Pattern printing regulation and control method and system based on photochromic printing

Abstract

The invention relates to a pattern printing regulation and control method and system based on photochromic printing. The method comprises the following steps: performing multispectral space-time coding mapping on a target color pattern corresponding to a pattern printing task to obtain initial time sequence exposure sequence data; performing inverse problem optimization based on light-chemical kinetics on the initial time sequence exposure sequence data to obtain optimized time sequence exposure sequence data; according to the optimized time sequence exposure sequence data, performing multispectral structured light exposure perception analysis on a printing body corresponding to the pattern printing task to obtain actually measured color field analysis data; and according to the actually measured color field analysis data, closed-loop iterative optimization is carried out on the optimized time sequence exposure sequence data until the actual color error value meets a preset color error threshold value, and pattern printing control data is obtained. With the adoption of the method, the production throughput and yield can be effectively improved in the production process under comprehensive conditions so as to meet the production standard.

Description

Technical Field

[0001] This application relates to the field of automatic control technology, and in particular to a pattern printing control method and system based on photochromic printing. Background Technology

[0002] In traditional printing techniques, the control of pattern printing is mainly achieved through screen printing, gravure printing, flexographic printing, and inkjet printing. This involves adjusting ink viscosity and surface tension, squeegee / imprint pressure, screen or roller gap, and belt speed and tension, along with substrate cleaning and simple primer application to stabilize ink application and line width. Simultaneously, registration marks and mechanical alignment are commonly used on the production floor to minimize misalignment, and drying curves are controlled by oven temperature, airflow, and speed. Environmental management in the workshop is achieved using temperature and humidity meters. The quality of pattern printing relies primarily on first-piece comparison, random sampling, and visual / magnifying glass inspection, with simple thickness measurements or resistance / continuity tests used when necessary to determine acceptance. However, while traditional techniques are low-cost and quick to learn, their production throughput and yield under mass production conditions still struggle to simultaneously meet established production standards. Summary of the Invention

[0003] Therefore, it is necessary to provide a pattern printing control method and system based on photochromic printing that can effectively improve production throughput and yield to meet production standards under comprehensive conditions, addressing the aforementioned technical problems.

[0004] In a first aspect, this application provides a method for controlling pattern printing based on photochromic printing, comprising: Multispectral spatiotemporal coding mapping is performed on the target color pattern corresponding to the pattern printing task to obtain the initial temporal exposure sequence data; The initial time-series exposure sequence data is optimized by performing an inverse problem based on photo-chemical dynamics to obtain optimized time-series exposure sequence data; Based on the optimized timing exposure sequence data, multispectral structured light exposure sensing analysis is performed on the substrate corresponding to the pattern printing task to obtain measured color field analysis data. Based on the measured color field analysis data, the optimized timing exposure sequence data is subjected to closed-loop iterative optimization until the actual color error value meets the preset color error threshold, thus obtaining the pattern printing control data.

[0005] Secondly, this application also provides a pattern printing control system based on photochromic printing, the system including: computer equipment and data acquisition terminal; The computer device is used to perform multispectral spatiotemporal coding mapping on the target color pattern corresponding to the pattern printing task to obtain initial temporal exposure sequence data; the target color pattern is obtained through the data acquisition terminal. The computer device is used to perform inverse problem optimization based on photochemical dynamics on the initial time-series exposure sequence data to obtain optimized time-series exposure sequence data; The computer device is used to perform multispectral structured light exposure sensing analysis on the substrate corresponding to the pattern printing task based on the optimized timing exposure sequence data, and obtain measured color field analysis data. The computer device is used to perform closed-loop iterative optimization of the optimized timing exposure sequence data based on the measured color field analysis data until the actual color error value meets the preset color error threshold, thereby obtaining pattern printing control data.

[0006] The aforementioned pattern printing control method and system based on photochromic printing converts the target color pattern corresponding to the pattern printing task into a target color field, performs multispectral spatiotemporal encoding, and drives it with structured light sequential exposure. By combining the inverse problem optimization of photo-chemical dynamics with the closed-loop iterative correction of the measured color field, the obtained pattern printing control data can accurately compensate for material and process errors under nonlinear and time-varying processes such as exposure-development. This achieves synchronous high-fidelity reproduction of color and spatial details, significantly reduces actual color errors, expands the achievable color gamut and dynamic range, improves consistency and stability across batches and across substrates, reduces trial and error and calibration costs, and shortens on-machine debugging time. Under the overall conditions, it can effectively improve production throughput and yield to meet production standards, while reducing energy consumption and material consumption, and improving the overall quality and reliability of pattern printing. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is an application environment diagram of a pattern printing control method based on photochromic printing in one embodiment; Figure 2 This is a flowchart illustrating a pattern printing control method based on photochromic printing in one embodiment. Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0010] This application provides a method for controlling pattern printing based on photochromic printing, which can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with computer device 104 via a network. A data storage system can store the data that computer device 104 needs to process. The data storage system can be integrated onto computer device 104, or it can be located in the cloud or on other network servers. Computer device 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0011] In one exemplary embodiment, such as Figure 2 As shown, a method for controlling pattern printing based on photochromic printing is provided, which can be applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 202 to 208. Wherein:

[0012] Step 202: Perform multispectral spatiotemporal coding mapping on the target color pattern corresponding to the pattern printing task to obtain the initial time-series exposure sequence data.

[0013] Step 204: Perform inverse problem optimization based on photo-chemical dynamics on the initial time-series exposure sequence data to obtain optimized time-series exposure sequence data.

[0014] Step 206: Based on the optimized time-series exposure sequence data, perform multispectral structured light exposure sensing analysis on the substrate corresponding to the pattern printing task to obtain measured color field analysis data.

[0015] Step 208: Based on the measured color field analysis data, perform closed-loop iterative optimization on the optimized time-series exposure sequence data until the actual color error value meets the preset color error threshold, and obtain the pattern printing control data.

[0016] The pattern printing task is the overall production and quality control process of faithfully transferring a target color pattern onto a designated substrate under the constraints of established processes and equipment.

[0017] The target color pattern is a target image represented by a specified color space and resolution, and its color and spatial distribution are used as a reference for printing reproduction.

[0018] Among them, multispectral spatiotemporal coding mapping is a mapping process that decomposes the target color field into a combination of exposure commands in multiple bands and multiple frames based on the available spectral channels and time resources of the device.

[0019] The initial time-series exposure sequence data refers to the unoptimized set of exposure parameters arranged by frame and channel, which includes at least band, power or dose, duty cycle and frame time information.

[0020] Among them, photochemical kinetics refers to the excitation, transformation, and restoration reactions of photochromic materials under light irradiation and their evolution over time and temperature.

[0021] Inverse problem optimization is the process of working backward from the desired color output to deduce feasible combinations of exposure parameters and minimize color error under physical and device constraints.

[0022] Among them, the optimized timing exposure sequence data is a set of frame-by-frame and channel exposure parameters that are manufacturable, executable, and have lower color error, obtained after inverse problem optimization and constraint verification.

[0023] The substrate is the base material or workpiece that carries the pattern, including its surface coating, geometric and optical properties, and is the object to be exposed.

[0024] Among them, multispectral structured light exposure perception analysis is an analysis process in which multi-band structured light exposure is performed according to an optimized sequence and hyperspectral data is collected simultaneously. Then, the data is corrected and reconstructed to obtain observation results that can be used for evaluation.

[0025] Among them, the measured color field analysis data is a comprehensive evaluation data including voxel-level color vectors, error heatmaps, and quality markers obtained after dealiasing, dediffusion, flattening, and color transformation processing.

[0026] Among them, closed-loop iterative optimization is a control strategy that uses measured analysis data to continuously correct exposure parameters and repeatedly predict and verify until the performance indicators are met.

[0027] The actual color error value is the error between the measured color and the target color calculated under a specified metric (such as ΔE or equivalent index).

[0028] The preset color error threshold is the maximum permissible color error limit used to determine whether the printing quality meets the standard.

[0029] Among them, the pattern printing control data is a complete instruction package that is ultimately used for production line execution, including frame sequence instructions, channel configuration, dosage and duty cycle lists, and synchronization trigger tables.

[0030] Specifically, in the multispectral spatiotemporal encoding and mapping stage of the target color pattern corresponding to the pattern printing task, the computer device 104 aligns the target color pattern, the substrate coordinates, and the process resolution to obtain grid-by-grid target color field data. Then, based on the available spectral channels, frame time resources, power, and duty cycle boundaries of the printing equipment, it selects a channel combination that can cover its target color vector for each grid cell and assigns time slots, initial dose, and duty cycle. Conflict resolution and transition smoothing are performed within the spatial neighborhood to suppress channel interference, flicker, and jagged artifacts. Color gamut consistency checks and energy safety window verifications are also completed. Finally, the channel selection, timing allocation, and dose duty cycle parameters are packaged into initial timing exposure sequence data with frame order index and channel load list via the control interface.

[0031] Based on the initial time-series exposure sequence data, time-resolved modeling of the spectral absorption and excitation-reduction reactions within the material layer is performed. During the modeling process, the local effective irradiance of each frame is estimated according to the absorption cross section of the channel band, the substrate thickness, and the scattering coefficient to obtain voxel-level luminous flux data. Then, the evolution of chromophore concentration over time is updated according to quantum yield, color development and restoration rate, triplet branch probability, and nonradiative relaxation path to obtain voxel-level reaction process data. Simultaneously, the forward evolution is corrected according to the influence of temperature rise on the rate constant and the diffusion coupling of adjacent voxels to obtain kinetically consistent spectral response data. The chromophore integral number accumulated over time is mapped to voxel-level spectral reflectance and transmittance, and color space conversion is performed in combination with instrument response and observation geometry to obtain printed color field prediction data. Then, according to the printed color field prediction data and the target color field data, the channel dose, duty cycle, and frame position are combined with sensitivity calculation and error direction determination to obtain the constrained update amount. At the same time, the feasible domain of variables is pruned according to the energy upper limit, temperature rise threshold, interlayer coupling amplitude, and equipment real-time performance to obtain constrained candidate sequence data. The candidate sequence data is subjected to thermal steady-state verification, optical stability verification, and time budget verification to obtain updated sequence data that has passed the verification. If the color error decreases slowly, the step size and regularization weight are adaptively adjusted and multi-starting point perturbation is triggered to avoid getting trapped in local minima. The prediction, comparison, update, and verification are performed cyclically until the convergence criterion is met, and optimized time-series exposure sequence data that can be directly issued under photochemical kinetics and process constraints are obtained.

[0032] Based on the optimized temporal exposure sequence data, a synchronous trigger table for exposure and perception is generated, and multispectral structured light exposure is performed frame by frame on the substrate. At the same time, raw hyperspectral image data is acquired using a co-phase strategy. After acquisition, temporal aliasing component identification is performed first to estimate inter-frame crosstalk and wake, and then imaging point diffusion component decomposition is performed to quantify the optical diffusion effect. The two estimation results are used to guide dealiasing and dediffusion reconstruction. The reconstructed data is flat-field corrected and noise subtracted under white board reference and dark field baseline and mapped to the target color space to obtain measured color field analysis data containing voxel-level measured color vectors, error heatmaps, spectral channel contributions, and suspicious area annotations.

[0033] By registering and comparing measured color field analysis data with the target color field, high-error sub-regions are located and dominant error channels are identified. Based on the sensitivity ranking of dose, duty cycle, and frame time, adjustment suggestions with minimal modifications are generated within the boundaries of energy, temperature rise, interlayer coupling, and device rate. Rapid simulation is then used to evaluate the impact on neighboring regions and subsequent frames. The evaluated adjustments are fed back into the inverse problem optimization process to generate new candidate sequences. Small-sample reexposure and rapid sensing are used to verify the convergence trend. If the threshold is not reached, iteration continues according to the step size and threshold strategy until the actual color error of the global and critical regions simultaneously falls below the preset threshold. Once the condition is met, the sequence is solidified, and pattern printing control data including frame order instructions, spectral channel configuration, dose and duty cycle lists, and synchronization trigger tables are exported.

[0034] In the aforementioned pattern printing control method based on photochromic printing, the target color pattern corresponding to the pattern printing task is converted into a target color field, then multispectral spatiotemporal encoding is performed, and structured light sequential exposure is used for driving. By combining the inverse problem optimization of photo-chemical dynamics and the closed-loop iterative correction of the measured color field, the obtained pattern printing control data can accurately compensate for material and process errors under nonlinear and time-varying processes such as exposure-development. This achieves synchronous high-fidelity reproduction of color and spatial details, significantly reduces actual color errors, expands the achievable color gamut and dynamic range, improves consistency and stability across batches and across substrates, reduces trial and error and calibration costs, and shortens the on-machine debugging time. Under the overall conditions, it can effectively improve production throughput and yield to meet production standards, while reducing energy consumption and material consumption, and improving the overall quality and reliability of pattern printing.

[0035] In an exemplary embodiment, the initial time-series exposure sequence data is optimized using an inverse problem based on photo-chemical dynamics to obtain optimized time-series exposure sequence data, including steps 302 to 306. Wherein:

[0036] Step 302: Based on the initial time-series exposure sequence data and the preset photo-chemical kinetic parameters, perform forward prediction analysis on the spectral and color response of the photochromic material to obtain the printing color field prediction data; Step 304: Based on the predicted color field data and the target color field data determined by the target color pattern, perform constraint optimization on the initial time-series exposure sequence data with the goal of minimizing color error to obtain candidate time-series exposure sequence data; Step 306: Perform stability verification on the candidate time-series exposure sequence data to obtain optimized time-series exposure sequence data.

[0037] Among them, the photochemical kinetic parameters are a set of physicochemical constants used to calculate the time evolution of the photochromic system, such as the reaction and recovery rate, quantum yield, absorption cross section, thermodynamic coefficient, and diffusion coupling, under irradiation at different wavelengths.

[0038] Photochromic materials are functional polymers or composite materials that can undergo reversible color development and fading under specific light exposure and retain or restore their color state after the irradiation stops.

[0039] Among them, forward predictive analysis is a causal link calculation process based on known exposure sequences and dynamic parameters, from light field to material state to spectral and color output.

[0040] Among them, the printing color field prediction data is a voxel-level color vector distribution aligned with the substrate coordinates, obtained from forward predictive analysis, and is used to evaluate the expected color effect of a single exposure.

[0041] Among them, the target color field data is a reference color vector distribution obtained by mapping the target color pattern to the device resolution and coordinates, which serves as the benchmark for color difference evaluation.

[0042] Among them, minimizing color error involves adjusting exposure variables within process and equipment constraints to minimize the measurement error (such as ΔE) between the predicted color and the target color.

[0043] Among them, the candidate time-series exposure sequence data is a set of frame-by-frame and channel-by-channel exposure parameters obtained through one or more limited optimizations but which have not yet passed the final verification.

[0044] Among them, stability verification is to conduct compliance verification of candidate sequences under multi-dimensional constraints such as thermal steady state, real-time performance, optical consistency and device execution boundaries to determine their manufacturability and executability.

[0045] Specifically, using initial temporal exposure sequence data and preset photochemical kinetic parameters as joint inputs, the frame sequence index, channel identifier, and substrate coordinates are first unified to complete data registration. Then, based on the band and power or dose, duty cycle and exposure time, absorption cross section, scattering coefficient, and geometric thickness of each frame, the voxel-level effective irradiance and energy deposition distribution are calculated. Under the drive of this light field, the chromophore concentration is developed and restored over time according to the reversible color development and fading kinetic rate. The temperature rise is used to correct the rate constant, and the weak coupling effect of adjacent voxels is introduced to obtain a kinetically consistent state evolution trajectory. The chromophore integrals at each time point are mapped to the reflection and transmission of the voxel-level spectrum, and the instrument response and observation geometry are superimposed to complete the temporal spectral signal. Finally, after frame-level integration and color space conversion, the predicted printing color field data aligned with the substrate coordinates is output.

[0046] A unified error metric is established using the predicted and target color field data of the printed color field, and the voxel-by-voxel error field is calculated and weighted. Then, within the boundaries of energy limit, peak illuminance, temperature rise threshold, interlayer coupling amplitude, device dynamic range, and trigger bandwidth, adjustable variables are determined as channel dose, duty cycle, and frame position, and a small number of channel replacement bits are enabled as needed. The direction and magnitude of the error with respect to each variable are obtained through adjoint sensitivity or equivalent gradient, and an objective function with the primary goal of minimizing color error is constructed accordingly. Temporal smoothing, spectral sparsity, and spatial consistency regularization are added to suppress oscillations and noise amplification. Then, restricted iterative updates are performed, with step size adaptation and trust region control performed at each step, followed by feasible region projection and saturation clipping. A block-first strategy is used to accelerate convergence in high residual regions and continuously monitor the error reduction rate and constraint activity. At the end of the iteration, rapid thermal steady-state evaluation, real-time budgeting, and optical robustness sampling are performed to eliminate infeasible solutions as early as possible and retain only candidate sequences that pass the verification. When the convergence condition is met or the error reduction is below the threshold, the current optimal candidate temporal exposure sequence data is output.

[0047] Using optimized temporal exposure sequence data, a synchronous trigger table for exposure and perception is generated, and phase alignment between each pair of frames, channels, power, and duty cycles is completed. The data acquisition terminal 102 is controlled to expose frames one by one on the substrate according to the trigger table and acquire hyperspectral raw images using a co-phase strategy. After acquisition, temporal aliasing identification is performed to quantify inter-frame crosstalk and wake, followed by imaging point diffusion component decomposition to characterize the effect of optical diffusion. Subsequently, dealiasing and dediffusion reconstruction are performed based on the two types of estimation results, and flat field correction and noise subtraction are performed using a white board reference and dark field baseline. The reconstructed spectral vector is then mapped to the target color space, and quality screening is completed through spatial consistency and abnormal pixel detection. Finally, measured color field analysis data containing voxel-level measured color vectors, error distribution, and spectral channel contribution assessment are obtained.

[0048] In this embodiment, forward prediction based on photochemical dynamics reduces the reliance on empirical trial and error, making the exposure parameters highly consistent with the actual material response. This provides an interpretable and quantifiable error basis for subsequent solutions. In the constraint optimization of minimizing color error, the energy upper limit, temperature rise threshold, and equipment timing boundary are simultaneously observed, which significantly improves the color matching accuracy without sacrificing manufacturability. Finally, solutions with thermal instability, insufficient real-time performance, and optical consistency deviations are screened out after stability verification, making the output time-series exposure sequence both executable and robust. Overall, this results in reduced color difference, faster convergence speed, reduced material and time costs, and improved yield and repeatability.

[0049] In an exemplary embodiment, based on the predicted printing color field data and the target color field data determined by the target color pattern, the initial temporal exposure sequence data is subjected to constraint optimization with the goal of minimizing color error to obtain candidate temporal exposure sequence data, including steps 402 to 406. Wherein:

[0050] Step 402: Perform multi-physics coupling feasible domain clipping on the multispectral time-series dose configuration parameters in the initial time-series exposure sequence data to obtain restricted time-series sequence template data.

[0051] Step 404: Based on the predicted color field data and the target color field data, perform color error analysis on the restricted time sequence template data to obtain color error analysis data.

[0052] Step 406: Based on the color error analysis data, perform spectral-temporal joint variational solution on the restricted time-series template data to obtain candidate time-series exposure sequence data.

[0053] Among them, the multispectral time-series dose configuration parameters are a set of exposure variables organized by band and frame order, which usually include adjustable parameters such as dose, power or duty cycle and time slot for each channel in each frame.

[0054] Among them, multiphysics coupling feasible domain clipping is a process of constraining and shrinking the range and combination of the above exposure parameters under optical, thermal and control constraints such as energy, temperature rise, interlayer coupling, bandwidth and buffer, and eliminating out-of-bounds values.

[0055] Among them, the restricted time sequence template data is the frame-by-frame and channel parameter skeleton retained after feasible region clipping, which gives the allowed dose interval, duty interval, available time slot and phase window and their position markers.

[0056] Color error analysis is an evaluation process that involves registering the predicted color field with the target color field, calculating the color difference, and decomposing its spectral, temporal, and spatial contributions to locate the main sources of error and the priority of correction.

[0057] Color error analysis data is a structured set of results obtained from color error analysis, including residual heatmaps, lists of dominant channels and keyframes, variable sensitivity and allowable stride, sub-region priority queues, and update masks.

[0058] Among them, the spectral-temporal joint variational solution is a solution method that uses dose, duty cycle and temporal shift as joint variables in a limited search space, and minimizes color error by co-updating in the spectral and temporal domains through variational optimization with regularization.

[0059] Specifically, the initial time-series exposure sequence data is aligned with frame order index, channel identifier, substrate coordinates, and layer thickness information. Hardware boundaries such as minimum step size, minimum pulse width, duty cycle limit, channel switching bandwidth, trigger jitter tolerance, buffer, and time budget are uniformly loaded as a constraint set along with material energy limits, temperature rise thresholds, cumulative fatigue limits, and interlayer coupling coefficients. Based on channel band power, duty cycle, and frame time, the effective irradiance and energy deposition per voxel in a single frame are calculated. A rolling time window is used to statistically analyze cross-frame energy to assess the safety margins of peak and mean values. Simultaneously, temperature rise and neighborhood thermal diffusion effects are estimated using a thermal response kernel or equivalent diffusion model, and interlayer coupling effects are superimposed to obtain the thermal and structural linkage constraints. Further checks are performed on spectral and temporal domain interference risks, including crosstalk within the same frame band, overlapping wakes in adjacent frames, phase synchronization boundary collisions, and sensing trigger conflicts. Non-compliant doses, duty cycles, and time slots are subject to ordered pruning and rearrangement. For example, amplitude saturation, pulse splitting or merging, timing micro-shifting, guard gap insertion, quantization to hardware step size and channel mutual exclusion rerouting are performed. If necessary, stricter derating coefficients or cooling frame insertion are applied to high-risk areas to restore thermal steady state and control bandwidth, ultimately generating restricted timing sequence template data.

[0060] After accurately registering the predicted color field data and the target color field data in the substrate coordinates and frame sequence, and unifying the color space and observation geometric space, then selecting the error metric and weighting strategy and establishing a quality mask to exclude invalid or low signal-noise regions of the restricted temporal sequence template data, the color difference is calculated voxel by voxel, band by band, and frame by frame to form a three-dimensional error tensor and its statistics. At the same time, the total error is additively decomposed according to the spectral domain contribution, temporal contribution, and spatial neighborhood contribution to locate the source of error. Based on this, the directional sensitivity and confidence interval of the restricted temporal sequence template data are calculated, and variable adjustability scores and priority rankings are generated by combining the upper and lower limits of the feasible region. Then, high residual pixels are aggregated into sub-regions to be corrected through region growing or superpixel clustering, and edge and fine texture regions are separately labeled to avoid over-smoothing. Robust filtering and outlier suppression are then applied to the error tensor to reduce the risk of noise amplification and to evaluate cross-frame error correlation to identify temporal aliasing signs. Finally, color error analysis data is obtained, which includes residual heatmaps for each frame and channel, a list of dominant channels and keyframes, allowable step size and expected correction direction of variables, sub-region priority queues and weight coefficients, and an update mask consistent with the feasible region.

[0061] When performing spectral joint variational solution on a constrained time-series template, the residual heatmap, variable priority, and update mask provided by color error analysis data are used as guiding signals. Dose, duty cycle, and time slot shift are used as joint optimization variables. With weighted color error as the main objective, a composite regularization of temporal smoothing, spectral sparsity, spatial consistency, and energy temperature rise penalty is introduced. Constrained solution is performed within the dose interval, duty cycle, available time slot mask, and phase window provided by the feasible region. The solution process involves block-based solving from high residual sub-regions to low residual sub-regions, employing an alternating update strategy in the spectral and temporal domains. Within each sub-block, gradient directions based on the forward model and adjoint sensitivity are first calculated. Candidate increments are then obtained through trust region step size or backtracking search. Subsequently, feasible region projection and hardware quantization are performed, increments are pruned and raster aligned, and analytical or semi-analytical updates of regularized subproblems are completed using proximal operators. Simultaneously, rapid thermal steady-state estimation and real-time budgeting are performed to promptly eliminate candidate solutions that may trigger temperature rise thresholds or bandwidth limitations. Guard gaps or pulse rearrangements are added at the cross-frame level to suppress crosstalk and wakes. The entire process is driven by a triple criterion of error reduction, constraint activity, and step size stability. When improvements fall below the threshold or the trust region shrinks to its minimum scale, the block results are globally summarized and boundary stitched to form a consistent variable field. Finally, the parameter instructions are restored to frame-by-frame and channel-by-channel, and synchronization phase and trigger sequence information are supplemented to obtain candidate temporal exposure sequence data.

[0062] In this embodiment, multiphysics coupling feasible domain pruning eliminates parameter combinations that do not meet constraints such as energy, temperature rise, bandwidth, buffering, and interlayer coupling in advance, ensuring manufacturability from the source and significantly reducing the search space while avoiding the risks of overheating and mutual interference. Subsequently, color error analysis is used to accurately locate deviations in the spectral, temporal, and spatial dimensions and to provide variable sensitivity and priority, enabling targeted corrections that only need to be made and reducing iteration overhead. Finally, spectral-temporal joint variational solution is used to collaboratively update dose, duty cycle, and timing within a confined space while taking into account smoothness, sparsity, and consistency, enabling the solution to converge quickly and maintaining the real-time performance and optical stability of the equipment. This results in a significant reduction in color error, a reduction in artifacts and crosstalk, and an improvement in thermal steady-state performance and repeatability, while simultaneously reducing experimental costs and energy consumption, as well as increasing production line throughput and yield.

[0063] In an exemplary embodiment, based on color error analysis data, a spectral-temporal joint variational solution is performed on the restricted temporal sequence template data to obtain candidate temporal exposure sequence data, including steps 502 to 508. Wherein:

[0064] Step 502: Based on the color error analysis data, perform spectral-temporal mutual derivative prior distillation on the restricted time series template data to obtain spectral-temporal prior constraint data.

[0065] Step 504: Based on the target color field data and the printed color field prediction data, the prior constraint data of the spectrum is weighted according to the target difference to obtain the weighted residual data.

[0066] Step 506: Based on the weighted residual data, perform spectral-time gated alternating minimization on the spectral-time prior constraint data to obtain intermediate time-series exposure sequence data.

[0067] Step 508: Perform energy temperature rise threshold verification on the intermediate time-series exposure sequence data to obtain candidate time-series exposure sequence data.

[0068] Among them, spectral-temporal mutual guidance prior distillation is the process of extracting and compressing the low-dimensional prior information most useful for variable updates under the mutual guidance of the spectral and temporal domains.

[0069] Among them, the spectral time prior constraint data is a prior set of channel and frame activation, stride, smoothing bandwidth and gating window obtained by distillation, which can directly constrain optimization variables.

[0070] Among them, target difference weighting is a strategy that weights the color difference between the target color field and the predicted color field according to task focus and prior confidence to highlight key areas and key channels.

[0071] The weighted residual data is a voxel-level residual tensor obtained after weighting the target difference, which contains the weighted error signals of each channel and each frame.

[0072] Among them, the spectral-temporal gated alternating minimization process is a solution method that updates variables alternately in blocks according to the spectral and temporal domains and gradually reduces the weighted residuals under the constraint of a priori gated mask.

[0073] Among them, the intermediate time-series exposure sequence data is a set of frame-by-frame and channel-by-channel exposure parameters formed after gating alternating minimization and which has not yet passed the final threshold verification.

[0074] Among them, the energy temperature rise threshold verification is a check process that involves quickly estimating the energy and temperature rise of intermediate sequences and comparing them with safety thresholds to screen out solutions that are thermally non-compliant or exceed limits.

[0075] Specifically, residual heatmaps, variable sensitivity, and sub-region priorities are read from color error analysis data. Then, dose, duty cycle, and time slot shift in the constrained time-series template are mapped to both the spectral and temporal domains, forming channel weights and frame weights respectively. Using a mutual guidance approach, the spectral domain provides compression hints to the temporal domain, and the temporal domain provides noise suppression hints to the spectral domain. Iteratively, mutually agreed-upon low-dimensional priors are extracted, such as channel activation probability, frequency band gating window, temporal smoothing bandwidth, and allowable step size. These priors are then solidified into spectral-temporal prior constraint data that can directly constrain variable updates.

[0076] In the target difference weighting process, the target color field data and the predicted print color field data are aligned to the same coordinates and frame order. Voxel-level color difference is calculated and weighted according to the task's area of ​​interest. This weighted difference is then multiplied by the channel weights and frame weights in the spectral prior constraint data to form a weighted residual that simultaneously reflects the target difficulty and prior confidence. This residual assigns larger coefficients to high-impact channels, keyframes, and edge details, while reducing the weights for low-confidence or noisy regions, thus obtaining the weighted residual data.

[0077] Driven by weighted residual data and gating with spectral-temporal prior constraints, adjustable variables are divided into spectral and temporal sub-blocks for alternating updates. The update order is controlled by a gating mask, with single-step minimization performed only at gated locations, channels, and within frames. After each step, feasible region projection, hardware step quantization, and regularized near-end updates are performed to maintain sparsity, smoothness, and spatial consistency. This alternating cycle continues until the residual decreases gradually, resulting in a structurally complete intermediate temporal exposure sequence data that remains within the feasible region.

[0078] For intermediate time-series exposure sequence data, calculate the single-frame energy and cross-frame energy accumulation frame by frame, combine the thermal response kernel to estimate the peak and steady-state temperature rise and compare them with the threshold; at the same time, check the channel switching bandwidth, duty cycle limit and trigger synchronization margin. Perform automatic derating or pulse rearrangement on edge items, mark those that cannot be repaired as non-compliant and roll back the previous gating step size, and output candidate time-series exposure sequence data that meet the energy and temperature rise constraints after passing all thermal and real-time checks.

[0079] In this embodiment, the variable dimension is compressed and the usability boundaries of channels and frames are strengthened by prior distillation of spectral-temporal mutual guidance, thereby reducing the search space and noise sensitivity from the source. Then, key regions and key bands are highlighted by target difference weighting, making the residual signal more concentrated and more identifiable. Next, in the spectral-temporal gated alternating minimization, only high-value positions are updated by prior gating, taking into account smoothness, sparsity and consistency, resulting in faster and more robust convergence. Finally, the energy temperature rise threshold is used to check and eliminate thermal non-compliance and real-time potential problems in the candidate stage in a timely manner, ensuring manufacturability and executability. This significantly reduces color error, crosstalk and artifacts, reduces the number of iterations and computing power, and improves the robustness and repeatability of the production line.

[0080] In an exemplary embodiment, based on the weighted residual data, spectral-time prior constraint data is subjected to spectral-time gated alternating minimization to obtain intermediate time-series exposure sequence data, including steps 602 to 608. Wherein:

[0081] Step 602: Based on the weighted residual data, perform error significance gating on the spectral time prior constraint data to obtain spectral time gated mask data.

[0082] Step 604: Based on the spectral-time gated mask data, perform alternating minimization subproblem modeling on the spectral-time prior constraint data to obtain subproblem description data.

[0083] Step 606: Based on the subproblem description data, perform gated spectral domain freezing and temporal domain unfreezing minimization updates on the spectral-temporal prior constraint data to obtain transitional temporal exposure sequence data.

[0084] Step 608: Perform a trust region step size test on the transition time series exposure sequence data to obtain the intermediate time series exposure sequence data.

[0085] Among them, the error significance gating process is a screening step that calculates significance scores for each channel and each frame based on weighted residuals and prior confidence, and only allows high-value positions to participate in subsequent updates.

[0086] Among them, the spectral-temporal gating mask data is a set of information that marks which channels and frames are turned on or off in the spectral and temporal domains, as well as the corresponding maximum stride and priority.

[0087] Alternating minimization subproblem modeling involves grouping the enabled variables into spectral domain blocks and temporal blocks, and constructing a solvable subproblem containing objectives, regularization, and constraints for each block.

[0088] The subproblem description data is a structured description of the configuration of each block, including the objective function, regularization term, variables and boundaries, projection and quantization operators, step size and stopping rules.

[0089] Among them, gated spectral domain freezing is a strategy that keeps the parameters of unopened or low-value channels unchanged under gating constraints, allowing only a small number of channels to make minor adjustments.

[0090] Among them, the temporal domain unfreezing minimization update is an update operation that performs single-step minimization on time slots, doses and duty cycles on gated frames, combined with feasible domain projection and quantization.

[0091] Among them, the transitional timing exposure sequence data is a frame-by-frame and channel parameter sequence obtained after completing a round of spectral domain freezing and timing update, but which has not yet passed the final test.

[0092] Among them, the trust region step size test is an effectiveness evaluation step that compares the expected improvement of the model with the actual improvement to adjust the step size and trust region, and simultaneously performs rapid checks on energy, temperature rise and bandwidth.

[0093] Specifically, after aligning the weighted residual data and spectral-temporal prior constraint data by channel, frame order, and coordinates, the residual strength and robust statistics (median bias, local contrast, and cross-frame consistency) of each voxel in each channel and frame are calculated. Then, saliency analysis data is generated by combining the allowable stride, confidence level, and feasible region boundaries from the spectral-temporal prior constraint data. Open and closed regions are determined according to multiple thresholds and connectivity rules, and buffer bands are set for edges and fine textures to prevent over-suppression. Simultaneously, low signal-to-noise ratio or locations strongly constrained by hardware quantization are removed, and mutual exclusion and phase window constraints are inherited. Finally, spectral-temporal gated mask data containing open / closed markers, variable update priorities, and maximum stride indications are output.

[0094] Based on spectral-temporal gating mask data, spectral-temporal prior constraint data, and weighted residual data, the indexes of the enabled variables are extracted, and spectral blocks and temporal blocks are grouped according to channel sets and frame sets. Simultaneously, the unenabled locations are fixed as boundary conditions. For each block, the sensitivity vector of the weighted residual to dose, duty cycle, and time slot displacement is calculated based on the forward model and adjoint link, and the local curvature scale is estimated to determine the first-order direction and step size of the objective function. Based on this, a block-level objective is constructed with residual reduction as the main term, supplemented by three types of regularization: temporal smoothing, spectral sparsity, and spatial consistency. The feasible region constraints are encoded into projective operators and hardware quantization operators, covering dose and duty cycle intervals, time slots and phase windows, channel mutual exclusion, minimum pulse width, and step size quantization, generating intra-block solution primitives. The update formula for proximal gradient or small-scale trust region updates is defined, the concatenation order of feasible region projection and quantization is given, the intra-block update order and inter-block rotation strategy are formulated, and the initialization point, regularization weights, and initial step size are set based on the sensitivity amplitude and prior confidence. Simultaneously, criteria for improvement rate, constraint activity, and residual monotonicity are established to trigger step size backtracking or trust region shrinkage. After completing the above calculations and configurations, the sub-problem description data is summarized, which naturally includes the target and regularization parameters, variable and boundary list, projection and quantization operators, step size and trust region rules, update and rotation strategies, and stopping and early stopping conditions for each spectral and temporal blocks.

[0095] Using the sub-problem description data as the execution blueprint and loading the spectral gating mask and prior constraints, all unopened channels are frozen on the spectral domain side, with only a small number of gated channels retained for fine-tuning. On the temporal side, the gated frame shifts, doses, and duty cycles are unfrozen and single-step updates are initiated. The update direction is calculated using adjoint sensitivity or proximal gradient, and the step size is determined by trust region or backtracking search. Subsequently, feasible region projection and hardware quantization are performed on the update amount to satisfy the dose range, duty cycle limit, time slot mask, and phase window. Guard gaps and necessary pulse rearrangements are added to suppress cross-frame crosstalk and wakes. After completing the intra-block update, the process alternates between the spectral domain block and the temporal block according to a predetermined rotation strategy, accumulating improvements and monitoring residual decrease and constraint activity. Once the intra-block stopping criterion is met, the process moves to the next block until the global round ends. Finally, the variables of each block are summarized, and consistent stitching and light smoothing are performed at the boundaries to output transitional temporal exposure sequence data that satisfies gating and constraints.

[0096] Based on the transitional time-series exposure sequence data in the trust region step-size test, the ratio of the model's expected improvement to the actual improvement is calculated to assess the reliability of this update. If the ratio is insufficient, the trust region is narrowed or the model reverts to the previous feasible solution with a reduced step size. If the ratio is good, the step size is appropriately increased to accelerate convergence. Then, a rapid compliance check is performed, evaluating single-frame energy and cross-frame energy accumulation frame by frame. Based on thermal response kernel estimation of peak and steady-state temperature rise, the channel switching bandwidth, duty cycle limit, time slots and phase windows, trigger synchronization margin, and hardware quantization step size are checked. Automatic derating, pulse rearrangement, or time slot micro-shifting are performed on edge terms to eliminate risks. Finally, the complete sequence is reconstructed and a light smoothing and boundary consistency correction are performed, outputting the intermediate time-series exposure sequence data that passes the test.

[0097] In this embodiment, by using error significance gating to concentrate updates on high-value channels and keyframes, the search space is narrowed from the source and noise interference is suppressed. Then, by modeling the alternating minimization subproblems, the complex global optimization is decomposed into solvable blocks with regularization and constraints, improving the structure and controllability of the solution. Then, the gating-driven spectral domain freezing and temporal domain unfreezing achieve "stable spectrum and fast time" directional updates, reducing weighted residuals faster without destroying priors and feasible regions. Finally, the trust region step size test is used to jointly check the improvement magnitude and physical constraints, automatically adjust the step size and eliminate potential out-of-limit solutions, thereby obtaining intermediate temporal exposure sequence data with faster convergence, higher stability, more reliable energy and real-time performance, and significantly reduced chromatic aberration and artifacts.

[0098] In an exemplary embodiment, multi-physics coupling feasible domain clipping is performed on the multispectral time-series dose configuration parameters in the initial time-series exposure sequence data to obtain restricted time-series sequence template data, including steps 702 to 708. Wherein:

[0099] Step 702: Based on the equipment capability data and media response data corresponding to the pattern printing task, perform multi-physics proxy prior inference on the multi-spectral time-series dose configuration parameters to obtain constrained prior spectrum data. Step 704: Based on the constrained prior spectral data, the resolution quantization and peak clamping of the multispectral time-series dose configuration parameters are performed to obtain quantized clamping parameter data; Step 706: Based on the quantized clamping parameter data, the multi-spectral time-series dose configuration parameters are subjected to safety quota trimming to obtain multi-domain quota parameter data; Step 708: Perform process safety window verification on the multi-domain quota parameter data to obtain restricted time sequence template data.

[0100] Among them, equipment capability data is a set of parameters describing the reachable and limited boundaries of printing equipment, such as available spectrum channels, power and duty cycle limits, minimum pulse width, channel switching bandwidth, buffer and timing budget, and synchronization trigger accuracy.

[0101] Among them, the medium response data are the optical, thermal and photochemical properties of the substrate and material at different wavelengths and doses, such as absorption spectrum, quantum yield, thermal conductivity, specific heat, diffusion and color recovery rate.

[0102] Among them, multiphysics proxy prior inference is based on a reduced-order model of optics, thermodynamics and photochemistry to quickly assess the feasibility and narrow the range of given exposure parameters, so as to form prior constraints for manufacturability.

[0103] Among them, the constrained prior spectral data is a structured map that marks the allowable dose, duty cycle and time slot window, safety margin and mutual exclusion relationship of each channel and each frame in three dimensions: space, spectral domain and time.

[0104] Among them, resolution quantization discretizes the continuous exposure parameters according to the minimum hardware step size, minimum pulse width, and duty granularity to make them consistent with the device resolution.

[0105] Peak clamping involves truncating or backing up the quantized peak parameters based on power, illuminance, and thermal transient limits to prevent instantaneous overshoot.

[0106] Among them, the quantization clamping parameter data refers to the set of frame-by-frame and channel-by-channel parameters obtained after discretization and peak limiting, which are consistent with the hardware and do not exceed the peak.

[0107] Among them, security quota pruning involves setting quotas for multiple domains such as energy, temperature rise, bandwidth, cache and channel concurrency, and performing peak shaving, rerouting or derating to eliminate conflicts when over-provisioning occurs.

[0108] Among them, the multi-domain quota parameter data is a set of parameters that simultaneously satisfy the quota constraints of each domain and record quota utilization rate and conflict resolution information.

[0109] Among them, process safety window verification is a comprehensive verification of the quota results in terms of energy and temperature rise, bandwidth and buffer, synchronization phase and quantization consistency, in order to confirm that it is manufacturable and executable.

[0110] Specifically, the equipment capability data and media response data corresponding to the pattern printing task are used as joint inputs to align the channel identifier, frame sequence, and substrate coordinates. Dimensions such as dose, duty cycle, and time slot are normalized. For each record (band, frame, dose, duty cycle, time slot) in the multispectral time-series dose configuration parameters, reduced-order optical, thermal, and photochemical kinetic proxy models are invoked for rapid evaluation. The optical sub-model estimates voxel-level effective irradiance and channel crosstalk tendency based on the light source spectral power, numerical aperture, and scattering parameters. The thermal sub-model provides the peak and steady-state temperature rise response of the record within its time window based on thermal conductivity, specific heat, and convection boundaries. The kinetic sub-model infers the color development threshold, saturation range, and reversible recovery degree based on absorption cross-section, quantum yield, and color recovery rate. The three types of responses, along with device bandwidth, minimum pulse width, and buffer budget, perform item-by-item judgment and interval shrinking on the input multispectral time-series dose configuration parameters, generating the corresponding permissible dose range, duty cycle, available time slot window, safety margin, and mutual exclusion relationship. Finally, they are aggregated into constrained prior spectral data in the spatial, spectral, and temporal three dimensions.

[0111] Multispectral time-series dose configuration parameters are quantized according to the hardware minimum step size, minimum pulse width, and duty granularity. Peak clamping of the quantization results is performed based on the channel power limit, instantaneous illuminance limit, and thermal transient peak threshold. Simultaneously, a rolling time window is used to statistically analyze cross-frame energy deposition to limit energy accumulation over short periods. Edge-triggered terms are either rolled back in place or slightly shifted between adjacent frames while maintaining the synchronization phase window. The final result is quantized clamping parameter data that satisfies hardware discretization constraints and does not exceed peak limits.

[0112] Based on quantized clamping parameter data, a quota ledger is established for multiple domains such as energy, temperature rise, channel bandwidth and buffer usage, and channel mutual exclusion concurrency. The total quota is first distributed from top to bottom according to region and frame granularity. Then, local weighted refinement is performed based on color importance and residual heat map. When quota over-allocation is encountered, time domain peak shifting and channel rerouting are performed first, and high-risk positions that cannot be shifted are reduced in quota. At the same time, mutual exclusion relationship and minimum guard gap are maintained to suppress crosstalk and wake. The output is multi-domain quota parameter data that meets the quota constraints in each domain.

[0113] Integrated and rapid verification of multi-domain quota parameter data is performed. Frame-by-frame checks are conducted on single-frame energy and cross-frame accumulation, and peak and steady-state temperature rise are estimated using a thermal response kernel. Channel switching bandwidth and buffer usage are verified to be within budget. Synchronization phase and trigger jitter margins, as well as hardware quantization consistency, are checked. If necessary, small-sample Monte Carlo perturbations are applied to key tolerances to assess robustness. For identified out-of-limit and critical terms, automatic derating, pulse rearrangement, or time slot fine-tuning are performed followed by verification. Finally, restricted time-series template data is output. This template provides permissible dose and duty cycle, available time slots and phase window masks, and safety annotations for each frame and channel.

[0114] In this embodiment, multi-physics proxy prior inference is used to pre-align multi-spectral time-series dose configuration parameters with device boundaries and medium characteristics, eliminating infeasible combinations from the source and significantly reducing the search space. Subsequently, resolution quantization and peak clamping ensure that parameters naturally meet the minimum step size, minimum pulse width, and power limit, avoiding instantaneous peaks and hardware inconsistencies. Then, safety quota pruning is used to eliminate over-spec conflicts and achieve peak shifting and rerouting in multiple domains such as energy, temperature rise, bandwidth, buffer, and channel concurrency, making the timing more stable and crosstalk lower. Finally, the process safety window verification solidifies it into a manufacturable template, resulting in faster optimization convergence, significantly reduced thermal and real-time risks, lower testing and computing costs, and improved production line consistency and yield.

[0115] In an exemplary embodiment, based on optimized timing exposure sequence data, multispectral structured light exposure sensing analysis is performed on the substrate corresponding to the pattern printing task to obtain measured color field analysis data, including steps 802 to 810. Wherein:

[0116] Step 802: Perform exposure-aware co-phase triggering arrangement on the optimized timing exposure sequence data to obtain exposure-aware synchronization command data; Step 804: Based on the exposure sensing synchronization command data, the substrate is exposed to multispectral structured light to obtain the original hyperspectral image data. Step 806: Perform temporal aliasing component identification and point diffusion component decomposition on the original hyperspectral image data to obtain temporal aliasing estimation data and imaging point diffusion component estimation data. Step 808: Based on the temporal aliasing estimation data and the imaging point diffusion component estimation data, the original hyperspectral image data is reconstructed by temporal aliasing and imaging point diffusion component deconvolution to obtain voxel-level measured spectral data. Step 810: Based on the voxel-level measured spectral data and the whiteboard reference data for the pattern printing task, perform flat field correction and color space conversion on the voxel-level measured spectral data to obtain measured color field analysis data.

[0117] Among them, exposure sensing co-phase triggering orchestration is a unified planning of the triggering timing and phase of exposure and sensing devices, so that the projection and acquisition of each frame are executed synchronously within the same phase window.

[0118] Among them, the exposure sensing synchronization command data is a frame-level execution list generated by co-phase orchestration, which includes synchronization trigger parameters such as band, power or dose, duty cycle, exposure duration, and sensing sampling start and end.

[0119] The original hyperspectral image data consists of hyperspectral observation data acquired synchronously frame by frame and band by band, without correction or reconstruction processing, along with its timestamps and channel labels.

[0120] Among them, temporal aliasing component identification is a process of analyzing the correlation between adjacent frames along the time axis to detect and quantify cross-frame crosstalk caused by trails and overlaps.

[0121] Point spread component decomposition is a process of decomposing spatial fuzziness into direct and scattering components based on the point spread function of the imaging system to characterize the effects of optical diffusion.

[0122] Among them, the temporal aliasing estimation data is the parameterized result of the temporal aliasing kernel and its intensity and confidence in each channel of each frame obtained from component identification.

[0123] Among them, the imaging point diffusion component estimation data is the estimation result of the spatial point diffusion function obtained by decomposition and its component weights, scale and uncertainty.

[0124] Among them, temporal aliasing and imaging point spread component deconvolution reconstruction is a process that uses both temporal aliasing kernel and spatial point spread function to jointly deconvolve the observed data in order to recover a more realistic signal.

[0125] Among them, the voxel-level measured spectral data are corrected spectral reflectance or radiance curves obtained at each pixel position and wavelength sampling point after joint deconvolution.

[0126] The whiteboard reference data is the response of a standard high-reflectivity target acquired under the same optical and geometric conditions, serving as a benchmark for flat-field and gain correction.

[0127] The process of flat field correction and color space conversion involves using a white and dark field to eliminate channel inhomogeneities and fix noise, and then integrating and mapping the corrected spectrum to the target color space.

[0128] Specifically, based on optimized time-series exposure sequence data, the channel, frame sequence, and substrate coordinates are first aligned, and the time reference and numbering are unified. Then, the trigger delay and jitter of the light source and camera or spectrometer are calibrated, and the phase compensation amount and trigger sequence relationship are calculated. A synchronization timeline is generated frame by frame, aligning the band, power or dose, duty cycle, and exposure duration of each frame with the corresponding sensing sampling start and end to the same phase window. At the same time, guard gaps and buffer frames are inserted according to the equipment switching bandwidth and buffer budget, and emergency slots for retry and derating are reserved. Finally, instruction consistency checks and time conflict clearing are completed, and exposure sensing synchronization instruction data is output.

[0129] Using the exposure-sensing synchronization command data as the execution list, the device presets and substrate positioning are completed. Then, frame-level parameters such as band and power, duty cycle, exposure time, and phase window are loaded, and the light source output stability, camera or spectrometer integration time and gain, and white board and dark field baselines are calibrated. Structured light projection and in-phase acquisition are triggered frame by frame, and timestamps, channel numbers, temperature and power telemetry are recorded synchronously. During execution, channel switching bandwidth, buffer usage, and frame loss risk are monitored in real time, and a guard gap or derating retry is activated when limits are exceeded. A lightweight consistency check is performed immediately after each frame to confirm that the exposure dose and sampling window are aligned, and the data is written to the metadata index. After the task is completed, the hyperspectral cubes of all frames, the corresponding device telemetry, and calibration information are aggregated to form the raw hyperspectral image data.

[0130] A correlation sequence of adjacent frames is constructed along the time axis from the raw hyperspectral image data to identify wakes and overlaps and form temporal aliasing estimation data. Simultaneously, in the spatial domain, based on edge response and micro-point calibration or a referenceless blind estimation framework, the imaging point spread function is extracted and decomposed into direct component and scattering tail to form imaging point spread component estimation data. During the process, robust weights are applied to low signal-noise regions and local windows are enabled for high-contrast edges to reduce bias. Finally, parameter maps, confidence maps, and availability masks are output for each channel and frame for both types of estimations.

[0131] Using raw hyperspectral image data as observations and temporal aliasing estimation data and imaging point spread component estimation data as priors, channel and frame alignment and dimensional normalization are completed, and confidence weights and availability masks are generated. Then, a joint deconvolution objective is constructed to simultaneously incorporate the aliasing kernel on the temporal axis and the point spread function in the spatial domain into the constraints. Non-negativity and mild smoothing or total variational regularization are used to suppress noise amplification and minimize it alternately. The process is updated cyclically between "temporal deconvolution - spatial deconvolution". During the update, mirror or reflection extension is applied to the boundaries, stronger regularization is applied to high-noise regions, and the step size is relaxed in high-confidence regions to accelerate convergence. The step size and regularization weights are adaptively adjusted through improvement rate monitoring and L-shaped curves or cross-validation. After convergence, the output is voxel-level measured spectral data per pixel and band, along with quality indicators such as residual plots, signal-to-noise ratio gain, and reconstruction confidence.

[0132] Combining voxel-level measured spectral data and whiteboard reference data (with dark field baselines added when necessary), dark current and fixed noise subtraction, channel response normalization and vignetting correction, and wavelength position micro-offset calibration are performed. Gains and biases for each band are unified to the absolute reflectance domain using whiteboard reflectance as a standard, and robust interpolation and neighborhood constraint smoothing are applied to high-noise and saturated pixels to stabilize the spectral shape. Then, the spectrum is integrated into tristimulus quantities according to the specified illuminator and observer functions and converted to the target color space. Simultaneously, gamut clipping, gamma uniformization, and device color matrix fine-tuning are performed to eliminate systematic biases. Finally, spatial consistency checks, edge region gradient correction, and outlier pixel removal are performed to generate voxel-level color vector maps, full-frame error heatmaps, quality confidence scores, and defect masks, which are then aggregated into measured color field analysis data.

[0133] In this embodiment, co-phase triggering is used to ensure strict synchronization between exposure and perception, avoiding sampling deviations caused by cross-frame misalignment. Subsequently, multispectral structured light exposure is performed according to the schedule to acquire raw hyperspectral data, ensuring complete consistency between band labels and timestamps. Then, temporal aliasing and optical diffusion in the raw data are modeled and estimated separately to accurately separate the sources of crosstalk. On this basis, joint deconvolution reconstruction is performed to effectively suppress wakes and blurring and improve the signal-to-noise ratio and resolution of voxel-level spectra. Finally, flat field and color conversion are completed with whiteboard reference, and measured color field analysis data that can be directly used for evaluation and closed-loop control are output, thereby achieving higher acquisition stability, more accurate color difference evaluation, fewer artifacts, and significantly improved repeatability and traceability.

[0134] In an exemplary embodiment, the substrate is subjected to multispectral structured light exposure according to the exposure sensing synchronization command data to obtain raw hyperspectral image data, including steps 902 to 910. Wherein:

[0135] Step 902: Based on the exposure sensing synchronization command data, perform exposure accuracy analysis on the substrate to obtain substrate exposure accuracy data. Step 904: Based on the exposure accuracy data of the substrate, perform multispectral exposure optimization scheduling on the exposure sequence of the substrate to obtain multispectral light source scheduling data; Step 906: Based on the multispectral light source scheduling data, perform spectral band synchronization calibration on the exposure process of the substrate to obtain exposure synchronization configuration data; Step 908: Based on the exposure synchronization configuration data, the dynamic exposure accuracy of the printing substrate is enhanced to obtain the exposure adjustment configuration data; Step 910: Based on the exposure adjustment configuration data, the substrate is subjected to multispectral structured light exposure to obtain the original hyperspectral image data.

[0136] Among them, exposure accuracy analysis is a process of evaluating and quantifying the feasibility of clock deviation, energy deviation and phase deviation for each frame and each channel based on synchronization commands and equipment / environment telemetry.

[0137] Among them, the substrate exposure accuracy data is a structured result output from the exposure accuracy analysis, which includes a timing alignment offset map, an energy and phase deviation map, an executability mask, and a confidence map.

[0138] Among them, multispectral exposure optimization scheduling is a scheduling process that allocates time slots and guard gaps to each band and sorts the loading order within the constraints of equipment bandwidth and cache, so as to reduce the risk of crosstalk and frame loss.

[0139] Among them, the multispectral light source scheduling data is the instruction-based representation of the scheduling results, recording the mapping of "frame → channel → time slot → duty and dose" as well as the guard gap and conflict resolution annotation.

[0140] Among them, spectral band synchronous calibration is a calibration process that uses delay compensation and phase fine-tuning to ensure that the exposure start and end points and the sensing sampling window of each band fall into the same co-phase window.

[0141] Among them, the exposure synchronization configuration data is a configuration list generated after synchronization calibration, which includes parameters such as phase offset, trigger order and synchronization tolerance for each frame and each channel.

[0142] The exposure adjustment configuration data is the final execution parameter table formed by combining online telemetry with small-step compensation of power, duty cycle and time slot based on synchronous configuration.

[0143] Specifically, the exposure sensing synchronization command data is registered with channel, frame sequence, and substrate coordinates, and a unified time reference is established. Then, key parameters such as band, power or dose, duty cycle, and exposure duration are extracted frame by frame and compared with the device boundary model to check the feasibility of minimum pulse width, switching bandwidth, buffer, and trigger jitter. Combining offline calibration curves and online telemetry (source power fluctuation, temperature, camera integration time, and gain), clock drift, energy deviation, and phase deviation for each channel and each frame are estimated, and confidence data is provided. At the same time, rolling window statistics are performed on cross-frame energy to assess transient and steady-state temperature rise risks and mark potential over-limit points. Finally, substrate exposure accuracy data is obtained, including a timing alignment offset map, energy and phase deviation map, execution feasibility mask, and confidence map.

[0144] Using the substrate exposure accuracy data as a constraint, the available time slots and guard gaps for each band are first determined, and channels and keyframes are prioritized according to confidence and importance. While ensuring minimum pulse width, switching bandwidth, and buffer budget, the loading order of "frame → channel → time slot" is optimized, prioritizing high-confidence channels and employing peak-shifting and derating strategies for low-confidence channels. A rolling window limiting method is used for cross-frame energy to suppress transient temperature rise, and soft transitions are set for adjacent high-contrast regions to reduce crosstalk. When a mismatch with camera integration time or trigger phase is detected, the time slots and duty cycles are automatically fine-tuned while maintaining the synchronization window. The final output is multispectral light source scheduling data, including time slot assignments, duty cycles and dose recommendations, guard gap markings, and conflict resolution records for each frame and channel.

[0145] Using multispectral light source scheduling data as a skeleton, a baseline measurement is performed on the trigger delay, rise time, and camera integration window of each channel, and converted to a unified time base. The phase difference between channels is fine-tuned according to the delay compensation table and online phase probe, ensuring that the exposure start and end points and the sensor sampling start and end points fall within the same co-phase window. For detected drift and jitter, phase offsets and micro-shifts are generated for each channel and applied to the corresponding time slots without compromising minimum pulse width, bandwidth, and buffer budget. Subframe-level guard gaps are inserted when necessary to isolate residual crosstalk. Adaptive convergence is performed on frames that do not match the camera integration time, prioritizing adjustments to exposure duration and small quantization steps with low duty cycles, while maintaining total energy and cross-frame energy limits. After full-channel calibration, consistency and playback verification are performed, outputting exposure synchronization configuration data including phase offset, trigger order, synchronization tolerance, and applied micro-shifts for each frame and channel.

[0146] Based on the exposure synchronization configuration data, online telemetry and scene information are loaded to read the light source power stability, internal temperature, camera integration time and gain, local albedo and edge contrast of the substrate, and generate frame-level and region-level compensation requirements. According to the compensation requirements, the fine-tuning of power, duty cycle, and exposure time is calculated using small step-size rules. Priority is given to derating and soft transitions for high-albedo or high-contrast edges, and mild gain compensation is applied to low-brightness areas. Compensation employs dual constraints of trust region and step-size quantization to ensure that any fine-tuning does not exceed the channel energy limit, cross-frame energy limit, minimum pulse width, bandwidth, and buffer budget, while maintaining the phase window and trigger order. Minimum guard gaps are inserted or time slot shifts are performed on adjacent frames that may cause crosstalk to eliminate the risk of overlap. A fast consistency check and rollback strategy are performed after each application. After completing a full-frame traversal, the compensation results and constraint margins for each frame and channel are summarized to form the exposure adjustment configuration data.

[0147] After loading and interlocking self-checks of the light source and sensing device based on the exposure adjustment configuration data as the instruction set, frame-by-frame projection and acquisition begin. In each frame, structured light is triggered according to the assigned band, power or dose, duty cycle, and time slot, and hyperspectral acquisition is initiated within the co-phase window. Simultaneously, timestamps, channel numbers, power and temperature telemetry data, and camera integration parameters are recorded. At the end of each frame, a lightweight consistency check is performed to confirm dose and sampling window alignment. If energy or synchronization deviations are detected, a guard gap, derating, or retry is executed according to the pre-defined plan, and an anomaly flag is written. During cross-frame periods, switching bandwidth and buffer usage are continuously monitored. When limits are exceeded, adaptive peak shifting is implemented to avoid frame loss and crosstalk. Upon completion of the task, all frames' hyperspectral cubes and accompanying metadata, including calibration information, telemetry trajectories, and event logs, are aggregated, and raw hyperspectral image data with complete time sequence and channel annotations are output.

[0148] In this embodiment, clock, energy, and phase deviations in each frame and channel are located through exposure accuracy analysis, eliminating unexecutable risks at the source. Then, multispectral exposure optimization scheduling is used to rationally allocate time slots and guard gaps and optimize loading order under bandwidth, buffer, and energy constraints, significantly reducing crosstalk and frame loss probability. Subsequently, spectral band synchronous calibration is performed to ensure that the projection and sensing are strictly in phase, eliminating sampling deviations caused by cross-frame misalignment and phase drift. On this basis, dynamic exposure accuracy enhancement is implemented, and power, duty cycle, and time slots are fine-tuned according to online telemetry, taking into account thermal safety and real-time performance, making the energy distribution more stable and uniform. Finally, multispectral structured light exposure is performed according to the optimized parameters to obtain original hyperspectral image data with complete timing and channel annotation, higher signal-to-noise ratio, fewer artifacts, and better repeatability, thereby improving the accuracy of subsequent reconstruction and color difference assessment and reducing setup and testing costs.

[0149] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0150] In one exemplary embodiment, a pattern printing control system based on photochromic printing is provided, the system including: computer equipment and data acquisition terminal; Computer equipment is used to perform multispectral spatiotemporal coding mapping on the target color pattern corresponding to the pattern printing task to obtain initial temporal exposure sequence data; the target color pattern is obtained through a data acquisition terminal. Computer equipment is used to perform inverse problem optimization based on photochemical dynamics on the initial time-series exposure sequence data to obtain optimized time-series exposure sequence data; Computer equipment is used to perform multispectral structured light exposure sensing analysis on the substrate corresponding to the pattern printing task based on optimized temporal exposure sequence data, and obtain measured color field analysis data. Computer equipment is used to perform closed-loop iterative optimization of the optimized time-series exposure sequence data based on measured color field analysis data until the actual color error value meets the preset color error threshold, thereby obtaining pattern printing control data.

[0151] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. This computer device includes a processor, memory, input / output interfaces (I / O), and communication interfaces. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0152] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0153] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0154] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps in the above method embodiments.

[0155] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0156] Those skilled in the art will understand that all or part of the processes in 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 the computer program is executed, it can include the processes of the embodiments of the above methods.

[0157] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0158] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for regulating pattern printing based on photochromic printing, characterized by, The method includes: Multispectral spatiotemporal coding mapping is performed on the target color pattern corresponding to the pattern printing task to obtain the initial temporal exposure sequence data; The initial time-series exposure sequence data is optimized by performing an inverse problem based on photo-chemical dynamics to obtain optimized time-series exposure sequence data; Based on the optimized timing exposure sequence data, multispectral structured light exposure sensing analysis is performed on the substrate corresponding to the pattern printing task to obtain measured color field analysis data. Based on the measured color field analysis data, the optimized timing exposure sequence data is subjected to closed-loop iterative optimization until the actual color error value meets the preset color error threshold, thus obtaining the pattern printing control data.

2. The method of claim 1, wherein, The optimization of the initial time-series exposure sequence data based on photochemical dynamics, to obtain optimized time-series exposure sequence data, includes: Based on the initial time-series exposure sequence data and the preset photo-chemical kinetic parameters, a forward prediction analysis is performed on the spectral and color response of the photochromic material to obtain the printing color field prediction data. Based on the predicted printing color field data and the target color field data determined by the target color pattern, the initial time-series exposure sequence data is subjected to constraint optimization with the goal of minimizing color error, to obtain candidate time-series exposure sequence data. The candidate time-series exposure sequence data is subjected to stability verification to obtain the optimized time-series exposure sequence data.

3. The method of claim 2, wherein, The step of performing constraint optimization on the initial temporal exposure sequence data with the objective of minimizing color error, based on the predicted printing color field data and the target color field data determined by the target color pattern, to obtain candidate temporal exposure sequence data, includes: Multi-physics coupling feasible domain clipping is performed on the multispectral time-series dose configuration parameters in the initial time-series exposure sequence data to obtain restricted time-series sequence template data; Based on the printed color field prediction data and the target color field data, color error analysis is performed on the restricted time sequence template data to obtain color error analysis data. Based on the color error analysis data, the spectral-temporal joint variational solution is performed on the restricted time-series template data to obtain the candidate time-series exposure sequence data.

4. The method of claim 3, wherein, The step of performing a spectral-temporal joint variational solution on the restricted temporal sequence template data based on the color error analysis data to obtain the candidate temporal exposure sequence data includes: Based on the color error analysis data, spectral-temporal mutual derivative prior distillation is performed on the restricted time-series template data to obtain spectral-temporal prior constraint data. Based on the target color field data and the printed color field prediction data, the target difference weighting is applied to the spectral time prior constraint data to obtain weighted residual data. Based on the weighted residual data, the spectral-time prior constraint data is subjected to spectral-time gated alternating minimization to obtain intermediate time-series exposure sequence data. The intermediate time-series exposure sequence data is subjected to energy temperature rise threshold verification to obtain the candidate time-series exposure sequence data.

5. The method of claim 4, wherein, The step of performing spectral-time gated alternating minimization on the spectral-time prior constraint data based on the weighted residual data to obtain intermediate time-series exposure sequence data includes: According to the weighted residual error data, error significance gating is performed on the spectral-time prior constraint data to obtain spectral-time gating mask data; According to the spectral-time gating mask data, an alternating minimization sub-problem modeling is performed on the spectral-time prior constraint data to obtain sub-problem description data; According to the sub-problem description data, a gated spectral domain freezing and time domain unfreezing minimization update is performed on the spectral-time prior constraint data to obtain transition time sequence exposure sequence data; A reliability domain step inspection is performed on the transition time sequence exposure sequence data to obtain the intermediate time sequence exposure sequence data.

6. The method of claim 3, wherein, The multi-physical field coupling feasible region clipping of the multi-spectral time sequence dose configuration parameter in the initial time sequence exposure sequence data includes: According to the equipment capability data and medium response data corresponding to the pattern printing task, a multi-physical field agent prior inference is performed on the multi-spectral time sequence dose configuration parameter to obtain constraint prior graph data; According to the constraint prior graph data, a resolution quantization and peak clamping is performed on the multi-spectral time sequence dose configuration parameter to obtain quantized clamped parameter data; According to the quantized clamped parameter data, a safety quota clipping is performed on the multi-spectral time sequence dose configuration parameter to obtain multi-domain quota parameter data; A process safety window verification is performed on the multi-domain quota parameter data to obtain the restricted time sequence sequence template data.

7. The method of claim 1, wherein, The multi-spectral structured light exposure perception analysis of the printing body corresponding to the pattern printing task according to the optimized time sequence exposure sequence data includes: An exposure perception co-phase trigger arrangement is performed on the optimized time sequence exposure sequence data to obtain exposure perception synchronization instruction data; According to the exposure perception synchronization instruction data, a multi-spectral structured light exposure is performed on the printing body to obtain original hyperspectral image data; A time sequence aliasing component identification and a point spread component decomposition are respectively performed on the original hyperspectral image data to obtain time sequence aliasing estimation data and imaging point spread component estimation data; According to the time sequence aliasing estimation data and the imaging point spread component estimation data, a time sequence aliasing and imaging point spread component deconvolution reconstruction is performed on the original hyperspectral image data to obtain voxel-level measured spectral data; According to the voxel-level measured spectral data and the whiteboard reference data of the pattern printing task, a flat field correction and a color space conversion are performed on the voxel-level measured spectral data to obtain the measured color field analysis data.

8. The method of claim 7, wherein, The multi-spectral structured light exposure of the printing body according to the exposure perception synchronization instruction data includes: According to the exposure perception synchronization instruction data, an exposure precision analysis is performed on the printing body to obtain printing body exposure precision data; According to the printing body exposure precision data, a multi-spectral exposure optimization scheduling is performed on the exposure time sequence of the printing body to obtain multi-spectral light source scheduling data; According to the multi-spectral light source scheduling data, a spectral band synchronization calibration is performed on the exposure process of the printing body to obtain exposure synchronization configuration data; According to the exposure synchronization configuration data, dynamic exposure precision enhancement is performed on the printing body to obtain exposure adjustment configuration data; According to the exposure adjustment configuration data, multi-spectrum structured light exposure is performed on the printing body to obtain the original hyperspectral image data.

9. A pattern printing regulation system based on photochromic printing, characterized in that, The system comprises a computer device and a data acquisition terminal; The computer device is configured to perform multi-spectrum space-time coding mapping on a target color pattern corresponding to a pattern printing task to obtain initial time sequence exposure sequence data, wherein the target color pattern is obtained by the data acquisition terminal; The computer device is configured to perform inverse problem optimization based on photo-chemical kinetics on the initial time sequence exposure sequence data to obtain optimized time sequence exposure sequence data; The computer device is configured to perform multi-spectrum structured light exposure sensing analysis on the printing body corresponding to the pattern printing task according to the optimized time sequence exposure sequence data to obtain actual color field analysis data; The computer device is configured to perform closed-loop iterative optimization on the optimized time sequence exposure sequence data according to the actual color field analysis data until an actual color error value meets a preset color error threshold to obtain pattern printing control data.

10. The system of claim 9, the computer device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 8.

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