A method and system for dynamic control of brightness of a display screen

Abstract

This invention relates to the field of display screen brightness control, and more particularly to a method and system for dynamic brightness control of a display screen. The method includes the following steps: real-time acquisition of ambient light sensing parameters; feature analysis based on the ambient light sensing parameters to construct a light feature set; brightness interval matching calculation based on the light feature set to extract a reference brightness adjustment interval; identification of the content frame to be displayed; brightness value constraint optimization of the reference brightness adjustment interval based on the content frame to be displayed to determine the target brightness deviation and brightness adjustment direction; progressive segmented brightness interpolation based on the target brightness deviation and brightness adjustment direction to obtain a brightness adjustment sequence; and driving the display screen to perform progressive brightness adjustment based on the brightness adjustment sequence. This invention dynamically adjusts the display screen brightness according to the display scene and real-time display content, improving the user's visual experience and the display screen's intelligent adaptability to different scenarios.

Description

Technical Field

[0001] This invention relates to the field of display screen brightness control, and more particularly to a method and system for dynamic brightness control of a display screen. Background Technology

[0002] As displays are widely used in complex environments such as outdoor high light, low-light nighttime conditions, vehicle-mounted dimming, and rapid switching between multiple scenarios, the accuracy and smoothness of their brightness adjustment have become increasingly prominent issues. In actual use, ambient lighting conditions continuously change dynamically, and the displayed content exhibits significant differences in color saturation, dynamic and static element composition, and visual information density. Traditional brightness adjustment schemes that rely solely on a single light intensity signal are insufficient to fully respond to these complex changes. A lack of precise control over the adjustment amplitude and rate during brightness adjustment can easily lead to problems such as abrupt brightness fluctuations, screen flickering, and visual discomfort, severely impacting the user's continuous experience and even causing visual fatigue and eye health damage in certain scenarios. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a method and system for dynamic brightness control of a display screen, thereby resolving at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, the present invention provides a method for dynamic brightness control of a display screen, comprising the following steps: Step S1: Real-time acquisition of ambient light sensor parameters; feature analysis based on ambient light sensor parameters to construct an illumination feature set; Step S2: Perform brightness range matching calculation based on the illumination feature set, and extract the reference brightness adjustment range; Step S3: Identify the content frame to be displayed; optimize the brightness value constraint of the reference brightness adjustment range based on the content frame to be displayed, and determine the target brightness deviation and brightness adjustment direction; Step S4: Perform progressive segmented brightness interpolation based on the target brightness deviation and brightness adjustment direction to obtain a brightness adjustment sequence; Step S5: Drive the display screen to perform progressive brightness adjustment based on the brightness adjustment sequence.

[0005] This specification provides a dynamic brightness control system for a display screen, used to execute the dynamic brightness control method for the display screen as described above, including: The acquisition unit is used to acquire ambient light sensing parameters in real time; and to perform feature analysis based on the ambient light sensing parameters to construct a light feature set. The reference unit is used to perform brightness range matching calculations based on the illumination feature set and extract the reference brightness adjustment range. The constraint optimization unit is used to identify the content frame to be displayed; and to perform brightness value constraint optimization on the reference brightness adjustment range based on the content frame to be displayed, thereby determining the target brightness deviation and the brightness adjustment direction. The segmented interpolation unit is used to perform progressive segmented brightness interpolation based on the target brightness deviation and brightness adjustment direction to obtain a brightness adjustment sequence. A brightness adjustment unit is used to drive the display screen to perform progressive brightness adjustment based on a brightness adjustment sequence.

[0006] The beneficial effects of this invention are specifically as follows: By collecting ambient light sensing parameters and constructing a light feature set, environmental perception is expanded from single light intensity detection to multi-dimensional feature analysis, comprehensively reflecting the characteristics of light intensity, direction, and color temperature changes. Introducing time-series variation parameters allows for the quantification of dynamic environmental changes, thereby improving the ability to identify complex lighting scenes and reducing the risk of misjudgment caused by sudden environmental changes or local interference. Environmental classification based on the light feature set and matching of brightness ranges enable scene-adaptive brightness adjustment, avoiding the incompatibility issues caused by fixed brightness strategies. By assigning different brightness ranges to different lighting types, excessively bright or dark conditions can be effectively prevented.

[0007] By combining visual load assessment with display content characteristics, brightness adjustment is expanded from a purely environment-driven model to a joint "environment + content" control mode. By identifying dynamic elements and color saturation distribution, the visual complexity of the image can be quantified, and the brightness range can be optimized accordingly, thereby reducing visual fatigue, improving viewing comfort under complex images, and making brightness adjustment more aligned with actual display needs. A brightness adjustment sequence is generated through progressive segmented brightness interpolation, transforming abrupt brightness changes into smooth transitions, effectively reducing flickering and jumps. The introduction of adjustment rate and windowing mechanisms allows the brightness change speed to dynamically and adaptively adjust with the environment, ensuring improved response speed and smooth transitions, enhancing visual comfort. By driving the display with the brightness adjustment sequence and combining it with feedback, closed-loop optimization of brightness control is achieved, enabling real-time correction of execution deviations and reducing hysteresis errors. Residual path replanning based on the deviation trajectory gives the brightness adjustment process adaptive correction capabilities, thereby improving convergence accuracy and stability, and achieving a smoother and more reliable dynamic brightness control effect. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating the steps of a dynamic brightness control method for a display screen according to the present invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0009] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0010] This application provides a method and system for dynamic brightness control of a display screen. The execution entities of the dynamic brightness control method and system include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio-visual management system, an information management system, and a cloud data management system.

[0011] Please see Figures 1 to 3 This invention provides a method for dynamic brightness control of a display screen, comprising the following steps: Step S1: Real-time acquisition of ambient light sensor parameters; feature analysis based on ambient light sensor parameters to construct an illumination feature set; Step S2: Perform brightness range matching calculation based on the illumination feature set, and extract the reference brightness adjustment range; Step S3: Identify the content frame to be displayed; optimize the brightness value constraint of the reference brightness adjustment range based on the content frame to be displayed, and determine the target brightness deviation and brightness adjustment direction; Step S4: Perform progressive segmented brightness interpolation based on the target brightness deviation and brightness adjustment direction to obtain a brightness adjustment sequence; Step S5: Drive the display screen to perform progressive brightness adjustment based on the brightness adjustment sequence.

[0012] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of a dynamic brightness control method for a display screen according to the present invention. In this example, the steps of the dynamic brightness control method for the display screen include: Step S1: Real-time acquisition of ambient light sensor parameters; feature analysis based on ambient light sensor parameters to construct an illumination feature set; Step S2: Perform brightness range matching calculation based on the illumination feature set, and extract the reference brightness adjustment range; Step S3: Identify the content frame to be displayed; optimize the brightness value constraint of the reference brightness adjustment range based on the content frame to be displayed, and determine the target brightness deviation and brightness adjustment direction; Step S4: Perform progressive segmented brightness interpolation based on the target brightness deviation and brightness adjustment direction to obtain a brightness adjustment sequence; Step S5: Drive the display screen to perform progressive brightness adjustment based on the brightness adjustment sequence.

[0013] In this embodiment, specifically, the display screen synchronously collects ambient light through a built-in 3×3 light sensor array. The average light intensity output by each sensor is 1200 lux, and the ambient light characteristics are calculated by combining the RGB signals (R=210, G=200, B=180). The overall light intensity L=1200 lux is obtained by weighted averaging and normalized to Ln=0.12. The incident angle of the light source (θ=35°, φ=10°) is obtained by fitting the spatial light distribution, and the color temperature CCT≈5200K is calculated by using an RGB to XYZ model. Differential analysis of the time-series light data yields a light intensity change rate ΔL=200 lux / s, a direction drift rate of approximately 2° / s, and a color temperature shift gradient of approximately 200K / s, thereby constructing a light feature set F=[0.12,0.4,0.2,0.3]. Based on this feature set, the environment is classified as a mixed transitional lighting scene, and the corresponding brightness range [120, 500 cd / m²] is matched. The baseline brightness B0 = 120 + (0.12 × 380) = 166 cd / m² is calculated through linear mapping, and this value is used as the base brightness input for subsequent content adjustment.

[0014] The current display frame is structurally analyzed, dividing the screen into a text area (40%), an image area (15%), a dynamic video area (35%), and an interactive control area (10%). The average motion amplitude of the dynamic area is calculated to be 15 px / frame using optical flow, and after normalization, the motion amplitude coefficient M = 0.6. The average saturation of the static area is obtained as S = 0.75 through HSV conversion. Based on the visual load model V = 0.6M + 0.4S, the visual load index V = 0.66 is calculated. The baseline brightness range is constrained and optimized according to the visual load, using the relationship Btarget = B0 × (1 0.3V), the target brightness value Btarget = 166 × (1 0.198) = 133 cd / m². The current screen brightness, obtained through feedback sampling, is 180 cd / m², therefore the brightness deviation ΔB is calculated to be 133. 180= The light intensity was 47 cd / m², and the adjustment direction was determined to be a decrease in brightness. Combined with the light intensity change rate of 200 lux / s, the adjustment rate index R = 0.7 was obtained, and the adjustment window W = 30 × (1 0.7) = 9 frames.

[0015] Based on the target brightness deviation and adjustment window, piecewise interpolation processing is performed on the brightness changes. Within a 9-frame window, a brightness adjustment sequence is generated using a linear decreasing method: 180, 175, 170, 165, 160, 155, 150, 143, 133 cd / m². This sequence is sent frame-by-frame to the brightness drive module at a 60Hz refresh rate for progressive adjustment, while brightness re-sampling is performed simultaneously with a sampling period of 20ms. The re-sampling results show that the actual brightness deviates in some frames; for example, when the target is 165 cd / m², the actual brightness is 168 cd / m², and when the target is 150 cd / m², the actual brightness is 155 cd / m², forming a deviation trajectory E = [+3, +5, +5]. Mean analysis of the deviation trajectory yields Eavg = 4.3 cd / m², indicating a slight lag. Based on this deviation, the remaining adjustment sequence is reprogrammed, and the subsequent frame step is adjusted to decrease by 6 cd / m², so that the brightness convergence path is corrected to 150, 144, 138, 133 cd / m², thereby shortening the convergence time and reducing overshoot, and achieving a smooth and stable final brightness output. In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Real-time acquisition of ambient light sensing parameters based on the built-in light sensor array of the display screen; Calculate the light intensity value, the incident angle of the light source, and the ambient color temperature component based on the ambient light sensor parameters; A time-series analysis of the light intensity values ​​was performed to obtain the rate of change of light intensity; Transient fluctuations in the incident angle of the light source are identified to obtain the drift rate of the light source direction; The color temperature shift gradient is calculated based on the ambient color temperature component; an illumination feature set is constructed based on the light intensity change rate, the light source direction drift rate, and the color temperature shift gradient.

[0016] In this embodiment, ambient light is synchronously collected from multiple points using a light sensor array integrated inside or at the edge of the display screen. The sensor array typically employs a 3×3 or 4×4 distribution structure, with each sensing unit possessing independent illumination detection capabilities and including an RGB three-channel response module for acquiring illuminance and spectral distribution information. The sampling frequency can be set to 100Hz to ensure responsiveness to rapid changes in ambient light. Each acquisition output includes an illumination intensity value (in lux), RGB three-channel signal values, and a corresponding time series marker. To reduce random noise interference, the raw data undergoes sliding window averaging, with a window length of 5 frames, thereby improving signal stability while maintaining response speed. To reduce response differences between different sensing units, consistency calibration is performed during the initialization phase to keep the output deviation of each sensor within a small range (e.g., within ±3%). The acquisition results are stored in a multi-dimensional matrix format, containing spatial distribution and time series characteristics. After obtaining multi-point illumination data, the light intensity information from each sensor is fused and calculated to obtain the overall ambient light intensity. The fusion method employs a weighted average model, with weights allocated based on the spatial location of the sensors. This gives higher weights to sampling points closer to the center of the display area, thus reducing the impact of edge errors. Illumination intensity results are normalized to the range of 0-1 for subsequent processing. The calculation of the incident angle of the light source is based on the light intensity differences between sensors at different locations. By constructing a spatial illumination distribution model and using a least-squares fitting method to estimate the illumination gradient, the incident direction of the main light source, including both horizontal and vertical angles, is deduced. For the ambient color temperature component, the RGB channel response values ​​are used for calculation. First, they are converted to the XYZ color space, then the correlated color temperature (CCT) value is obtained based on the chromaticity coordinates, and further decomposed into warm and cool component proportions. The color temperature range generally covers approximately 3000K to 7000K.

[0017] The continuously collected light intensity data is sequenced chronologically and analyzed within a fixed time window (e.g., 2 seconds, corresponding to 200 sampling points). To reduce the impact of high-frequency noise, the sequence is exponentially smoothed with a smoothing coefficient of approximately 0.3. The change in light intensity between adjacent time points is then calculated using a first-order difference method and divided by the time interval (approximately 0.01 seconds) to obtain the rate of change in light intensity (in lux / s). To avoid interference from abnormal fluctuations, median filtering or amplitude limiting can be introduced to constrain the rate of change sequence. The rate of change in light intensity varies significantly under different environments; for example, the rate of change is usually low in slowly changing scenarios, while it increases significantly in abruptly changing scenarios. By setting a threshold (e.g., 100 lux / s), different change states can be distinguished, and the time series of the rate of change in light intensity and its statistical characteristics (e.g., average, maximum, etc.) are output to describe the degree of dynamic change in light intensity.

[0018] The orientation angle data is converted into a unit vector representation to avoid discontinuities caused by angle jumps. Then, the orientation vectors at adjacent time points are differentially calculated to determine the angle change, and this difference, combined with the time interval, yields the orientation drift rate (unit: ° / s). To identify transient fluctuations, an angle change threshold (e.g., 3°) can be set, and the frequency of change is statistically analyzed within a certain time window (e.g., 0.5 seconds). Fluctuations exceeding a set proportion are considered orientation fluctuations. To improve data stability, the orientation angle sequence can be filtered, for example, using a recursive estimation method to reduce the impact of random disturbances. The orientation drift rate reflects changes in the light source position or equipment movement; its magnitude can be used to distinguish between stable and dynamic lighting environments. The final output includes the orientation drift rate and fluctuation identification information. Time series modeling of color temperature data is performed, and its changing trends are analyzed. First, continuous color temperature values ​​are smoothed, for example, using a low-pass filter to suppress measurement noise. Then, the color temperature change at adjacent time points is calculated using first-order difference, and divided by the time interval to obtain the color temperature shift gradient (unit: K / s). To enhance stability, color temperature values ​​can be normalized before calculating the rate of change, thereby reducing the impact of dimensional differences. Color temperature changes typically reflect changes in the type of ambient light source, such as a transition from warm to cool light or vice versa. Significant changes can be identified by setting a gradient threshold (e.g., 30K / s). Further calculations can be made of the trend (increasing or decreasing) and the duration of the change, describing the dynamic characteristics of color temperature from multiple dimensions. The final output is the color temperature shift gradient and its trend parameters.

[0019] The light intensity change rate, directional drift rate, and color temperature shift gradient are integrated to form a multi-dimensional illumination feature vector. First, each parameter is normalized to ensure a consistent numerical range (e.g., mapped to the 0-1 interval) for unified analysis. Then, statistical features, such as mean, standard deviation, and maximum variation amplitude, can be extracted within a fixed time window, expanding the feature dimension to 6 dimensions or higher. To reduce redundant information, dimensionality reduction methods can be used to compress the features while retaining the main trends. The resulting illumination feature set comprehensively reflects the intensity, direction, and color temperature changes of ambient light, serving as input for subsequent dynamic brightness adjustment models, thereby achieving smoother and more visually perceptible display brightness control.

[0020] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Environmental classification and assessment are performed based on the lighting feature set to obtain the lighting type of the current environment; the lighting type includes strong direct sunlight scene, diffuse indoor light scene, low-illuminance nighttime environment, and mixed transitional lighting scene. Based on the above, a brightness range matching calculation is performed to extract the reference brightness adjustment range.

[0021] In this embodiment, the aforementioned constructed illumination feature set is used as input to classify and determine the ambient illumination state. The illumination feature set typically includes multi-dimensional parameters such as the rate of change of light intensity, the rate of drift of the light source direction, and the color temperature shift gradient, and can be extended to include statistical features (such as mean, variance, etc.). First, each feature is normalized to uniformly map its numerical range to between 0 and 1, thus eliminating the influence between different physical quantities. Then, a classification model is constructed based on the multi-dimensional features, which can employ rule-based thresholding or machine learning-based classification methods, such as Support Vector Machines (SVM) or lightweight decision tree models. Under the rule-based classification method, multiple threshold intervals are set to distinguish scenes. For example, when the light intensity is high and the directionality is strong (direction drift rate is less than 1° / s, and the light intensity change rate is low), it is identified as a strong direct sunlight scene; when the light intensity is medium and the directionality is weak and the change is gradual, it is identified as a diffuse indoor light scene; when the overall light intensity is below a certain threshold (e.g., less than 50 lux), it is identified as a low-light nighttime environment; when the light intensity, direction, and color temperature all exhibit high fluctuations (e.g., the change rate exceeds the threshold and the direction drift rate is greater than 5° / s), it is identified as a mixed transitional lighting scene. By jointly judging through multi-dimensional conditions, the classification robustness can be effectively improved, enabling different complex environments to be accurately identified and outputting corresponding lighting type labels.

[0022] After determining the ambient light type, brightness range matching calculations are performed based on the mapping relationship between different light types and preset brightness. This process establishes a mapping model based on the "ambient light intensity to display brightness response curve," which is typically fitted using a piecewise linear or logarithmic function to conform to the non-linear perception characteristics of human eye brightness. First, corresponding baseline brightness ranges are established for different light types. For example, strong direct sunlight scenes correspond to a higher brightness range (e.g., 400–800 cd / m²), diffuse indoor light scenes correspond to a medium brightness range (e.g., 150–400 cd / m²), low-illuminance nighttime environments correspond to a lower brightness range (e.g., 10–150 cd / m²), and mixed transitional lighting scenes correspond to a dynamically fluctuating range (e.g., 100–500 cd / m²). In the actual calculation process, based on the relative position of the current light intensity value within its type, an interpolation method is used to determine the specific baseline brightness value. For example, linear interpolation or exponential smoothing interpolation methods are used to make brightness changes more continuous and natural. The range boundary is dynamically expanded based on the rate of change of illumination. For example, when the rate of change of light intensity is large, the brightness adjustment range is automatically expanded to enhance adaptability. The final output is a reference brightness adjustment range, which serves as the constraint range for subsequent dynamic smooth control of brightness, thereby ensuring that the display brightness has reasonable adaptability and visual comfort under different environmental conditions.

[0023] In this embodiment, step S3 includes the following steps: Identify the content frame to be displayed; parse the display elements of the content frame to be displayed, and identify multiple display elements; the display elements include a text information area, an image and graphics area, a dynamic video area, and an interface interaction control area. The display elements are judged for their dynamic and static attributes, and dynamic and static elements are marked. Calculate the motion amplitude coefficient of the dynamic elements and the color saturation distribution of the static elements; The visual load of the image is evaluated based on the motion amplitude coefficient and color saturation distribution to obtain the visual load index. Based on the visual load index, the brightness value of the baseline brightness adjustment range is constrained and optimized, and the target display brightness value is output. Based on the target display brightness value, analyze the brightness deviation of the display screen to determine the target brightness deviation and the direction of brightness adjustment.

[0024] In this embodiment, the continuous image frames to be displayed are acquired and cached frame by frame, with each frame serving as an independent analysis unit input into the subsequent parsing process. By performing structured parsing of the image frames, the screen is divided into multiple semantic-level regions. A method combining edge detection and semantic segmentation is used to identify different display element regions. Edge detection uses gradient operators to initially segment high-contrast regions, while semantic segmentation uses a lightweight convolutional feature extraction model to classify regions of the screen, thereby identifying different functional regions. During the parsing process, the displayed content is divided into four core display elements: text information areas, image and graphic areas, dynamic video areas, and interface interaction control areas. Text information areas typically exhibit high-frequency edges and regular structures; image and graphic areas exhibit medium-texture complexity; dynamic video areas exhibit significant changes between consecutive frames; and interface interaction control areas have obvious button or control boundary features. During processing, each type of region is numbered and spatially labeled, recording its positional proportion, area ratio, and brightness distribution characteristics within the screen. To improve parsing stability, a multi-frame fusion method can be adopted, which involves performing consistency comparison on 3 to 5 consecutive frames to reduce misjudgment of a single frame.

[0025] Pixel-level or feature-level comparative analysis is performed on the same element region in consecutive frames to calculate its inter-frame difference. If the structural change of a region between adjacent frames exceeds a preset threshold (e.g., pixel change rate greater than 15% or feature vector cosine distance greater than 0.2), it is determined to be a dynamic element; otherwise, it is determined to be a static element. Dynamic elements typically include video playback areas, animation areas, or scrolling content areas, which have a high frequency of inter-frame changes; static elements include text descriptions, fixed icons, or interface backgrounds, which have weak or no temporal changes. A time window mechanism can also be introduced during processing, such as using a 1-second time window (approximately 30 frames) for statistical analysis to improve the stability of the determination. For regions with blurred boundaries, a weighted determination method is used, and its dynamic / static attributes are determined by a comprehensive score based on the frequency of change and spatial proportion.

[0026] Different visual features are extracted for dynamic and static elements. For dynamic elements, the motion amplitude coefficient is mainly calculated, which characterizes the intensity of image changes. Specifically, this is done by calculating the element's displacement vector between consecutive frames through optical flow estimation or pixel displacement analysis, and then statistically normalizing the displacement amplitude. The motion amplitude coefficient is typically set to a dimensionless value between 0 and 1; for example, an average displacement exceeding 20 pixels corresponds to a high motion intensity region. For static elements, the color saturation distribution is mainly analyzed. This is done by converting the RGB color space to the HSV space to extract the S component, and then statistically analyzing the saturation of different regions. Statistical indicators include average saturation, saturation variance, and the proportion of high-saturation regions. For example, an average saturation higher than 0.7 can be considered a high-visual-stimulation region, while a saturation lower than 0.3 indicates a low-stimulation region. During the calculation process, different display elements are weighted and fused according to area weights, making large areas have a more significant impact on the overall indicators.

[0027] This study integrates the motion amplitude characteristics of dynamic elements with the color saturation characteristics of static elements to assess the overall visual stimulus intensity of the image. First, all features are normalized to map them uniformly to the 0-1 range. Then, a visual load calculation model is constructed, employing either a weighted linear model or a nonlinear regression model. The weight for dynamic motion amplitude is typically set to 0.6, and the weight for static color saturation is set to 0.4 to reflect the stronger impact of dynamic changes on visual fatigue. The calculated visual load index usually falls between 0 and 1, with higher values ​​indicating stronger visual stimulation. To improve accuracy, correction factors can be introduced; for example, when the dynamic area accounts for more than 50%, the motion amplitude weight is adjusted. Additionally, a penalty term can be applied to high-saturation areas to reflect the visual pressure caused by strong color stimulation.

[0028] The brightness range is dynamically adjusted by shrinking or expanding based on the visual load index. When the visual load is high (e.g., index greater than 0.7), the upper limit of brightness is appropriately lowered and the range is narrowed to reduce visual stimulation; when the visual load is low, the upper limit of brightness can be appropriately increased to enhance display clarity. Brightness adjustment is achieved using piecewise functions or continuous mapping functions, such as using an exponential decay model to make the brightness decrease more gradual under high load. Subsequently, within the optimized range, interpolation calculations are performed based on the current ambient light intensity to determine the final target brightness value, ensuring that it meets both environmental adaptation requirements and content visual comfort requirements. To avoid abrupt brightness changes, a smoothing constraint mechanism is introduced, such as limiting the brightness change between adjacent frames to no more than 10% or using a moving average filter for transition processing.

[0029] In this embodiment, the specific steps for performing display screen brightness deviation analysis based on the target display brightness value to determine the target brightness deviation and brightness adjustment direction are as follows: Identify the current screen brightness value; The difference between the target display brightness value and the screen brightness value is calculated to obtain the target brightness deviation; Identify the direction of the target brightness deviation to obtain the brightness adjustment direction.

[0030] In this embodiment, the actual brightness state of the current display output is acquired and quantitatively analyzed in real time. The screen brightness value is usually obtained through the backlight control feedback channel in the display driver module, or indirectly calculated through the mapping relationship between brightness sensing feedback and PWM duty cycle. Specifically, the backlight drive current, PWM modulation duty cycle, and panel transmission efficiency are jointly modeled to derive the current actual brightness output value. The brightness value is usually expressed in cd / m² and varies within the range of 0 to 800 cd / m². During the acquisition process, periodic sampling is performed at fixed time intervals (e.g., 20ms or 50ms) to ensure continuous recording of brightness changes. To reduce errors caused by instantaneous jitter, the sampling results are processed by moving average, for example, using a 5-frame window for smoothing calculation to make the brightness curve more stable. An ambient reflection light compensation model can also be combined to correct the reflected brightness of the display panel, thereby improving the accuracy of brightness recognition.

[0031] A direct difference operation is performed between the two, i.e., the target brightness minus the current brightness, to obtain a signed brightness deviation value. This deviation value is used to characterize the distance between the current display state and the desired display state. In actual processing, to avoid extreme values ​​impacting the control strategy, the brightness deviation is normalized, for example, mapped to the range of -1 to 1, where positive values ​​indicate that brightness needs to be increased and negative values ​​indicate that brightness needs to be decreased. To enhance stability, an average deviation calculation within a time window can be introduced, for example, taking the average deviation over the most recent second as the basis for the current decision, thereby reducing the impact of instantaneous fluctuations. In some cases, the deviation change rate is also combined for auxiliary analysis; for example, if the deviation continues to increase in a short period of time, it indicates that the environment is changing rapidly, and the adjustment response speed needs to be improved.

[0032] When the target brightness deviation is positive, it indicates that the current screen brightness is lower than the target brightness, and a brightness increase operation needs to be performed; when the deviation is negative, it indicates that the current brightness is higher than the target brightness, and a brightness decrease operation needs to be performed. To improve the stability of the judgment, a multi-frame consistency judgment mechanism is introduced in the direction recognition process. For example, the final adjustment direction is only confirmed when the deviation direction remains consistent within 3 to 5 consecutive sampling periods, thereby avoiding frequent direction switching due to noise. A graded judgment can also be performed based on the deviation magnitude. For example, the adjustment direction can be divided into four states: "slight increase," "rapid increase," "slight decrease," and "rapid decrease." The grading criteria can be set as a threshold for the absolute value of the deviation; for example, less than 50 cd / m² is a slight adjustment, and greater than 150 cd / m² is a rapid adjustment. A trend prediction method can also be introduced in the direction recognition process. For example, based on short-time linear fitting, the deviation change direction at the next moment can be predicted to adjust the adjustment strategy in advance and improve response smoothness.

[0033] In this embodiment, step S4 includes the following steps: Based on the rate of change of light intensity, the adjustment rate analysis is performed to obtain the adjustment rate index; The adjustment window length is calculated based on the target brightness deviation and the adjustment rate index to obtain the adjustment window. Based on the target brightness deviation and brightness adjustment direction, progressive segmented brightness interpolation is performed on the adjustment window to obtain a brightness adjustment sequence.

[0034] In this embodiment, the light intensity change rate sequence is subjected to time window statistical processing. For example, continuous change rate data within the most recent 1-2 seconds (corresponding to approximately 100-200 sampling points) are selected, and statistical characteristics such as mean, maximum, and variance are calculated to reflect the overall trend and fluctuation of ambient light changes. Based on this, a regulation rate mapping model is constructed to map the light intensity change rate to a regulation rate exponent. This exponent is typically normalized to the range of 0-1, where a larger value indicates more drastic environmental changes and a faster brightness response speed is required. The mapping method can employ a piecewise function or an exponential function. For example, a low-sensitivity mapping is used in the low change rate range (less than 50 lux / s), and a high-response mapping is used in the high change rate range (greater than 300 lux / s) to enhance adaptability to abrupt environmental changes. To avoid the influence of instantaneous noise, a moving average or exponential smoothing mechanism can be introduced to perform secondary smoothing processing on the regulation rate exponent, making its changes more continuous and stable.

[0035] The target brightness deviation is normalized by mapping it to the 0-1 range, where a larger deviation indicates a more significant difference between the current brightness and the target brightness. A window length calculation model is then constructed using the adjustment rate index, employing an inverse adjustment mechanism: the adjustment window is shortened when environmental changes are drastic (higher adjustment rate index) to improve response speed, and lengthened when environmental changes are gradual to enhance smoothness. The window length is typically expressed in frames or time units; for example, at a 50Hz refresh rate, the window range can be set to vary between 5 and 60 frames. The calculation can use a weighted function, where the window length is positively correlated with the target deviation and negatively correlated with the adjustment rate index, achieving a dynamic balance. To avoid frequent fluctuations in window length, a limiting mechanism is introduced, such as limiting the change between adjacent windows to no more than 20%, ensuring the stability of the adjustment process.

[0036] The interpolation trend is determined based on the direction of brightness adjustment. When the adjustment direction is upward, the brightness sequence shows an increasing trend; when it is downward, it shows a decreasing trend. The adjustment window is then segmented, for example, into 5 to 20 equally spaced sub-intervals, each corresponding to an intermediate brightness state value. Linear interpolation or exponential smoothing interpolation can be used. Linear interpolation ensures uniform change, while exponential interpolation achieves a smoother convergence effect when approaching the target brightness. To enhance visual comfort, a rate-of-change limit is introduced during interpolation, for example, the brightness change in a single frame does not exceed the maximum allowable gradient (e.g., 10 cd / m² or 5% of the current brightness) to avoid flickering or abrupt changes. The interpolation density is adjusted based on the magnitude of the target brightness deviation. When the deviation is large, the number of interpolation points is increased to improve transition smoothness; when the deviation is small, the number of interpolation points is reduced to reduce computational burden. The final generated brightness adjustment sequence is a continuously progressive set of brightness values. This sequence is executed step-by-step within the adjustment window time, thus achieving a smooth transition from the current brightness to the target brightness.

[0037] In this embodiment, the specific steps of step S5 are as follows: Based on the brightness adjustment sequence, the display brightness driving module is sent to perform progressive brightness adjustment and continuously collects screen brightness values. The execution time deviation of the brightness adjustment sequence is calculated based on the screen brightness value to obtain the brightness adjustment deviation trajectory. Based on the brightness adjustment deviation trajectory, the remaining brightness value is adjusted and replanned to achieve dynamic and smooth brightness adjustment.

[0038] In this embodiment, the previously generated brightness adjustment sequence is sent to the display brightness driving module frame by frame or cycle by cycle in chronological order to control the backlight or pixel luminous intensity to achieve gradual brightness changes. The brightness adjustment sequence typically contains multiple discrete brightness target values, each corresponding to a time segment; for example, at a 50Hz refresh rate, each brightness value lasts for 20ms. The brightness driving module, based on the received brightness command, implements the actual brightness output change through PWM duty cycle adjustment or current control. During execution, a brightness retrieval channel is activated, and the current actual output brightness value is obtained in real time through the screen's built-in light sensor feedback or brightness estimation model. The sampling period is typically set to 20ms to 50ms to ensure continuous monitoring of brightness changes. The retrieval data is processed by moving average filtering (e.g., 5-frame window) to reduce noise. By synchronously recording the execution process and the retrieval data, a brightness execution trajectory can be formed. Each target brightness value in the adjustment sequence is matched with the actual brightness value at the corresponding time point, and the difference between the two is calculated to obtain the brightness execution deviation sequence. This deviation includes not only numerical differences in brightness but also delays or advances in the time dimension, such as the difference between the actual time required to reach a target brightness value and the preset time. By continuously statistically analyzing the deviations at multiple time points, a brightness adjustment deviation trajectory can be formed, reflecting the overall consistency of the brightness adjustment process. During processing, a time alignment tolerance range can be set, for example, allowing ±1 sampling period error, to avoid slight timing jitter affecting the analysis results. The deviation trajectory is smoothed, for example, using an exponentially weighted moving average method, to make the trajectory changes more continuous and stable. If a continuous positive deviation appears in the deviation trajectory, it indicates that the brightness adjustment speed is too slow; if a negative deviation appears, it indicates that the adjustment is too fast or overshoot.

[0039] The overall execution status is determined based on the current deviation trajectory. For example, if the deviation is consistently positive and large, it indicates that the brightness adjustment is lagging, requiring an increase in subsequent adjustment gain. If the deviation fluctuates or overshoots, the adjustment amplitude needs to be reduced to avoid flickering. Based on this, the remaining brightness adjustment sequence is re-interpolated to adjust the target brightness value at each time step, making it closer to the actual execution capability. A predictive correction mechanism can be introduced during the replanning process, such as predicting the future brightness convergence point based on the deviation trend of the most recent frames, thereby adjusting the remaining path in advance. Adaptive control of the adjustment step size can also be implemented, using a larger step size for rapid convergence during periods of large deviation and a smaller step size for smooth transition when approaching the target brightness. To avoid jitter caused by frequent replanning, a replanning trigger threshold is introduced, for example, adjusting is only triggered when the deviation exceeds 5% or the duration exceeds 200ms.

[0040] In this embodiment, a dynamic brightness control system for a display screen is provided, used to execute the dynamic brightness control method for the display screen as described above, including: The acquisition unit is used to acquire ambient light sensing parameters in real time; and to perform feature analysis based on the ambient light sensing parameters to construct a light feature set. The reference unit is used to perform brightness range matching calculations based on the illumination feature set and extract the reference brightness adjustment range. The constraint optimization unit is used to identify the content frame to be displayed; and to perform brightness value constraint optimization on the reference brightness adjustment range based on the content frame to be displayed, thereby determining the target brightness deviation and the brightness adjustment direction. The segmented interpolation unit is used to perform progressive segmented brightness interpolation based on the target brightness deviation and brightness adjustment direction to obtain a brightness adjustment sequence. A brightness adjustment unit is used to drive the display screen to perform progressive brightness adjustment based on a brightness adjustment sequence.

[0041] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0042] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for dynamically controlling the brightness of a display screen, characterized in that, Includes the following steps: Step S1: Real-time acquisition of ambient light sensor parameters; Feature analysis is performed based on ambient light sensing parameters to construct an illumination feature set; Step S2: Perform brightness range matching calculation based on the illumination feature set, and extract the reference brightness adjustment range; Step S3: Identify the content frame to be displayed; Based on the content frame to be displayed, the brightness value constraint optimization is performed on the reference brightness adjustment range to determine the target brightness deviation and the brightness adjustment direction; Step S4: Perform progressive segmented brightness interpolation based on the target brightness deviation and brightness adjustment direction to obtain a brightness adjustment sequence; Step S5: Drive the display screen to perform progressive brightness adjustment based on the brightness adjustment sequence.

2. The method for dynamic brightness control of a display screen according to claim 1, characterized in that, The specific steps of step S1 are as follows: Real-time acquisition of ambient light sensing parameters based on the built-in light sensor array of the display screen; Calculate the light intensity value, the incident angle of the light source, and the ambient color temperature component based on the ambient light sensor parameters; A time-series analysis of the light intensity values ​​was performed to obtain the rate of change of light intensity; Transient fluctuations in the incident angle of the light source are identified to obtain the drift rate of the light source direction; The color temperature shift gradient is calculated based on the ambient color temperature component; an illumination feature set is constructed based on the light intensity change rate, the light source direction drift rate, and the color temperature shift gradient.

3. The method for dynamic brightness control of a display screen according to claim 2, characterized in that, The specific steps of step S2 are as follows: Environmental classification and assessment are performed based on the lighting feature set to obtain the lighting type of the current environment; the lighting type includes strong direct sunlight scene, diffuse indoor light scene, low-illuminance nighttime environment and mixed transitional lighting scene. Based on the above, a brightness range matching calculation is performed to extract the reference brightness adjustment range.

4. The method for dynamic brightness control of a display screen according to claim 3, characterized in that, Step S3 is as follows: Identify the content frame to be displayed; parse the display elements of the content frame to be displayed, and identify multiple display elements; The display elements are judged for their dynamic and static attributes, and dynamic and static elements are marked. Calculate the motion amplitude coefficient of the dynamic elements and the color saturation distribution of the static elements; The visual load of the image is evaluated based on the motion amplitude coefficient and color saturation distribution to obtain the visual load index. Based on the visual load index, the brightness value of the baseline brightness adjustment range is constrained and optimized, and the target display brightness value is output. Based on the target display brightness value, analyze the brightness deviation of the display screen to determine the target brightness deviation and the direction of brightness adjustment.

5. The method for dynamic brightness control of a display screen according to claim 4, characterized in that, The display elements include a text information area, an image and graphics area, a dynamic video area, and an interface interaction control area.

6. The method for dynamic brightness control of a display screen according to claim 4, characterized in that, The specific steps for analyzing the brightness deviation of the display screen based on the target display brightness value to determine the target brightness deviation and the direction of brightness adjustment are as follows: Identify the current screen brightness value; The difference between the target display brightness value and the screen brightness value is calculated to obtain the target brightness deviation; Identify the direction of the target brightness deviation to obtain the brightness adjustment direction.

7. The method for dynamic brightness control of a display screen according to claim 6, characterized in that, The specific steps of step S4 are as follows: Based on the rate of change of light intensity, the adjustment rate analysis is performed to obtain the adjustment rate index; The adjustment window length is calculated based on the target brightness deviation and the adjustment rate index to obtain the adjustment window. Based on the target brightness deviation and brightness adjustment direction, progressive segmented brightness interpolation is performed on the adjustment window to obtain a brightness adjustment sequence.

8. The method for dynamic brightness control of a display screen according to claim 7, characterized in that, The specific steps of step S5 are as follows: Based on the brightness adjustment sequence, the display brightness driving module is sent to perform progressive brightness adjustment and continuously collects screen brightness values. The execution time deviation of the brightness adjustment sequence is calculated based on the screen brightness value to obtain the brightness adjustment deviation trajectory. Based on the brightness adjustment deviation trajectory, the remaining brightness value is adjusted and replanned to achieve dynamic and smooth brightness adjustment.

9. A dynamic brightness control system for a display screen, characterized in that, The method for performing dynamic brightness control of a display screen as described in claim 1 includes: The acquisition unit is used to acquire ambient light sensing parameters in real time; and to perform feature analysis based on the ambient light sensing parameters to construct a light feature set. The reference unit is used to perform brightness range matching calculations based on the illumination feature set and extract the reference brightness adjustment range. The constraint optimization unit is used to identify the content frame to be displayed; and to perform brightness value constraint optimization on the reference brightness adjustment range based on the content frame to be displayed, thereby determining the target brightness deviation and the brightness adjustment direction. The segmented interpolation unit is used to perform progressive segmented brightness interpolation based on the target brightness deviation and brightness adjustment direction to obtain a brightness adjustment sequence. A brightness adjustment unit is used to drive the display screen to perform progressive brightness adjustment based on a brightness adjustment sequence.

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