CN112202962B - Screen brightness adjusting method and device and storage medium - Google Patents
Screen brightness adjusting method and device and storage medium Download PDFInfo
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- CN112202962B CN112202962B CN202010887665.4A CN202010887665A CN112202962B CN 112202962 B CN112202962 B CN 112202962B CN 202010887665 A CN202010887665 A CN 202010887665A CN 112202962 B CN112202962 B CN 112202962B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/02—Constructional features of telephone sets
- H04M1/22—Illumination; Arrangements for improving the visibility of characters on dials
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05B—ELECTRIC HEATING; ELECTRIC LIGHT SOURCES NOT OTHERWISE PROVIDED FOR; CIRCUIT ARRANGEMENTS FOR ELECTRIC LIGHT SOURCES, IN GENERAL
- H05B47/00—Circuit arrangements for operating light sources in general, i.e. where the type of light source is not relevant
- H05B47/10—Controlling the light source
- H05B47/105—Controlling the light source in response to determined parameters
- H05B47/11—Controlling the light source in response to determined parameters by determining the brightness or colour temperature of ambient light
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B20/00—Energy efficient lighting technologies, e.g. halogen lamps or gas discharge lamps
- Y02B20/40—Control techniques providing energy savings, e.g. smart controller or presence detection
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Abstract
The disclosure relates to a screen brightness adjusting method, a screen brightness adjusting device and a storage medium. The screen brightness adjusting method is applied to a terminal, the terminal comprises a front light sensor and a rear light sensor, and the screen brightness adjusting method comprises the following steps: acquiring first light sensing data acquired by the front light sensor and second light sensing data acquired by the rear light sensor, and determining current attitude data of the terminal; and determining the current light sensing data of the terminal according to the first light sensing data, the second light sensing data and the current posture data, and adjusting the screen brightness of the terminal according to the current light sensing data. By the method and the device, the current light sensing data of the terminal can be accurately obtained, and the screen brightness of the terminal can be accurately adjusted.
Description
Technical Field
The present disclosure relates to the field of terminal technologies, and in particular, to a method and an apparatus for adjusting screen brightness, and a storage medium.
Background
At present, all devices such as terminals are provided with light sensors for detecting the light intensity of the environment. When the terminal detects that the light intensity of the environment changes through the light sensor, the screen brightness can be automatically adjusted, and therefore a user can clearly view contents displayed in the screen.
In practical applications, the intensity of the light detected by the light sensor may be affected by various factors, such as the light sensor being blocked. Especially, with the development of a full-screen terminal, a sensor under the screen is adopted by each large terminal manufacturer, and the condition that light is shielded is more obvious. The condition of shielding the light sensor can occur in a plurality of use scenes, so that the light data reported by the light sensor is inaccurate, and further the condition of inaccurate screen brightness adjustment occurs.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides a screen brightness adjusting method, apparatus, and storage medium.
According to a first aspect of the embodiments of the present disclosure, there is provided a screen brightness adjusting method, where the screen brightness adjusting method is applied to a terminal, the terminal includes a front light sensor and a rear light sensor, and the screen brightness adjusting method includes: acquiring first light sensing data acquired by the front light sensor and second light sensing data acquired by the rear light sensor, and determining current attitude data of the terminal; and determining the current light sensing data of the terminal according to the first light sensing data, the second light sensing data and the current posture data, and adjusting the screen brightness of the terminal according to the current light sensing data.
In one example, the determining the current light sensing data of the terminal according to the first light sensing data, the second light sensing data and the current posture includes: calling a preset model; and inputting the first light sensing data, the second light sensing data and the current posture data into the preset model, and determining the current light sensing data based on the output of the preset model.
In one example, the screen brightness adjusting method further includes: and obtaining the preset model based on feedforward neural network training.
In one example, the training based on the feedforward neural network to obtain the preset model includes: acquiring a plurality of groups of training data, wherein each group of training data in the plurality of groups of training data comprises first light sensing data, second light sensing data and posture data of a terminal at the same moment; inputting a plurality of groups of training data into a feedforward neural network, and outputting predicted light sensing data of the plurality of groups of training data through the feedforward neural network; and adjusting parameters of the feedforward neural network based on the predicted light sensing data and the loss function to obtain the preset model meeting the loss value.
In an example, the terminal is installed with an inertial measurement unit IMU, and the acquiring current attitude data of the terminal includes: acquiring current attitude data of the terminal on a three-dimensional coordinate plane based on the IMU; preprocessing the current attitude data of the terminal on a three-dimensional coordinate plane to obtain the current inclination angle of the terminal relative to the ground; and taking the current inclination angle of the terminal relative to the ground as the current attitude data of the terminal.
According to a second aspect of the embodiments of the present disclosure, there is provided a screen brightness adjusting apparatus applied to a terminal including a front light sensor and a rear light sensor, the screen brightness adjusting apparatus including: the acquisition unit is configured to acquire first light sensing data acquired by the front light sensor and second light sensing data acquired by the rear light sensor and determine current attitude data of the terminal; and the processing unit is configured to determine current light sensing data of the terminal according to the first light sensing data, the second light sensing data and the current posture data, and adjust the screen brightness of the terminal according to the current light sensing data.
In an example, the processing unit determines current light sensing data of the terminal according to the first light sensing data, the second light sensing data and the current posture in the following manner: calling a preset model; and inputting the first light sensing data, the second light sensing data and the current posture data into the preset model, and determining the current light sensing data based on the output of the preset model.
In an example, the apparatus further comprises a training unit; the training unit is configured to derive the preset model based on a feed-forward neural network training.
In one example, the training unit is trained to obtain the preset model based on a feedforward neural network in the following manner: acquiring a plurality of groups of training data, wherein each group of training data in the plurality of groups of training data comprises first light sensing data, second light sensing data and posture data of a terminal at the same moment; inputting a plurality of groups of training data into a feedforward neural network, and outputting predicted light sensing data of the plurality of groups of training data through the feedforward neural network; and adjusting parameters of the feedforward neural network based on the predicted light sensing data and the loss function to obtain the preset model meeting the loss value.
In an example, the terminal is provided with an inertial measurement unit IMU, and the obtaining unit obtains current attitude data of the terminal in the following manner: acquiring current attitude data of the terminal on a three-dimensional coordinate plane based on the IMU; preprocessing the current attitude data of the terminal on a three-dimensional coordinate plane to obtain the current inclination angle of the terminal relative to the ground; and taking the current inclination angle of the terminal relative to the ground as the current attitude data of the terminal.
According to a third aspect of the present disclosure, there is provided a screen brightness adjusting apparatus including: a memory configured to store instructions. And a processor configured to invoke instructions to perform the screen brightness adjustment method in the foregoing first aspect or any example of the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform the screen brightness adjustment method of the foregoing first aspect or any one of the examples of the first aspect.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: by determining the terminal pose data, the current pose of the terminal can be determined. And then according to the current posture of the terminal, the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor, the reliability of the light sensing data collected by the front light sensor and the reliability of the light sensing data collected by the rear light sensor in the current light sensing data of the terminal can be determined, the reliability of the front light sensor and the reliability of the rear light sensor are determined, the weight respectively occupied by the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor is determined, the current light sensing data of the terminal can be accurately obtained, and the aim of accurately adjusting the screen brightness of the terminal is fulfilled.
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 present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart illustrating a screen brightness adjustment method according to an exemplary embodiment.
Fig. 2 is a diagram illustrating an acquisition of first light sensing data, second light sensing data, and current pose data of a terminal according to an exemplary embodiment.
Fig. 3 is a flowchart illustrating a screen brightness adjustment method according to an exemplary embodiment.
FIG. 4 is a flow diagram illustrating training a predictive model according to an exemplary embodiment.
Fig. 5 is a schematic diagram illustrating application of the screen brightness adjustment method of the present disclosure using a feedforward neural network model according to an exemplary embodiment.
Fig. 6 is a block diagram illustrating a screen brightness adjustment apparatus according to an exemplary embodiment.
Fig. 7 is a block diagram illustrating a device for adjusting screen brightness according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The technical scheme of the exemplary embodiment of the present disclosure can be applied to an application scenario in which the screen brightness of the terminal is adjusted. In the exemplary embodiments described below, a terminal is sometimes also referred to as an intelligent terminal device, where the terminal may be a Mobile terminal, and may also be referred to as a User Equipment (UE), a Mobile Station (MS), and the like. A terminal is a device that provides voice and/or data connectivity to a user, or a chip disposed within the device, such as a handheld device, a vehicle-mounted device, etc. with wireless connectivity. Examples of terminals may include, for example: the Mobile terminal comprises a Mobile phone, a tablet computer, a notebook computer, a palm computer, mobile Internet Devices (MID), a wearable device, a Virtual Reality (VR) device, an Augmented Reality (AR) device, a wireless terminal in industrial control, a wireless terminal in unmanned driving, a wireless terminal in remote operation, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home and the like.
In the related art, when the brightness of a screen of a terminal is adjusted, the brightness is mainly adjusted by using light data collected by a front light sensor installed on the terminal. Because the visual angle of a person is opposite to the direction of light collected by the front light sensor, when the screen of the terminal is adjusted by utilizing light data collected by the front light sensor, the brightness of the screen is often different from the expected brightness, and discomfort of the eyes of the person is caused. Furthermore, the rear light sensor is installed in the terminal, and the possibility of stable and accurate screen brightness of the terminal is provided based on the fact that the front light sensor is matched with the rear light sensor.
However, in practical applications, situations such as the terminal being horizontally placed on a desktop often occur, so that the rear light sensor is shielded and loses effect, or in the using process of the terminal, the front light sensor is shielded, so that light data reported based on the light sensor is inaccurate, screen brightness adjustment is inaccurate, backlight of the terminal screen is unstable, and the use of a user is affected.
The embodiment of the disclosure provides a screen brightness adjusting method. In the screen brightness adjusting method, the current posture of the terminal can be determined by determining the posture data of the terminal. And then according to the current posture of the terminal, the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor, the weight respectively occupied by the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor in the current light sensing data of the terminal can be determined, so that the current light sensing data of the terminal can be accurately obtained, and the purpose of accurately adjusting the screen brightness of the terminal is realized.
Fig. 1 is a flowchart illustrating a screen brightness adjusting method according to an exemplary embodiment, where the screen brightness adjusting method is used in a terminal, as shown in fig. 1, and includes the following steps.
In step S11, first light sensing data collected by the front light sensor and second light sensing data collected by the rear light sensor are obtained, and current attitude data of the terminal is determined.
In the present disclosure, the terminal includes a front light sensor and a rear light sensor. For convenience of description, the light sensing data collected by the front light sensor is referred to as first light sensing data, and the light sensing data collected by the rear light sensor is referred to as second light sensing data.
In order to avoid the instability of the first light sensing data acquired when the front light sensor is shielded or the instability of the second light sensing data acquired when the rear light sensor is shielded, the light sensing data of the terminal is acquired with errors, and the accuracy of the screen brightness is affected. In one embodiment, after light sensing data are respectively obtained by a front light sensor and a rear light sensor, current attitude data of the terminal in a three-dimensional coordinate plane are obtained according to an Inertial Measurement Unit (IMU) including an acceleration sensor and a gyroscope. And preprocessing the acquired current attitude data of the terminal on the three-dimensional coordinate plane according to the current attitude data of the terminal on the three-dimensional coordinate plane to obtain the current inclination angle of the terminal relative to the ground. According to the obtained current inclination angle of the terminal relative to the ground, the first light sensing data collected by the front light sensor and the second light sensing data collected by the rear light sensor, whether the front sensor or the rear sensor of the current terminal is shielded or not can be determined.
For example, the terminal may have one side of the terminal screen as an X-axis and the other side of the terminal screen as a Y-axis in a three-dimensional coordinate plane, and a direction perpendicular to the terminal screen as a Z-axis in the three-dimensional coordinate plane. After the IMU acquires the current attitude data of the terminal on a three-dimensional coordinate plane, the terminal is determined to be in a motion state at present, and according to the fixed relation between the accelerometer sensor coordinate system and the terminal position coordinate system, the inclination angle between the current terminal and the ground can be obtained to be 30 degrees, so that the situation that the terminal is in upward motion and the light sensing data acquired by the front light sensor device and the rear light sensor device are reliable can be determined.
For another example, after the IMU acquires the current attitude data of the terminal on the three-dimensional coordinate plane, the terminal is determined to be in the static state at present, and according to the fixed relation between the accelerometer sensor coordinate system and the terminal position coordinate system, the inclination angle between the current terminal and the ground can be obtained to be 10 degrees, so that the terminal can be determined to be in the static state, and according to the current static state of the terminal and the light sensing data acquired by the front light sensor and the rear light sensor, whether the light sensor is shielded or not and whether the light sensing data acquired by the light sensor is reliable or not can be comprehensively obtained.
Fig. 2 is a diagram illustrating an acquisition of first light sensing data, second light sensing data, and current pose data of a terminal according to an exemplary embodiment. In fig. 2, before the brightness of the terminal screen is adjusted, first light sensing data L1 is acquired by a front light sensor of the terminal, second light sensing data L2 is acquired by a rear light sensor of the terminal, and a current inclination angle θ of the terminal relative to the ground is obtained after preprocessing current attitude data of the terminal obtained based on the IMU.
In step S12, current light sensing data of the terminal is determined according to the first light sensing data, the second light sensing data, and the current posture data, and the screen brightness of the terminal is adjusted according to the current light sensing data.
In a real-time mode, after first light sensing data, second light sensing data and current terminal attitude data are acquired, whether the light sensing data acquired by a front light sensor and the light sensing data acquired by a rear light sensor are reliable or not can be determined according to the first light sensing data, the second light sensing data and the current terminal attitude data, and further, the weights respectively occupied by the light sensing data acquired by the front light sensor and the light sensing data acquired by the rear light sensor can be obtained according to the reliability of the light sensing data acquired by the front light sensor and the reliability of the light sensing data acquired by the rear light sensor, so that the current light sensing data of the terminal can be accurately obtained, and the aim of accurately adjusting the screen brightness of the terminal can be fulfilled.
For example, before the brightness of a screen of a terminal is adjusted, the first light sensing data is 300 lux, the second light sensing data is 20 lux, the current inclination angle of the terminal relative to the ground is 0 according to the current attitude data of the terminal, and according to the first light sensing data, the second light sensing data and the current inclination angle of the terminal relative to the ground, it can be determined that a rear light sensor of the terminal is shielded, the reliability of the light sensing data acquired by the front light sensor is high, and when the current light sensing data of the terminal is determined, the current light sensing data of the terminal can be determined and obtained mainly based on the first light sensing data acquired by the front light sensor, and the brightness of the screen of the terminal is adjusted.
For another example, before the screen brightness of the terminal is adjusted, the first light sensing data is 500 lux, the second light sensing data is 300 lux, the current inclination angle of the terminal relative to the ground is 0% according to the current attitude data of the terminal, and the terminal can be determined to be used under the backlight according to the first light sensing data, the second light sensing data and the current inclination angle of the terminal relative to the ground, and the first light sensing data and the second light sensing data have high reliability and accuracy. For example, the weight of the first light sensing data collected by the front light sensor is set to 0.7, the weight of the second light sensing data collected by the rear light sensor is set to 0.3, the current light sensing data of the terminal is determined, and the brightness of the screen of the terminal is adjusted.
In an exemplary embodiment of the present disclosure, by determining terminal pose data, the current pose of the terminal may be determined. And then according to the current posture of the terminal, the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor, the reliability of the light sensing data collected by the front light sensor and the reliability of the light sensing data collected by the rear light sensor in the current light sensing data of the terminal can be determined, the reliability of the front light sensor and the reliability of the rear light sensor are determined, the weight respectively occupied by the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor is determined, the current light sensing data of the terminal can be accurately obtained, and the aim of accurately adjusting the screen brightness of the terminal is fulfilled.
In order to further accurately judge the light sensing data of the terminal, in one embodiment, the method and the device predict the input first light sensing data and second light sensing data and the current attitude data of the terminal according to a pre-trained model, accurately output the current light sensing data of the terminal, and achieve the purpose of accurately adjusting the screen brightness of the terminal.
Fig. 3 is a flowchart illustrating a screen brightness adjusting method according to an exemplary embodiment, where the screen brightness adjusting method is used in a terminal, as shown in fig. 3, and includes the following steps.
In step S21, first light sensing data collected by the front light sensor and second light sensing data collected by the rear light sensor are obtained, and current attitude data of the terminal is determined.
In step S22, a preset model is called, the first light sensing data, the second light sensing data and the current posture data are input into the preset model, the current light sensing data is determined based on the output of the preset model, and the screen brightness of the terminal is adjusted according to the current light sensing data.
The preset model in the present disclosure may be a feedforward neural network, a convolutional neural network, or the like, and the present disclosure is not limited herein.
The feedforward neural network has the characteristics of classification, clustering and prediction, and the feedforward neural network has the characteristic that the feedforward neural network can learn the implicit knowledge in the data through training the historical data according to a certain amount of historical data, so that a model capable of accurately predicting the ambient light data is trained according to the historical light data.
In one embodiment, the first light sensing data, the second light sensing data and the current-time posture data of the terminal, which are acquired in real time, can be input into a pre-trained feedforward neural network model, the current light sensing data of the terminal is output through the pre-trained feedforward neural network model, and the screen brightness of the terminal is adjusted according to the current light sensing data of the terminal, which is output by the pre-trained feedforward neural network model.
In an exemplary embodiment of the present disclosure, by determining the terminal attitude data, the tilt angle of the terminal with respect to the ground may be determined. And then the inclination angle of the terminal relative to the ground, the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor are input into a pre-trained model, and the current light sensing data of the terminal is output through the pre-trained model, so that the aim of accurately adjusting the screen brightness of the terminal is fulfilled.
Before the screen brightness of the terminal is adjusted by the screen brightness adjusting method, the preset model is trained.
FIG. 4 is a flowchart illustrating a method of training a predictive model, as shown in FIG. 4, including the following steps, in accordance with an exemplary embodiment.
In step S31, a plurality of sets of training data are acquired.
In the disclosure, each set of training data in the multiple sets of training data includes the first light sensing data and the second light sensing data at the same time, and the posture data of each set of terminal is preprocessed, so that the current inclination angle of the terminal relative to the ground is obtained. And actual ambient light data corresponding to each set of training data.
In order to enable the trained preset model to accurately determine the ambient light data, in a real-time manner, the acquired multiple sets of training data may be training data covering all usage scenarios.
For example, the sets of training data may include: the method comprises the steps of aiming at multiple groups of front light sensing data and rear light sensing data of a scene with a shielded front light sensor and attitude data of a terminal. The method is used for acquiring the front light sensing data and the rear light sensing data of the rear light sensor. The method comprises the steps of using multiple groups of front light sensing data, rear light sensing data and terminal posture data for terminal backlight. The method comprises the steps of using multiple groups of front light sensing data, rear light sensing data and terminal posture data for terminal backlight. For a terminal in a game or video, a scene with a high-brightness screen is required. And the terminal is used under a night desk lamp in a scene needing soft screen brightness, and the like.
In step S32, a plurality of sets of training data are input into the preset model, and the predicted light sensing data of the plurality of sets of training data is output through the preset model.
In the present disclosure, each set of training data and the actual environment light data corresponding to each set of training data may be input to the preset model, and the predicted light sensing data of each set of training data may be output through the preset model. And adjusting parameters of the preset model according to each set of predicted light sensing data and the loss function until the difference between the predicted light sensing data and the actual environment light data meets the loss value, so as to obtain the trained preset model.
In step S33, parameters of the feedforward neural network are adjusted based on the predicted light sensing data and the loss function, so as to obtain a preset model satisfying the loss value.
In an exemplary embodiment of the disclosure, a trained model is obtained by acquiring a plurality of sets of training data covering all usage scenarios, inputting the acquired plurality of sets of training data into a training model, outputting predicted light sensing data according to the training model, and adjusting parameters of the training model according to the predicted light sensing data and a loss function until a difference between the predicted light sensing data and actual environment light data satisfies a loss value. When the trained model is used for predicting the ambient light, accurate and stable light sensing data can be predicted according to various scenes where the terminal is located, and the screen brightness of the terminal can be adjusted accurately and stably.
The present disclosure takes a preset model as a feedforward neural network model as an example, and explains the screen brightness adjustment to which the present disclosure is applied.
Fig. 5 is a schematic diagram illustrating application of the screen brightness adjustment method of the present disclosure using a feedforward neural network model according to an exemplary embodiment.
In fig. 5, the screen brightness adjusting method for implementing the present disclosure mainly includes a data acquisition phase, a model training phase and a model using phase.
The data acquisition stage mainly comprises the following steps: the method comprises the steps of collecting light sensing data through a front light sensor, collecting light sensing data through a rear light sensor, obtaining current attitude data of a terminal on a three-dimensional coordinate plane through an IMU, and preprocessing the current attitude data of the terminal on the three-dimensional coordinate plane through the IMU to obtain the inclination angle of the terminal relative to the ground.
The model training stage mainly comprises: light sensing data collected by a front light sensor, light sensing data collected by a rear light sensor and an inclination angle of a terminal relative to the ground are input into the BP feedforward neural network through a Back propagation inverse algorithm (BP feedforward neural network for short), the terminal light sensing data are predicted through the BP feedforward neural network to obtain predicted terminal light sensing data, and parameters of the BP feedforward neural network, namely weight and offset of each layer in the BP feedforward neural network, are corrected through a loss function, such as an MSE (mean square error) loss function. And obtaining a trained BP feedforward neural network model until the difference between the predicted terminal light sensing data and the actual environment light data meets a loss value.
The method mainly comprises the following steps in the model using stage: the method comprises the steps of writing a trained BP feedforward neural network model into a terminal hardware system for use, acquiring first light sensing data acquired by a front light sensor and second light sensing data acquired by a rear light sensor in real time in the using process, acquiring current posture data of a terminal on a three-dimensional coordinate plane through an IMU at the same time with the first light sensing data and the second light sensing data, preprocessing to obtain the inclination angle of the terminal relative to the ground, inputting the inclination angle to the trained BP feedforward neural network model, outputting current environment light data in real time through the BP feedforward neural network model, namely current terminal light sensing data, and adjusting screen backlight according to the current terminal light sensing data.
Based on the same conception, the embodiment of the disclosure also provides a screen brightness adjusting device.
It is understood that, in order to implement the above functions, the screen brightness adjusting apparatus provided in the embodiments of the present disclosure includes a hardware structure and/or a software module for performing each function. The disclosed embodiments can be implemented in hardware or a combination of hardware and computer software, in combination with the exemplary elements and algorithm steps disclosed in the disclosed embodiments. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
Fig. 6 is a block diagram illustrating a screen brightness adjustment apparatus according to an exemplary embodiment. Referring to fig. 6, the screen brightness adjusting apparatus 600 is applied to a terminal including a front light sensor and a rear light sensor, and includes: an acquisition unit 601 and a processing unit 602.
The acquiring unit 601 is configured to acquire first light sensing data acquired by the front light sensor and second light sensing data acquired by the rear light sensor, and determine current attitude data of the terminal; a processing unit configured to determine current light sensing data of the terminal according to the first light sensing data, the second light sensing data and the current posture data, and adjust screen brightness of the terminal according to the current light sensing data.
In an embodiment, the processing unit 602 determines the current light sensing data of the terminal according to the first light sensing data, the second light sensing data and the current gesture by the following method: calling a preset model; and inputting the first light sensing data, the second light sensing data and the current posture data into the preset model, and determining the current light sensing data based on the output of the preset model.
In an embodiment, the apparatus further comprises a training unit 603; the training unit is configured to derive the preset model based on a feedforward neural network training.
In an embodiment, the training unit 603 trains the preset model based on a feedforward neural network in the following manner: acquiring a plurality of groups of training data, wherein each group of training data in the plurality of groups of training data comprises first light sensing data, second light sensing data and posture data of a terminal at the same time; inputting a plurality of groups of training data into a feedforward neural network, and outputting predicted light sensing data of the plurality of groups of training data through the feedforward neural network; and adjusting parameters of the feedforward neural network based on the predicted light sensing data and the loss function to obtain the preset model meeting the loss value.
In an embodiment, the terminal is installed with an inertial measurement unit IMU, and the obtaining unit 601 obtains current attitude data of the terminal by: acquiring current attitude data of the terminal on a three-dimensional coordinate plane based on the IMU; preprocessing the current attitude data of the terminal on a three-dimensional coordinate plane to obtain the current inclination angle of the terminal relative to the ground; and taking the current inclination angle of the terminal relative to the ground as the current attitude data of the terminal.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 7 is a block diagram illustrating an apparatus 700 for screen brightness adjustment according to an exemplary embodiment. For example, the apparatus 700 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 7, apparatus 700 may include one or more of the following components: a processing component 702, a memory 704, a power component 706, a multimedia component 708, an audio component 710, an input/output (I/O) interface 712, a sensor component 714, and a communication component 716.
The processing component 702 generally controls overall operation of the device 700, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 702 may include one or more processors 720 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 702 may include one or more modules that facilitate interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate interaction between the multimedia component 708 and the processing component 702.
The memory 704 is configured to store various types of data to support operations at the apparatus 700. Examples of such data include instructions for any application or method operating on device 700, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 704 may be implemented by any type or combination of volatile or non-volatile 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 disks.
The power component 706 provides power to the various components of the device 700. The power components 706 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the apparatus 700.
The multimedia component 708 includes a screen that provides an output interface between the device 700 and a 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 an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 708 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 700 is in an operation mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 710 is configured to output and/or input audio signals. For example, audio component 710 includes a Microphone (MIC) configured to receive external audio signals when apparatus 700 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 704 or transmitted via the communication component 716. In some embodiments, audio component 710 further includes a speaker for outputting audio signals.
The I/O interface 712 provides an interface between the processing component 702 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 714 includes one or more sensors for providing status assessment of various aspects of the apparatus 700. For example, sensor assembly 714 may detect an open/closed state of device 700, the relative positioning of components, such as a display and keypad of device 700, sensor assembly 714 may also detect a change in position of device 700 or a component of device 700, the presence or absence of user contact with device 700, orientation or acceleration/deceleration of device 700, and a change in temperature of device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 714 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 714 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 716 is configured to facilitate wired or wireless communication between the apparatus 700 and other devices. The apparatus 700 may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 716 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 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 apparatus 700 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, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 704 comprising instructions, executable by the processor 720 of the device 700 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It is understood that "a plurality" in this disclosure means two or more, and other words are analogous. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It will be further understood that the terms "first," "second," and the like are used to describe various information and that such information should not be limited by these terms. These terms are only used to distinguish one type of information from another and do not denote a particular order or importance. Indeed, the terms "first," "second," and the like are fully interchangeable. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure.
It will be further understood that, unless otherwise specified, "connected" includes direct connections between the two without the presence of other elements, as well as indirect connections between the two with the presence of other elements.
It is further to be understood that while operations are depicted in the drawings in a particular order, this is not to be understood as requiring that such operations be performed in the particular order shown or in serial order, or that all illustrated operations be performed, to achieve desirable results. In certain environments, multitasking and parallel processing may be advantageous.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (12)
1. A screen brightness adjusting method is applied to a terminal, wherein the terminal comprises a front light sensor and a rear light sensor, and the method comprises the following steps:
acquiring first light sensing data acquired by the front light sensor and second light sensing data acquired by the rear light sensor, and determining current attitude data of the terminal;
determining the reliability of the light sensing data collected by the front light sensor and the reliability of the light sensing data collected by the rear light sensor according to the first light sensing data, the second light sensing data and the current attitude data;
according to the reliability of the light sensing data collected by the front light sensor and the reliability of the light sensing data collected by the rear light sensor, the weights respectively occupied by the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor are obtained;
determining current light sensing data of the terminal according to the weight, and adjusting the screen brightness of the terminal according to the current light sensing data;
the weight of the light sensing data collected by the shielded light sensor is smaller than that of the light sensing data collected by the unshielded light sensor;
the shielded light sensor comprises the front light sensor or the rear light sensor, and is determined in the following way:
preprocessing the acquired current attitude data of the terminal on the three-dimensional coordinate plane according to the current attitude data of the terminal on the three-dimensional coordinate plane to obtain a current inclination angle of the terminal relative to the ground;
and determining the shielded light sensor according to the obtained current inclination angle of the terminal relative to the ground, the first light sensing data and the second light sensing data.
2. The method for adjusting the brightness of the screen according to claim 1, wherein the determining the current light sensing data of the terminal according to the first light sensing data, the second light sensing data and the current gesture comprises:
calling a preset model;
and inputting the first light sensing data, the second light sensing data and the current posture data into the preset model, and determining the current light sensing data based on the output of the preset model.
3. The screen brightness adjustment method according to claim 2, further comprising:
and obtaining the preset model based on feedforward neural network training.
4. The method for adjusting screen brightness according to claim 3, wherein the deriving the preset model based on the feedforward neural network training comprises:
acquiring a plurality of groups of training data, wherein each group of training data in the plurality of groups of training data comprises first light sensing data, second light sensing data and posture data of a terminal at the same time;
inputting a plurality of groups of training data into a feedforward neural network, and outputting predicted light sensing data of the plurality of groups of training data through the feedforward neural network;
and adjusting parameters of the feedforward neural network based on the predicted light sensing data and the loss function to obtain the preset model meeting the loss value.
5. The screen brightness adjusting method according to claim 1, wherein the terminal is installed with an inertial measurement unit IMU, and the acquiring current attitude data of the terminal comprises:
acquiring current attitude data of the terminal on a three-dimensional coordinate plane based on the IMU;
preprocessing the current attitude data of the terminal on a three-dimensional coordinate plane to obtain the current inclination angle of the terminal relative to the ground;
and taking the current inclination angle of the terminal relative to the ground as the current attitude data of the terminal.
6. A screen brightness adjusting device is applied to a terminal, the terminal comprises a front light sensor and a rear light sensor, and the device comprises:
the acquisition unit is configured to acquire first light sensing data acquired by the front light sensor and second light sensing data acquired by the rear light sensor and determine current attitude data of the terminal;
the processing unit is configured to determine the reliability of the light sensing data collected by the front light sensor and the reliability of the light sensing data collected by the rear light sensor according to the first light sensing data, the second light sensing data and the current posture data;
according to the reliability of the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor, the weights respectively occupied by the light sensing data collected by the front light sensor and the light sensing data collected by the rear light sensor are obtained;
determining current light sensing data of the terminal according to the weight, and adjusting the screen brightness of the terminal according to the current light sensing data;
the weight of the light sensing data collected by the shielded light sensor is smaller than that of the light sensing data collected by the unshielded light sensor;
the shielded light sensor comprises the front light sensor or the rear light sensor, and is determined in the following way:
preprocessing the acquired current attitude data of the terminal on the three-dimensional coordinate plane according to the current attitude data of the terminal on the three-dimensional coordinate plane to obtain a current inclination angle of the terminal relative to the ground;
and determining the shielded light sensor according to the obtained current inclination angle of the terminal relative to the ground, the first light sensing data and the second light sensing data.
7. The screen brightness adjustment apparatus according to claim 6, wherein the processing unit determines the current light sensing data of the terminal according to the first light sensing data, the second light sensing data and the current gesture in the following manner:
calling a preset model;
and inputting the first light sensing data, the second light sensing data and the current posture data into the preset model, and determining the current light sensing data based on the output of the preset model.
8. The screen brightness adjustment device of claim 7, wherein the device further comprises a training unit;
the training unit is configured to derive the preset model based on a feedforward neural network training.
9. The screen brightness adjusting apparatus according to claim 8, wherein the training unit is configured to train the preset model based on a feedforward neural network in the following manner:
acquiring a plurality of groups of training data, wherein each group of training data in the plurality of groups of training data comprises first light sensing data, second light sensing data and posture data of a terminal at the same time;
inputting a plurality of groups of training data into a feedforward neural network, and outputting predicted light sensing data of the plurality of groups of training data through the feedforward neural network;
and adjusting parameters of the feedforward neural network based on the predicted light sensing data and the loss function to obtain the preset model meeting the loss value.
10. The screen brightness adjusting apparatus according to claim 6, wherein the terminal is installed with an inertial measurement unit IMU, and the obtaining unit obtains the current attitude data of the terminal by:
acquiring current attitude data of the terminal on a three-dimensional coordinate plane based on the IMU;
preprocessing the current attitude data of the terminal on a three-dimensional coordinate plane to obtain the current inclination angle of the terminal relative to the ground;
and taking the current inclination angle of the terminal relative to the ground as the current attitude data of the terminal.
11. A screen brightness adjustment apparatus, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to: performing the screen brightness adjustment method of any one of claims 1-5.
12. A non-transitory computer-readable storage medium, wherein instructions, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the screen brightness adjustment method of any one of claims 1-5.
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| CN113241044B (en) * | 2021-05-18 | 2022-08-05 | 北京小米移动软件有限公司 | Screen brightness adjusting method, device, equipment and storage medium |
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| CN105280158A (en) * | 2014-07-24 | 2016-01-27 | 扬升照明股份有限公司 | Display device and control method of backlight module thereof |
| CN110970003A (en) * | 2019-12-24 | 2020-04-07 | 维沃移动通信有限公司 | Screen brightness adjusting method and device, electronic equipment and storage medium |
| CN111128092B (en) * | 2020-02-11 | 2021-07-23 | 北京小米移动软件有限公司 | Screen brightness adjusting method, screen brightness adjusting device and computer storage medium |
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