Universe dynamic optimization system of ultra-high-definition glass-based display screen

Abstract

The invention discloses a global dynamic optimization system for an ultra-high-definition glass-based display screen. The operation process of the system specifically comprises the following steps: acquiring a real-time state data set of the display screen; generating an initial optimization parameter set based on a preset optimization strategy and the user behavior model; capturing a user interaction signal and an environment change signal in real time; generating a dynamic correction coefficient set according to the user interaction signal and the environment change signal; performing global parameter fusion based on the initial optimization parameter set and the dynamic correction coefficient set to generate a target optimization instruction set; and feeding back an optimization effect evaluation table based on the optimization effect. The method has the following advantages and effects: collaborative optimization and thermodynamic balance of global display parameters are realized, the system can balance visual experience improvement and thermal diffusion suppression requirements in real time through multi-dimensional data fusion and global parameter collaborative calculation, and the system can improve the visual experience while enhancing local contrast and optimizing color rendition. And actively inhibiting the temperature rise of the screen through thermodynamic equilibrium constraint.

Description

Technical Field

[0001] The present invention relates to the field of display technology, and in particular to a global dynamic optimization system for an ultra-high-definition glass-based display screen. Background Art

[0002] The current dynamic optimization technology of ultra-high-definition glass-based displays generally faces a core bottleneck: under the real-time coupling of multi-dimensional dynamic factors, such as user interaction behavior, ambient light changes, and local heat load, it is difficult for existing systems to achieve coordinated optimization and thermal balance of global display parameters. Traditional methods usually adopt independent closed-loop control strategies, such as global brightness adjustment based on ambient light, backlight control of fixed partitions, or static color temperature compensation. Although these solutions can partially improve the display effect in isolated scenes, they cannot cope with the complex correlation between local heat accumulation caused by user gaze area migration, touch operation, and dynamic interference of ambient light. Especially in long-term high-load scenarios, there is often a contradiction between the local temperature rise of the screen and the need to adjust the display parameters: simply increasing the local brightness will lead to increased heat diffusion, while the forced temperature control strategy will sacrifice display quality. The limitations of this parameter decoupling optimization make it difficult for existing systems to simultaneously meet the requirements of visual experience consistency, color reproduction accuracy, and thermal stability in dynamic scenes, becoming a key technical barrier to further improve the performance of ultra-high-definition glass-based displays. Summary of the invention

[0003] The purpose of the present invention is to provide a global dynamic optimization system for an ultra-high-definition glass-based display screen to solve the problems raised in the background technology.

[0004] The above technical objectives of the present invention are achieved through the following technical solutions: A global dynamic optimization system for an ultra-high-definition glass-based display screen, wherein the system operation process specifically includes the following steps: S100, obtaining a real-time status data set of a display screen; wherein the real-time status data set includes brightness distribution data, color temperature gradient data, ambient light intensity data, and screen surface temperature data; S200, generating an initial optimization parameter set based on a preset optimization strategy and a user behavior model; wherein the preset optimization strategy includes a dynamic contrast enhancement rule, a color gamut adaptive matching rule, and a thermal balance constraint rule, the user behavior model is obtained by training historical operation data, and the initial optimization parameter set includes a partition backlight intensity parameter, a pixel drive voltage parameter, and a color compensation coefficient matrix; S300, capturing user interaction signals and environment change signals in real time; wherein the user interaction signals include touch track data, gaze area coordinates and operation response duration, and the environment change signals are synchronously collected by ambient light sensors and infrared thermal sensing arrays; S400. Generate a set of dynamic correction factors according to the user interaction signal and the environmental change signal; wherein, the set of dynamic correction factors includes a brightness attenuation compensation factor, a color temperature shift correction factor, and a thermal diffusion suppression parameter; S500. Perform global parameter fusion based on the initial optimization parameter set and the set of dynamic correction factors to generate a target optimization instruction set; wherein, the global parameter fusion includes weight superposition of backlight zones, gamma value iterative adjustment of pixel matrices, and remapping processing of color spaces; S600. Optimize the optimization effect evaluation table based on the optimization effect feedback.

[0005] By adopting the above technical solutions, by establishing a full-domain state perception system for the display screen, it is possible to integrate multi-dimensional data such as brightness distribution, color temperature gradient, ambient light intensity, and screen surface temperature in real time, forming a comprehensive and dynamically updated operation portrait of the display screen; this multi-modal data fusion mechanism breaks through the limitations of traditional optimization solutions that only rely on single-dimensional parameters, enabling the system to accurately identify the conflict areas between display quality and thermal stability. For example, in a scenario where strong ambient light coexists with a high screen temperature, traditional methods often lead to parameter imbalance due to isolated adjustment of brightness or color temperature. However, through the coupled analysis of ambient light intensity and screen temperature data, this system can intelligently balance the demand for brightness enhancement and the risk of heat dissipation, thus ensuring hardware safety while maintaining the visual experience; the generation of the initial optimization parameter set combines the dual driving of preset optimization strategies and user behavior models, following both the basic rules of display technology and integrating user personalized preferences, realizing the upgrade from static rules to dynamic adaptation; the multi-level control variable design of the zoned backlight intensity parameter, pixel driving voltage parameter, and color compensation coefficient matrix ensures a full range of coverage of the display effect, avoiding optimization blind spots caused by parameter omission; the real-time capture mechanism for user interaction signals and environmental change signals further endows the system with dynamic response capabilities, enabling display parameters to be quickly adjusted following the instantaneous changes in user behavior and environmental status, effectively solving the problem of degraded display effects caused by the response lag of traditional fixed strategies; during the full-domain parameter fusion process, the weight superposition of backlight zones, the gamma value iterative adjustment of the pixel matrix, and the remapping process of the color space form a collaborative optimization network, eliminating the mutual interference between backlight, color, and temperature adjustments through multi-parameter joint calculations. For example, when increasing the local backlight intensity, the system synchronously adjusts the gamma value of the corresponding area to compensate for the color deviation that may be caused by the brightness jump, and at the same time restricts the temperature rise rate of this area through heat dissipation suppression parameters, thereby achieving dual optimization of display quality and thermal management; the generation of the target optimization instruction set converts abstract algorithm parameters into hardware-executable instructions, ensuring the efficient connection between the optimization logic at the software layer and the physical layer driving devices, significantly reducing the system response delay; the introduction of the optimization effect evaluation table constructs a closed-loop feedback mechanism. By continuously monitoring core indicators such as brightness uniformity error, color temperature deviation value, and temperature rise suppression efficiency, the system can dynamically correct the parameter weights of the preset strategy and user model, forming the optimization ability of self-diagnosis and continuous iteration, and long-term ensuring the stability and reliability of the display screen performance.

[0006] A further setting is that the S200 specifically includes the following steps: S210. According to the dynamic contrast enhancement rule, perform regional segmentation on the brightness distribution data, calculate the local contrast gain value of each partition, and generate a dynamic allocation table for backlight intensity, specifically including: Divide the brightness distribution data in the real-time status dataset into multiple backlight partitions according to a preset grid, and the division of each partition is dynamically adjusted according to the gaze area coordinate distribution in the user behavior model; For each backlight partition, extract the mean and standard deviation of its brightness distribution data, and combine the multi-directional incident light intensity in the ambient light intensity data to calculate the local contrast gain value: Perform dynamic attenuation compensation on the local contrast gain value according to the heat accumulation value of the corresponding partition in the screen surface temperature data to generate a dynamic backlight intensity allocation table; S220. Based on the gamut adaptive matching rule, perform difference analysis on the color temperature gradient data to generate a three-dimensional color compensation coefficient matrix, specifically including: Extract the red, green, and blue sub-pixel color coordinate offsets of the color temperature gradient data from the color temperature gradient data in the real-time status dataset, and construct a three-dimensional color difference mapping matrix in the color space; Compare the three-dimensional color difference mapping matrix with the color temperature range of the preset standard gamut pixel by pixel, calculate the color difference value, and generate a difference quantization table including the color difference weight factor; among them, the generation steps of the difference quantization table are: According to the color preference data in the user behavior model, assign a color difference weight factor to the color difference value to generate a difference quantization table, where the color difference weight factor is positively correlated with the color adjustment frequency in the user's historical operations; According to the temperature gradient of each pixel region in the screen surface temperature data, perform non-linear scaling on the color difference value in the high-temperature region and correct it; Multiply the color difference weight factor in the difference quantization table by the corrected color difference value to generate independent compensation coefficients for the RGB channels, specifically including: For each pixel coordinate position, calculate the compensation coefficients for the red, green, and blue channels respectively; Integrate the red, green, and blue channel compensation coefficients at each pixel position into a three-dimensional color compensation coefficient matrix according to the spatial coordinates; S230. Through the thermal equilibrium constraint rule, combine the screen surface temperature data and the user interaction signal to establish a heat diffusion suppression model and generate a pixel voltage attenuation curve, specifically including: According to the screen surface temperature data in the real-time status dataset, construct a temperature field distribution map with pixel coordinates as variables, and establish a heat source intensity distribution model based on the historical touch pressure distribution data stored in the user behavior model; Based on the principles of thermodynamics, establish a heat conduction equation, and then use the finite volume method for discretized solution to output the predicted value of the temperature change rate of each pixel unit; Generate a piecewise continuous pixel voltage attenuation curve according to the historical operation response duration and touch trajectory data in the user behavior model, combined with the predicted value of the temperature change rate.

[0007] By adopting the above technical solutions, the dynamic contrast enhancement rule dynamically divides the backlight zones according to the user's fixation area based on the luminance distribution data, significantly improving the contrast performance in the visual focus area while reducing the ineffective energy consumption in the non-fixation area; the traditional fixed-zone backlight control often causes resource waste due to the mismatch between the zone boundaries and the user's attention area, while the present invention ensures the efficient allocation of backlight resources by real-time tracking the fixation coordinates and dynamically adjusting the zone grid; the calculation of the local contrast gain value is not only based on the luminance mean and standard deviation, but also incorporates multi-directional ambient light intensity data, which can effectively offset the interference of ambient light reflection on the display effect. For example, in a strong lateral light environment, the system analyzes the incident light angle and intensity and enhances the backlight output of the corresponding area of the screen accordingly to maintain the visibility of the picture; the gain attenuation compensation mechanism driven by the heat accumulation value avoids the risk of screen aging or thermal failure caused by excessive pursuit of contrast by dynamically adjusting the upper limit of the backlight gain in the high-temperature area; the generation of the three-dimensional color compensation coefficient matrix is based on the precise quantification of the color coordinate offsets of the red, green, and blue sub-pixels. Compared with the traditional global color temperature adjustment, it can achieve pixel-level color deviation detection and compensation; the associated design of the color difference weight factor and the user's historical operation data makes the color compensation process closely fit the user's personalized preferences. For example, for users who frequently adjust the saturation, the color difference compensation intensity is automatically increased; the non-linear scaling strategy of the color difference value in the high-temperature area effectively suppresses the color drift phenomenon caused by the temperature gradient, ensuring the consistency of color reproduction in a complex thermal environment; the thermal diffusion suppression model constructs a temperature field distribution map and a heat source intensity distribution model to accurately predict the local temperature rise trend caused by the user's interaction behavior, such as the temperature accumulation effect in the area of long-term touch operation; based on the discretized solution of the heat conduction equation and the prediction of the pixel-level temperature change rate, the system can scientifically generate a segmented continuous pixel voltage attenuation curve to balance the temperature control requirements and the display stability.

[0008] A further setting is that in the S210, according to the heat accumulation value of the corresponding zone in the screen surface temperature data, a dynamic attenuation compensation is performed on the local contrast gain value to generate a dynamic backlight intensity allocation table, including: Extract the average temperature value of each backlight zone and calculate the temperature difference between it and the preset temperature threshold; if the temperature difference is greater than zero, the local contrast gain value is corrected based on the following logic: Calculate the ratio of the temperature difference to the safe operating temperature range of the display screen, and multiply it by the heat attenuation coefficient to obtain the gain attenuation ratio; wherein, the safe operating temperature range of the display screen is the difference between the maximum allowable operating temperature and the preset temperature threshold; the heat attenuation coefficient is obtained by training the historical temperature rise data in the user behavior model and is used to adaptively adjust the heat attenuation intensity in different usage scenarios; Dynamically reduce the original local contrast gain value according to the gain attenuation ratio to ensure that the backlight intensity in the high-temperature area does not exceed the thermal stability threshold; Write the corrected local contrast gain value into the backlight intensity dynamic allocation table according to the partition number, and associate the corresponding backlight driver chip control address.

[0009] By adopting the above technical solution, by calculating the temperature difference between backlight partitions in real time and dynamically adjusting the thermal attenuation coefficient, intelligent attenuation of the backlight gain is achieved. The training of the thermal attenuation coefficient enables the system to adapt to the temperature rise law in different usage scenarios. For example, in the game scenario, due to the high load of the GPU, the overall screen temperature rises, and the system automatically increases the attenuation ratio to match the change in thermal load. The above dynamic balance design of temperature and backlight intensity not only extends the service life of the display screen but also reduces the probability of hardware failures through preventive thermal management. The calculation logic of the gain attenuation ratio combines the safe operating temperature range of the display screen and historical temperature rise data to ensure that the backlight intensity in the high-temperature area is always within the thermal stability threshold, and at the same time, minimizes the negative impact on the display effect through an optimized algorithm.

[0010] A further setting is that the S200 specifically further includes the following steps: Integrate the backlight intensity dynamic allocation table, the three-dimensional color compensation coefficient matrix, and the pixel voltage attenuation curve into an initial optimization parameter set, specifically including: Perform spatial alignment on the local contrast gain value of the partition in the backlight intensity dynamic allocation table and the three-dimensional color compensation coefficient matrix to generate a multi-channel parameter mapping table; Synchronize the pixel voltage attenuation curve with the multi-channel parameter mapping table in time sequence to ensure that backlight intensity adjustment, color compensation, and voltage attenuation take effect collaboratively within the refresh cycle; Based on the dynamic anti-reflection suppression coefficient in the ambient light intensity data, perform global normalization processing on the initial optimization parameter set to generate a binary control instruction sequence that can be directly input into the display driver chip.

[0011] By adopting the above technical solution, the generation of the multi-channel parameter mapping table solves the problem of display effect imbalance caused by parameter space misalignment by spatially aligning the backlight intensity and color compensation parameters; the time sequence synchronization mechanism prevents screen tearing or flickering caused by phase differences in parameter updates within the refresh cycle by coordinating the effective time sequences of backlight adjustment, color compensation, and voltage attenuation; the global normalization processing driven by the dynamic anti-reflection suppression coefficient can intelligently offset the impact of sudden changes in ambient light on display parameters. For example, in the case of suddenly enhanced ambient light, the system automatically reduces the global brightness and enhances color compensation to maintain the visibility of the displayed content; the spatio-temporal consistency guarantee mechanism for multi-parameter collaboration enables seamless connection of backlight, color, and temperature control adjustments in the time and space dimensions, thereby enhancing the coherence and stability of the overall optimization effect.

[0012] A further setting is that the S300 specifically includes the following steps: Record the acceleration and pressure distribution of the touch trajectory through the capacitive touch layer to generate a touch behavior feature vector; the touch behavior feature vector includes the standardization of the average touch pressure, acceleration amplitude, and trajectory curvature radius; Use the eye tracking module to obtain the coordinate sequence of the user's gaze area, and construct a time decay model of the gaze heat map in combination with the time stamp; Collect the spectral intensity data of the ambient light sensor in the visible and infrared bands, and eliminate the instantaneous interference noise through the Kalman filtering algorithm; Synchronize multiple frames of temperature distribution maps of the infrared thermal sensor array, and use the optical flow method to calculate the moving trajectory and intensity change gradient of the environmental heat source.

[0013] By adopting the above technical solutions, the generation of the touch behavior feature vector accurately identifies the user's operation intention and interaction mode by analyzing the acceleration, pressure distribution, and curvature radius of the touch trajectory. For example, a high-speed sliding trajectory may indicate an upcoming display content switch, and the system preloads and optimizes parameters in advance to reduce the response delay accordingly; the time decay model of the gaze heat map dynamically constructs the law of the user's visual focus migration by weighting the historical gaze coordinates and time stamp data, enabling the system to preferentially optimize the current gaze area while reserving the parameter adjustment margin for the historical attention area; the Kalman filtering algorithm of the ambient light sensor effectively eliminates the instantaneous interference noise; the optical flow method analysis of the infrared thermal sensor array can accurately track the moving trajectory and intensity change of the external heat source, and the system predicts the local temperature rise trend of the screen and starts the suppression measures in advance based on this; this multi-dimensional signal fusion perception ability enables the system to have a deep understanding of the user's intention and environmental interference, providing high-confidence input data for dynamic optimization.

[0014] A further setting is that the S400 specifically includes the following steps: S410. Input the touch behavior feature vector into the first convolutional neural network, and output the spatial distribution map of the brightness attenuation compensation coefficient, specifically including: Perform normalization processing on the touch behavior feature vector; Extract the spatio-temporal correlation features of the touch behavior through the three-dimensional convolutional layer. After compressing the feature dimension through the pooling layer, the fully connected layer outputs the brightness attenuation compensation coefficient corresponding to the backlight partition; Train the first convolutional neural network based on historical touch data, and the optimization goal is to minimize the error between the predicted brightness attenuation value and the actual backlight adjustment amount; Map the brightness attenuation compensation coefficient output by the first convolutional neural network to a spatial distribution map according to the backlight partition number; S420. Match the time decay model of the gaze heat map with the key areas of the current display content, and generate a color temperature offset correction factor through an attention weight distribution algorithm, specifically including: Extract the high-contrast areas and the user's historical attention areas in the display content as key areas, and calculate the spatial matching degree score by combining the time decay weight of the gaze heat map and the key area mask; Normalize the matching degree score to generate an attention weight distribution, and weighted-correct the current color temperature gradient data to obtain a color temperature offset correction factor matrix; S430. Based on the moving trajectory of the environmental heat source, use a thermodynamic simulation model to predict the local temperature rise trend of the screen and generate a heat diffusion suppression parameter, specifically including: According to the moving trajectory and intensity change gradient of the environmental heat source, construct a heat flux density distribution model on the screen surface to obtain the heat flux input density of each pixel unit; For each pixel unit, according to its heat flux input density and historical temperature rise response curve, fit the predicted temperature rise amount within the future time window, and at the same time, combine the touch operation frequency in the user behavior model to add a temperature rise compensation coefficient to the high-frequency interaction area; If the corrected predicted temperature rise amount of a pixel unit exceeds the preset safety threshold, calculate the heat diffusion suppression parameter; specifically including: Calculate the difference between the corrected predicted temperature rise amount and the preset safety threshold to obtain the over-temperature rise amount; Divide the over-temperature rise amount by the difference between the maximum allowable temperature rise and the preset safety threshold to obtain the normalized temperature rise risk ratio; Finally, subtract the temperature rise risk ratio from 1 to obtain the heat diffusion suppression parameter; If the predicted temperature rise amount does not exceed the preset safety threshold, assign the heat diffusion suppression parameter as 1; Integrate the heat diffusion suppression parameters of all pixel units according to the coordinates into a gradient mask matrix, and each element in the matrix corresponds to the heat diffusion suppression parameter of a pixel; Integrate the heat diffusion suppression parameters of all pixels and generate a heat diffusion suppression parameter gradient mask matrix according to the physical coordinates of the screen.

[0015] By adopting the above technical solution, the first convolutional neural network extracts the spatio-temporal correlation features of touch behaviors through three-dimensional convolutional layers, capturing the inherent laws of complex interaction patterns, such as the synergistic effect of multi-touch operations or the persistent heat source features of long-press operations; continuous training of historical touch data enables the neural network to adapt to the interaction habits of different users and gradually improve the prediction accuracy of the brightness attenuation compensation coefficient; the matching algorithm between the gaze heat map and the key areas of the display content dynamically allocates attention weights by calculating the spatial matching degree score, ensuring that high-priority areas obtain more refined color temperature correction; the generation of the heat diffusion suppression parameter is based on the joint prediction of the heat flux density distribution model and the historical temperature rise response curve, which can accurately predict the pixel-level temperature rise trend and formulate suppression strategies; the design of the temperature rise compensation coefficient for high-frequency interaction areas strengthens the heat diffusion suppression ability for touch-intensive areas, avoiding touch failure or display anomalies caused by local overheating.

[0016] A further setting is that the S500 specifically includes the following steps: S510. Perform weighted superposition processing on the backlight partitions, specifically including: Extract the backlight intensity dynamic allocation table from the initial optimization parameter set, and at the same time obtain the spatial distribution map of the brightness attenuation compensation coefficient from the dynamic correction coefficient set; Perform per-zone multiplication operation on the local contrast gain value of the backlight partition and the brightness attenuation compensation coefficient of the corresponding partition to generate a fused backlight weight matrix; Convert the fused backlight weight matrix into a backlight control signal waveform through the physical addressing mapping relationship of the backlight driver chip; S520. Iteratively adjust the gamma value of the pixel matrix, specifically including: Based on the independent compensation coefficients of the RGB channels in the three-dimensional color compensation coefficient matrix, construct a gamma correction parameter mapping table; Extract the color temperature offset correction factor matrix from the dynamic correction coefficient set and align its channels with the gamma correction parameter mapping table; Iteratively adjust the original gamma value through the per-pixel weighted average algorithm to generate a dynamic gamma correction curve; S530. Perform remapping processing on the color space, specifically including: Input the dynamic gamma correction curve into the color management engine, and combine it with the heat diffusion suppression parameter gradient mask matrix to establish a gamut constraint condition; Perform non-linear transformation on the RGB color space based on the gamut constraint condition to generate a three-dimensional lookup table of the color space; Use the bilinear interpolation algorithm to map the three-dimensional lookup table to the native gamut space of the display screen to complete the recalibration of the color space; S540. Generate a target optimization instruction set, specifically including: Perform time-sequence synchronous encoding on the fused backlight weight matrix, dynamic gamma correction curve, and recalibrated color space parameters; Based on the instruction set architecture of the display driver chip, convert the encoded parameters into an executable binary optimized instruction sequence; Real-time send the binary optimized instruction sequence to the backlight controller, gamma correction module, and color processing unit through a high-speed serial interface to achieve collaborative optimization of display parameters.

[0017] By adopting the above technical solution, the fused backlight weight matrix realizes the dynamic balance between brightness and thermal management through zone-by-zone product operation. When there is a high brightness requirement in the high-temperature zone, the system limits the maximum gain value according to the thermal stability threshold and compensates for the brightness loss through a color compensation mechanism; the channel alignment of the gamma correction parameter mapping table and the color temperature offset correction factor solves the problem of parameter coupling across color spaces. For example, when compensating the red channel, the influence of color temperature offset on the overall white balance needs to be considered synchronously; the dynamic gamma curve generated by the per-pixel weighted average algorithm can adapt to local display characteristic differences; the color space remapping ensures that the conversion process does not exceed the physical limit of the native color gamut of the display screen through gamut constraint conditions; the combination of the three-dimensional lookup table and the bilinear interpolation algorithm greatly reduces the computational complexity and meets the real-time requirement; the time-sequence synchronous encoding of the instruction set architecture eliminates the time jitter of parameter updates and ensures the action consistency of the backlight, gamma, and color units.

[0018] A further setting is that the S600 specifically includes the following steps: S610, Real-time collect the screen performance indicators after the display parameters are executed; wherein, the screen performance indicators include the actual brightness uniformity error, color temperature deviation value, and temperature rise suppression efficiency; S620, Compare the screen performance indicators with the preset target thresholds to generate an optimization effect evaluation table; the optimization effect evaluation table records the brightness error ratio, color temperature deviation level, and heat diffusion suppression success rate of each backlight zone.

[0019] By adopting the above technical solution, the optimization effect evaluation table can provide intuitive feedback on the optimization effect and guide the subsequent parameter adjustment direction.

[0020] In summary, the present invention has the following beneficial effects: Realize the collaborative optimization and thermal balance of global display parameters; through multi-dimensional data fusion and global parameter collaborative calculation, the system can balance the visual experience improvement and heat diffusion suppression requirements in real time. While enhancing local contrast and optimizing color restoration, actively suppress the screen temperature rise through thermal equilibrium constraints to ensure the long-term coexistence of ultra-high definition display effects and hardware security. Description of the Drawings

[0021] Figure 1Schematic diagram of the main process of the embodiment; Figure 2 Schematic diagram of the process of S200 in the embodiment; Figure 3 Schematic diagram of the process of generating a dynamic allocation table of backlight intensity by dynamically attenuating and compensating the local contrast gain value according to the thermal accumulation value of the corresponding partition in the screen surface temperature data in S210 of the embodiment; Figure 4 Schematic diagram of the process of S400 in the embodiment. Detailed implementation manners

[0022] The present invention will be further described in detail below with reference to the accompanying drawings.

[0023] As shown in the attached Figures 1 to 4 figure; This embodiment discloses a full - domain dynamic optimization system for an ultra - high - definition glass - based display screen. The specific process of the system operation includes the following steps: S100, obtaining a real - time status data set of the display screen; wherein, the real - time status data set includes brightness distribution data, color temperature gradient data, ambient light intensity data, and screen surface temperature data; The brightness distribution data is collected by a partitioned backlight sensor; the color temperature gradient data is obtained by performing frame - by - frame spectral scanning on the display image by a multi - spectral imaging unit, extracting the color coordinate offsets of red, green, and blue sub - pixels, and constructing a three - dimensional matrix of the color temperature gradient; the ambient light intensity data is synchronously collected by a multi - direction ambient light sensor array for the spectral intensities in the visible and infrared bands; the screen surface temperature data is obtained by a high - density infrared thermal sensor grid for real - time monitoring of the thermal radiation values of each pixel area.

[0024] Specifically, the partitioned backlight sensor adopts a high - sensitivity photodiode array with a 256×144 grid distribution, and each partition contains 16×16 pixels; the multi - spectral imaging unit integrates a CMOS sensor with narrow - band filters; the multi - direction ambient light sensor adopts a six - direction circular layout, and each direction is configured with visible and infrared dual - channel spectral detectors with a sampling frequency of 1 kHz; the high - density infrared thermal sensor grid is based on a 640×480 thermal imaging array of microbolometers, with a temperature resolution of 0.05 °C and a spatial resolution of 0.1 mm² / pixel.

[0025] S200, generating an initial optimization parameter set based on a preset optimization strategy and a user behavior model; wherein, the preset optimization strategy includes a dynamic contrast enhancement rule, a color gamut adaptive matching rule, and a thermal equilibrium constraint rule, the user behavior model is trained by historical operation data, and the initial optimization parameter set includes partitioned backlight intensity parameters, pixel drive voltage parameters, and a color compensation coefficient matrix; S300. Capture user interaction signals and environmental change signals in real time; among them, the user interaction signals include touch trajectory data, gaze area coordinates, and operation response duration, and the environmental change signals are synchronously collected by an ambient light sensor and an infrared thermal sensing array; S400. Generate a set of dynamic correction coefficients according to the user interaction signals and environmental change signals; among them, the set of dynamic correction coefficients includes a brightness attenuation compensation coefficient, a color temperature offset correction factor, and a heat diffusion suppression parameter; S500. Perform global parameter fusion based on the initial optimization parameter set and the set of dynamic correction coefficients to generate a target optimization instruction set; among them, the global parameter fusion includes weight superposition of backlight partitions, gamma value iterative adjustment of the pixel matrix, and remapping processing of the color space; S600. Optimize the optimization effect evaluation table based on the optimization effect feedback.

[0026] Specifically, S200 specifically includes the following steps: S210. According to the dynamic contrast enhancement rule, perform regional segmentation on the brightness distribution data, calculate the local contrast gain value of each partition, and generate a backlight intensity dynamic allocation table, specifically including: Divide the brightness distribution data in the real-time status dataset into multiple backlight partitions according to a preset grid, and the division of each partition is dynamically adjusted according to the gaze area coordinate distribution in the user behavior model; For each backlight partition, extract the mean and standard deviation of its brightness distribution data, and combine the multi-directional incident light intensity in the ambient light intensity data to calculate the local contrast gain value: Specifically, calculate the local contrast gain value through the following formula: , Among them, is the local contrast gain value; is the brightness mean of the backlight partition; is the brightness standard deviation of the backlight partition; is the ambient light intensity; is the maximum allowable backlight intensity; and are weight coefficients obtained by training based on the user behavior model; According to the heat accumulation value of the corresponding partition in the screen surface temperature data, perform dynamic attenuation compensation on the local contrast gain value to generate a backlight intensity dynamic allocation table; S220. Based on the gamut adaptive matching rule, perform difference analysis on the color temperature gradient data to generate a three-dimensional color compensation coefficient matrix, specifically including: Extract the red, green, and blue sub-pixel color coordinate offsets of the color temperature gradient data from the color temperature gradient data in the real-time status dataset, and construct a three-dimensional color difference mapping matrix of the color space; Perform a pixel-by-pixel comparison between the three-dimensional color difference mapping matrix and the color temperature range of the preset standard color gamut, calculate the color difference value, and generate a difference quantization table including the color difference weight factor; among them, the steps for generating the difference quantization table are as follows: According to the color preference data in the user behavior model, assign a color difference weight factor to the color difference value to generate a difference quantization table, where the color difference weight factor is positively correlated with the color adjustment frequency in the user's historical operations; According to the temperature gradient of each pixel region in the screen surface temperature data, perform non-linear scaling on the color difference value of the high-temperature region and make corrections; Specifically, the correction is made through the following formula: , where, is the local temperature; is the reference temperature threshold; is the maximum allowable temperature; is the thermal attenuation coefficient; is the color difference value before correction; is the color difference value after correction; Multiply the color difference weight factor in the difference quantization table by the color difference value after correction to generate independent compensation coefficients for the RGB channels, specifically including: For each pixel coordinate position, calculate the compensation coefficients for the red, green, and blue channels respectively; Integrate the compensation coefficients of the red, green, and blue channels at each pixel position into a three-dimensional color compensation coefficient matrix according to the spatial coordinates; In the three-dimensional color compensation coefficient matrix: The first dimension represents the row coordinates of the pixels; The second dimension represents the column coordinates of the pixels; The third dimension corresponds to the compensation coefficient values of the red, green, and blue color channels respectively.

[0027] S230. Through the thermal equilibrium constraint rule, combine the screen surface temperature data and the user interaction signal to establish a heat diffusion suppression model and generate a pixel voltage attenuation curve, specifically including: According to the screen surface temperature data in the real-time status dataset, construct a temperature field distribution map with pixel coordinates as variables, and establish a heat source intensity distribution model based on the historical touch pressure distribution data stored in the user behavior model; Based on the principles of thermodynamics, establish a heat conduction equation, and then use the finite volume method for discretization and solution to output the predicted values of the temperature change rate of each pixel unit; Specifically: Construct a partial differential equation of heat conduction: , where, is the thermal conductivity of the display screen glass substrate; is the pixel driving power, which is calculated from the pixel driving current and voltage in the real-time status dataset; is the specific heat capacity of the display screen material; is the density of the display screen material; represents the spatial second derivative of the temperature field and is used to describe the heat diffusion effect; represents the rate of change of the temperature field with time. By quantifying the speed of temperature change, it predicts the temperature rise trend of each area of the screen at future moments, thereby dynamically adjusting the pixel driving parameters to suppress the display distortion caused by heat diffusion; According to the historical operation response duration and touch trajectory data in the user behavior model, combined with the predicted value of the temperature change rate, a piecewise continuous pixel voltage attenuation curve is generated.

[0028] Specifically, in S210, according to the heat accumulation value of the corresponding partition in the screen surface temperature data, a dynamic attenuation compensation is performed on the local contrast gain value to generate a dynamic backlight intensity allocation table, including: Extract the average temperature value of each backlight partition and calculate the temperature difference between it and the preset temperature threshold; if the temperature difference is greater than zero, the local contrast gain value is corrected based on the following logic: Calculate the ratio of the temperature difference to the safe operating temperature range of the display screen, and multiply it by the heat attenuation coefficient to obtain the gain attenuation ratio; among them, the safe operating temperature range of the display screen is the difference between the maximum allowable operating temperature and the preset temperature threshold; the heat attenuation coefficient is obtained by training the historical temperature rise data in the user behavior model and is used to adaptively adjust the heat attenuation intensity in different usage scenarios; Dynamically reduce the original local contrast gain value according to the gain attenuation ratio to ensure that the backlight intensity in the high-temperature area does not exceed the thermal stability threshold; Specifically: , wherein, is the temperature difference; is the maximum allowable operating temperature of the display screen; is the preset temperature threshold; is the heat attenuation coefficient; is the local contrast gain value before correction; is the local contrast gain value after correction.

[0029] Write the corrected local contrast gain value into the dynamic backlight intensity allocation table according to the partition number and associate it with the corresponding backlight driver chip control address.

[0030] Embodiment 1 Based on the user's gaze heat map, it is determined that 80% of the gaze points are concentrated in the 200×200 area at the center of the screen, and the grid density of the central area is increased to 2 times; Among them, in one backlight partition is 1500nits, is 30nits, is 300lux, is 2000nits, is 0.6, is 0.4; After calculation, is 30.06; This backlight partition is 50℃, is 40℃, is 0.8; ; Example 2 The color difference value of one pixel point , the proportion of the user's historical adjustment frequency of green is 60%, and the color difference weight factor is 0.8; if the local temperature is 45℃, is 40℃, is 0.5; then ; The final compensation coefficient .

[0031] Example 3 Using the COMSOL Multiphysics solver, perform a transient analysis on the temperature field distribution, and predict that the temperature rise rate in a certain high-power consumption area will reach 0.8℃ / s within the next 5 minutes; Generate a piecewise continuous pixel voltage attenuation curve. For example, when the temperature is greater than 42℃, the pixel drive voltage decreases by 0.2V for every 1℃ increase.

[0032] Specifically, S200 specifically further includes the following steps: Integrate the backlight intensity dynamic allocation table, the three-dimensional color compensation coefficient matrix, and the pixel voltage attenuation curve into an initial optimization parameter set, specifically including: Perform spatial alignment on the local contrast gain value of the partition in the backlight intensity dynamic allocation table and the three-dimensional color compensation coefficient matrix to generate a multi-channel parameter mapping table; Synchronize the pixel voltage attenuation curve with the multi-channel parameter mapping table in time sequence to ensure that the backlight intensity adjustment, color compensation, and voltage attenuation take effect together within the refresh cycle; Based on the dynamic specular reflection suppression coefficient in the ambient light intensity data, perform global normalization on the initial set of optimized parameters to generate a binary control instruction sequence that can be directly input into the display driver chip.

[0033] Specifically, S300 specifically includes the following steps: Record the acceleration and pressure distribution of the touch trajectory through the capacitive touch layer to generate a touch behavior feature vector; the touch behavior feature vector includes the normalization of the average touch pressure, acceleration amplitude, and trajectory curvature radius; Use the eye movement tracking module to obtain the coordinate sequence of the user's gaze area, and construct a time decay model of the gaze heat map in combination with the time stamp; specifically: Capture the user's eye movement data through an infrared camera, extract the pupil center coordinates and gaze dwell time, map the pupil center coordinates to the physical coordinate space of the display screen to generate a gaze area coordinate sequence, and associate the time stamp to mark the start time and duration of each gaze point; Construct a time decay model of the gaze heat map based on the time stamp, and specifically use an exponential decay function to calculate the weight values of each gaze point: Finally, superimpose the weight values of each gaze point according to the spatial coordinates to generate a dynamically updated gaze heat map; Collect the spectral intensity data of the ambient light sensor in the visible and infrared bands, and eliminate the instantaneous interference noise through the Kalman filter algorithm; Synchronize multiple frames of temperature distribution maps of the infrared thermal sensing array, and use the optical flow method to calculate the moving trajectory and intensity change gradient of the environmental heat source.

[0034] Embodiment 4 The capacitive touch layer records the touch trajectory at a sampling rate of 1000 Hz and calculates the curvature radius; The eye movement tracking module uses the Tobii 4C module to output the gaze point coordinate sequence; The optical flow method detects that the environmental heat source on the right moves at a speed of 0.5 m / s, and the heat flux density gradient .

[0035] Specifically, S400 specifically includes the following steps: S410. Input the touch behavior feature vector into the first convolutional neural network to output the spatial distribution map of the brightness attenuation compensation coefficient, specifically including: Normalize the touch behavior feature vector; Extract the spatio-temporal correlation features of the touch behavior through a three-dimensional convolutional layer. After compressing the feature dimensions through a pooling layer, the brightness attenuation compensation coefficient corresponding to the backlight partition is output by a fully connected layer; Specifically, the structure of the first convolutional neural network is as follows: Input layer: Receive the spatio-temporal feature tensor; 3D convolutional layer: Extract spatio-temporal correlation features of touch behavior, with a convolutional kernel size of (3×3×3); Pooling layer: Use max pooling to compress the feature dimension; Fully connected layer: Output the brightness attenuation compensation coefficient corresponding to the screen backlight partition; Train the first convolutional neural network based on historical touch data, with the optimization goal of minimizing the error between the predicted brightness attenuation value and the actual backlight adjustment amount; Map the brightness attenuation compensation coefficient output by the first convolutional neural network to a spatial distribution map according to the backlight partition number; S420. Match the time decay model of the gaze heat map with the key areas of the current display content, and generate a color temperature offset correction factor through the attention weight distribution algorithm, specifically including: Extract the high-contrast areas and the user's historical attention areas in the display content as key areas, and combine the time decay weight of the gaze heat map with the key area mask to calculate the spatial matching degree score; Normalize the matching degree score to generate an attention weight distribution, and weighted-correct the current color temperature gradient data to obtain a color temperature offset correction factor matrix; S430. Based on the movement trajectory of the environmental heat source, use a thermodynamic simulation model to predict the local temperature rise trend of the screen and generate a heat diffusion suppression parameter, specifically including: According to the movement trajectory and intensity change gradient of the environmental heat source, construct a heat flux density distribution model on the screen surface to obtain the heat flux input density of each pixel unit; For each pixel unit, according to its heat flux input density and historical temperature rise response curve, fit the predicted temperature rise amount within the future time window, and at the same time, combined with the touch operation frequency in the user behavior model, add a temperature rise compensation coefficient to the high-frequency interaction area; Specifically: , Among them, is the touch frequency; is the compensation weight; The predicted temperature rise amount before correction; is the predicted temperature rise amount after correction; If the predicted temperature rise amount after correction of the pixel unit exceeds the preset safety threshold, then calculate the heat diffusion suppression parameter; specifically including: Calculate the difference between the predicted temperature rise amount after correction and the preset safety threshold to obtain the over-temperature rise amount; Divide the over-temperature rise amount by the difference between the maximum allowable temperature rise and the preset safety threshold to obtain the normalized temperature rise risk ratio; Finally, subtract the temperature rise risk ratio from 1 to obtain the heat diffusion suppression parameter; The specific formula is as follows: , wherein, is the preset safety threshold; is the maximum allowable temperature rise; is the thermal diffusion suppression parameter.

[0036] If the predicted temperature rise does not exceed the preset safety threshold, the thermal diffusion suppression parameter is assigned as 1; Integrate the thermal diffusion suppression parameters of all pixel units according to the coordinates into a gradient mask matrix, and each element in the matrix corresponds to the thermal diffusion suppression parameter of a pixel; Integrate the thermal diffusion suppression parameters of all pixels and generate a thermal diffusion suppression parameter gradient mask matrix according to the physical coordinates of the screen.

[0037] Embodiment 5 In the first convolutional neural network, the average touch pressure input is 0.5 N and the acceleration is 2 m / s². After 3 layers of convolution, the central region compensation coefficient of 0.8 is output; When the overlap degree between the user's gaze area and the movie subtitle area reaches 70%, the color temperature correction factor of 1.2 is given to this area; For one of the pixel points is 8 °C, is 5 °C, is 10 °C, is 0.2, is 15 times / minute; After calculation, , exceeding the preset safety threshold; , force to set , complete suppression.

[0038] Specifically, S500 specifically includes the following steps: S510. Perform weighted superposition processing on the backlight partitions, specifically including: Extract the backlight intensity dynamic allocation table from the initial optimization parameter set, and at the same time obtain the spatial distribution map of the brightness attenuation compensation coefficient from the dynamic correction coefficient set; Perform a zone-by-zone product operation on the local contrast gain value of the backlight partition and the brightness attenuation compensation coefficient of the corresponding partition to generate a fused backlight weight matrix; Convert the fused backlight weight matrix into a backlight control signal waveform through the physical addressing mapping relationship of the backlight driving chip; S520. Iteratively adjust the gamma value of the pixel matrix, specifically including: Based on the independent compensation coefficients of the RGB channels in the three-dimensional color compensation coefficient matrix, construct a gamma correction parameter mapping table; Extract the color temperature offset correction factor matrix from the dynamic correction coefficient set and align its channels with the gamma correction parameter mapping table; Iteratively adjust the original gamma value through the per-pixel weighted average algorithm to generate a dynamic gamma correction curve; The adjustment formula for the dynamic gamma correction curve is: , where, is the adjusted dynamic gamma value; is the original gamma value; is the set weight factor; is the independent compensation coefficient of the RGB channels in the three-dimensional color compensation coefficient matrix; is the color temperature offset correction factor in the color temperature offset correction factor matrix.

[0039] S530. Perform remapping processing of the color space, specifically including: Input the dynamic gamma correction curve into the color management engine, and combine it with the thermal diffusion suppression parameter gradient mask matrix to establish a gamut constraint condition; Based on the gamut constraint condition, perform a non-linear transformation on the RGB color space to generate a three-dimensional lookup table for the CIE XYZ intermediate color space; Use the bilinear interpolation algorithm to map the three-dimensional lookup table to the native gamut space of the display screen to complete the recalibration of the color space; S540. Generate a target optimization instruction set, specifically including: Perform timing synchronization encoding on the fused backlight weight matrix, the dynamic gamma correction curve, and the recalibrated color space parameters; Based on the instruction set architecture of the display driver chip, convert the encoded parameters into an executable binary optimization instruction sequence; Send the binary optimization instruction sequence to the backlight controller, the gamma correction module, and the color processing unit in real time through a high-speed serial interface to achieve the collaborative optimization of display parameters.

[0040] Specifically, S600 specifically includes the following steps: S610. Real-time collect the screen performance indicators after the display parameters are executed; among them, the screen performance indicators include the actual brightness uniformity error, the color temperature deviation value, and the temperature rise suppression efficiency; S620. Compare the screen performance indicators with the preset target thresholds to generate an optimization effect evaluation table; the optimization effect evaluation table records the brightness error ratio, the color temperature deviation level, and the thermal diffusion suppression success rate of each backlight zone.

[0041] Example 6 The screen performance indicators are as follows: Brightness uniformity: The measured brightness deviation in the central area is less than 5%, and the target threshold is 8%.

[0042] Color temperature consistency: White screen Less than 1.5, target value Less than 2.

[0043] Temperature rise control: The heating rate in the high-temperature area is reduced from 0.8 °C / s to 0.3 °C / s.

[0044] The evaluation form example is shown in the following table; Table 1 Evaluation form example

[0045] This specific embodiment is only an explanation of the present invention, and it is not a limitation of the present invention. Those skilled in the art can make modifications without creative contributions to this embodiment after reading this specification, but as long as it is within the scope of the claims of the present invention, it is protected by the patent law.

Claims

1. An overall dynamic optimization system for an ultra-high definition glass-based display screen, characterized in that, The specific process of the system operation includes the following steps: S100. Obtain the real-time status data set of the display screen; wherein, the real-time status data set includes brightness distribution data, color temperature gradient data, ambient light intensity data, and screen surface temperature data; S200. Generate an initial optimization parameter set based on a preset optimization strategy and a user behavior model; wherein, the preset optimization strategy includes a dynamic contrast enhancement rule, a gamut adaptive matching rule, and a thermal equilibrium constraint rule, the user behavior model is trained by historical operation data, and the initial optimization parameter set includes partition backlight intensity parameters, pixel drive voltage parameters, and a color compensation coefficient matrix; S300. Capture user interaction signals and environmental change signals in real time; wherein, the user interaction signals include touch trajectory data, gaze area coordinates, and operation response duration, and the environmental change signals are synchronously collected by an ambient light sensor and an infrared thermal sensing array; S400. Generate a dynamic correction coefficient set according to the user interaction signals and environmental change signals; wherein, the dynamic correction coefficient set includes a brightness attenuation compensation coefficient, a color temperature offset correction factor, and a heat diffusion suppression parameter; S500. Perform global parameter fusion based on the initial optimization parameter set and the dynamic correction coefficient set to generate a target optimization instruction set; wherein, the global parameter fusion includes weight superposition of backlight partitions, gamma value iterative adjustment of pixel matrices, and remapping processing of color spaces; S600. Optimize the optimization effect evaluation table based on the optimization effect feedback.

2. The global dynamic optimization system of an ultra-high definition glass-based display screen according to claim 1, characterized in that, The S200 specifically includes the following steps: S210. According to the dynamic contrast enhancement rule, perform regional segmentation on the brightness distribution data, calculate the local contrast gain value of each partition, and generate a dynamic backlight intensity allocation table, specifically including: Divide the brightness distribution data in the real-time status data set into multiple backlight partitions according to a preset grid, and the division of each partition is dynamically adjusted according to the gaze area coordinate distribution in the user behavior model; For each backlight partition, extract the mean and standard deviation of its brightness distribution data, and calculate the local contrast gain value in combination with the multi-directional incident light intensity in the ambient light intensity data: Perform dynamic attenuation compensation on the local contrast gain value according to the heat accumulation value of the corresponding partition in the screen surface temperature data to generate a dynamic backlight intensity allocation table; S220. Based on the gamut adaptive matching rule, perform difference analysis on the color temperature gradient data to generate a three-dimensional color compensation coefficient matrix, specifically including: Extract the red, green, and blue sub-pixel color coordinate offsets of the color temperature gradient data from the color temperature gradient data in the real-time status data set, and construct a three-dimensional color difference mapping matrix of the color space; Compare the three-dimensional color difference mapping matrix with the color temperature range of the preset standard gamut pixel by pixel, calculate the color difference value, and generate a difference quantization table including color difference weight factors; wherein, the generation step of the difference quantization table is: Assign a color difference weight factor to the color difference value according to the color preference data in the user behavior model to generate a difference quantization table, wherein the color difference weight factor is positively correlated with the color adjustment frequency in the user's history operations. According to the temperature gradient of each pixel area in the screen surface temperature data, non-linearly scale and correct the color difference value of the high-temperature area; Multiply the color difference weight factor in the difference quantization table by the corrected color difference value to generate independent compensation coefficients for the RGB channels, specifically including: For each pixel coordinate position, calculate the compensation coefficients for the red, green, and blue channels respectively; Integrate the compensation coefficients of the red, green, and blue channels at each pixel position into a three-dimensional color compensation coefficient matrix according to the spatial coordinates; S230. Through the thermal equilibrium constraint rule, combine the screen surface temperature data and the user interaction signal to establish a heat diffusion suppression model and generate a pixel voltage attenuation curve, specifically including: According to the screen surface temperature data in the real-time status dataset, construct a temperature field distribution map with pixel coordinates as variables, and establish a heat source intensity distribution model based on the historical touch pressure distribution data stored in the user behavior model; Based on the thermodynamic principle, establish a heat conduction equation, and then use the finite volume method for discretization and solution to output the predicted value of the temperature change rate of each pixel unit; According to the historical operation response duration and touch trajectory data in the user behavior model, combine with the predicted value of the temperature change rate to generate a piecewise continuous pixel voltage attenuation curve.

3. The global dynamic optimization system of an ultra-high definition glass-based display screen according to claim 2, characterized in that, In the S210, according to the heat accumulation value of the corresponding partition in the screen surface temperature data, perform dynamic attenuation compensation on the local contrast gain value to generate a backlight intensity dynamic allocation table, including: Extract the average temperature value of each backlight partition and calculate the temperature difference between it and the preset temperature threshold; if the temperature difference is greater than zero, correct the local contrast gain value based on the following logic: Calculate the ratio of the temperature difference to the safe operating temperature range of the display screen, and multiply it by the heat attenuation coefficient to obtain the gain attenuation ratio; wherein, the safe operating temperature range of the display screen is the difference between the maximum allowable operating temperature and the preset temperature threshold; the heat attenuation coefficient is obtained through training with the historical temperature rise data in the user behavior model and is used to adaptively adjust the heat attenuation intensity in different usage scenarios; Dynamically reduce the original local contrast gain value according to the gain attenuation ratio to ensure that the backlight intensity in the high-temperature area does not exceed the thermal stability threshold; Write the corrected local contrast gain value into the backlight intensity dynamic allocation table according to the partition number and associate the corresponding backlight driver chip control address.

4. An overall dynamic optimization system for an ultra-high definition glass-based display screen according to claim 3, characterized in that, The S200 specifically further includes the following steps: Integrate the backlight intensity dynamic allocation table, the three-dimensional color compensation coefficient matrix, and the pixel voltage attenuation curve into an initial optimization parameter set, specifically including: Perform spatial alignment on the local contrast gain value of the partition in the backlight intensity dynamic allocation table and the three-dimensional color compensation coefficient matrix to generate a multi-channel parameter mapping table; Synchronize the pixel voltage attenuation curve with the multi-channel parameter mapping table in time sequence to ensure that the backlight intensity adjustment, color compensation, and voltage attenuation take effect synergistically within the refresh cycle; Based on the dynamic anti-reflection suppression coefficient in the ambient light intensity data, perform global normalization processing on the initial optimization parameter set to generate a binary control instruction sequence that can be directly input into the display driver chip.

5. The global dynamic optimization system of an ultra-high definition glass-based display screen according to claim 2, characterized in that, The S300 specifically includes the following steps: Record the acceleration and pressure distribution of the touch trajectory through the capacitive touch layer to generate a touch behavior feature vector; the touch behavior feature vector includes the normalization of the average touch pressure, acceleration amplitude, and trajectory curvature radius. Use the eye movement tracking module to obtain the coordinate sequence of the user's gaze area and construct a time decay model of the gaze heat map in combination with timestamps. Collect the spectral intensity data of the ambient light sensor in the visible and infrared bands, and eliminate the instantaneous interference noise through the Kalman filtering algorithm. Synchronize multiple frames of temperature distribution maps of the infrared thermal sensing array, and use the optical flow method to calculate the movement trajectory and intensity change gradient of the environmental heat source.

6. The global dynamic optimization system of an ultra-high definition glass-based display screen according to claim 5, characterized in that, The specific steps of S400 are as follows: S410: Input the touch behavior feature vector into the first convolutional neural network to output the spatial distribution map of the brightness attenuation compensation coefficient, specifically including: Perform normalization processing on the touch behavior feature vector. Extract the spatio-temporal correlation features of the touch behavior through a three-dimensional convolutional layer. After compressing the feature dimension through a pooling layer, the fully connected layer outputs the brightness attenuation compensation coefficient corresponding to the backlight partition. Train the first convolutional neural network based on historical touch data, and the optimization goal is to minimize the error between the predicted brightness attenuation value and the actual backlight adjustment amount. Map the brightness attenuation compensation coefficient output by the first convolutional neural network to a spatial distribution map according to the backlight partition number. S420: Match the time decay model of the gaze heat map with the key areas of the current display content, and generate a color temperature offset correction factor through the attention weight distribution algorithm, specifically including: Extract the high-contrast areas and the user's historical attention areas in the display content as key areas, and calculate the spatial matching degree score in combination with the time decay weight of the gaze heat map and the key area mask. Perform normalization processing on the matching degree score to generate an attention weight distribution, and weighted correct the current color temperature gradient data to obtain a color temperature offset correction factor matrix. S430: Based on the movement trajectory of the environmental heat source, use the thermodynamic simulation model to predict the local temperature rise trend of the screen and generate a heat diffusion suppression parameter, specifically including: According to the movement trajectory and intensity change gradient of the environmental heat source, construct a heat flux density distribution model on the screen surface to obtain the heat flux input density of each pixel unit. For each pixel unit, according to its heat flux input density and historical temperature rise response curve, fit the predicted temperature rise amount within the future time window. At the same time, in combination with the touch operation frequency in the user behavior model, an additional temperature rise compensation coefficient is added to the high-frequency interaction area. If the corrected predicted temperature rise amount of the pixel unit exceeds the preset safety threshold, calculate the heat diffusion suppression parameter; specifically including: Calculate the difference between the corrected predicted temperature rise amount and the preset safety threshold to obtain the over-limit temperature rise amount. Divide the over-limit temperature rise amount by the difference between the maximum allowable temperature rise and the preset safety threshold to obtain the normalized temperature rise risk ratio. Finally, subtract the temperature rise risk ratio from 1 to obtain the heat diffusion suppression parameter. If the predicted temperature rise amount does not exceed the preset safety threshold, assign the heat diffusion suppression parameter as 1. Integrate the heat diffusion suppression parameters of all pixel units according to the coordinates into a gradient mask matrix, and each element in the matrix corresponds to the heat diffusion suppression parameter of a pixel. Integrate the thermal diffusion suppression parameters of all pixels to generate a thermal diffusion suppression parameter gradient mask matrix according to the physical coordinates of the screen.

7. An overall dynamic optimization system for an ultra-high definition glass-based display screen according to claim 6, characterized in that, The specific steps of S500 are as follows: S510. Perform weighted superposition processing on the backlight partitions, specifically including: Extract the backlight intensity dynamic allocation table from the initial optimization parameter set, and at the same time obtain the spatial distribution map of the brightness attenuation compensation coefficient from the dynamic correction coefficient set; Perform a per-zone multiplication operation on the local contrast gain value of the backlight partition and the brightness attenuation compensation coefficient of the corresponding partition to generate a fused backlight weight matrix; Convert the fused backlight weight matrix into a backlight control signal waveform through the physical addressing mapping relationship of the backlight driver chip; S520. Iteratively adjust the gamma value of the pixel matrix, specifically including: Based on the independent compensation coefficients of the RGB channels in the three-dimensional color compensation coefficient matrix, construct a gamma correction parameter mapping table; Extract the color temperature offset correction factor matrix from the dynamic correction coefficient set and align its channels with the gamma correction parameter mapping table; Iteratively adjust the original gamma value through a per-pixel weighted average algorithm to generate a dynamic gamma correction curve; S530. Perform remapping processing on the color space, specifically including: Input the dynamic gamma correction curve into the color management engine, and combine it with the thermal diffusion suppression parameter gradient mask matrix to establish a color gamut constraint condition; Perform a non-linear transformation on the RGB color space based on the color gamut constraint condition to generate a three-dimensional lookup table of the color space; Use the bilinear interpolation algorithm to map the three-dimensional lookup table to the native color gamut space of the display screen to complete the recalibration of the color space; S540. Generate a target optimization instruction set, specifically including: Perform timing synchronization encoding on the fused backlight weight matrix, the dynamic gamma correction curve, and the recalibrated color space parameters; Based on the instruction set architecture of the display driver chip, convert the encoded parameters into an executable binary optimization instruction sequence; Send the binary optimization instruction sequence to the backlight controller, the gamma correction module, and the color processing unit in real time through a high-speed serial interface to achieve collaborative optimization of the display parameters.

8. The global dynamic optimization system of an ultra-high definition glass-based display screen according to claim 7, characterized in that, The specific steps of S600 are as follows: S610. Real-time collect the screen performance indicators after the display parameters are executed; among them, the screen performance indicators include the actual brightness uniformity error, the color temperature deviation value, and the temperature rise suppression efficiency; S620. Compare the screen performance indicators with the preset target thresholds to generate an optimization effect evaluation table; the optimization effect evaluation table records the brightness error ratio, the color temperature deviation level, and the thermal diffusion suppression success rate of each backlight partition.

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