Dynamic backlight control method based on mini LED

Abstract

The invention relates to the technical field of backlight control, in particular to a dynamic backlight control method based on mini LEDs. The method comprises the following steps: acquiring current environment data, analyzing the current environment data, and predicting a human eye perception level; determining a target backlight brightness value according to the human eye perception level and preset heat dissipation performance; acquiring real-time temperature data of the backlight partition; determining local brightness duration according to the real-time temperature data, the target backlight brightness value and preset heat dissipation performance; and dynamically adjusting a brightness output strategy of the backlight partition according to the target backlight brightness value and the local brightness duration. And the peak brightness is dynamically excited while the service life of equipment is guaranteed. Local brightness output is optimized, sacrifice of a global heat dissipation strategy is avoided, and the overall brightness output capability and the equipment reliability are improved. The visual experience is maximized within the heat dissipation limit, the subarea light control fineness is improved, and the cooperation of the heat dissipation performance, the human eye perception model and the subarea light control is achieved.

Description

Technical Field

[0001] The present application relates to the field of backlight control technology, and in particular to a dynamic backlight control method based on mini LED. Background Art

[0002] In the display technology field, mini LED backlight technology is widely used in high-end display devices due to its high brightness, high contrast, and energy-saving characteristics. Compared with traditional LED backlights, mini LEDs, with their smaller chip size and higher arrangement density, can achieve more precise zoned light control, partially approaching the pixel-level light control effect of OLED.

[0003] However, existing mini LED backlight technology, when implementing dynamic backlight control, suffers from limited heat dissipation. This causes localized heat to accumulate rapidly during backlight adjustment. Failure to dissipate heat in a timely manner can accelerate light decay and shorten the lifespan. This also reduces peak brightness potential, preventing the desired visual impact and negatively impacting the viewing experience. Summary of the Invention

[0004] This application provides a dynamic backlight control method based on mini LED to solve the above problems.

[0005] In a first aspect, the present application provides a dynamic backlight control method based on mini LED, the method comprising: Acquiring current environmental data, analyzing the current environmental data, and predicting a human eye perception level; Determining a target backlight brightness value based on the human eye perception level and preset heat dissipation performance; Acquire real-time temperature data of the backlight partition; determine the local brightness duration based on the real-time temperature data, the target backlight brightness value and the preset heat dissipation performance; The brightness output strategy of the backlight partition is dynamically adjusted according to the target backlight brightness value and the local brightness duration.

[0006] This solution captures and analyzes current environmental data to predict human eye perception levels, enabling brightness control to better align with the actual needs of the eye, avoiding rigid brightness strategies and improving visual comfort and accuracy. Based on human eye perception and preset heat dissipation performance, it determines the target backlight brightness value, enabling intelligent optimization of brightness output. This dynamically stimulates peak brightness while ensuring device lifespan. Real-time temperature data is captured for each backlight zone, eliminating the disconnect between zone brightness control and heat dissipation optimization and indirectly alleviating heat dissipation limitations. Based on real-time temperature data, the target backlight brightness value, and preset heat dissipation performance, it determines the local brightness duration, enabling zone-level heat dissipation prediction and compensation. This optimizes local brightness output and avoids sacrificing the global heat dissipation strategy. This improves overall brightness output and device reliability while maintaining the same hardware heat dissipation structure. Based on the target backlight brightness value and local brightness duration, it dynamically adjusts the brightness output strategy for each backlight zone, ensuring the maximum visual experience within heat dissipation constraints. This dynamic adjustment prevents temperature overshoot and improves the precision of zone-based light control, achieving a synergistic effect between heat dissipation performance, human eye perception models, and zone-based light control.

[0007] Optionally, analyzing the current environmental data to predict the human eye perception level includes: Analyze the current environment data to determine the ambient light brightness and color temperature data; determining the contrast sensitivity of the human eye according to the ambient light brightness and the color temperature data; Based on the contrast sensitivity, the human eye perception level is predicted.

[0008] This solution analyzes current environmental data to determine ambient light brightness and color temperature, ensuring a digital representation of ambient light characteristics. Based on this data, the human eye's contrast sensitivity is determined, reflecting the impact of environmental factors on visual sensitivity. Based on this contrast sensitivity, the human eye's perception level is predicted to guide backlight brightness control decisions, optimizing the visual experience.

[0009] Optionally, determining the contrast sensitivity of the human eye according to the ambient light brightness and the color temperature data includes: Get the current display content; Analyze the current display content to determine the current scene type; Determining a region sensitivity weight according to the current scene type; Analyze the current display content to determine average brightness and dynamic range; The contrast sensitivity of the human eye is determined according to the regional sensitivity weight, the average brightness, and the dynamic range.

[0010] This solution allows you to obtain the current display content, avoid using outdated or static data, and support dynamic optimization. Analyze the current display content, determine the current scene type, and ensure that the brightness control strategy is aligned with the content characteristics to avoid rigid processing. Based on the current scene type, determine the regional sensitivity weight, provide spatial weight input for contrast sensitivity calculations, and guide regional optimization of brightness resource allocation. Analyze the current display content, determine the average brightness and dynamic range, quantify the visual characteristics of the content, and provide a numerical basis for contrast sensitivity calculations to avoid fixed values based on assumptions. Based on the regional sensitivity weight, average brightness, and dynamic range, determine the contrast sensitivity of the human eye, guide partition brightness adjustment, and optimize the visual experience.

[0011] Optionally, determining the local brightness duration according to the real-time temperature data, the target backlight brightness value, and the preset heat dissipation performance includes: Get the component attribute information of the backlight partition; Constructing a heat conduction model based on the component attribute information and the preset heat dissipation performance; determining temperature gradients of adjacent backlight partitions based on the real-time temperature data and the heat conduction model; The local brightness duration is determined according to the target backlight brightness value and the temperature gradient.

[0012] This solution captures component attribute information for each backlight partition, avoiding model deviations caused by missing parameters. Based on component attribute information and preset heat dissipation performance, a heat conduction model is constructed to simulate the heat accumulation process without relying on additional assumptions beyond real-time calculations. Based on real-time temperature data and the heat conduction model, the temperature gradient between adjacent backlight partitions is determined, reflecting local heat flow dynamics and identifying potential overheating areas. Based on the target backlight brightness value and temperature gradient, the local brightness duration is determined to ensure that the backlight partition maintains peak brightness, thereby maximizing visual output within heat dissipation constraints.

[0013] Optionally, determining the local brightness duration according to the target backlight brightness value and the temperature gradient includes: Obtaining a current brightness value; determining a brightness change based on the current brightness value and the target backlight brightness value; determining a heat diffusion rate according to the heat conduction model and the brightness change; The local brightness duration is determined according to the component property information, the heat diffusion rate, and the temperature gradient.

[0014] This solution obtains the current brightness value, ensuring that brightness adjustments are based on actual measurements rather than estimates, thereby improving the accuracy and real-time nature of brightness control. Based on the current brightness value and the target backlight brightness value, the brightness change is determined, bridging the relationship between brightness change and thermal behavior. Based on the thermal conduction model and brightness change, the heat diffusion rate is determined, simulating the impact of heat generation caused by brightness change on heat diffusion. The brightness change is mapped to thermal diffusion behavior, providing thermodynamic dynamic parameters for calculating local brightness duration. Based on component property information, heat diffusion rate, and temperature gradient, the local brightness duration is determined to ensure that brightness output is within a safe temperature range, thereby preventing shortened device life due to overheating.

[0015] Optionally, analyzing the currently displayed content to determine the current scene type includes: Analyzing the current display content to determine a color histogram distribution and a motion trajectory; Determining a hue proportion according to the color histogram distribution; Determining a dynamic change ratio according to the motion trajectory; The current scene type is determined according to the hue proportion and the dynamic change proportion.

[0016] This solution analyzes the current display content, determines the color histogram distribution and motion trajectory, and provides a quantitative representation of the image's overall color composition. It also provides a quantitative representation of the image's dynamic characteristics, ensuring that the color and dynamic characteristics of the current display content are objectively captured. Based on the color histogram distribution, the hue percentage is determined, simplifying the image's hue distribution and focusing on the tonal characteristics. Based on the motion trajectory, the dynamic change percentage is determined, providing a standardized measure of the image's dynamics. Based on the hue percentage and dynamic change percentage, the current scene type is determined, used to characterize the characteristics of the current display content.

[0017] Optionally, dynamically adjusting the brightness output strategy of the backlight partition according to the target backlight brightness value and the local brightness duration includes: Dividing the display content according to the current scene type to obtain a number of human eye gaze areas; Determining priority weights of several eye gaze areas according to the current scene type; Obtaining distribution data of backlight partitions; determining geometric positional relationships between the backlight partitions and a plurality of human eye gaze areas based on the distribution data; generating a partition brightness compensation coefficient according to the priority weight and the geometric position relationship; The brightness output strategy of the backlight partition is dynamically adjusted according to the target backlight brightness value, the partition brightness compensation coefficient and the local brightness duration.

[0018] Through this solution, the display content is divided according to the current scene type, and several human eye gaze areas are obtained to ensure that the brightness adjustment is focused on the human eye perception area. According to the current scene type, the priority weights of several human eye gaze areas are determined to provide a weight basis for generating partition brightness compensation coefficients, ensuring that the areas given priority by the brightness adjustment strategy. The distribution data of the backlight partitions is obtained to provide an accurate physical reference for the calculation of the geometric position relationship to ensure that the brightness adjustment can be mapped to the hardware partition. Based on the distribution data, the geometric position relationship between the backlight partition and several human eye gaze areas is determined to ensure that the brightness adjustment is geometrically aligned with the image content. Based on the priority weight and geometric position relationship, the partition brightness compensation coefficient is generated to provide a quantitative adjustment basis for dynamically adjusting the brightness output strategy. Based on the target backlight brightness value, the partition brightness compensation coefficient and the local brightness duration, the brightness output strategy of the backlight partition is dynamically adjusted to achieve dynamic optimization, improve the visual experience and ensure the life of the device.

[0019] Optionally, determining the heat diffusion rate according to the heat conduction model and the brightness change includes: Get real-time fan speed data and historical operating temperature data; Calculating a heat dissipation attenuation coefficient based on the fan speed data and historical operating temperature data; Dynamically updating thermal coupling parameters of adjacent backlight partitions according to the heat dissipation attenuation coefficient; The heat diffusion rate is determined by the heat conduction model according to the brightness change and the thermal coupling parameter.

[0020] This solution acquires fan speed data and historical operating temperature data in real time, preventing thermal model distortion caused by the use of outdated information. Based on fan speed data and historical operating temperature data, the heat dissipation attenuation coefficient is calculated, providing a basis for the dynamic update of thermal coupling parameters and adaptive heat dissipation attenuation. Based on the heat dissipation attenuation coefficient, the thermal coupling parameters of adjacent backlight partitions are dynamically updated to reflect the impact of heat dissipation and optimize heat distribution prediction. Using the heat conduction model, the heat diffusion rate is determined based on brightness changes and thermal coupling parameters, ensuring the coordinated optimization of heat dissipation and brightness.

[0021] Optionally, determining the contrast sensitivity of the human eye according to the regional sensitivity weight, the average brightness, and the dynamic range includes: Analyze the ambient light brightness to determine the light change rate and light change direction; determining a pupil dynamic response model according to the light change direction; Based on the pupil dynamic response model, the contrast sensitivity of the human eye is determined according to the regional sensitivity weight, the average brightness and the dynamic range.

[0022] This solution analyzes ambient light brightness and determines the rate and direction of light change, avoiding rigid brightness control caused by ignoring ambient light data and improving the accuracy of the response to ambient light changes. Based on the direction of light change, a pupil dynamic response model is determined to simulate the physiological behavior of the human eye under different ambient light trends, ensuring that the brightness control strategy adapts to differences in human perception and avoids visual fatigue or loss of detail. Based on the pupil dynamic response model, the human eye's contrast sensitivity is determined based on regional sensitivity weights, average brightness, and dynamic range, achieving intelligent dynamic optimization of brightness output, thereby maximizing the visual experience while protecting device life.

[0023] Optionally, constructing a heat conduction model according to the component attribute information and the preset heat dissipation performance includes: Acquiring display screen operating data; analyzing the display screen operating data to determine the degree of component aging; Determining the heat dissipation impact of the component based on the aging degree of the component and the component attribute information; A heat conduction model is constructed based on the heat dissipation impact of the components and the preset heat dissipation performance.

[0024] This solution captures display screen operating data, ensuring a complete record of real-time operating parameters and avoiding processing interruptions caused by missing data. This data is analyzed to determine component aging, reflecting the actual lifespan degradation of components. Based on component aging and component attribute information, the heat dissipation impact of components is determined, quantifying the degradation of heat dissipation capacity caused by aging. Based on the heat dissipation impact of components and pre-set heat dissipation performance, a heat conduction model is constructed, ensuring that the model output is used for heat dissipation optimization decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0026] Figure 1 A schematic diagram of an application scenario provided in one embodiment of the present application; Figure 2 This is a flow chart of a dynamic backlight control method based on mini LED provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0028] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates an "or" relationship between the related objects.

[0029] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0030] Existing mini LED backlight technology, when implementing dynamic backlight control, suffers from limited heat dissipation. This causes localized heat to accumulate rapidly during backlight adjustment. Failure to dissipate heat in a timely manner accelerates light decay and shortens the lifespan. This also reduces peak brightness potential, preventing the desired visual impact and negatively impacting the viewing experience.

[0031] Based on this, this application provides a dynamic backlight control method based on mini LEDs. This method acquires and analyzes current environmental data, predicts the human eye's perception level, and optimizes brightness control to better meet the actual needs of the human eye, avoiding rigid brightness strategies and improving the comfort and accuracy of the visual experience. Based on the human eye's perception level and preset heat dissipation performance, a target backlight brightness value is determined to achieve intelligent optimization of brightness output, dynamically stimulating peak brightness while ensuring device lifespan. Real-time temperature data is acquired for each backlight zone, eliminating the disconnect between zone brightness control and heat dissipation optimization and indirectly alleviating heat dissipation limitations. Based on real-time temperature data, the target backlight brightness value, and preset heat dissipation performance, local brightness duration is determined to achieve zone-level heat dissipation prediction and compensation, optimizing local brightness output and avoiding sacrificing the global heat dissipation strategy. This improves overall brightness output capability and device reliability while maintaining the same hardware heat dissipation structure. Based on the target backlight brightness value and local brightness duration, the brightness output strategy of each backlight zone is dynamically adjusted to ensure the maximum visual experience within heat dissipation constraints. Dynamic adjustment prevents temperature overshoot, improves the precision of zone light control, and achieves a synergistic effect among heat dissipation performance, human eye perception model, and zone light control.

[0032] Figure 1This is a schematic diagram of an application scenario provided by this application. When performing dynamic backlight control of mini LED, the method provided by this application is applied.

[0033] Specifically, the method provided in this application is applied to the processing chip of any mini LED display screen. The processing chip interacts with the ambient light sensor in any mini LED display screen, obtains the current environmental data in real time through the ambient light sensor, analyzes the current environmental data, and predicts the human eye perception level. According to the human eye perception level and the preset heat dissipation performance, the target backlight brightness value is determined. The real-time temperature data of the backlight partition is obtained. According to the real-time temperature data, the target backlight brightness value and the preset heat dissipation performance, the local brightness duration is determined, the heat dissipation prediction and compensation at the partition level are realized, the local brightness output is optimized, and the sacrifice of the global heat dissipation strategy is avoided, thereby improving the overall brightness output capability and equipment reliability while the hardware heat dissipation structure remains unchanged. According to the target backlight brightness value and the local brightness duration, the brightness output strategy of the backlight partition is dynamically adjusted to ensure that the visual experience is maximized within the heat dissipation limit. At the same time, the temperature is avoided from exceeding the standard through dynamic adjustment, the precision of the partition light control is improved, and the coordination of the heat dissipation performance, the human eye perception model and the partition light control is achieved.

[0034] For specific implementation methods, please refer to the following embodiments.

[0035] Figure 2 This is a flow chart of a dynamic backlight control method based on mini LED provided in one embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes: S201, obtaining current environmental data, analyzing the current environmental data, and predicting the human eye perception level; The current environment data may be lighting information of the environment in which the display device is located.

[0036] The human eye perception level may be a sensitivity level of the human eye to brightness changes.

[0037] Specifically, the current environmental data is obtained in real time through the ambient light sensor; then, the current environmental data is filtered and calibrated, and converted into standardized ambient light brightness values and color temperature values; finally, the ambient light brightness values and color temperature values are input into a preset human eye perception model established based on the principle of visual perception to predict the human eye perception level.

[0038] S202, determining a target backlight brightness value based on human eye perception and preset heat dissipation performance; The preset heat dissipation performance may be a preset heat dissipation capability parameter of the device, which is pre-stored in the server and called when used.

[0039] The target backlight brightness value may be an ideal light output intensity value set for the backlight zone.

[0040] Specifically, a preset heat dissipation performance is established based on the principles of thermodynamics; then, the initial target brightness value is mapped according to the human eye perception level, and then combined with the preset heat dissipation performance, a brightness decision algorithm is applied for constrained optimization to determine the target backlight brightness value.

[0041] S203, obtaining real-time temperature data of the backlight partition; determining the local brightness duration based on the real-time temperature data, the target backlight brightness value and the preset heat dissipation performance; The backlight partition can be the smallest light control unit in the mini LED backlight device that can independently adjust the brightness.

[0042] The real-time temperature data may be the current temperature value of the backlight partition.

[0043] The local brightness duration may be the maximum length of time that a backlight partition maintains a target backlight brightness value.

[0044] Specifically, the mini LED display is equipped with a temperature sensor to monitor the temperature of the backlight zones. This sensor collects real-time temperature data for each backlight zone. This data is then compared with the temperature safety threshold set in the pre-set heat dissipation performance. This data is also combined with the target backlight brightness. A duration decision model based on the thermal time constant principle is then applied to determine the local brightness duration by experimentally fitting the temperature decay curve.

[0045] S204: Dynamically adjust the brightness output strategy of the backlight partition according to the target backlight brightness value and the local brightness duration.

[0046] A brightness output policy may be a set of rules that controls the brightness output level and duration of a backlight zone.

[0047] Specifically, the target backlight brightness value is converted into a driving signal; then, combined with the local brightness duration, the time control logic is set to dynamically adjust the brightness output strategy of the backlight partition.

[0048] This solution captures and analyzes current environmental data to predict human eye perception levels, enabling brightness control to better align with the actual needs of the eye, avoiding rigid brightness strategies and improving visual comfort and accuracy. Based on human eye perception and preset heat dissipation performance, it determines the target backlight brightness value, enabling intelligent optimization of brightness output. This dynamically stimulates peak brightness while ensuring device lifespan. Real-time temperature data is captured for each backlight zone, eliminating the disconnect between zone brightness control and heat dissipation optimization and indirectly alleviating heat dissipation limitations. Based on real-time temperature data, the target backlight brightness value, and preset heat dissipation performance, it determines the local brightness duration, enabling zone-level heat dissipation prediction and compensation. This optimizes local brightness output and avoids sacrificing the global heat dissipation strategy. This improves overall brightness output and device reliability while maintaining the same hardware heat dissipation structure. Based on the target backlight brightness value and local brightness duration, it dynamically adjusts the brightness output strategy for each backlight zone, ensuring the maximum visual experience within heat dissipation constraints. This dynamic adjustment prevents temperature overshoot and improves the precision of zone-based light control, achieving a synergistic effect between heat dissipation performance, human eye perception models, and zone-based light control.

[0049] In some embodiments, current environmental data is analyzed to determine ambient light brightness and color temperature data; based on the ambient light brightness and color temperature data, the contrast sensitivity of the human eye is determined; and based on the contrast sensitivity, the human eye perception level is predicted.

[0050] The ambient light brightness may be a light intensity value in the current environment.

[0051] The color temperature data may be a color characteristic value of the current ambient light.

[0052] The human eye may be a human's visual organ.

[0053] Contrast sensitivity can be a measure of the human eye's ability to perceive changes in brightness.

[0054] Specifically, the data parsing module decodes the current ambient data, extracts light intensity values and converts them into ambient brightness, and simultaneously extracts color information and converts it into color temperature data. Then, based on the ambient brightness and color temperature data, adjacent reference points are located in an interpolation table and linear interpolation is applied to determine the contrast sensitivity of the human eye. Subsequently, based on this contrast sensitivity, a scaling algorithm is applied, combined with a scaling factor determined through empirical calibration, to predict the level of human perception.

[0055] This solution analyzes current environmental data to determine ambient light brightness and color temperature, ensuring a digital representation of ambient light characteristics. Based on this data, the human eye's contrast sensitivity is determined, reflecting the impact of environmental factors on visual sensitivity. Based on this contrast sensitivity, the human eye's perception level is predicted to guide backlight brightness control decisions, optimizing the visual experience.

[0056] In some embodiments, the currently displayed content is obtained; the currently displayed content is analyzed to determine the current scene type; the regional sensitivity weight is determined based on the current scene type; the currently displayed content is parsed to determine the average brightness and dynamic range; and the contrast sensitivity of the human eye is determined based on the regional sensitivity weight, average brightness and dynamic range.

[0057] The currently displayed content may be image or video frame data currently presented on the display device.

[0058] The current scene type may be a classification category of displayed content.

[0059] The region-sensitive weight may be an importance weight value assigned to the backlight partition.

[0060] The average brightness may be an overall average brightness value of the displayed content.

[0061] Dynamic range can be the difference between the maximum and minimum brightness values in the displayed content.

[0062] Specifically, the current display content is read from the display device's frame buffer or video stream. A preset scene classification model is then trained on a labeled image dataset through supervised learning. The current display content is then input into the preset scene classification model to determine a probability vector. The category with the highest probability is then taken as the current scene type. A weight mapping table stored in non-volatile memory is then accessed. The corresponding weight matrix is then retrieved based on the current scene type to determine the region-sensitive weight. Based on the current display content, the brightness values of several pixels are extracted and the average brightness is calculated. Simultaneously, the brightness values of several pixels are scanned to find the maximum and minimum values and determine the dynamic range. Finally, a global average weight is calculated based on the region-sensitive weights. The average brightness and dynamic range are then combined to determine the contrast sensitivity of the human eye.

[0063] This solution allows you to obtain the current display content, avoid using outdated or static data, and support dynamic optimization. Analyze the current display content, determine the current scene type, and ensure that the brightness control strategy is aligned with the content characteristics to avoid rigid processing. Based on the current scene type, determine the regional sensitivity weight, provide spatial weight input for contrast sensitivity calculations, and guide regional optimization of brightness resource allocation. Analyze the current display content, determine the average brightness and dynamic range, quantify the visual characteristics of the content, and provide a numerical basis for contrast sensitivity calculations to avoid fixed values based on assumptions. Based on the regional sensitivity weight, average brightness, and dynamic range, determine the contrast sensitivity of the human eye, guide partition brightness adjustment, and optimize the visual experience.

[0064] In some embodiments, component attribute information of the backlight partition is obtained; a heat conduction model is constructed based on the component attribute information and preset heat dissipation performance; the temperature gradient of adjacent backlight partitions is determined based on real-time temperature data and the heat conduction model; and the local brightness duration is determined based on the target backlight brightness value and temperature gradient.

[0065] The component attribute information may be physical characteristic parameters of the backlight partition.

[0066] The heat conduction model may be a mathematical model that simulates heat transfer between backlight partitions.

[0067] Adjacent backlight partitions may be backlight partitions that are adjacent in position in the backlight array.

[0068] The temperature gradient may be a temperature difference between adjacent backlight partitions.

[0069] Specifically, the component attribute information for each backlight zone is retrieved from the device's non-volatile memory. Then, based on the thermal conductivity and position coordinates in the component attribute information, the thermal coupling coefficient between zones is calculated. Furthermore, combined with the preset heat dissipation performance, heat dissipation boundary conditions are defined. Subsequently, the thermal coupling coefficient and heat dissipation boundary conditions are integrated to construct a heat conduction model. Next, based on real-time temperature data and the heat conduction model, for each backlight zone, adjacent backlight zones are identified and the temperature gradient between adjacent backlight zones is calculated. Finally, the target backlight brightness value is mapped to the heat generation rate. Furthermore, combined with the temperature gradient, the local brightness duration is determined by storing a lookup table of brightness-gradient-duration mapping relationships.

[0070] This solution captures component attribute information for each backlight partition, avoiding model deviations caused by missing parameters. Based on component attribute information and preset heat dissipation performance, a heat conduction model is constructed to simulate the heat accumulation process without relying on additional assumptions beyond real-time calculations. Based on real-time temperature data and the heat conduction model, the temperature gradient between adjacent backlight partitions is determined, reflecting local heat flow dynamics and identifying potential overheating areas. Based on the target backlight brightness value and temperature gradient, the local brightness duration is determined to ensure that the backlight partition maintains peak brightness, thereby maximizing visual output within heat dissipation constraints.

[0071] In some embodiments, a current brightness value is obtained; a brightness change is determined based on the current brightness value and the target backlight brightness value; a heat diffusion rate is determined based on a heat conduction model and brightness changes; and a local brightness duration is determined based on component property information, heat diffusion rate, and temperature gradient.

[0072] The current brightness value may be a brightness measurement value output by the backlight partition in real time.

[0073] The brightness change may be a difference between a target backlight brightness value and a current brightness value.

[0074] The heat diffusion rate can be the rate at which heat is transferred between backlight partitions.

[0075] Specifically, the current brightness value is acquired in real time through the brightness sensor of the backlight partition. The difference between the target backlight brightness value and the current brightness value is then calculated to determine the brightness change. This brightness change is then input into a heat conduction model, and the rate of heat diffusion between adjacent partitions is determined based on the mapping between brightness change and heat generation rate. Finally, the duration of local brightness is determined based on component property information, heat diffusion rate, and temperature gradient, using a preset safety temperature threshold constructed from experimental data on the material's thermal attenuation characteristics and a heat accumulation formula constructed based on the principle of heat generation and diffusion balance.

[0076] This solution obtains the current brightness value, ensuring that brightness adjustments are based on actual measurements rather than estimates, thereby improving the accuracy and real-time nature of brightness control. Based on the current brightness value and the target backlight brightness value, the brightness change is determined, bridging the relationship between brightness change and thermal behavior. Based on the thermal conduction model and brightness change, the heat diffusion rate is determined, simulating the impact of heat generation caused by brightness change on heat diffusion. The brightness change is mapped to thermal diffusion behavior, providing thermodynamic dynamic parameters for calculating local brightness duration. Based on component property information, heat diffusion rate, and temperature gradient, the local brightness duration is determined to ensure that brightness output is within a safe temperature range, thereby preventing shortened device life due to overheating.

[0077] In some embodiments, the currently displayed content is analyzed to determine the color histogram distribution and motion trajectory; based on the color histogram distribution, the hue proportion is determined; based on the motion trajectory, the dynamic change proportion is determined; and based on the hue proportion and the dynamic change proportion, the current scene type is determined.

[0078] The color histogram distribution may be a statistical histogram of the distribution of color values in the currently displayed content.

[0079] The motion trajectory can be a set of pixel motion vectors between consecutive frames.

[0080] The hue ratio may be the ratio of the number of pixels of each hue category to the total number of pixels in the current display content.

[0081] The dynamic change ratio may be a normalized proportional value of the degree of dynamic change of the currently displayed content.

[0082] Specifically, the image of the currently displayed content is divided into a pixel grid, and the color value of each pixel is quantized and bucketed. Then, the number of pixels in each color bucket is counted to form a color histogram distribution. Simultaneously, by comparing the differences in pixel brightness values between adjacent frames of the currently displayed content, motion areas are detected. Then, based on the optical flow method, a motion trajectory is generated. Subsequently, based on the color histogram distribution, hue information is extracted. Then, the pixel frequency of each hue bucket is counted. Then, the proportion of the number of pixels in each hue category, i.e., the hue proportion, is calculated. Then, based on the motion trajectory, dynamic change information is extracted. Then, the size of each motion vector is counted, and the average amplitude value of several vectors is calculated. Then, the average amplitude value is normalized to determine the dynamic change proportion. Finally, based on the hue proportion and dynamic change proportion, the current scene category is determined using preset scene type classification rules established based on human eye perception and display content characteristics.

[0083] This solution analyzes the current display content, determines the color histogram distribution and motion trajectory, and provides a quantitative representation of the image's overall color composition. It also provides a quantitative representation of the image's dynamic characteristics, ensuring that the color and dynamic characteristics of the current display content are objectively captured. Based on the color histogram distribution, the hue percentage is determined, simplifying the image's hue distribution and focusing on the tonal characteristics. Based on the motion trajectory, the dynamic change percentage is determined, providing a standardized measure of the image's dynamics. Based on the hue percentage and dynamic change percentage, the current scene type is determined, used to characterize the characteristics of the current display content.

[0084] In some embodiments, the display content is divided according to the current scene type to obtain several human eye gaze areas; the priority weights of the several human eye gaze areas are determined according to the current scene type; the distribution data of the backlight partition is obtained; the geometric position relationship between the backlight partition and the several human eye gaze areas is determined according to the distribution data; the partition brightness compensation coefficient is generated according to the priority weight and the geometric position relationship; the brightness output strategy of the backlight partition is dynamically adjusted according to the target backlight brightness value, the partition brightness compensation coefficient and the local brightness duration.

[0085] The human eye gaze area may be a sub-area in the displayed content that the user preferentially gazes at.

[0086] The priority weight may be a numerical value assigned to each eye gaze area.

[0087] The distribution data may be physical layout information of the backlight partitions.

[0088] The geometric position relationship may be a spatial coverage ratio between the backlight partition and the human eye gaze area.

[0089] The zone brightness compensation coefficient may be a scaling factor used to adjust the brightness output of each backlight zone.

[0090] Specifically, the image frame data of the currently displayed content is loaded. Then, based on the current scene type, edge detection is applied to divide the image frame data into several sub-regions, which serve as the eye gaze areas. Subsequently, based on a preset weight mapping table storing regional features for different scene types, a weight rule is queried according to the current scene type to determine the priority weights of the eye gaze areas. Then, backlight partition distribution data is retrieved from the display device's hardware configuration memory. Furthermore, based on the backlight partition distribution data, the coordinate boundaries of the eye gaze areas are traversed to calculate the geometric positional relationship between each backlight partition and the eye gaze areas. Then, based on the priority weights, the overlap ratio between the backlight partition and the eye gaze area is extracted. This overlap ratio is multiplied by the priority weight of the corresponding eye gaze area, and the sum is calculated to obtain the partition brightness compensation coefficient. Furthermore, based on the target backlight brightness value and the local brightness duration, combined with the partition brightness compensation coefficient, an adjusted brightness value is calculated. Finally, the brightness output value of each backlight partition is set via the backlight control interface, thereby dynamically adjusting the backlight partition brightness output strategy.

[0091] Through this solution, the display content is divided according to the current scene type, and several human eye gaze areas are obtained to ensure that the brightness adjustment is focused on the human eye perception area. According to the current scene type, the priority weights of several human eye gaze areas are determined to provide a weight basis for generating partition brightness compensation coefficients, ensuring that the areas given priority by the brightness adjustment strategy. The distribution data of the backlight partitions is obtained to provide an accurate physical reference for the calculation of the geometric position relationship to ensure that the brightness adjustment can be mapped to the hardware partition. Based on the distribution data, the geometric position relationship between the backlight partition and several human eye gaze areas is determined to ensure that the brightness adjustment is geometrically aligned with the image content. Based on the priority weight and geometric position relationship, the partition brightness compensation coefficient is generated to provide a quantitative adjustment basis for dynamically adjusting the brightness output strategy. Based on the target backlight brightness value, the partition brightness compensation coefficient and the local brightness duration, the brightness output strategy of the backlight partition is dynamically adjusted to achieve dynamic optimization, improve the visual experience and ensure the life of the device.

[0092] In some embodiments, fan speed data and historical operating temperature data are acquired in real time; based on the fan speed data and according to the historical operating temperature data, the heat dissipation attenuation coefficient is calculated; based on the heat dissipation attenuation coefficient, the thermal coupling parameters of adjacent backlight partitions are dynamically updated; and through the heat conduction model, the heat diffusion rate is determined according to the brightness change and the thermal coupling parameters.

[0093] The fan speed data may be a fan speed value read in real time.

[0094] The historical operating temperature data may be a stored sequence of backlight partition operating temperature data.

[0095] The heat dissipation attenuation coefficient may be a scalar value indicating a degree of attenuation of heat dissipation efficiency.

[0096] The thermal coupling parameter may be a parameter indicating the intensity of heat transfer between adjacent backlight partitions at physical locations.

[0097] Specifically, real-time fan speed data is acquired from the device's cooling fan sensor. Simultaneously, historical operating temperature data within a specified time window is retrieved from the temperature sensor database. The historical temperature change rate is then calculated based on the historical operating temperature data and the time window length. Furthermore, a preset speed-heat dissipation efficiency mapping table is queried based on the fan speed data to calculate the heat dissipation attenuation coefficient. Subsequently, based on the heat dissipation attenuation coefficient and the initial heat dissipation attenuation coefficient preset by the hardware layout, calculation rules are used to dynamically update the thermal coupling parameters of adjacent backlight partitions. Finally, the brightness change and thermal coupling parameters are input into the thermal conduction model, and a preset partial differential equation is used to determine the heat diffusion rate.

[0098] This solution acquires fan speed data and historical operating temperature data in real time, preventing thermal model distortion caused by the use of outdated information. Based on fan speed data and historical operating temperature data, the heat dissipation attenuation coefficient is calculated, providing a basis for the dynamic update of thermal coupling parameters and adaptive heat dissipation attenuation. Based on the heat dissipation attenuation coefficient, the thermal coupling parameters of adjacent backlight partitions are dynamically updated to reflect the impact of heat dissipation and optimize heat distribution prediction. Using the heat conduction model, the heat diffusion rate is determined based on brightness changes and thermal coupling parameters, ensuring the coordinated optimization of heat dissipation and brightness.

[0099] In some embodiments, the ambient light brightness is analyzed to determine the rate of light change and the direction of light change; based on the direction of light change, the pupil dynamic response model is determined; based on the pupil dynamic response model, the contrast sensitivity of the human eye is determined according to the regional sensitivity weight, average brightness and dynamic range.

[0100] The light change rate may be a rate of change of ambient light brightness over time.

[0101] The light change direction may be a trend of change in the brightness of the ambient light.

[0102] The pupil dynamic response model may be a preset pupil physiological response function.

[0103] Specifically, the system analyzes the ambient light brightness and reads the ambient light brightness in the current time window. Then, using numerical differentiation, it determines the rate of light change by dividing the brightness difference between adjacent sampling points within the time window by the time interval. The current ambient light brightness is then compared with the ambient light brightness at the previous sampling point. Based on the comparison results, the direction of light change is determined. Three types of pupil response models are preset during device initialization based on the direction of light change. A library of preset models is then queried to determine the pupil dynamic response model. Finally, based on the pupil dynamic response model, a weighted combination of regional sensitivity weights, average brightness, and dynamic range is used to determine the contrast sensitivity of the human eye.

[0104] This solution analyzes ambient light brightness and determines the rate and direction of light change, avoiding rigid brightness control caused by ignoring ambient light data and improving the accuracy of the response to ambient light changes. Based on the direction of light change, a pupil dynamic response model is determined to simulate the physiological behavior of the human eye under different ambient light trends, ensuring that the brightness control strategy adapts to differences in human perception and avoids visual fatigue or loss of detail. Based on the pupil dynamic response model, the human eye's contrast sensitivity is determined based on regional sensitivity weights, average brightness, and dynamic range, achieving intelligent dynamic optimization of brightness output, thereby maximizing the visual experience while protecting device life.

[0105] In some embodiments, display screen operation data is obtained; the display screen operation data is analyzed to determine the degree of component aging; the heat dissipation impact of the component is determined based on the degree of component aging and component attribute information; and a heat conduction model is constructed based on the heat dissipation impact of the component and preset heat dissipation performance.

[0106] The display screen operation data may be a collection of data collected in real time by built-in sensors during the operation of the display screen.

[0107] The degree of component aging can represent the aging status of the mini LED in the backlight partition.

[0108] The heat dissipation effect of a component can represent the effect of aging on heat dissipation efficiency.

[0109] Specifically, real-time display screen operating data is collected from the display's built-in sensors and control devices. Temperature values and operating time series are then extracted from this data. A pre-defined aging calculation model, constructed based on the principle of temperature-accelerated aging, combines the accumulated operating time with high-temperature exposure to determine the component's aging degree. Subsequently, the component's aging degree is used as an aging factor to adjust the thermal conductivity within the component's property information. Based on this adjusted thermal conductivity, the component's heat dissipation impact is determined. Finally, the component's heat dissipation impact is integrated with pre-defined heat dissipation performance parameters to form a comprehensive heat dissipation parameter. A heat conduction model is then established using the heat conduction equation.

[0110] This solution captures display screen operating data, ensuring a complete record of real-time operating parameters and avoiding processing interruptions caused by missing data. This data is analyzed to determine component aging, reflecting the actual lifespan degradation of components. Based on component aging and component attribute information, the heat dissipation impact of components is determined, quantifying the degradation of heat dissipation capacity caused by aging. Based on the heat dissipation impact of components and pre-set heat dissipation performance, a heat conduction model is constructed, ensuring that the model output is used for heat dissipation optimization decisions.

Claims

1. A dynamic backlight control method based on mini LED, characterized in that: include: Acquiring current environmental data, analyzing the current environmental data, and predicting a human eye perception level; Determining a target backlight brightness value based on the human eye perception level and preset heat dissipation performance; Get real-time temperature data of backlight partitions; determining a local brightness duration according to the real-time temperature data, the target backlight brightness value, and the preset heat dissipation performance; The brightness output strategy of the backlight partition is dynamically adjusted according to the target backlight brightness value and the local brightness duration.

2. The method according to claim 1, characterized in that The analyzing the current environmental data and predicting the human eye perception level includes: Analyze the current environment data to determine the ambient light brightness and color temperature data; determining the contrast sensitivity of the human eye according to the ambient light brightness and the color temperature data; Based on the contrast sensitivity, the human eye perception level is predicted.

3. The method according to claim 2, characterized in that The determining the contrast sensitivity of the human eye according to the ambient light brightness and the color temperature data includes: Get the current display content; Analyze the current display content to determine the current scene type; Determining a region sensitivity weight according to the current scene type; Analyze the current display content to determine average brightness and dynamic range; The contrast sensitivity of the human eye is determined according to the regional sensitivity weight, the average brightness, and the dynamic range.

4. The method according to claim 1, wherein The determining of the local brightness duration according to the real-time temperature data, the target backlight brightness value, and the preset heat dissipation performance includes: Get the component attribute information of the backlight partition; Constructing a heat conduction model based on the component attribute information and the preset heat dissipation performance; determining temperature gradients of adjacent backlight partitions based on the real-time temperature data and the heat conduction model; The local brightness duration is determined according to the target backlight brightness value and the temperature gradient.

5. The method according to claim 4, characterized in that The determining the local brightness duration according to the target backlight brightness value and the temperature gradient includes: Obtaining a current brightness value; determining a brightness change based on the current brightness value and the target backlight brightness value; determining a heat diffusion rate according to the heat conduction model and the brightness change; The local brightness duration is determined according to the component property information, the heat diffusion rate, and the temperature gradient.

6. The method according to claim 3, characterized in that The analyzing the current display content to determine the current scene type includes: Analyzing the current display content to determine a color histogram distribution and a motion trajectory; Determining a hue proportion according to the color histogram distribution; Determining a dynamic change ratio according to the motion trajectory; The current scene type is determined according to the hue proportion and the dynamic change proportion.

7. The method according to claim 3, characterized in that The dynamically adjusting the brightness output strategy of the backlight partition according to the target backlight brightness value and the local brightness duration includes: Dividing the display content according to the current scene type to obtain a number of human eye gaze areas; Determining priority weights of several eye gaze areas according to the current scene type; Obtaining distribution data of backlight partitions; determining geometric positional relationships between the backlight partitions and a plurality of human eye gaze areas based on the distribution data; generating a partition brightness compensation coefficient according to the priority weight and the geometric position relationship; The brightness output strategy of the backlight partition is dynamically adjusted according to the target backlight brightness value, the partition brightness compensation coefficient and the local brightness duration.

8. The method according to claim 5, characterized in that The determining of the heat diffusion rate according to the heat conduction model and the brightness change includes: Get real-time fan speed data and historical operating temperature data; Calculating a heat dissipation attenuation coefficient based on the fan speed data and historical operating temperature data; Dynamically updating thermal coupling parameters of adjacent backlight partitions according to the heat dissipation attenuation coefficient; The heat diffusion rate is determined by the heat conduction model according to the brightness change and the thermal coupling parameter.

9. The method according to claim 3, characterized in that The determining the contrast sensitivity of the human eye according to the regional sensitivity weight, the average brightness, and the dynamic range includes: Analyze the ambient light brightness to determine the light change rate and light change direction; determining a pupil dynamic response model according to the light change direction; Based on the pupil dynamic response model, the contrast sensitivity of the human eye is determined according to the regional sensitivity weight, the average brightness and the dynamic range.

10. The method according to claim 4, characterized in that The step of constructing a heat conduction model based on the component attribute information and the preset heat dissipation performance includes: Acquiring display screen operating data; analyzing the display screen operating data to determine the degree of component aging; Determining the heat dissipation impact of the component based on the aging degree of the component and the component attribute information; A heat conduction model is constructed based on the heat dissipation impact of the components and the preset heat dissipation performance.

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