Self-calibration system and method for optical performance of vehicle-mounted display screen in extreme environment

Abstract

The application relates to the technical field of vehicle-mounted intelligent display, and discloses a self-calibration system and method for optical performance of a vehicle-mounted display screen in an extreme environment, which comprises the following steps: obtaining multi-dimensional environmental parameters and physical field states of the vehicle-mounted display screen in the extreme environment, constructing a multi-field coupling state matrix reflecting the physical pixel space state of the vehicle-mounted display screen based on the multi-dimensional environmental parameters and the physical field states, and obtaining the current actual chroma coordinates and target standard chroma coordinates of the vehicle-mounted display screen to represent the initial optical performance of the vehicle-mounted display screen. The multi-dimensional environmental parameters and the physical field states are used to construct the multi-field coupling state matrix, and a metric tensor is constructed on the basis to map a color driving space into a Riemann manifold space dynamically deformed according to the environment, so that the technical limitation that a simple linear compensation algorithm cannot accurately fit the light-emitting material is broken, and the self-calibration precision of the optical performance of the vehicle-mounted display screen is improved.

Description

Technical Field

[0001] This invention relates to the field of automotive intelligent display technology, specifically to a self-calibration system and method for the optical performance of automotive displays under extreme environments. Background Technology

[0002] With the rapid popularization of automotive intelligence, in-vehicle displays have gradually become the core hub of human-machine interaction in smart cockpits. Unlike conventional consumer electronics, in-vehicle displays need to face extremely harsh physical application environments during their actual service life, such as extremely cold climates, high temperatures and direct sunlight in summer, high humidity airflow, and complex and ever-changing external ambient light. Under these extreme environments, the semiconductor light-emitting materials and driving substrates inside the display panel will inevitably be affected by the superposition of multiple physical factors such as thermal stress and long-term electrical stress, resulting in varying degrees of changes in physical properties. This underlying physical degradation will ultimately manifest in the macroscopic vision as serious optical performance distortion phenomena such as color gamut drift, white point shift, gamma curve distortion, and a sharp drop in contrast. This not only reduces the visual experience in the cockpit, but may even affect the driver's accurate interpretation of information in key information display areas such as the instrument panel, thereby threatening driving safety.

[0003] Currently, conventional calibration methods for optical performance distortion of displays mainly rely on 3D lookup tables (3D LUTs) pre-installed before the panel leaves the factory and simple linear interpolation algorithms. Some improved solutions for the automotive environment attempt to introduce external temperature or light sensors, attempting to collect single environmental variables such as temperature or light intensity, map and match corresponding empirical compensation coefficients in the system lookup table, and then change the effective value of the target pixel driving voltage by adjusting the standard pulse width modulation (PWM) duty cycle of the display driver chip, in order to restore the panel's initial color performance when the environment changes.

[0004] However, the performance degradation of the panel in the extreme environment of the vehicle is the result of the deep coupling of multiple physical fields such as temperature field, time-series aging field and external ambient light field. The decay law of the light-emitting material caused by this multivariate exhibits highly nonlinear and distorted spatial evolution characteristics in the color space. The core algorithm of the traditional linear compensation model and the lookup table interpolation method based on discrete nodes still regards the color driving space as a flat and uniform Euclidean geometric space. This makes it difficult to accurately fit the complex spatial topological deformation under the action of multiple physical fields at the theoretical level. As a result, it is very easy to produce severe color banding and calculation deviation under the boundary conditions of drastic fluctuation of environmental variables. At the same time, due to the special light trajectory and air conditioning airflow distribution in the vehicle, the physical thermodynamic state of the screen surface often exhibits an extremely uneven gradient distribution, making it difficult to maintain the optical stability of the display screen throughout its entire life cycle in the harsh vehicle environment. Therefore, this invention designs a self-calibration system and method for the optical performance of vehicle display screens in extreme environments based on the above-mentioned problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a self-calibration system and method for the optical performance of vehicle-mounted displays under extreme environments. This solves the problem that the accuracy of traditional linear calibration algorithms is severely insufficient in extreme environments with multi-physics coupling, due to the nonlinear and non-uniform degradation of light-emitting devices.

[0006] To achieve the above objectives, the present invention provides a self-calibration method for the optical performance of an in-vehicle display screen under extreme environments, comprising the following steps:

[0007] S1. First, obtain the multi-dimensional environmental parameters and physical field state of the vehicle display screen under extreme environment. Based on the multi-dimensional environmental parameters and physical field state, construct a multi-field coupling state matrix that reflects the physical pixel space state of the vehicle display screen. Then, obtain the current actual chromaticity coordinates and target standard chromaticity coordinates of the vehicle display screen to characterize the initial optical performance of the vehicle display screen.

[0008] S2. Subsequently, based on the gradient distribution of the multi-field coupling state matrix in the physical pixel space, the physical pixel space is adaptively divided into multiple independently evolving micromanifolds. Within each micromanifold, a metric tensor is constructed based on the multi-field coupling state matrix to map the color driving space of the vehicle display screen into a Riemannian manifold space that dynamically deforms under extreme environments.

[0009] S3. Solve the geodesic differential equation based on the metric tensor to obtain the optimal compensated continuous trajectory that regresses from the actual chromaticity coordinates to the target standard chromaticity coordinates in the Riemannian manifold space;

[0010] S4. Calculate the spatial curvature of the optimal compensated continuous trajectory, adaptively reconstruct the underlying driving waveform corresponding to the color driving space based on the spatial curvature, and output the reconstructed underlying driving waveform to drive the pixels in the micromanifold to emit light.

[0011] S5. Finally, the actual optical feedback features after the pixel emits light are collected, the updated chromaticity coordinates in the actual optical feedback features are extracted, the residual between the updated chromaticity coordinates and the target standard chromaticity coordinates in the Riemann manifold space is calculated, and the construction parameters of the metric tensor are updated based on the residual. Adaptive reconstruction is performed cyclically to perform closed-loop iteration until the residual meets the preset convergence condition, thus completing the self-calibration of the optical performance of the vehicle display screen under the extreme environment.

[0012] Preferably, step S1 specifically includes:

[0013] Collect temperature data of the vehicle display screen at the physical pixel spatial coordinates, and construct a temperature field distribution matrix;

[0014] Extract the historical driving current and cumulative working time of each pixel area of ​​the vehicle display screen, calculate the local physical attenuation by integration, and generate the aging field distribution matrix.

[0015] The illuminance and color temperature of the incident light from outside the vehicle display screen are obtained to construct the ambient light field vector.

[0016] The temperature field distribution matrix, the aging field distribution matrix, and the ambient light field vector are fused to generate the multi-field coupling state matrix.

[0017] Preferably, step S2 specifically includes:

[0018] Calculate the thermodynamic gradient vector field of the temperature field distribution matrix in the physical pixel space;

[0019] Determine whether the magnitude of the thermodynamic gradient vector field in the local region is greater than a preset gradient threshold;

[0020] If the gradient is greater than the gradient threshold, the contour lines of the thermodynamic gradient vector field are used as the topological space boundary to divide the physical pixel space into multiple independent display sub-regions.

[0021] Each of the aforementioned display sub-regions is defined as an independent micromanifold in terms of topology.

[0022] Preferably, the step of mapping the color driving space of the in-vehicle display to a Riemannian manifold space that dynamically deforms under extreme environments includes:

[0023] Obtain the background metric tensor under standard conditions;

[0024] The nonlinear degradation function corresponding to the temperature field distribution matrix, the nonlinear degradation function corresponding to the aging field distribution matrix, and the correction function corresponding to the ambient light field vector are processed by second-order partial derivatives respectively.

[0025] By combining the preset physical field coupling weight coefficients as the construction parameters, the background metric tensor is linearly superimposed with each of the processed functions to generate a metric tensor that dynamically evolves with the extreme environment.

[0026] Preferably, step S3 specifically includes:

[0027] Calculate the second type of Christofel symbol based on the metric tensor and its inverse matrix;

[0028] The initial driving voltage state corresponding to the actual chromaticity coordinates is used as the starting boundary condition, and the target driving voltage state corresponding to the target standard chromaticity coordinates is used as the ending boundary condition.

[0029] The geodesic differential equation is established using the second type of Christofer notation, and the path function that varies with the evolution parameters is solved by numerical algorithm. The path function is then used as the optimal compensated continuous trajectory.

[0030] Preferably, the step of calculating the spatial curvature of the optimal compensated continuous trajectory includes:

[0031] Extract the first-order and second-order derivative vectors of the optimal compensated continuous trajectory with respect to the evolution parameters;

[0032] Calculate the magnitude of the cross product of the first derivative vector and the second derivative vector, and divide by the cube of the magnitude of the first derivative vector to obtain the spatial curvature that evolves with the optimal compensated continuous trajectory.

[0033] Preferably, step S4 specifically includes:

[0034] Determine whether the spatial curvature is greater than a preset curvature safety threshold;

[0035] If the spatial curvature is less than or equal to the curvature safety threshold, then the underlying driving waveform is generated using pulse width modulation mode, the peak waveform voltage is kept constant, and the pulse duty cycle is adjusted based on the trajectory integral.

[0036] If the spatial curvature is greater than the curvature safety threshold, a hybrid reconstruction mechanism of pulse amplitude modulation and pulse width modulation is triggered to actively reduce the instantaneous voltage peak of the underlying driving waveform and extend the pulse duration according to the compensation energy integral ratio, outputting a non-standard square wave as the reconstructed underlying driving waveform.

[0037] Preferably, step S5 specifically includes:

[0038] In the Riemannian manifold space, the geodesic distance between the updated chromaticity coordinates and the target standard chromaticity coordinates is calculated, and the geodesic distance is used as the residual.

[0039] If the geodesic distance is greater than a preset tolerance threshold, the partial derivative of the residual with respect to the physical field coupling weight coefficient is calculated using the gradient descent method.

[0040] The physical field coupling weight coefficients are finely adjusted and updated based on the partial derivatives, thus completing the update of the metric tensor construction parameters.

[0041] Preferably, the step of continuing until the residual satisfies the preset convergence condition specifically includes:

[0042] After closed-loop iteration, the updated geodesic distance is calculated again;

[0043] When the calculated geodesic distance is less than or equal to the tolerance threshold, the residual is determined to meet the preset convergence condition, the current physical field coupling weight coefficient is saved, and the closed-loop iteration ends.

[0044] Preferably, a self-calibration system for the optical performance of an in-vehicle display under extreme environments includes:

[0045] The data acquisition module is used to acquire multi-dimensional environmental parameters and physical field states of the vehicle-mounted display screen under extreme environments;

[0046] The manifold segmentation module is used to adaptively segment the physical pixel space into multiple independently evolving micromanifolds based on the gradient distribution of the multi-field coupling state matrix in the physical pixel space.

[0047] The tensor construction and tracking module is used to construct a metric tensor based on the multi-field coupling state matrix within each of the micromanifolds.

[0048] The waveform reconstruction driving module is used to calculate the spatial curvature of the optimal compensation continuous trajectory and adaptively reconstruct the underlying driving waveform corresponding to the color driving space based on the spatial curvature.

[0049] The feedback iteration module is used to collect the actual optical feedback features after the pixel emits light, extract the updated chromaticity coordinates from the actual optical feedback features, and repeatedly trigger the waveform reconstruction driving module to perform adaptive reconstruction for closed-loop iteration.

[0050] This invention provides a self-calibration system and method for the optical performance of vehicle-mounted displays under extreme environments. It offers the following advantages:

[0051] 1. This invention constructs a multi-field coupled state matrix by acquiring multi-dimensional environmental parameters and physical field states, and on this basis, constructs a metric tensor to map the color-driven space into a Riemannian manifold space that dynamically deforms with the environment. This breaks through the technical limitation of the existing simple linear compensation algorithm that cannot accurately fit the multivariate nonlinear degradation of luminescent materials under extreme environments, thereby improving the self-calibration accuracy of the optical performance of vehicle-mounted displays.

[0052] 2. This invention reconstructs the actual optical feedback characteristics after emission by collecting the underlying driving waveform, calculates the geodesic distance residual between the updated chromaticity coordinates and the target standard chromaticity coordinates in the Riemann manifold space, and constructs parameters based on the dynamic micro-scheduling tensor of the residual for closed-loop iterative solution, thus ensuring the long-term optical stability of the display screen in the face of complex and extreme environments throughout its entire life cycle.

[0053] 3. This invention obtains the optimal compensation continuous trajectory from actual chromaticity coordinates to target standard chromaticity coordinates in Riemannian manifold space by solving the geodesic differential equation based on the metric tensor. It successfully transforms the process of finding the optimal compensation voltage into the problem of finding the shortest smooth path in non-Euclidean geometric space. Compared with traditional solutions, it can achieve dynamic optimization calculation in all scenarios without consuming a large amount of hardware storage capacity.

[0054] 4. This invention creatively realizes the direct coordination between abstract mathematical topological features and underlying semiconductor driving timing by calculating the spatial curvature of the optimal compensation continuous trajectory and adaptively reconstructing the underlying driving waveform based on the spatial curvature. This prevents the safety hazards of thermal breakdown of light-emitting devices or exacerbation of secondary thermal color difference caused by simply forcibly increasing the compensation voltage at the edge of extreme physical conditions, and enhances the working reliability of hardware under extreme compensation conditions.

[0055] 5. This invention effectively addresses the problem of extremely uneven physical degradation in different areas of the screen in complex automotive scenarios by calculating the gradient distribution of the multi-field coupled state matrix in the physical pixel space and adaptively dividing the physical space into multiple independently evolving micromanifolds based on the contour lines of the gradient distribution. This avoids the local overcompensation or undercompensation phenomenon caused by the traditional global unified compensation model. Attached Figure Description

[0056] Figure 1 This is a system architecture diagram of the method of the present invention;

[0057] Figure 2 This is one of the schematic diagrams of the method flow of the present invention;

[0058] Figure 3 This is a second schematic diagram of the method flow of the present invention;

[0059] Figure 4 This is the third schematic diagram of the method flow of the present invention;

[0060] Figure 5 This is the fourth schematic diagram of the method flow of the present invention;

[0061] Figure 6 This is the fifth schematic diagram of the method flow of the present invention. Detailed Implementation

[0062] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Example 1:

[0064] Please see the appendix Figure 1 -Appendix Figure 6 This invention provides a self-calibration method for the optical performance of an in-vehicle display screen under extreme environments, comprising the following steps:

[0065] S1. First, acquire the multi-dimensional environmental parameters and physical field state of the vehicle display screen under extreme conditions. Based on the multi-dimensional environmental parameters and physical field state, construct a multi-field coupling state matrix reflecting the physical pixel spatial state of the vehicle display screen. Also, acquire the current actual chromaticity coordinates and target standard chromaticity coordinates of the vehicle display screen to characterize the initial optical performance of the vehicle display screen. The specific steps include: collecting temperature data of the vehicle display screen at the physical pixel spatial coordinates and constructing a temperature field distribution matrix; extracting the historical driving current and cumulative working time of each pixel area of ​​the vehicle display screen, integrating to calculate local physical attenuation, and generating an aging field distribution matrix; acquiring the illuminance and color temperature of the external incident light of the vehicle display screen and constructing an ambient light field vector; and fusing the temperature field distribution matrix, the aging field distribution matrix, and the ambient light field vector to generate a multi-field coupling state matrix.

[0066] Specifically, due to the extremely harsh in-vehicle environment, the degradation of the display panel is not caused by a single variable. This step is the underlying data acquisition layer of the system, responsible for digitizing the complex physical environment. Assume the physical pixel space of the display screen has a two-dimensional coordinate system, with coordinate points denoted as... The system acquires multi-dimensional environmental parameters and physical field states through a multi-source sensor array:

[0067] First, a flexible thermal sensor array attached to the back panel of the display is used to collect coordinate data at each point. Real-time temperature data at the location is used to construct a temperature field distribution matrix. ;

[0068] The system then extracts the historical drive current of each pixel region from the timing controller (TCON). With cumulative working hours Since the aging of luminescent materials is a cumulative process of long-term electrical stress, the system calculates local physical decay by integration and multiplies it by the material's half-life constant. Generate the aging field distribution matrix Its expression is:

[0069] ;

[0070] The illuminance of external incident light is obtained again by using an ambient light sensor installed inside the vehicle. With color temperature Construct ambient light field vector ;

[0071] Finally, the above , and The data is fused into a multi-field coupled state matrix. Simultaneously, the system drives miniature photodetectors located in the dummy area of ​​the display screen to acquire the current actual chromaticity coordinates under a preset reference image. And retrieve the target standard chromaticity coordinates from memory. .

[0072] S2. Subsequently, based on the gradient distribution of the multi-field coupling state matrix in the physical pixel space, the physical pixel space is adaptively divided into multiple independently evolving micromanifolds. Within each micromanifold, a metric tensor is constructed based on the multi-field coupling state matrix, mapping the color driving space of the vehicle display to a Riemannian manifold space dynamically deformed by extreme environments. Specifically, the above steps include: calculating the thermodynamic gradient vector field of the temperature field distribution matrix in the physical pixel space; determining whether the magnitude of the thermodynamic gradient vector field in a local region is greater than a preset gradient threshold; if it is greater than the gradient threshold, using the contour lines of the thermodynamic gradient vector field as the boundary of the topological space, the physical pixel space is... The process involves dividing the element space into multiple independent display sub-regions, defining each sub-region as an independent micromanifold in terms of topology, and mapping the color driving space of the vehicle display to a Riemannian manifold space that dynamically deforms under extreme environments. This includes: obtaining the baseline metric tensor under standard conditions; performing second-order partial derivative processing on the nonlinear degradation function corresponding to the temperature field distribution matrix, the nonlinear degradation function corresponding to the aging field distribution matrix, and the correction function corresponding to the ambient light field vector; and combining the preset physical field coupling weight coefficients as construction parameters, linearly superimposing the baseline metric tensor with the processed functions to generate a metric tensor that dynamically evolves with extreme environments.

[0073] Specifically, in automotive applications (such as when sunlight shines at an angle, causing one half of the screen to be exposed to strong light while the other half is exposed to cold air from the air conditioner), the optical degradation of different areas of the screen will be highly non-uniform. If the entire screen is treated as a whole and compensated with a uniform straight line, it will lead to local overcompensation or undercompensation.

[0074] The system calculates the temperature field distribution matrix. Thermodynamic gradient vector field in physical pixel space: Then the magnitude of the vector field is calculated. The system presets a gradient threshold. When the local area meets When this occurs, it indicates a significant difference in thermal stress. The system uses the contour lines of this thermodynamic gradient vector field as the topological space boundary to adaptively divide the full-screen physical pixel space into... Each display subregion is defined as an independent micromanifold in terms of mathematical topology. ( This can reduce the global solution dimension of subsequent complex partial differential equations, thereby achieving accurate local calibration;

[0075] In extreme environments, the drift of color gamut and white point coordinates is nonlinear and spatially distorted, especially in arbitrary micromanifolds. Within this, the fundamental coordinate variables defining the color driving space (e.g., a three-dimensional space composed of RGB driving voltages) are: ( The system introduces background tensors from a standard laboratory environment. Dynamic tensors are constructed by combining the nonlinear degradation properties of physical fields. :

[0076] ;

[0077] In the formula, and These are the nonlinear degradation functions for temperature and aging, respectively; A positive value for ambient light correction; The Kronecker function; , , As a parameter for constructing the base tensor, the physical field coupling weight coefficient accurately describes the bending deformation of the color-driven space within the micromanifold due to extreme environments (Riemannian manifold space).

[0078] S3. Solve the geodesic differential equation based on the metric tensor to obtain the optimal compensation continuous trajectory from the actual chromaticity coordinates to the target standard chromaticity coordinates in the Riemannian manifold space. Specifically, the above steps include: calculating the Christofel symbol of the second kind based on the metric tensor and its inverse matrix; using the initial driving voltage state corresponding to the actual chromaticity coordinates as the starting boundary condition and the target driving voltage state corresponding to the target standard chromaticity coordinates as the ending boundary condition; establishing the geodesic differential equation using the Christofel symbol of the second kind, and solving the path function that varies with the evolution parameters through a numerical algorithm, using the path function as the optimal compensation continuous trajectory;

[0079] Specifically, after spatial deformation, the optimal transition between two points is no longer a straight line, but a geodesic. This step aims to find the shortest path in this curved space.

[0080] First, based on the metric tensor and its inverse matrix Compute the second type of Christofel notation describing spatial curvature connectivity. :

[0081] ;

[0082] The system introduces path evolution parameters. ( ), to use the actual chromaticity coordinates As a starting point ( ), target standard chromaticity coordinates As the end point ( Substituting into the geodesic differential equation:

[0083] ;

[0084] The equation is solved using an in-vehicle edge computing platform (such as an NPU) and the Runge-Kutta numerical algorithm to obtain a parameterized path function. This path is the optimal compensation continuous trajectory for overcoming nonlinear distortion in the current deformation space.

[0085] S4. Calculate the spatial curvature of the optimal compensated continuous trajectory. Based on the spatial curvature, adaptively reconstruct the underlying driving waveform corresponding to the color driving space, and output the reconstructed underlying driving waveform to drive the pixels within the micromanifold to emit light. Specifically, the above steps include: extracting the first and second derivative vectors of the optimal compensated continuous trajectory with respect to the evolution parameters; calculating the cross product magnitude of the first and second derivative vectors and dividing it by the cube of the magnitude of the first derivative vector to obtain the spatial curvature that evolves with the optimal compensated continuous trajectory; determining whether the spatial curvature is greater than a preset curvature safety threshold; if the spatial curvature is less than or equal to the curvature safety threshold, then using pulse width modulation mode to generate the underlying driving waveform, maintaining a constant peak voltage, and adjusting the pulse duty cycle based on the trajectory integral; if the spatial curvature is greater than the curvature safety threshold, then triggering a hybrid reconstruction mechanism of pulse amplitude modulation and pulse width modulation, actively reducing the instantaneous peak voltage of the underlying driving waveform, and extending the pulse duration according to the compensation energy integral ratio, outputting a non-standard square wave as the reconstructed underlying driving waveform.

[0086] Specifically, if the voltage compensation value is only mathematically derived and applied, it may cause thermal breakdown of the device under extreme conditions. This step, however, directly maps the characteristics of the mathematical space to the underlying pulse control of the semiconductor:

[0087] System extracts trajectory First derivative vector With the second derivative vector Calculate the scalar of spatial curvature :

[0088] ;

[0089] The system will With the preset curvature safety threshold Comparison: If This indicates that the trajectory is smooth, the device can withstand the current compensation stress, and the display driver chip uses standard pulse width modulation (PWM) to generate the underlying drive waveform, maintaining a constant voltage peak and only changing the duty cycle. This indicates that the color space is extremely distorted and on the edge of physical limits. The system will trigger a hybrid reconstruction mechanism, which uses pulse amplitude modulation (PAM) combined with PWM to actively reduce the instantaneous voltage peak of the waveform (to avoid instantaneous high voltage causing heat accumulation and exacerbating color difference). At the same time, the pulse duration is extended according to the geometric area equivalence principle, so as to output a non-standard square wave as the underlying driving waveform after reconstruction, driving the pixel to emit light safely.

[0090] S5. Finally, the actual optical feedback features after pixel emission are collected, the updated chromaticity coordinates are extracted from the actual optical feedback features, the residuals between the updated chromaticity coordinates and the target standard chromaticity coordinates in the Riemannian manifold space are calculated, and the construction parameters of the metric tensor are updated based on the residuals. Adaptive reconstruction is performed iteratively in a closed loop until the residuals meet the preset convergence conditions. This completes the self-calibration of the optical performance of the vehicle display in extreme environments. Specifically, the above steps include: calculating the geodesic distance between the updated chromaticity coordinates and the target standard chromaticity coordinates in the Riemannian manifold space. The geodesic distance is used as the residual. If the geodesic distance is greater than the preset tolerance threshold, the partial derivative of the residual with respect to the physical field coupling weight coefficient is calculated using the gradient descent method. The physical field coupling weight coefficient is finely adjusted and updated based on the partial derivative to complete the update of the metric tensor construction parameters until the residual meets the preset convergence condition. The specific steps are as follows: After the closed-loop iteration, the updated geodesic distance is calculated again. When the calculated geodesic distance is less than or equal to the tolerance threshold, it is determined that the residual meets the preset convergence condition, the current physical field coupling weight coefficient is saved, and the closed-loop iteration ends.

[0091] Specifically, since the initial construction of the metric tensor depends on the model preset, system errors need to be eliminated through optical feedback closed loop. After completing one round of reconstructed waveform driving emission, the photodetector collects the actual optical feedback features again to extract updated chromaticity coordinates. In the previously constructed Riemannian manifold space, the updated chromaticity coordinates are calculated using the metric tensor integral. Target standard chromaticity coordinates The geodesic distance between them is defined as the residual. The system has a preset tolerance threshold. ,like If the system has reached convergence, save the current metric tensor construction parameters, and calibration is complete, then... This indicates that the model has biases, and the system is based on residuals. The gradient descent method is used to calculate the coupling weight coefficients of the physical field. The partial derivatives are used to fine-tune and update these construction parameters. Then, the above steps are returned to perform a closed-loop iteration of geodesic solution and waveform adaptive reconstruction until the optical performance self-calibration under extreme conditions is completed.

[0092] Example 2:

[0093] Please see the appendix Figure 1 The present invention also provides a self-calibration system for the optical performance of vehicle-mounted displays under extreme environments, comprising:

[0094] The data acquisition module is used to acquire multi-dimensional environmental parameters and physical field states of the vehicle-mounted display screen under extreme environments;

[0095] Specifically, this module is the physical sensing layer of the entire self-calibration system. It is responsible for transforming the complex and multivariable physical environment faced by the display screen into a mathematical matrix that the system can compute. At the hardware level, this module is connected to various external sensors and internal controllers. Through a flexible thermal sensor array attached to the back of the display panel, it reads the coordinates of each point in the physical pixel space in real time. The temperature values ​​are used to construct the temperature field distribution matrix. Simultaneously, this module connects to the timing controller, extracts historical driving current data for each pixel region, calculates the cumulative physical degradation degree using a time integration algorithm combined with the material's half-life constant, and generates an aging field distribution matrix. In addition, this module also obtains the illuminance and color temperature of external incident light through the in-vehicle ambient light sensor, and constructs an ambient light field vector. The aforementioned data is fused to construct a multi-field coupled state matrix. While acquiring environmental data, this module drives miniature photodetectors deployed in the non-display area of ​​the screen to collect the actual chromaticity coordinates of the current screen under the reference image. And read the target standard chromaticity coordinates from the system's non-volatile memory. This establishes the initial state and ultimate goal of the calibration.

[0096] The manifold segmentation module is used to adaptively segment the physical pixel space into multiple independently evolving micromanifolds based on the gradient distribution of the multi-field coupling state matrix in the physical pixel space.

[0097] Specifically, this module is mainly used to address the problem of extremely uneven screen degradation in extreme automotive environments (e.g., large temperature differences caused by localized sun exposure). This module receives the temperature field distribution matrix transmitted from the data acquisition module. The module performs partial derivative operations on the gradient vector field in the physical pixel space to calculate the thermodynamic gradient vector field. A gradient threshold is preset in the system, and this module analyzes the gradient magnitude of local regions. When the magnitude exceeds the threshold, it determines that a severe thermal stress step exists in that region. At this point, the module uses the contour lines of the gradient vector field as the topological spatial boundary, adaptively dividing the physical pixel space of the entire screen into multiple independent display sub-regions. Mathematically, these sub-regions are given independent topological structures, defined as multiple independently evolving micromanifolds. Through spatial partitioning, this module reduces the complex overall calculation of the entire screen to parallel calculations of multiple local regions, accurately allocating system computing power to subsequent modules.

[0098] The tensor construction and tracking module is used to construct a metric tensor based on a multi-field coupled state matrix within each micromanifold;

[0099] Specifically, this module is the core mathematical operation engine of this system, usually implemented using an onboard NPU (Neural Processing Unit) or a high-performance FPGA. After receiving the micromanifolds divided by the manifold segmentation module, this module operates independently within each micromanifold. This module abandons the traditional Euclidean flat space assumption, introduces the background metric tensor under standard conditions, and linearly superimposes it with the nonlinear degradation function and correction function (after processing the second-order partial derivative) corresponding to the multi-field coupling state matrix. With the help of preset physical field coupling weight coefficients, a metric tensor matrix that dynamically deforms with extreme environments is constructed. This metric tensor accurately maps the color-driven space to a curved Riemannian manifold space. Subsequently, this module uses the metric tensor and its inverse matrix to calculate the second kind of Christofel symbol, and uses the voltage state of the actual chromaticity coordinates and the target standard chromaticity coordinates as boundary conditions to establish a system of geodesic ordinary differential equations. This module incorporates numerical solving algorithms such as Runge-Kutta to solve the system of differential equations and outputs a parameterized spatial curve, which is the optimal compensated continuous trajectory for regressing from the actual chromaticity to the target chromaticity in the current distorted Riemannian manifold space.

[0100] The waveform reconstruction driver module is used to calculate the spatial curvature of the optimal compensation continuous trajectory and adaptively reconstruct the underlying driver waveform corresponding to the color driving space based on the spatial curvature.

[0101] Specifically, this module serves as a crucial bridge connecting mathematical algorithms and the underlying semiconductor hardware, directly interacting with the Display Driver IC (DDIC) via signaling. Since compensation in an extremely distorted space can trigger device overload, this module first performs geometric feature analysis on the optimal compensation continuous trajectory output by the tensor construction and tracking module. It extracts the first and second derivative vectors of the trajectory with respect to evolution parameters and calculates the curvature scalar of the trajectory as it evolves in space. This module incorporates a curvature safety threshold for safe hardware operation: when the calculated spatial curvature is less than or equal to the safety threshold, the module issues a standard command to the DDIC using pulse width modulation. The modulation (PWM) mode generates the underlying drive waveform. At this time, the voltage peak of the waveform remains unchanged, and the pulse duty cycle is adjusted only based on the trajectory integral. When the spatial curvature is greater than the safety threshold, it indicates that the compensation path is at the edge of extreme physical limits. The module immediately triggers the hybrid reconstruction mechanism of pulse amplitude modulation (PAM) and PWM to actively weaken the instantaneous voltage peak of the underlying drive waveform (to prevent high voltage transient-induced heat generation and breakdown), and extends the pulse duration backward according to the equivalent energy integral ratio to reconstruct a non-standard square wave drive signal. Finally, the module sends the reconstructed waveform to the display panel to drive the pixels in the micromanifold to emit light safely.

[0102] The feedback iteration module is used to collect the actual optical feedback features after the pixel emits light, extract the updated chromaticity coordinates from the actual optical feedback features, and repeatedly trigger the waveform reconstruction drive module to perform adaptive reconstruction for closed-loop iteration.

[0103] Specifically, this module is responsible for establishing an adaptive closed loop to eliminate open-loop deviations between the theoretical mathematical model and the actual state of the physical device. After the waveform reconstruction driving module completes the driving of a frame of image emission, this module controls the photodetector to capture the actual optical feedback characteristics of the screen again, extracting the updated chromaticity coordinates after driving. Subsequently, in the Riemannian manifold space established by the tensor construction and tracking module, this module uses the Riemann metric integral formula to calculate the geodesic distance between the updated chromaticity coordinates and the target standard chromaticity coordinates, defining this distance as the residual. This module compares the residual with a preset tolerance threshold: if the residual is greater than the tolerance threshold, this module uses the residual to calculate the partial derivative with respect to the physical field coupling weight coefficient, and uses the gradient descent method to fine-tune and update the metric tensor construction parameters in the tensor construction and tracking module, triggering the aforementioned calculation and waveform reconstruction module to enter the closed-loop iteration of the next frame; until the geodesic distance residual is less than or equal to the tolerance threshold, this module determines that the calibration has reached the convergence requirement, locks the current system parameters, and ends the calibration process, thereby ensuring the optical stability of the display screen throughout its entire life cycle.

[0104] In summary, this invention provides a self-calibration system and method for the optical performance of automotive displays under extreme environments. By acquiring multi-dimensional environmental parameters and physical field states, a multi-field coupled state matrix is ​​constructed, and a metric tensor is built on this basis. The color driving space is mapped to a Riemannian manifold space that dynamically deforms with the environment. This overcomes the technical limitations of existing simple linear compensation algorithms (such as three-dimensional lookup table interpolation) that cannot accurately fit the multivariate nonlinear degradation of luminescent materials under extreme environments. This improves the self-calibration accuracy of the optical performance of automotive displays. It solves the serious color gamut drift and white point distortion problems faced by automotive displays under extreme environments from the theoretical model level. Furthermore, by acquiring the underlying driving waveform to reconstruct the actual optical feedback characteristics after emission, the geodesic distance residual between the updated chromaticity coordinates and the target standard chromaticity coordinates is calculated in the Riemannian manifold space. Based on this residual, a dynamic micro-scheduling tensor is used to construct parameters for closed-loop iterative solution. This constructs a feedback adaptive control architecture with self-correction capabilities, eliminating the open-loop system deviation caused by the pre-set static mathematical model parameters and the individual differences in the aging of actual physical devices. This ensures the long-term optical stability of the display in complex extreme environments throughout its entire life cycle.

[0105] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A self-calibration method for the optical performance of an in-vehicle display screen under extreme environments, characterized in that, Includes the following steps: S1. First, obtain the multi-dimensional environmental parameters and physical field state of the vehicle display screen under extreme environment. Based on the multi-dimensional environmental parameters and physical field state, construct a multi-field coupling state matrix that reflects the physical pixel space state of the vehicle display screen. Then, obtain the current actual chromaticity coordinates and target standard chromaticity coordinates of the vehicle display screen to characterize the initial optical performance of the vehicle display screen. S2. Subsequently, based on the gradient distribution of the multi-field coupling state matrix in the physical pixel space, the physical pixel space is adaptively divided into multiple independently evolving micromanifolds. Within each micromanifold, a metric tensor is constructed based on the multi-field coupling state matrix to map the color driving space of the vehicle display screen into a Riemannian manifold space that dynamically deforms under extreme environments. S3. Solve the geodesic differential equation based on the metric tensor to obtain the optimal compensated continuous trajectory that regresses from the actual chromaticity coordinates to the target standard chromaticity coordinates in the Riemannian manifold space; S4. Calculate the spatial curvature of the optimal compensated continuous trajectory, adaptively reconstruct the underlying driving waveform corresponding to the color driving space based on the spatial curvature, and output the reconstructed underlying driving waveform to drive the pixels in the micromanifold to emit light. S5. Finally, the actual optical feedback features after the pixel emits light are collected, the updated chromaticity coordinates in the actual optical feedback features are extracted, the residual between the updated chromaticity coordinates and the target standard chromaticity coordinates in the Riemann manifold space is calculated, and the construction parameters of the metric tensor are updated based on the residual. Adaptive reconstruction is performed cyclically to perform closed-loop iteration until the residual meets the preset convergence condition, thus completing the self-calibration of the optical performance of the vehicle display screen under the extreme environment.

2. The self-calibration method for the optical performance of a vehicle-mounted display screen under extreme environments according to claim 1, characterized in that, Step S1 specifically includes: Collect temperature data of the vehicle display screen at the physical pixel spatial coordinates, and construct a temperature field distribution matrix; Extract the historical driving current and cumulative working time of each pixel area of ​​the vehicle display screen, calculate the local physical attenuation by integration, and generate the aging field distribution matrix. The illuminance and color temperature of the incident light from outside the vehicle display screen are obtained to construct the ambient light field vector. The temperature field distribution matrix, the aging field distribution matrix, and the ambient light field vector are fused to generate the multi-field coupling state matrix.

3. The self-calibration method for the optical performance of a vehicle-mounted display screen under extreme environments according to claim 1, characterized in that, Step S2 specifically includes: Calculate the thermodynamic gradient vector field of the temperature field distribution matrix in the physical pixel space; Determine whether the magnitude of the thermodynamic gradient vector field in the local region is greater than a preset gradient threshold; If the gradient is greater than the gradient threshold, the contour lines of the thermodynamic gradient vector field are used as the topological space boundary to divide the physical pixel space into multiple independent display sub-regions. Each of the aforementioned display sub-regions is defined as an independent micromanifold in terms of topology.

4. The self-calibration method for the optical performance of a vehicle-mounted display screen under extreme environments according to claim 1, characterized in that, The step of mapping the color driving space of the vehicle display to a Riemannian manifold space that dynamically deforms under extreme environments includes: Obtain the background metric tensor under standard conditions; The nonlinear degradation function corresponding to the temperature field distribution matrix, the nonlinear degradation function corresponding to the aging field distribution matrix, and the correction function corresponding to the ambient light field vector are processed by second-order partial derivatives respectively. By combining the preset physical field coupling weight coefficients as the construction parameters, the background metric tensor is linearly superimposed with each of the processed functions to generate a metric tensor that dynamically evolves with the extreme environment.

5. The self-calibration method for the optical performance of a vehicle-mounted display screen under extreme environments according to claim 1, characterized in that, Step S3 specifically includes: Calculate the second type of Christofel symbol based on the metric tensor and its inverse matrix; The initial driving voltage state corresponding to the actual chromaticity coordinates is used as the starting boundary condition, and the target driving voltage state corresponding to the target standard chromaticity coordinates is used as the ending boundary condition. The geodesic differential equation is established using the second type of Christofer notation, and the path function that varies with the evolution parameters is solved by numerical algorithm. The path function is then used as the optimal compensated continuous trajectory.

6. The self-calibration method for the optical performance of a vehicle-mounted display screen under extreme environments according to claim 1, characterized in that, The step of calculating the spatial curvature of the optimal compensated continuous trajectory includes: Extract the first-order and second-order derivative vectors of the optimal compensated continuous trajectory with respect to the evolution parameters; Calculate the magnitude of the cross product of the first derivative vector and the second derivative vector, and divide by the cube of the magnitude of the first derivative vector to obtain the spatial curvature that evolves with the optimal compensated continuous trajectory.

7. The self-calibration method for the optical performance of a vehicle-mounted display screen under extreme environments according to claim 1, characterized in that, Step S4 specifically includes: Determine whether the spatial curvature is greater than a preset curvature safety threshold; If the spatial curvature is less than or equal to the curvature safety threshold, then the underlying driving waveform is generated using pulse width modulation mode, the peak waveform voltage is kept constant, and the pulse duty cycle is adjusted based on the trajectory integral. If the spatial curvature is greater than the curvature safety threshold, a hybrid reconstruction mechanism of pulse amplitude modulation and pulse width modulation is triggered to actively reduce the instantaneous voltage peak of the underlying driving waveform and extend the pulse duration according to the compensation energy integral ratio, outputting a non-standard square wave as the reconstructed underlying driving waveform.

8. The self-calibration method for the optical performance of a vehicle-mounted display screen under extreme environments according to claim 1, characterized in that, Step S5 specifically includes: In the Riemannian manifold space, the geodesic distance between the updated chromaticity coordinates and the target standard chromaticity coordinates is calculated, and the geodesic distance is used as the residual. If the geodesic distance is greater than a preset tolerance threshold, the partial derivative of the residual with respect to the physical field coupling weight coefficient is calculated using the gradient descent method. The physical field coupling weight coefficients are finely adjusted and updated based on the partial derivatives, thus completing the update of the metric tensor construction parameters.

9. The self-calibration method for the optical performance of a vehicle-mounted display screen under extreme environments according to claim 1, characterized in that, The specific steps until the residual satisfies the preset convergence condition are as follows: After closed-loop iteration, the updated geodesic distance is calculated again; When the calculated geodesic distance is less than or equal to the tolerance threshold, the residual is determined to meet the preset convergence condition, the current physical field coupling weight coefficient is saved, and the closed-loop iteration ends.

10. A self-calibration system for the optical performance of an in-vehicle display screen under extreme environments, characterized in that, The self-calibration method for the optical performance of an in-vehicle display screen under extreme environments according to any one of claims 1-9 includes: The data acquisition module is used to acquire multi-dimensional environmental parameters and physical field states of the vehicle-mounted display screen under extreme environments; The manifold segmentation module is used to adaptively segment the physical pixel space into multiple independently evolving micromanifolds based on the gradient distribution of the multi-field coupling state matrix in the physical pixel space. The tensor construction and tracking module is used to construct a metric tensor based on the multi-field coupling state matrix within each of the micromanifolds. The waveform reconstruction driving module is used to calculate the spatial curvature of the optimal compensation continuous trajectory and adaptively reconstruct the underlying driving waveform corresponding to the color driving space based on the spatial curvature. The feedback iteration module is used to collect the actual optical feedback features after the pixel emits light, extract the updated chromaticity coordinates from the actual optical feedback features, and repeatedly trigger the waveform reconstruction driving module to perform adaptive reconstruction for closed-loop iteration.

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