High-precision liquid crystal display calibration system and calibration method

Abstract

The application relates to the technical field of liquid crystal display calibration, and specifically discloses a high-precision liquid crystal display calibration system and a calibration method, a closed-loop calibration system is constructed in cooperation of a terminal and a cloud, original optical data of a preset test picture is collected on the terminal side, characteristic data such as brightness uniformity, gamma curve and color gamut coverage is extracted after pretreatment, initial factory calibration data and historical calibration characteristic data of the display screen are combined on the cloud, parameter dynamic drift characteristics are predicted through a digital twin model, a multi-dimensional collaborative compensation parameter matrix is generated, a customized calibration configuration file containing the matrix is issued to the terminal and embedded into a driving link, real-time compensation calibration is completed by the terminal according to the parameters, and the effect is quickly verified, a new round of calibration process is automatically started through periodic, environment-triggered and other multi-condition dynamic monitoring. The application realizes dynamic and accurate compensation of display parameters, solves the problems of traditional static calibration lag and poor adaptability, and effectively maintains high-precision display effect in the whole life cycle of the display screen.

Description

Technical Field

[0001] This invention relates to the field of liquid crystal display calibration technology, and in particular to a high-precision liquid crystal display calibration system and calibration method. Background Technology

[0002] LCD screens, with their advantages of being thin, light, low-power, and high-resolution, have become the core display components of terminal devices. Their display accuracy directly determines the user experience and the reliability of professional applications. Core display parameters such as brightness uniformity, gamma characteristics, and color gamut coverage are subject to dynamic drift over long-term use due to factors such as temperature fluctuations, cumulative usage time, and changes in ambient light. If not calibrated in time, problems such as uneven screen brightness, color distortion, and abnormal grayscale transitions will gradually appear, severely impacting the accuracy of judgments in professional scenarios.

[0003] Existing LCD calibration technologies suffer from several technical problems. Firstly, many technologies employ static factory calibration, setting fixed compensation values ​​based solely on initial parameters. This fails to adapt to dynamic parameter drift during use, resulting in significant lag in calibration effectiveness. Secondly, some local calibration schemes rely on a single algorithm, failing to integrate historical calibration data with initial factory calibration data. This makes it difficult to accurately predict parameter drift trends, limiting compensation accuracy. Furthermore, compensation parameters are often adjusted in a single dimension, neglecting the interplay between brightness, color, and gamma characteristics, which can easily introduce new display deviations. Thirdly, calibration triggers often use a single fixed cycle, failing to consider key factors such as environmental changes and usage intensity. This leads to a mismatch between calibration timing and actual needs, resulting in either excessively frequent calibrations that waste resources or delayed calibrations that fail to correct deviations in a timely manner. Finally, traditional calibration processes lack optimized extraction and calibration strategies for different trigger scenarios, failing to balance calibration efficiency and specificity, and thus failing to meet the high-precision display requirements throughout the entire lifecycle of the display screen.

[0004] Therefore, there is an urgent need for a high-precision liquid crystal display calibration system and calibration method to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a high-precision liquid crystal display calibration method, comprising the following steps: On the terminal device side, optical sensors are used to collect raw optical data of the display screen under a preset test screen, and the raw optical data is processed to extract feature data of the display parameters. The feature data is uploaded to the cloud server. On the cloud server side, based on the received feature data, the initial factory calibration data of the corresponding display model and historical calibration data, the digital twin model of the display is updated to predict the dynamic drift characteristics of the display parameters, and based on the prediction results, the collaborative compensation parameters used to compensate for the deviation of the display parameters are calculated. The calibration configuration file containing the collaborative compensation parameters is sent from the cloud server to the terminal device and configured into the drive link of the display screen; On the terminal device side, the display screen is driven according to the parameters in the calibration configuration file to complete the compensation calibration of the display screen and verify the calibration effect; Set and monitor calibration trigger conditions, and automatically start a new calibration process when any of the aforementioned trigger conditions are met.

[0006] Furthermore, the present invention also discloses a high-precision liquid crystal display calibration system, comprising: The extraction module is used on the terminal device side to collect the original optical data of the display screen under the preset test screen using an optical sensor, and to process the original optical data to extract the feature data of the display parameters. The calculation module is used to upload the feature data to the cloud server. On the cloud server side, based on the received feature data, the initial factory calibration data of the corresponding display model and historical calibration data, the digital twin model of the display is updated to predict the dynamic drift characteristics of the display parameters, and based on the prediction results, the collaborative compensation parameters used to compensate for the deviation of the display parameters are calculated. The sending module is used to send the calibration configuration file containing the collaborative compensation parameters from the cloud server to the terminal device and configure it in the drive link of the display screen; The calibration module is used on the terminal device side to drive the display screen according to the parameters in the calibration configuration file, complete the compensation calibration of the display screen, and verify the calibration effect; The trigger module is used to set and monitor calibration trigger conditions. When any of the trigger conditions is met, a new round of calibration process is automatically started.

[0007] Furthermore, the sending module includes: The integration unit is used to integrate the calculated collaborative compensation parameters through the cloud server, add identification information and verification codes, and generate a customized calibration configuration file. The verification unit is used to send the calibration configuration file to the terminal device via an encrypted link. After receiving the configuration file, the terminal device verifies its integrity and correctness. The parsing unit is used to parse the calibration configuration file after the verification is passed, and write the collaborative compensation parameters therein into the corresponding functional unit of the display driver module.

[0008] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described high-precision liquid crystal display calibration method.

[0009] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described high-precision liquid crystal display calibration method.

[0010] The beneficial effects of this application are as follows: Firstly, this application integrates initial factory calibration data, historical calibration feature data, and feature data extracted from the current terminal through a digital twin model. It accurately learns and predicts the dynamic drift characteristics of display parameters with time, temperature, and usage intensity, solving the problem that existing static calibration cannot adapt to dynamic parameter changes. This makes the compensation parameters more forward-looking and targeted, effectively improving calibration accuracy. Secondly, this application uses a collaborative optimization algorithm to generate a multi-dimensional collaborative compensation parameter matrix that includes a partitioned brightness compensation coefficient matrix, a three-dimensional color lookup table adjustment value, and gamma voltage optimization curve parameters. This achieves collaborative adjustment of brightness uniformity, color accuracy, and gamma characteristics, avoiding display imbalance caused by single-dimensional compensation and ensuring the overall consistency of display effect. Third, in the process of generating the customized configuration file, the model identifier, version information and verification code are added. The transmission adopts an encrypted link. After the terminal receives the data, it verifies the integrity by comparing the digital hash value, which ensures the security and adaptability of the compensation parameter transmission process and effectively avoids the risk of data tampering, loss or misuse across models. Fourth, this application adds a rapid verification step for calibration effect after the terminal compensation is performed. By collecting data in key areas through a miniature optical sensor and comparing it with the target value, a calibration closed loop is formed, which can promptly detect calibration anomalies and trigger subsequent processing, ensuring the effectiveness and reliability of the calibration effect. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of a method flow proposed in an embodiment of this application.

[0012] Figure 2 This is a schematic diagram of the system structure proposed in an embodiment of the present invention.

[0013] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0014] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0015] like Figure 1 As shown, this application provides a high-precision liquid crystal display calibration method, including the following steps: S1, On the terminal device side, a miniature optical sensor is used to collect the original optical data of the display screen under the preset test screen in real time, a lightweight image processing algorithm is run to preprocess the original optical data, and feature data of multiple display parameters are calculated and extracted. S2, the feature data is uploaded to the cloud server. On the cloud server side, based on the received feature data, combined with the initial factory calibration data and historical calibration feature data of the corresponding display model, the digital twin model of the display is updated and run to predict the dynamic drift characteristics of the display parameters. Based on the output of the digital twin model, a multi-dimensional collaborative compensation parameter matrix for compensating the current display parameter deviation is calculated through a collaborative optimization algorithm. S3, the customized calibration configuration file containing the multi-dimensional collaborative compensation parameter matrix is ​​sent from the cloud server to the terminal device and embedded into the drive link of the display screen; S4, on the terminal device side, the display screen is driven according to the parameters in the embedded customized calibration configuration file to complete the real-time compensation calibration of the display screen; S5. Set and monitor calibration trigger conditions. When any of the trigger conditions are met, automatically start a new round of calibration process starting from step S1 to achieve dynamic high-precision maintenance of the display screen throughout its entire life cycle.

[0016] As described in steps S1-S5 above, the high-precision liquid crystal display calibration method of the present invention, through a closed-loop architecture of terminal and cloud collaboration, sequentially completes terminal-side perception extraction, cloud-side model analysis and calculation, calibration configuration distribution and loading, terminal compensation execution closed loop, and dynamic trigger adaptive fine-tuning, thereby achieving dynamic and accurate calibration of display parameters throughout the entire life cycle of the display screen and maintaining stable display accuracy.

[0017] During long-term use, LCD screens are affected by factors such as temperature fluctuations, cumulative usage time, and changes in ambient light, causing display parameters to dynamically drift. This manifests as decreased brightness uniformity, gamma curve distortion, and color gamut coverage deviation, directly leading to reduced display accuracy. Therefore, it is necessary to establish a dynamic calibration mechanism to capture parameter drift and compensate for deviations in real time to ensure consistent display performance.

[0018] Existing calibration technologies are mostly static calibrations at the factory, setting fixed compensation values ​​based solely on initial parameters. They lack the ability to dynamically adjust throughout the entire lifecycle and often rely on local single-algorithm calibration without integrating cloud-based big data analysis. This makes them unable to accurately predict parameter drift trends, resulting in limited compensation accuracy and difficulty in adapting to the dynamic changes in complex usage scenarios. This solution adopts a closed-loop design that collaborates between the terminal and the cloud, combining a digital twin model and a collaborative optimization algorithm to achieve dynamic drift prediction and accurate compensation. Furthermore, through a multi-condition triggering mechanism, it achieves adaptive calibration throughout the entire lifecycle.

[0019] The core logic of this technical solution is to construct a closed-loop calibration system encompassing terminal perception, cloud analysis, parameter distribution, terminal execution, and dynamic iteration. Its principle revolves around the physical characteristics of display parameters dynamically drifting with changes in time, temperature, usage intensity, and environment. Through collaborative operation between the terminal and the cloud, high-precision dynamic maintenance is achieved throughout the entire lifecycle. Specifically, the terminal device first uses a miniature optical sensor to collect raw optical data under a preset test screen. After preprocessing with a lightweight image processing algorithm, feature data of multiple display parameters is extracted, providing basic data reflecting the current display state for subsequent analysis. This feature data is then uploaded to a cloud server. The cloud server combines the initial factory calibration data (standard display parameter benchmarks at the time of device manufacture) and historical calibration feature data (parameter change sequences accumulated during past calibration processes) of the corresponding display model to update and run a digital twin model based on a time-series memory network architecture. The model learns the parameter drift patterns to predict the next stage of drift. Based on its characteristics, the system utilizes a collaborative optimization algorithm to calculate a multi-dimensional collaborative compensation parameter matrix that can compensate for the deviation of current display parameters, solving the problem that traditional static calibration cannot adapt to dynamic drift. Subsequently, a customized calibration configuration file containing this matrix is ​​sent to the terminal and embedded into the display drive link, so that the compensation parameters can directly act on the driving process. The terminal device drives the display to complete real-time compensation calibration based on the embedded parameters, and at the same time, the calibration effect is ensured through rapid verification. Finally, by preset and monitoring various calibration trigger conditions such as periodicity, environmental triggering, usage intensity, and manual operation, a new round of calibration process is automatically started when any condition is met, forming a dynamic cycle that continuously adapts to parameter drift. Ultimately, it achieves stable maintenance of display accuracy throughout the entire life cycle of the display. Its core principle is to capture the state through data perception, predict and judge the deviation through model prediction, correct the deviation through collaborative compensation, and maintain accuracy through dynamic triggering. It replaces the traditional single static calibration with a closed-loop collaborative architecture, effectively addressing the problem of accuracy degradation caused by dynamic drift of display parameters.

[0020] In one embodiment, the step of calculating and extracting feature data for multiple display parameters includes: S11, on the terminal device side, drive the display screen to sequentially display a series of standard test screens covering different gray levels and primary colors, and simultaneously start the integrated or external micro optical sensor to collect the original image data under each standard test screen. When the micro optical sensor is enabled, load its factory calibration parameters. The calibration parameters are used to correct the nonlinear response of the sensor, subtract the background dark current noise, and balance the multi-channel gain. S12, perform preprocessing operations sequentially on each frame of raw image data acquired. The preprocessing operations include: background noise reduction based on sensor calibration parameters, gain equalization correction of red, green and blue multi-channels, and linearization processing of sensor response. S13, from the preprocessed image data, perform the following feature calculation operations for each standard test frame: (a) Calculate the luminance uniformity spectrum feature value: Divide the screen display area into multiple partitions, calculate the average luminance value of each partition, construct a luminance matrix based on the average luminance values ​​of all partitions, and calculate the luminance uniformity index that reflects the consistency of the screen luminance according to the luminance matrix. This index is characterized by the ratio of the luminance of the darkest partition of the screen to the average luminance of the whole screen. (b) Calculate the deviation of the actual gamma curve: For the grayscale test screen, obtain the average brightness value measured by the sensor at each grayscale level and compare it with the theoretical standard brightness target value corresponding to that grayscale. The root mean square error of the deviation between the measured brightness value and the theoretical target value of all sampled grayscale levels is used as the deviation of the actual gamma curve from the standard gamma curve. (c) Calculate the color gamut coverage characteristic value: For the primary color test screen, obtain the measured chromaticity coordinates of red, green and blue primary colors. Within the standard color space, determine the actual color gamut range of the current display screen based on the measured primary color coordinates, and compare it with the target standard color gamut range. By calculating the ratio of the overlapping area of ​​the actual color gamut and the standard color gamut to the area of ​​the standard color gamut, the color gamut coverage characteristic value is obtained. S14, the calculated brightness uniformity spectrum feature values, actual gamma curve deviation values ​​and color gamut coverage feature values ​​are compressed and formatted for encapsulation to form a feature data package to be uploaded.

[0021] As described in steps S11-S14 above, this invention, through a coherent process of test screen driving and sensor calibration, image data preprocessing, multi-dimensional display parameter feature calculation and feature data compression and encapsulation, accurately extracts the luminance uniformity spectrum feature value, actual gamma curve deviation value and color gamut coverage feature value, providing high-quality and standardized feature input for model analysis and compensation calculation on the cloud side, and ensuring the accuracy of subsequent calibration and compensation.

[0022] When acquiring optical data, miniature optical sensors exhibit nonlinear response characteristics, inherent dark current noise interference, and natural differences in gain between red, green, and blue channels. Directly acquired raw image data carries a significant amount of interference, failing to accurately reflect the actual display state. Furthermore, display deviations manifest in multiple core dimensions, including brightness uniformity, gamma curve, and color gamut coverage. These deviations require quantification to accurately capture and provide a clear basis for subsequent compensation calculations. Therefore, systematic processing of the raw data and extraction of multi-dimensional features are essential.

[0023] In existing technologies, feature extraction often fails to load sensor calibration parameters, directly using raw data for calculation. This results in errors introduced by sensor defects being incorporated into the feature results. Preprocessing operations are simplistic, addressing only a single type of interference and failing to comprehensively eliminate noise, channel imbalance, and other issues. Feature calculation dimensions are incomplete, often focusing on a single display parameter, making it difficult to fully reflect the overall deviation of the display screen. This solution addresses these shortcomings through a closed-loop design involving calibration loading, multi-step preprocessing, multi-dimensional feature calculation, and standardized encapsulation, achieving accurate and comprehensive feature data extraction.

[0024] In the test screen driving and sensor calibration data loading stage, the terminal device drives the display screen to sequentially display standard test screens covering different grayscale levels and primary colors. The grayscale test screen covers the full grayscale levels from 0 to 255, and the primary color test screen includes three basic colors: pure red, pure green, and pure blue, ensuring comprehensive acquisition of optical data from the display screen under different display states. The synchronously activated miniature optical sensor is either integrated or external. Its factory calibration parameters are obtained by the sensor manufacturer through standard light source calibration before leaving the factory, including nonlinear response correction coefficients, background dark current reference values, and multi-channel gain balance parameters. Loading these calibration parameters when activating the sensor can correct the sensor's nonlinear response in advance, avoid response distortion under strong or weak light, eliminate baseline offset caused by background dark current noise, and balance the gain differences of the red, green, and blue multi-channels, ensuring that the acquired raw image data can accurately reflect the optical output of the display screen.

[0025] In the image data preprocessing stage, three operations are performed on each frame of raw image data in a fixed order. First, background noise is subtracted based on the background dark current reference value in the sensor calibration parameters to directly remove noise interference generated by the sensor's own circuitry. Second, gain equalization correction is performed on the red, green, and blue channels using the multi-channel gain balance parameter in the calibration parameters to ensure consistent response output for the same light intensity across the three channels. Third, the nonlinear response correction coefficient in the calibration parameters is used to linearize the sensor response, converting the sensor's nonlinear output into data linearly related to the actual light intensity. These three preprocessing steps are progressively refined, gradually purifying the data and laying a clean data foundation for subsequent feature calculations.

[0026] In the multi-dimensional display parameter feature calculation stage, targeted calculations are performed for each standard test screen. When calculating the brightness uniformity spectrum feature value, the screen display area is divided into 16×16 uniform partitions. The average brightness value of each partition is obtained through pixel brightness statistics. A brightness matrix is ​​constructed based on the average brightness values ​​of all partitions. Then, the brightness uniformity index is obtained by the ratio of the brightness of the darkest partition to the average brightness of the entire screen. If the average brightness of a display screen is 250 cd / m² and the brightness of the darkest partition is 200 cd / m², the brightness uniformity index is 0.8, which intuitively quantifies the consistency of brightness across the entire screen. When calculating the actual gamma curve deviation, for the grayscale test image, the average brightness value measured by the sensor at each grayscale level is extracted and compared with the corresponding gamma 2.2 standard brightness target value. The deviation value of the actual gamma curve is obtained by calculating the root mean square error (RMSE) of the deviations between all sampled grayscale measured values ​​and the theoretical target values. If the measured brightness of a certain grayscale is 102 cd / m² and the theoretical target value is 100 cd / m², the deviation value is obtained after calculating the RMS of the deviations of multiple grayscales, accurately reflecting the degree of distortion of the gamma curve. When calculating the color gamut coverage characteristic value, for the primary color test image, the measured chromaticity coordinates of red, green, and blue primary colors are extracted. The actual color gamut range is determined within the CIE 1931 standard color space and compared with the sRGB target standard color gamut range. The proportion of the overlapping area to the standard color gamut area is calculated to obtain the color gamut coverage characteristic value. If the overlapping area ratio is 96%, it indicates that the color reproduction accuracy of the display screen reaches this level.

[0027] In the feature data compression and encapsulation stage, the LZ77 lightweight lossless compression algorithm is used to compress the three feature values, reducing data transmission volume and improving upload efficiency. Simultaneously, the data is formatted and encapsulated according to a preset data protocol. The encapsulated content includes feature type identifiers, numerical precision information, data acquisition timestamps, and corresponding test screen information, forming a standardized feature data package. This ensures that the cloud server can quickly parse and identify the data, accurately associating it with the corresponding display screen status, providing a standardized and reliable input basis for subsequent digital twin model updates and compensation calculations.

[0028] In one embodiment, the step of calculating the multidimensional collaborative compensation parameter matrix for compensating the current display parameter deviation using a collaborative optimization algorithm includes: S21, the cloud server receives the encrypted feature data packet uploaded from the terminal device, and decrypts it using a preset decryption key to recover the brightness uniformity spectrum feature value, the actual gamma curve deviation value, and the color gamut coverage feature value. S22, retrieve the initial factory calibration data that matches the current terminal device display model, and retrieve the historical calibration feature data sequence of the specific device from the historical calibration database. Use the currently decrypted feature data as a new time node data and input it into the digital twin model established for the display. The digital twin model adopts an architecture based on a time-series memory network. By integrating historical data, it learns the dynamic law of the display parameters changing with time, temperature and usage intensity. After the model is updated, it outputs the predicted value of the drift of key display parameters in the next stage. The predicted value includes the brightness attenuation of each partition of the screen, the distortion of the gamma curve, and the offset of the chromaticity coordinates of the red, green and blue primary colors. S23, based on the predicted drift output by the digital twin model, a collaborative optimization algorithm is executed to calculate the multidimensional collaborative compensation parameter matrix, which includes the following components: (a) Partition brightness compensation coefficient matrix: This matrix is ​​used to compensate for the brightness non-uniformity of each partition of the screen and the predicted brightness attenuation. Each coefficient corresponds to a screen partition. The calculation of the coefficient is aimed at optimizing the brightness uniformity of the whole screen after compensation. (b) 3D color lookup table adjustment value: This adjustment value is used to correct the influence of predicted primary color chromaticity coordinate offset and gamma curve distortion on color. Based on the predicted drift, the output color value corresponding to each input coordinate in the 3D color lookup table is re-determined through color space transformation and interpolation calculation, so that the corrected display color conforms to the target color gamut standard and gamma characteristics. (c) Gamma voltage optimization curve parameters: This set of parameters is used to directly adjust the mapping relationship between the driving voltage and brightness of the display screen to compensate for the predicted gamma curve distortion. By fitting the compensated target brightness and voltage relationship curve, a set of control point parameters is generated.

[0029] As described in steps S21-S23 above, the present invention accurately obtains a multi-dimensional collaborative compensation parameter matrix for compensating for the deviation of the current display parameters through a coherent process of data reception and decryption, digital twin model update and parameter drift prediction, and collaborative compensation parameter matrix calculation. This provides core technical support for subsequent calibration configuration distribution and terminal compensation execution, ensuring the pertinence and accuracy of calibration operations.

[0030] To ensure secure transmission, the feature data packets uploaded by the terminal device are encrypted. The cloud server must decrypt them before obtaining valid feature data. Meanwhile, the display parameters drift of the screen do not occur suddenly but change dynamically with time, temperature, and usage intensity. The deviation trend cannot be accurately grasped based solely on the current feature data. It is necessary to combine the initial factory calibration data and historical calibration feature data to predict the drift characteristics in order to formulate an appropriate compensation scheme. Therefore, a specific process must be used to complete data decryption, drift prediction, and compensation parameter calculation.

[0031] In existing technologies, feature data transmission often lacks encryption protection, posing risks of data leakage or tampering. Compensation parameter calculations are mostly based on current, single data points, failing to integrate historical and initial calibration data, making it impossible to predict parameter drift trends and resulting in lagging compensation. Furthermore, compensation parameters are often adjusted in a single dimension, neglecting the interrelationships between parameters and easily introducing new display deviations. This solution ensures data security through encryption and decryption, utilizes a digital twin model to predict drift characteristics, and generates a multi-dimensional, collaborative compensation parameter matrix through a collaborative optimization algorithm, systematically addressing the aforementioned shortcomings.

[0032] In the data reception and decryption phase, the cloud server receives encrypted feature data packets uploaded by the terminal device. These packets are generated by the terminal device using a preset encryption algorithm to encrypt the luminance uniformity spectrum feature values, actual gamma curve deviation values, and color gamut coverage feature values. The pre-installed decryption key on the cloud server is a dedicated key stored during system deployment and corresponds one-to-one with the encryption key on the terminal device. Using this decryption key to decrypt the encrypted feature data packets accurately recovers the original three feature data points. This ensures the security of data transmission, preventing data theft or tampering, and provides a reliable and effective data foundation for subsequent model analysis.

[0033] In the digital twin model update and parameter drift prediction stage, the initial factory calibration data for the corresponding display model is the standard display parameter data recorded by the display manufacturer at the time of manufacture and stored in the model calibration database on the cloud server. Historical calibration feature data is the feature data generated during each previous calibration of the terminal device and stored in the historical calibration database. The digital twin model adopts a temporal memory network architecture, which includes an input layer, a temporal memory layer, and an output layer. The temporal memory layer stores and integrates historical data information, establishing a mapping relationship between parameters and influencing factors by learning the dynamic changes in display parameters over time, temperature variations, and accumulated usage intensity. The currently decrypted feature data is used as new time-node data input to the model. The model combines the initial factory calibration data and the historical calibration feature data sequence to complete the update. The updated model can output the predicted value of the drift amount of key display parameters in the next stage. For example, after a display screen has been used for 1000 hours, the model combines its historical brightness decay data with the current brightness uniformity characteristic value to predict that the brightness decay of a certain zone in the next 500 hours will be 8 cd / m², the gamma curve distortion will be 0.08, and the chromaticity coordinate offsets of red, green and blue primary colors will be 0.01, 0.008 and 0.009 respectively, providing a clear deviation indication for subsequent compensation calculations.

[0034] In the collaborative compensation parameter matrix calculation stage, the collaborative optimization algorithm uses the predicted drift amount as input to construct a multi-objective optimization function, simultaneously considering optimization objectives such as brightness uniformity, color accuracy, and gamma characteristics, to avoid imbalances in other parameters caused by adjusting a single parameter. Each coefficient in the partition brightness compensation coefficient matrix corresponds one-to-one with a screen partition, with the optimization objective being to minimize the average brightness difference between all partitions of the entire screen after compensation. This is obtained through a linear programming algorithm. For example, if a partition's predicted brightness attenuation is 8 cd / m², and the target average brightness for the entire screen is 250 cd / m², then the compensation coefficient for that partition is calculated as 250 / (250-8)≈1.033, ensuring that the brightness of that partition reaches the target value and is uniform across the entire screen after compensation. During the calculation of the adjustment values ​​for the 3D color lookup table, based on the predicted primary color chromaticity coordinate offset, a color space transformation is performed within the CIE 1931 standard color space. A bilinear interpolation algorithm is then used to redetermine the output color value corresponding to each input coordinate in the 3D color lookup table. For example, if the measured chromaticity coordinate of a primary color deviates from the standard value by 0.01, the output value corresponding to the input coordinate of that primary color is adjusted to a value conforming to the target color gamut standard through transformation and interpolation calculations. When calculating the parameters of the gamma voltage optimization curve, the linear relationship between the compensated target brightness and the driving voltage is used as the objective. The target relationship curve is obtained by fitting using the least squares method. The voltage and brightness corresponding values ​​at key nodes on the curve are extracted as control point parameters. For example, a target brightness of 100 cd / m² corresponds to a driving voltage of 3.0V, and a target brightness of 200 cd / m² corresponds to a driving voltage of 3.5V, ensuring that the driving voltage accurately matches the target brightness and compensates for gamma curve distortion. These three factors together form a multi-dimensional collaborative compensation parameter matrix, achieving multi-parameter collaborative calibration and improving overall display accuracy.

[0035] In one embodiment, the step of sending the customized calibration profile containing the multidimensional collaborative compensation parameter matrix from the cloud server to the terminal device and embedding it into the drive link of the display screen includes: S31, the cloud server integrates the partition brightness compensation coefficient matrix, the three-dimensional color lookup table adjustment value and the gamma voltage optimization curve parameters obtained in step S2, and adds the configuration file version information, the applicable display model identifier, the generated timestamp and the check code for integrity verification to the integrated data, thereby generating a structured customized calibration configuration file. S32, the customized calibration configuration file is sent from the cloud server to the terminal device that initiated the calibration request through an encrypted secure transmission link. After receiving the file, the terminal device immediately calculates the digital hash value of the received configuration file and compares the hash value with the check code contained in the configuration file to verify that the configuration file has not been tampered with and is complete and error-free during transmission. S33, after verification, the terminal device parses the customized calibration configuration file and loads the parameters therein into the designated storage area of ​​the display driver module. The embedding process is as follows: the partition brightness compensation coefficient matrix is ​​written into the register of the backlight control unit, the three-dimensional color lookup table adjustment value is updated into the lookup table memory of the color management module, and the gamma voltage optimization curve parameters are configured into the gamma voltage generation circuit of the timing controller. After the embedding is completed, the display driver link will automatically call these new parameters in the subsequent image rendering.

[0036] As described in steps S31-S33 above, the present invention securely and accurately deploys the multi-dimensional collaborative compensation parameter matrix of cloud computing to the display driving link of the terminal device through a coherent process of customized configuration file generation, secure transmission and integrity verification, and driving link parameter embedding. This provides reliable parameter support for real-time compensation calibration on the terminal side and ensures that the compensation parameters can effectively act on the display driving process.

[0037] The partition brightness compensation coefficient matrix, 3D color lookup table adjustment values, and gamma voltage optimization curve parameters calculated by the cloud server are scattered sets of values. Direct transmission can easily lead to problems such as adaptation errors, data tampering, or loss. Furthermore, it is necessary to clearly associate them with the corresponding display model to avoid misuse. At the same time, the parameters need to be accurately embedded into the core module of the driver link so that the compensation parameters can be effectively called during image rendering. Therefore, it is necessary to achieve reliable implementation of parameters from the cloud to the terminal driver through structured encapsulation, secure transmission, and accurate embedding.

[0038] In existing technologies, compensation parameter transmission often lacks structured encapsulation and clear version and model identification, which can easily lead to parameter confusion between different devices. Furthermore, the lack of encryption and integrity verification measures during transmission poses a risk of data tampering or loss. Parameter embedding often uses general storage areas, failing to precisely correspond to the core hardware modules of the driver link, resulting in parameter call delays or failures. This solution systematically addresses these shortcomings by generating a customized configuration file containing identification information and a checksum, employing encrypted transmission and hash verification mechanisms to accurately write parameters into the designated hardware storage area of ​​the driver link.

[0039] In the customized configuration file generation stage, the partition brightness compensation coefficient matrix, 3D color lookup table adjustment values, and gamma voltage optimization curve parameters integrated by the cloud server are all derived from the calculation results of the collaborative optimization algorithm mentioned above. The added configuration file version information is used to record the number of parameter iterations, facilitating subsequent parameter updates and backtracking. The applicable display model identifier corresponds one-to-one with the terminal device's display model, ensuring that the configuration file is only compatible with the target device and avoiding cross-model misuse. The generation timestamp is used to mark the parameter generation time, providing a basis for historical data traceability. The checksum used for integrity verification is a unique identifier calculated from the integrated parameter data using a hash algorithm, providing a benchmark for subsequent transmission verification. For example, if a terminal display model is LCD-2560, the generated configuration file will clearly indicate this model, along with the version number V1.0, the generation timestamp, and the corresponding checksum, ensuring the file's uniqueness and traceability.

[0040] In the secure transmission and integrity verification phase, the encrypted secure transmission link is constructed using a symmetric encryption algorithm, effectively resisting the risk of data theft during transmission. After receiving the configuration file, the terminal device calculates the digital hash value of the received file using the same hash algorithm as the cloud. This digital hash value and the checksum in the configuration file are essentially unique feature codes generated based on the file content. Even a slight change in the file content will significantly alter the digital hash value. By comparing the two, if they match, it indicates that the configuration file has not been tampered with during transmission and is complete and error-free; if they do not match, a transmission anomaly is determined, triggering a retransmission process to ensure the authenticity and reliability of the parameter data received by the terminal.

[0041] In the driver link parameter embedding stage, the terminal device extracts various parameters from the customized configuration file through a parsing algorithm. Based on the parameter function and the hardware architecture of the display driver module, the parameters are loaded into the designated storage area. The partition brightness compensation coefficient matrix is ​​written to the register of the backlight control unit. Since the backlight control unit is directly responsible for adjusting the backlight intensity of each partition of the display screen, register storage ensures that the coefficient is called in real time during backlight control. The 3D color lookup table adjustment value is updated to the lookup table memory of the color management module. The color management module needs to realize the pixel color conversion output through the lookup table. The updated lookup table can directly affect the color correction process. The gamma voltage optimization curve parameters are configured to the gamma voltage generation circuit of the timing controller. One of the core functions of the timing controller is to generate the corresponding driving voltage according to the brightness requirements. This parameter configuration ensures that the output of the gamma voltage generation circuit meets the compensation requirements. For example, if the brightness compensation coefficient of a certain zone is 1.033, after it is written into the backlight control unit register, the backlight driver of that zone will automatically call this coefficient to adjust the backlight current to achieve the target brightness. After the parameter embedding is completed, the display driver link will automatically call these new parameters when rendering each subsequent frame, laying the hardware foundation for real-time compensation calibration.

[0042] In one embodiment, the step of completing the real-time compensation calibration of the displayed image includes: S41, after the driving link parameters are embedded, the display driving module of the terminal device activates all the compensation parameters in the loaded customized calibration configuration file when it receives the next frame synchronization signal, so that the new partition brightness compensation coefficient matrix, three-dimensional color lookup table and gamma voltage optimization curve officially take effect. S42, for each frame of image data input to the display driver module, the following collaborative compensation operations are performed sequentially: First, based on the brightness information of the pixel and its screen partition position, the corresponding partition brightness compensation coefficient is applied to adjust the partition brightness. Second, the pixel color value after partition brightness adjustment is used as input to query the updated three-dimensional color lookup table to obtain the color-corrected output value. Finally, the gamma voltage generation circuit configured with optimized curve parameters generates the corresponding driving voltage based on the corrected pixel brightness value, and finally outputs the collaboratively compensated display image on the display screen. S43, after the compensation takes effect, the terminal device controls the display screen to display a simplified verification test screen, and once again uses the micro optical sensor to quickly collect the brightness and chromaticity data of the specified area on the screen. The collected measured data is quickly compared with the expected calibration target value. If the comparison deviation is less than the preset allowable threshold, the closed-loop calibration is determined to be successful and a success log is recorded. Otherwise, it is marked as a calibration abnormality and a simplified feature data collection process can be triggered for subsequent analysis.

[0043] As described in steps S41-S43 above, the present invention transforms the parameters in the customized calibration configuration file into the actual driving actions of the display screen through a continuous process of compensation parameter activation, real-time screen driving and collaborative compensation, and rapid verification of calibration effect. This completes real-time and accurate compensation of the display screen, and ensures that the calibration effect meets the standards through closed-loop verification, thereby achieving effective correction of display parameter deviations.

[0044] The compensation parameters embedded in the driver chain need to be activated through a specific mechanism before they can be applied to image rendering. Furthermore, the display state of each frame must be adapted to the compensation parameters in real time to maintain calibration accuracy. Simultaneously, the calibration effect needs to be quickly verified after the calibration operation to avoid calibration becoming a mere formality due to parameter failure, driver anomalies, or other issues, failing to truly correct display deviations. Therefore, a complete process of activation, real-time compensation, and effect verification is necessary to ensure the effective implementation of calibration from parameters to actual display.

[0045] In existing technologies, the activation of compensation parameters often lacks a synchronization mechanism with image rendering, which can easily lead to image tearing or flickering. The compensation process typically involves a single parameter acting independently, failing to achieve coordinated adjustment of brightness, color, and gamma characteristics, potentially causing new display imbalances. Furthermore, it lacks a rapid and effective calibration verification process; the success of calibration depends on subsequent user experience, resulting in a lag and making it difficult to detect calibration anomalies in a timely manner. This solution systematically addresses these shortcomings through frame-synchronized parameter activation, multi-dimensional collaborative compensation, and rapid closed-loop verification, ensuring the effectiveness and stability of calibration.

[0046] In the compensation parameter activation phase, the display driver module continuously monitors the frame synchronization signal. The frame synchronization signal is the reference synchronization signal for controlling screen refresh, ensuring the timing consistency of each frame's image rendering. After embedding the driver link parameters, the compensation parameters are activated when the next frame synchronization signal arrives. This avoids screen tearing and flickering caused by asynchronous parameter switching and image rendering, achieving a smooth transition of the compensation parameters. At this point, the new partition brightness compensation coefficient matrix, 3D color lookup table, and gamma voltage optimization curve officially replace the original parameters, becoming the driving basis for subsequent image rendering and ensuring the compensation parameters take effect immediately.

[0047] In the real-time image driving and collaborative compensation phase, three collaborative operations are executed sequentially according to a fixed logic for each frame of image data input to the display driving module. First, zone brightness adjustment is performed. The display driving module identifies the brightness information of each pixel and its location within a screen zone, calls the corresponding zone brightness compensation coefficient, and adjusts the output current of the backlight control unit to correct the brightness. For example, if the compensation coefficient for a pixel's zone is 1.033, the original brightness information of that pixel corresponds to an actual output brightness of 242 cd / m². After applying the coefficient, the brightness is adjusted to 250 cd / m², consistent with the target brightness of the entire screen. Second, color correction is performed. The pixel color values ​​after brightness adjustment are input into an updated three-dimensional color lookup table. The lookup table outputs the corrected color values ​​according to a preset color mapping relationship, correcting color distortion caused by the offset of the primary color chromaticity coordinates. For example, if a red pixel appears orange-toned due to chromaticity coordinate offset, the correct red color value is output after looking up the value in the lookup table. Finally, gamma voltage optimization is performed. The gamma voltage generation circuit of the timing controller calls the gamma voltage optimization curve parameters based on the corrected pixel brightness value to generate the corresponding driving voltage, ensuring that the pixel brightness output conforms to the standard gamma characteristics and avoiding uneven grayscale transitions caused by gamma curve distortion. These three operations work together to independently correct display deviations in their respective dimensions, while also adapting to each other to avoid imbalances in other dimensions caused by adjusting a single parameter, achieving accurate compensation across the entire screen.

[0048] In the rapid verification phase of calibration, after compensation takes effect, the terminal device drives the display screen to show a simplified verification test screen. This screen includes key grayscale levels and red, green, and blue primary color blocks, which can quickly reflect core display indicators such as brightness uniformity and color accuracy. The miniature optical sensor is activated again to quickly collect brightness and chromaticity data from a designated area of ​​the screen. The designated area covers key locations such as the center and four corners of the screen to ensure the representativeness of the test. The expected calibration target value is a preset standard display parameter, including the target brightness value and the target chromaticity coordinate range, for example, a target brightness of 250 cd / m² and a target chromaticity coordinate range conforming to the sRGB standard. The measured data is compared with the target value. If the brightness deviation is less than ±5 cd / m² and the chromaticity coordinate deviation is less than ±0.005, that is, the comparison deviation is less than the preset allowable threshold, the calibration is considered successful and a success log is recorded. If the deviation exceeds the threshold, it is marked as a calibration anomaly, triggering a simplified feature data acquisition process. Only key parameters such as brightness and chromaticity are collected to provide data support for subsequent troubleshooting of parameter failures, driver anomalies, and other issues, forming a closed-loop control of the calibration effect and ensuring that the calibration operation truly achieves the purpose of correcting display deviations.

[0049] In one embodiment, the steps for achieving dynamic high-precision retention of the displayed image throughout its entire lifecycle include: S51, in the terminal device or cloud server, a variety of dynamically manageable external and internal calibration trigger conditions are preset. The trigger conditions include: periodic trigger conditions based on fixed time intervals, environmental trigger conditions based on sudden changes in light intensity detected by the ambient light sensor, usage intensity trigger conditions based on the cumulative power-on working time of the display screen, and manual trigger conditions in response to user active operation. S52, the background service process of the terminal device continues to run, and monitors the periodic timing, changes in ambient light intensity, cumulative usage time and user input commands in real time. The cloud server can also perform auxiliary analysis and trigger judgment based on the device status logs periodically reported by the terminal. S53, when the monitoring system determines that any preset trigger condition is met, the decision unit on the local terminal device or the decision module on the cloud server generates a calibration start command and sends the command to the calibration process control module of the terminal device. S54, the calibration process control module, based on the received start command and the type of triggering condition, selects to call the complete feature extraction process or the rapid extraction process of some key parameters, and automatically starts a new round of closed-loop calibration process from step S1, which includes sensing, uploading, calculation, distribution and execution. For calibration triggered by sudden changes in ambient light, rapid fine-tuning related to brightness and white balance is performed first to achieve adaptive matching between display effect and environment, thereby maintaining stable display accuracy of the display screen throughout its entire life cycle.

[0050] As described in steps S51-S54 above, the present invention achieves dynamic triggering and flexible adaptation of the calibration process through a coherent process of multi-condition triggering mechanism configuration, continuous monitoring of triggering conditions, triggering decision and calibration initiation, and adaptive fine-tuning process execution. This ensures that the display screen can initiate calibration in a timely manner according to changes in usage scenarios and status throughout its entire life cycle, maintaining long-term stability of display accuracy.

[0051] Display parameter drift is a continuous dynamic process. Its drift rate is related to various factors such as changes in ambient light, cumulative usage time, and usage frequency. Calibration at a single point in time cannot cover subsequent parameter changes in the long term. Moreover, drift caused by different factors requires different calibration. Sudden changes in ambient light only require adjustment of brightness-related parameters, while drift after long-term use requires comprehensive calibration. Therefore, it is necessary to establish a multi-condition triggering mechanism and an adaptive fine-tuning process to achieve accurate and efficient dynamic calibration.

[0052] In existing technologies, calibration triggering often adopts a single, fixed-cycle mode, failing to consider key influencing factors such as environmental changes and usage intensity. This leads to a mismatch between calibration timing and actual needs, resulting in either excessively frequent calibrations that waste resources or delayed calibrations that fail to correct deviations in a timely manner. Furthermore, all triggers execute the complete calibration process without optimizing the process based on the triggering cause, performing complex full-process calibration even for simple deviations, resulting in low efficiency. This solution systematically addresses these shortcomings by configuring multi-dimensional triggering conditions, a collaborative monitoring mechanism, intelligent decision-making for initiation, and adaptive process selection, achieving dynamic and efficient calibration.

[0053] The multi-condition triggering mechanism configuration allows trigger conditions to be preset in the terminal device or cloud server and dynamically modified to meet the personalized needs of different usage scenarios. Periodic trigger conditions based on fixed time intervals, with a preset interval of 7 days, ensure regular and comprehensive calibration of display parameters. Environmental trigger conditions based on ambient light sensor detection set a light intensity mutation threshold of ±500 lux; calibration is triggered when the ambient light sensor detects a light intensity change exceeding this threshold. Usage intensity trigger conditions based on cumulative display power-on time have a preset cumulative time threshold of 500 hours; calibration is triggered when the display's cumulative power-on time reaches this threshold, increasing the risk of parameter drift due to component wear. Manual trigger conditions respond to user-initiated operations; users can initiate calibration commands through the terminal device's settings interface to meet immediate calibration needs in special scenarios.

[0054] In the continuous monitoring phase triggered by the conditions, the background service process of the terminal device runs continuously in low-power mode, continuously calculating the periodic timing duration, collecting illuminance data from the ambient light sensor, accumulating the display screen's power-on time, and monitoring user input commands. The cloud server receives periodically reported device status logs from the terminal device, including information such as periodic timing progress, illuminance change records, and cumulative usage time, for auxiliary analysis. For example, if the terminal fails to detect the cumulative time target due to a local fault, the cloud can detect this through log analysis and trigger a judgment to ensure that no monitoring is missed.

[0055] In the trigger decision and calibration initiation phase, the monitoring system compares monitoring data with preset trigger condition thresholds in real time. When any condition is met, the local decision unit on the terminal device or the decision module on the cloud server immediately generates a calibration initiation command. The local decision unit on the terminal is suitable for scenarios with high real-time requirements, such as periodic events, sudden changes in ambient light, and manual triggering. The cloud decision module is suitable for usage intensity triggering scenarios that require comprehensive analysis of historical data. The command is sent to the calibration process control module on the terminal device through an internal communication link to ensure rapid transmission of the initiation command.

[0056] In the adaptive fine-tuning process, the calibration process control module receives the start command and parses the trigger condition type. If it is triggered periodically or based on cumulative usage time, the complete feature extraction process is invoked to comprehensively collect parameters such as brightness, gamma, and color gamut to ensure comprehensive calibration. If it is triggered by a sudden change in ambient light, the fast extraction process related to brightness and white balance is executed first, collecting only key brightness and white balance parameters and omitting the extraction and calculation of unnecessary parameters such as color gamut, quickly generating targeted compensation parameters to achieve adaptive matching between display effect and environment. If it is manually triggered by the user, the complete process is executed by default, while allowing the user to select a fast fine-tuning mode. By selecting the appropriate process according to the trigger type, calibration efficiency is greatly improved while ensuring calibration effect, avoiding resource waste, and ultimately achieving dynamic stability of display accuracy throughout the entire life cycle of the display screen.

[0057] like Figure 2 As shown, the present invention also discloses a high-precision liquid crystal display calibration system, comprising: Extraction module 1 is used on the terminal device side to collect the original optical data of the display screen under the preset test screen using an optical sensor, and to process the original optical data to extract the feature data of the display parameters. Calculation module 2 is used to upload the feature data to the cloud server. On the cloud server side, based on the received feature data, the initial factory calibration data of the corresponding display model and historical calibration data, the digital twin model of the display is updated to predict the dynamic drift characteristics of the display parameters, and based on the prediction results, the collaborative compensation parameters used to compensate for the deviation of the display parameters are calculated. Sending module 3 is used to send the calibration configuration file containing the collaborative compensation parameters from the cloud server to the terminal device and configure it in the drive link of the display screen; The calibration module 4 is used on the terminal device side to drive the display screen according to the parameters in the calibration configuration file, complete the compensation calibration of the display screen, and verify the calibration effect; Trigger module 5 is used to set and monitor calibration trigger conditions. When any of the trigger conditions is met, a new round of calibration process is automatically started.

[0058] In one embodiment, the sending module includes: The integration unit is used to integrate the calculated collaborative compensation parameters through the cloud server, add identification information and verification codes, and generate a customized calibration configuration file. The verification unit is used to send the calibration configuration file to the terminal device via an encrypted link. After receiving the configuration file, the terminal device verifies its integrity and correctness. The parsing unit is used to parse the calibration configuration file after the verification is passed, and write the collaborative compensation parameters therein into the corresponding functional unit of the display driver module.

[0059] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the high-precision liquid crystal display calibration system described above.

[0060] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the high-precision liquid crystal display calibration system described above.

[0061] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0062] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0063] The above description is merely a preferred embodiment of the present invention and does not limit the scope of this application. Any equivalent results or equivalent process transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.

Claims

1. A high-precision liquid crystal display calibration method, characterized in that, Includes the following steps: On the terminal device side, optical sensors are used to collect raw optical data of the display screen under a preset test screen, and the raw optical data is processed to extract feature data of the display parameters. Specific steps include: On the terminal device side, the display screen is driven to display a preset standard test screen, and the original image data under the standard test screen is collected simultaneously using an optical sensor; Preprocessing is performed on the acquired raw image data to eliminate inherent sensor errors and noise interference; Extract multi-dimensional display parameter feature data reflecting the brightness uniformity, gamma curve characteristics, and color gamut coverage of the display screen from the preprocessed image data; The brightness uniformity is characterized by dividing the screen display area into multiple partitions, calculating the average brightness value of each partition and constructing a brightness matrix, and using the ratio of the brightness of the darkest partition to the average brightness of the entire screen. The gamma curve characteristics are characterized by obtaining the average brightness value measured by the sensor at each gray level for the gray level test image, comparing it with the theoretical standard brightness target value corresponding to that gray level, and calculating the root mean square error of the deviation between the measured brightness value and the theoretical target value of all sampled gray levels. The color gamut coverage is characterized by obtaining the measured chromaticity coordinates of red, green and blue primary colors from the primary color test screen, determining the actual color gamut range of the current display screen based on the measured primary color coordinates within the standard color space, and calculating the ratio of the overlapping area of ​​the actual color gamut and the standard color gamut to the area of ​​the standard color gamut. The extracted multi-dimensional display parameter feature data is compressed and formatted for encapsulation to form a feature data package to be uploaded. The feature data is uploaded to a cloud server. On the cloud server side, based on the received feature data, the initial factory calibration data of the corresponding display model, and historical calibration data, the digital twin model of the display is updated to predict the dynamic drift characteristics of the display parameters. Based on the prediction results, collaborative compensation parameters for compensating for display parameter deviations are calculated. The specific steps include: The cloud server receives and decrypts the feature data packet from the terminal device to obtain the multi-dimensional display parameter feature data; Based on the multi-dimensional display parameter characteristics, the initial factory calibration data and historical calibration data sequence of the corresponding display screen, the digital twin model of the display screen is updated and run by adopting a time-series memory network architecture to predict the drift of display parameters. The drift amount includes the brightness attenuation of each partition of the screen, the distortion of the gamma curve, and the offset of the chromaticity coordinates of the red, green, and blue primary colors. Based on the predicted display parameter drift amount from the digital twin model, a multidimensional collaborative compensation parameter matrix is ​​calculated using a collaborative optimization algorithm. The multidimensional collaborative compensation parameter matrix includes: The partition brightness compensation coefficient matrix is ​​used to compensate for the brightness non-uniformity of each partition of the screen and the predicted brightness attenuation. Each coefficient corresponds to a screen partition, and the calculation of the coefficient is aimed at optimizing the brightness uniformity of the whole screen after compensation. The adjustment value of the three-dimensional color lookup table is used to correct the influence of the predicted primary color chromaticity coordinate offset and gamma curve distortion on color. Based on the predicted drift, the output color value corresponding to each input coordinate in the three-dimensional color lookup table is re-determined through color space transformation and interpolation calculation. Gamma voltage optimization curve parameters are used to directly adjust the mapping relationship between the display's driving voltage and brightness to compensate for the predicted gamma curve distortion. Control point parameters are generated by fitting the compensated target brightness and voltage relationship curve. The calibration configuration file containing the collaborative compensation parameters is sent from the cloud server to the terminal device and configured into the drive link of the display screen; On the terminal device side, the display screen is driven according to the parameters in the calibration configuration file to complete the compensation calibration of the display screen and verify the calibration effect; Set and monitor calibration trigger conditions, and automatically start a new calibration process when any of the aforementioned trigger conditions are met.

2. The high-precision liquid crystal display calibration method according to claim 1, characterized in that, The step of sending the calibration configuration file containing the collaborative compensation parameters from the cloud server to the terminal device and configuring it in the drive link of the display screen includes: The cloud server integrates the calculated collaborative compensation parameters, adds identification information and verification codes, and generates a customized calibration configuration file. The calibration configuration file is sent to the terminal device via an encrypted link. After receiving the configuration file, the terminal device verifies its integrity and correctness. After successful verification, the terminal device parses the calibration configuration file and writes the collaborative compensation parameters therein into the corresponding functional unit of the display driver module.

3. The high-precision liquid crystal display calibration method according to claim 1, characterized in that, The steps of completing the compensation calibration of the displayed image and verifying the calibration effect include: The terminal device activates the co-compensation parameters in the calibration configuration file in the display driver module; For the input image data, the display driver module performs collaborative processing on the screen, including zone brightness adjustment, color correction, and gamma characteristic optimization, based on the activated collaborative compensation parameters. After the compensation takes effect, the driver displays a verification screen and collects data. The collected data is compared with the expected target value, and the calibration is determined based on the comparison result.

4. The high-precision liquid crystal display calibration method according to claim 1, characterized in that, The steps for automatically initiating a new round of calibration include: Multiple calibration trigger conditions are preset, including periodic time conditions, ambient light change conditions, cumulative usage time conditions, and user-manual trigger conditions; Continuously monitor the parameters related to the triggering conditions and determine whether any triggering condition is met; When the triggering conditions are met, a calibration start command is generated, and depending on the type of triggering conditions, either a complete calibration process or a quick fine-tuning process for specific parameters is selected.

5. A high-precision liquid crystal display calibration system, characterized in that, include: The extraction module is used on the terminal device side to acquire raw optical data of the display screen under a preset test screen using an optical sensor, and to process the raw optical data to extract feature data of the display parameters. Specific steps include: On the terminal device side, the display screen is driven to display a preset standard test screen, and the original image data under the standard test screen is collected simultaneously using an optical sensor; Preprocessing is performed on the acquired raw image data to eliminate inherent sensor errors and noise interference; Extract multi-dimensional display parameter feature data reflecting the brightness uniformity, gamma curve characteristics, and color gamut coverage of the display screen from the preprocessed image data; The brightness uniformity is characterized by dividing the screen display area into multiple partitions, calculating the average brightness value of each partition and constructing a brightness matrix, and using the ratio of the brightness of the darkest partition to the average brightness of the entire screen. The gamma curve characteristics are characterized by obtaining the average brightness value measured by the sensor at each gray level for the gray level test image, comparing it with the theoretical standard brightness target value corresponding to that gray level, and calculating the root mean square error of the deviation between the measured brightness value and the theoretical target value of all sampled gray levels. The color gamut coverage is characterized by obtaining the measured chromaticity coordinates of red, green and blue primary colors from the primary color test screen, determining the actual color gamut range of the current display screen based on the measured primary color coordinates within the standard color space, and calculating the ratio of the overlapping area of ​​the actual color gamut and the standard color gamut to the area of ​​the standard color gamut. The extracted multi-dimensional display parameter feature data is compressed and formatted for encapsulation to form a feature data package to be uploaded. The calculation module is used to upload the feature data to the cloud server. On the cloud server side, based on the received feature data, the initial factory calibration data of the corresponding display model, and historical calibration data, the digital twin model of the display is updated to predict the dynamic drift characteristics of the display parameters. Based on the prediction results, collaborative compensation parameters for compensating for display parameter deviations are calculated. The specific steps include: The cloud server receives and decrypts the feature data packet from the terminal device to obtain the multi-dimensional display parameter feature data; Based on the multi-dimensional display parameter characteristics, the initial factory calibration data and historical calibration data sequence of the corresponding display screen, the digital twin model of the display screen is updated and run by adopting a time-series memory network architecture to predict the drift of display parameters. The drift amount includes the brightness attenuation of each partition of the screen, the distortion of the gamma curve, and the offset of the chromaticity coordinates of the red, green, and blue primary colors. Based on the predicted display parameter drift amount from the digital twin model, a multidimensional collaborative compensation parameter matrix is ​​calculated using a collaborative optimization algorithm. The multidimensional collaborative compensation parameter matrix includes: The partition brightness compensation coefficient matrix is ​​used to compensate for the brightness non-uniformity of each partition of the screen and the predicted brightness attenuation. Each coefficient corresponds to a screen partition, and the calculation of the coefficient is aimed at optimizing the brightness uniformity of the whole screen after compensation. The adjustment value of the three-dimensional color lookup table is used to correct the influence of the predicted primary color chromaticity coordinate offset and gamma curve distortion on color. Based on the predicted drift, the output color value corresponding to each input coordinate in the three-dimensional color lookup table is re-determined through color space transformation and interpolation calculation. Gamma voltage optimization curve parameters are used to directly adjust the mapping relationship between the display's driving voltage and brightness to compensate for the predicted gamma curve distortion. Control point parameters are generated by fitting the compensated target brightness and voltage relationship curve. The sending module is used to send the calibration configuration file containing the collaborative compensation parameters from the cloud server to the terminal device and configure it in the drive link of the display screen; The calibration module is used on the terminal device side to drive the display screen according to the parameters in the calibration configuration file, complete the compensation calibration of the display screen, and verify the calibration effect; The trigger module is used to set and monitor calibration trigger conditions. When any of the trigger conditions is met, a new round of calibration process is automatically started.

6. The high-precision liquid crystal display calibration system according to claim 5, characterized in that, The sending module includes: The integration unit is used to integrate the calculated collaborative compensation parameters through the cloud server, add identification information and verification codes, and generate a customized calibration configuration file. The verification unit is used to send the calibration configuration file to the terminal device via an encrypted link. After receiving the configuration file, the terminal device verifies its integrity and correctness. The parsing unit is used to parse the calibration configuration file after the verification is passed, and write the collaborative compensation parameters therein into the corresponding functional unit of the display driver module.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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