the user is adjusted at any time, the use experience of the user is improved, and the requirements of the user are met. And the weight information is determined by binary normal probability, so that the influence of outlier factors can be better avoided, the capability of an algorithm for resisting uncertainty of manually adjusting brightness data is improved, and the recommendation accuracy is improved. Different coefficients are given to the weight information at different time points, so that the premature historical screen brightness information is forgotten, and the self-updating capability is further realized.

Fig. 5 is a flowchart illustrating a method for adjusting screen brightness according to an exemplary embodiment, and as shown in fig. 5, the method for adjusting screen brightness is used in an electronic device, the electronic device includes a processor, a memory for storing instructions executable by the processor, and the method for adjusting screen brightness includes the steps of:

S31, acquiring target application scene information.

S32, acquiring history adjustment data.

S33, searching historical adjustment data in a traversing mode.

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

S34, determining whether screen brightness information exists in the history adjustment data.

In step S34, in the process of querying by the processor, if there is screen brightness information corresponding to the target application scene information, executing step S37-step S39; if there is no screen brightness information corresponding to the target application scene information, step S35 is performed.

S35, acquiring a preset recommended brightness adjustment model.

In step S35, a preset recommended brightness adjustment model is obtained, and the preset recommended brightness adjustment model is stored in the processor before shipment, so as to provide a basis for adjusting the brightness of the screen. For example, each application scene can be preset to have an independent adjustment model, and each preset recommended brightness value in the preset recommended brightness adjustment model has a corresponding relation with the application scene information.

S36, adjusting the screen brightness based on a 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, searching an adjusting model corresponding to the target application scene information according to a preset recommended brightness adjusting model, determining a preset recommended automatic brightness value based on the adjusting model, and adjusting the screen brightness of the electronic equipment according to the preset recommended automatic brightness value.

The preset recommended brightness adjustment model can be used for making a corresponding adjustment curve based on ambient light information, collecting favorites of different users, performing modeling analysis on the sample data, and calculating the corresponding adjustment curve, wherein the actual design is specific, and the method is not limited further. The preference of different users can be acquired, for example, data acquisition can be performed according to different user information, and each user can upload user information and application scene information to the cloud according to personal wish in the application process, so that analysis support is provided for the data.

S37, screen brightness information corresponding to the target application scene information in the history adjustment data is acquired.

S38, determining target brightness information according to the screen brightness information.

S39, determining recommended automatic brightness information according to the target brightness information.

The method performed in step S37 to step S39 is the same as the method performed in step S33 to step S35, and the detailed description is not repeated here.

According to the method, whether screen brightness information exists in the historical adjustment data or not is determined by the processor in a mode of searching the historical adjustment data, whether the screen brightness is manually adjusted by a user is further determined, if the screen brightness is not manually adjusted, the user is satisfied with a preset recommended brightness adjustment model, the processor adjusts the screen brightness of the electronic device based on the preset recommended brightness adjustment model, the current screen brightness meets the eye requirement of the user on target application scene information, and the visual effect of the user is improved.

Fig. 6 is a flowchart illustrating a method for adjusting screen brightness according to an exemplary embodiment, and as shown in fig. 6, the method for adjusting screen brightness is used in an electronic device, the electronic device includes a processor, a memory for storing instructions executable by the processor, and the method for adjusting screen brightness includes the steps of:

s41, acquiring target screen brightness information acting on the electronic equipment.

In step S41, the processor acquires 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 within the screen, and when the user touches the screen, who wants to adjust the screen brightness value, the user operates the brightness adjustment bar. The processor collects the user call-out brightness adjustment interface and adjusts the screen brightness value, and the processor obtains the screen brightness value, for example, the screen brightness value is adjusted to 100nit, or other screen brightness values meeting the user requirement.

The brightness adjusting interface can be arranged in a drop-down notification bar of the electronic equipment, can be arranged at any position of a screen display interface, or can be called out through a preset gesture, so that a brightness adjusting bar is displayed for a user, and the user can conveniently adjust the brightness adjusting bar.

When the manual adjustment by the user is completed, the notification bar may be slid up and down to hide the brightness adjustment bar. Alternatively, the processor conceals the brightness adjustment bar by clicking on a location other than the brightness adjustment bar. Or, if the processor does not acquire any operation of the brightness adjustment bar within the preset time period, the processor conceals the brightness adjustment bar.

The brightness adjustment bar may include a slider bar to facilitate a user's sliding adjustment of the brightness thereof. The brightness adjustment bar may further include a "+" - "key so that a user may click the" + "-" key to precisely adjust the brightness thereof. The brightness adjustment bar also includes a close key so that the user can quickly close the automatic recommended brightness adjustment function. The closing key can be represented in a graphic form, a text form or a symbol form, and the actual design is subject to.

S42, adjusting the screen brightness of the electronic equipment 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, a control instruction is sent to the screen to control the screen brightness of the electronic equipment to display with target screen brightness information.

S43, storing the target screen brightness information and the application scene information corresponding to the target screen brightness information into the history adjustment data.

In step S43, the processor stores the target screen brightness information and application scene information corresponding to the target screen brightness information to the history adjustment data. Under the target application scene information, if the user manually adjusts the screen brightness, the preset recommended automatic brightness value set by the preset recommended brightness adjustment model is indicated to not meet the current requirement of the user, and the processor can store the target screen brightness information and the current corresponding application scene information into the historical adjustment data so as to serve as sample data to provide data support for generating the target brightness adjustment model.

According to the method, the target screen brightness information of the electronic equipment acted by the user is obtained, the target screen brightness information is the screen brightness information value of the user when the user is currently manually adjusted, the screen is displayed according to the screen brightness value of the user when the user is currently manually adjusted, and the current requirement of the user is met. And the screen brightness information and the corresponding application scene information are synchronously stored in the historical adjustment data, so that a target brightness adjustment model applied in the future can conveniently provide data support, storage of targeted information is realized, personalized screen brightness designs of users are formulated, the requirements of different users are met, and the use experience of the users is improved.

In step S43, the target screen brightness information and the application scene information corresponding to the target screen brightness information are stored in the history adjustment data, as shown in fig. 7. The method also comprises the following steps:

s431, taking 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 takes current screen brightness information for screen brightness adjustment, and application scene information corresponding to target screen brightness information, as sample data. Wherein the sample data may include a plurality of target screen brightness information for screen brightness adjustment. For example, when the user a uses WeChat at 22 points in a plurality of days, each of the screen brightness is manually adjusted, and then each of the target screen brightness information for the screen brightness adjustment, and the corresponding application scene information, is used as sample data.

S432, analyzing the sample data to determine whether the sample data meets the condition of exciting the recommended automatic brightness.

In step S432, the processor analyzes the sample data to determine whether the sample data satisfies a condition for activating the recommended automatic brightness. Because of the possibility of accidental and erroneous adjustment when the user manually adjusts the screen brightness, conditions for activating the recommended automatic brightness effect can be set so as to determine whether the sample data is valid data.

In step S432, the analysis is performed on the sample data to determine whether the sample data meets the condition of activating the recommended automatic brightness, including the following steps:

s4321, determining the distribution state of the target screen brightness information in the sample data according to the sample data.

In step S4321, the processor determines a distribution state of the target screen brightness information in the sample data from the sample data. Wherein, the distribution state of the target screen brightness information in the sample data is determined by calculating the binary normal probability distribution (bivariate Gaussian Distribution) of each target screen brightness information by using a density clustering (density-based classification) method. The formula of the binary normal probability distribution is:

Where X is amblevel (ambient light level, i.e., ambient light level information), Y can be understood as time, δx and δy are the variances of X and Y, μx and μy are the mean of X and Y, ρ is the correlation coefficient of two variables X and Y; the calculation mode of ρ is:

for example, the category in which the user is currently located is short video APP, the processor acquires the current ambient light level of 12lux through the sensor, the service time of 22 points, and the processor acquires the screen brightness adjusted for several times to generate a plurality of sample data. The adjustment is performed several times, which means that the user cannot always adjust to the most satisfactory state once in the adjustment process, and when the adjusted screen brightness is not suitable, the adjustment is performed several times until the most satisfactory screen brightness is reached. When the binary normal probability distribution is calculated, the probability of the binary normal probability f in the current use time can be determined to be larger, and the probability that the user adjusts the screen brightness in the use time is larger.

And the user may have a false touch during the adjustment process. For example, the category in which the user is currently located is short video APP, the processor acquires that the current ambient light brightness is 1ux through the sensor, the service time is 2 points, and the processor acquires that the primary screen brightness is adjusted. When the binary normal probability distribution is calculated, the probability of the binary normal probability f in the current use time can be determined to be smaller, and the possibility of false touch of a user in the use time is high.

S4322, analyzing the distribution state of the brightness information of the target screen, and determining the relevance of two adjacent sample values in the sample data.

In step S4332, the processor analyzes the distribution state of the target screen brightness information to determine the correlation between two adjacent sample values in the sample data. Wherein, the relevance is characterized by the possibility that the user manually adjusts the brightness of the screen in a specific environment light brightness and a specific using time. The stronger the association, the greater the likelihood that the user will manually turn the screen brightness, rather than a false touch, and the weaker the association, the greater the likelihood that the user will false touch.

In one example, in a certain category, the processor collects a plurality of target screen brightness information, referring to fig. 12, the X-axis in fig. 12 represents ambient light brightness information (ambientlight), the y-axis represents time information (miniscale) such as from 0h to 24h, the unit is min, and the z-axis represents target screen brightness information (bright) after hand adjustment, and different color depths in fig. 12 represent different target screen brightness information, the values of which correspond to the right bar graph in fig. 12. In order to confirm the relevance of two adjacent sample values in sample data, a density clustering (density-based classification) method is utilized to calculate a binary normal probability distribution (bivariate Gaussian Distribution) of application scene information of brightness information of each target screen, and the relevance of the two sample values is determined according to the binary normal probability distribution. For example, when the ambient light level information is 12lux and the usage time is 22 points, the binary positive probability distribution of the points is shown with reference to fig. 13, in fig. 13, the X-axis represents amblevel (ambient light level, i.e., ambient light level information), the Y-axis represents time (time information), and the bar graph represents correlation (e.g., correlation coefficient, which represents probability of correlation degree of corresponding coordinate points) between different coordinate points. The probability is greater the closer the second point is to the point, representing a stronger correlation of the two points, and conversely the probability is less the farther the two points are from each other, the weaker the correlation.

For example, the probability of the two target screen brightness information being formed is f ₁ =0.6 and f ₂ =0.65, both probabilities are large and both are greater than 0.5, indicating a strong correlation of the two sample values.

S4323, based on the relevance, determining whether the sample data meets the condition of exciting the recommended automatic brightness.

In step S4323, the processor determines whether the sample data satisfies a condition that motivates recommended automatic brightness according to the correlation. The more correlated sample data, i.e., the condition of the excitation recommended automatic brightness is satisfied, the step S433 is executed in response to the sample data satisfying the condition of the excitation recommended automatic brightness, and the step S434 is executed in response to the sample data not satisfying the condition of the excitation recommended automatic brightness if the sample data with the weaker correlation is not satisfied.

And when the probability distribution of the application scene information of at least one target screen brightness information is positioned at a position larger than 0.5 or the probability distribution of the application scene information of at least two target screen brightness information is positioned at a position larger than 0.625 in the sample data, the condition of exciting the recommended automatic brightness is satisfied.

S433, responding to the sample data meeting the condition of exciting the recommended automatic brightness, and recording the sample data to the history adjustment data.

In step S433, the processor records sample data satisfying the condition for exciting the recommended automatic brightness to the history adjustment data for use in generating the target brightness adjustment model.

S434, discarding the sample data.

According to the method, when the probability distribution of at least one screen brightness is located at a position larger than 0.5 or the probability distribution of at least two screen brightness is located at a position larger than 0.625 in the sample data, the condition of exciting the recommended automatic brightness is met, the great influence of the misadjustment on the target brightness adjustment model is effectively avoided, the accuracy of the recommended automatic adjustment is improved, and the use experience of a user is improved.

It will be appreciated that when all the target screen brightness information is stored in the history data, the history screen brightness information is formed, i.e. the history screen brightness information is used for subsequent calculation and use.

Fig. 8 is a flowchart illustrating a method for adjusting screen brightness according to an exemplary embodiment, and as shown in fig. 8, the method for adjusting screen brightness is used in an electronic device, the electronic device includes a processor and a sensor, a memory for storing instructions executable by the processor, the sensor is used for acquiring ambient light brightness information and screen brightness information, and the method for adjusting screen brightness includes the steps of:

S61, acquiring current screen brightness information and environment brightness information.

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

And S62, displaying warning information when the absolute value of the difference value between the current screen brightness information and the environment brightness information is larger than a preset threshold value.

In step S62, the processor compares the current screen brightness information and the ambient light brightness information to determine a difference value between the current screen brightness information and the ambient light brightness information. And displaying warning information when the absolute value of the difference value between the current screen brightness information and the environment brightness information is larger than a preset threshold value. The display warning information can be displayed in a popup window mode in a display interface of a screen, and prompts a user that the current screen brightness information is not matched with the environment brightness information in the current environment, and the screen brightness information should be adjusted. The display warning information can be in the form of sound directly for prompting the user, and when the current screen brightness information is not matched with the environment brightness information in the current environment, a prompting voice can be sent, and the prompting voice is used as warning words such as unsafe, irregular screen brightness and the like.

When the warning information is displayed, the processor can send out an instruction for adjusting the screen brightness, so that the current screen brightness information is matched with the environment brightness information, and eyes of a user are protected.

In the method in this embodiment, by calculating the difference between the current screen brightness information and the ambient light brightness information, it is determined whether the current usage mode of the user is inappropriate. If the display device is improperly used, warning information is displayed to remind the user to change the current screen brightness information, so that the user is prevented from developing bad use habits, and eyes of the user are protected. For example, when a user looks at the mobile phone for a long time, the ambient light brightness information is 10lux, the current environment is dim, and the user adjusts the screen brightness information to 100nit, so that the screen brightness is bright, and the user can stimulate human eyes to cause injury. When a user looks at the mobile phone for a long time, the ambient light brightness information is 100lux, the current environment is brighter, and the user adjusts the screen brightness information to 10nit, so that the screen brightness is darker, and eye fatigue and even irreversible injury can be caused by long-time looking. In the method, the display of the warning information guides the brightness of the screen of the user to be kept in a proper brightness interval as much as possible, so that the health of a human body is protected.

It should be noted that, the method in the disclosure is not limited to monitoring the brightness information of the screen, but can also monitor the use time of the user, if the use time of the user is too long, the warning information can be displayed, and the screen is adjusted to be warm, so that the brightness of the screen is kept in the state of optimally protecting the eyes. And/or, the method in the disclosure can also monitor the display content of the screen display interface and determine the spectrum of the display content. The blue spectrum has the greatest damage to eyes of a user, the sleeping quality of the user can be affected, and if the user watches the content with the blue spectrum for a long time, warning information is displayed to remind the user, and the user is guided to protect eyes. The monitoring method can be used for setting specific conditions and numerical values of human eye health by recommending automatic brightness model setting so as to correct bad use habits of users and protect the physical health of the users.

According to the screen brightness adjusting method, a more complex nonlinear relation is simulated by using a density clustering probability algorithm or a deep learning neural network, application scene information is introduced and classified, requirements of users are refined according to the application scene information, and a targeted adjusting design is carried out on each user. Wherein the application scenario information includes at least two of: ambient light information, application information, time information, and geographic location information; wherein the time information includes a use time and a use date.

And automatically calculating a target brightness adjustment model according to a plurality of factors in the target application scene information and historical screen brightness information of the historical adjustment data when a user manually adjusts the target application scene information, so that a screen brightness value expected by the user is output in the same future application scene information, and based on the target brightness adjustment model, the screen brightness information corresponding to the same application scene information is determined to adjust the screen brightness of the electronic equipment, thereby meeting the requirements of different users.

Because the preference of the user has uncertainty and variability, the most likely expected screen brightness value of the user is calculated by using a probability algorithm of density clustering, the influence of outlier factors can be better avoided, the capability of the algorithm for resisting the uncertainty of manually adjusting brightness data is improved, and more accurate prediction is realized. And the screen brightness of each manual adjustment is calculated in a probability mode, the relevance between screen brightness information is determined, the influence caused by uncertain factors is reduced, and the requirements of users are predicted better.

And a deep learning neural network can be used for replacing a density clustering algorithm to simulate a more complex nonlinear relation so as to generate a target brightness adjustment model and improve accuracy.

The human eye protection mechanism is arranged in the human eye protection system, so that bad use habits of users are timely reminded, the damage to human health is avoided, and more humanized design is realized.

Fig. 9 is a block diagram illustrating a screen brightness adjusting apparatus according to an exemplary embodiment. Referring to fig. 9, the apparatus 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 obtaining module 710 is configured to obtain history adjustment data, where the history adjustment data is used to characterize a correspondence between an application scene and screen brightness information. The determining module 720 is configured to determine recommended brightness information based on the historical adjustment data and the target application scenario information. The adjustment module 730 is configured to adjust the screen brightness of the electronic device according to the recommended brightness information.

The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.

Fig. 10 is a block diagram of an electronic device 800, according to an example embodiment. For example, electronic device 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.

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

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

The memory 804 is configured to store various types of data to support operations at the 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, and so forth. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices 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 or optical disk.

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

The multimedia component 808 includes a screen between the electronic device 800 and the user that provides an output interface. 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 a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operational mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

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

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.

The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the electronic device 800. For example, the sensor assembly 814 may detect an on/off state of the device 800, a relative positioning of the components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in position of the electronic device 800 or a component of the electronic device 800, the presence or absence of a user's contact with the electronic device 800, an orientation or acceleration/deceleration of the electronic device 800, and a change in temperature of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The 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 assembly 814 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices, either wired or wireless. The electronic device 800 may 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 communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may 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, the electronic device 800 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 elements for executing the methods described above.

In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 804 including instructions executable by processor 820 of electronic device 800 to perform the above-described method. 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, which when executed by a processor of an electronic device, causes the electronic device to perform a method of adjusting screen brightness, the method comprising:

and acquiring target application scene information. And acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between the application scene and the screen brightness information. And determining recommended brightness information according to the historical adjustment data and the target application scene information. And adjusting the screen brightness of the electronic equipment according to the recommended brightness information.

Fig. 11 is a block diagram illustrating an adjustment apparatus 900 for screen brightness according to an exemplary embodiment. For example, apparatus 900 may be provided as a server. Referring to FIG. 11, apparatus 900 includes a processing component 922 that further includes one or more processors, and memory resources represented by memory 932, for storing instructions, such as applications, executable by processing component 922. The application programs stored in memory 932 may include one or more modules that each correspond to a set of instructions. Further, processing component 922 is configured to execute instructions to perform the above-described methods.

The apparatus 900 may also include a power component 926 configured to perform power management of the apparatus 900, a wired or wireless network interface 950 configured to connect the apparatus 900 to a network, and an input output (I/O) interface 958. The device 900 may operate based on an operating system stored in memory 932, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.

Other embodiments of the invention will be 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 following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. 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 invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.

Industrial applicability

According to the method, the recommended brightness information corresponding to the target application scene information is found through the corresponding relation between the application scene and the screen brightness information, the screen brightness is adjusted for the user, the user demand is refined according to the application scene, the brightness value expected by the user is output, the actual brightness demand of the user is more met, and the use experience of the user is improved.

Claims

A method for adjusting screen brightness, wherein the method for adjusting screen brightness comprises the following steps:

acquiring target application scene information;

acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between application scene information and screen brightness information;

determining recommended brightness information according to the history adjustment data and the target application scene information;

and adjusting the screen brightness of the electronic equipment according to the recommended brightness information.
The method of adjusting screen brightness according to claim 1, wherein the history adjustment data includes a target brightness adjustment model, the method of adjusting screen brightness further comprising:

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

determining target brightness information according to the historical screen brightness information; the target brightness information is a weighted operation value based on the brightness information of each historical screen;

And generating a target brightness adjustment model according to the target brightness information and the corresponding application scene information.
The method for adjusting screen brightness according to claim 2, wherein the determining target brightness information according to the historical screen brightness information comprises:

determining weight information of each historical screen brightness information;

and determining target brightness information according to the weight information and the historical screen brightness information.
A method of adjusting screen brightness according to claim 3, wherein said determining weight information for each of said historical screen brightness information comprises:

acquiring binary normal probability distribution of application scene information of each historical screen brightness information;

and determining weight information of each historical screen brightness information according to each binary normal probability distribution.
The screen brightness adjustment method according to claim 4, wherein the determining the weight information of each of the historical screen brightness information according to each of the binary normal probability distributions comprises:

acquiring time information of each historical screen brightness information;

determining a weight coefficient of each historical screen brightness information according to the time information;

And determining the weight information of each historical screen brightness information according to the weight coefficient and the binary normal probability distribution.
The method for adjusting screen brightness according to claim 1, wherein the method for adjusting screen brightness further comprises:

searching the historical adjustment data in a traversing mode, and acquiring a preset recommended brightness adjustment model if screen brightness information corresponding to the target application scene information does not exist;

and adjusting 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 comprises:

acquiring target screen brightness information acting on the electronic equipment; the target screen brightness information is used for representing screen brightness information after manual adjustment;

adjusting the screen brightness of the electronic equipment according to the target screen brightness information;

and storing the target screen brightness information and application scene information corresponding to the target screen brightness information into the history adjustment data.
The screen brightness adjustment method according to claim 7, wherein the storing the target screen brightness information and application scene information corresponding to the target screen brightness information to the history adjustment data includes:

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

and responsive to the sample data meeting a condition that motivates recommended automatic brightness, recording the sample data to historical adjustment data.
The method of adjusting screen brightness according to claim 8, wherein the recording the sample data to historical adjustment data in response to the sample data satisfying a condition that a recommended automatic brightness is excited comprises:

determining the distribution state of the target screen brightness information in the sample data according to the sample data;

analyzing the distribution state of the brightness information of the target screen, and determining the relevance of two adjacent sample values in the sample data;

based on the correlation, the sample data is recorded to historical adjustment data in response to the sample data meeting a condition that motivates recommended automatic brightness.
The method for adjusting screen brightness according to claim 1, wherein the method for adjusting screen brightness further comprises:

acquiring current screen brightness information and ambient light brightness information;

and displaying warning information when the absolute value of the difference value between the current screen brightness information and the environment brightness information is larger than a preset threshold value.
The method for adjusting screen brightness according to claim 1, wherein the application scene information includes at least two of:

ambient light information, application information, time information, and geographic location information; wherein the time information includes a use time and a use date.
A screen brightness adjusting device is applied to electronic equipment, and comprises:

the first acquisition module is used for acquiring the target application scene information;

the second acquisition module is used for acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between the application scene and the screen brightness information;

the determining module is used for determining recommended brightness information according to the historical adjustment data and the target application scene information;

and the adjusting module is used for adjusting the screen brightness of the electronic equipment according to the recommended brightness information.
An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to:

acquiring target application scene information;

acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between an application scene and screen brightness information;

Determining recommended brightness information according to the history adjustment data and the target application scene information;

and adjusting the screen brightness of the electronic equipment according to the recommended brightness information.
A non-transitory computer readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform a method of adjusting screen brightness, the method comprising:

acquiring target application scene information;

acquiring history adjustment data, wherein the history adjustment data is used for representing the corresponding relation between an application scene and screen brightness information;

determining recommended brightness information according to the history adjustment data and the target application scene information;

and adjusting the screen brightness of the electronic equipment according to the recommended brightness information.