A laser processing method for realizing holographic image anti-counterfeiting effect

Abstract

The application provides a laser processing method for realizing holographic image anti-counterfeiting effect, resolves preset phase change dot pattern and optical attribute target value from a digital design file of the holographic image, establishes a mapping model of laser parameters and material phase change depth and optical property change, calculates and generates a matching laser processing parameter sequence for each processing unit according to the model, collects a transient thermal radiation signal and compares it with a theoretical curve in real time during the processing process, dynamically judges phase change process deviation, adjusts subsequent laser pulse parameters to correct phase change region morphology according to the deviation, applies a gradient cooling field to control the cooling process after unit processing, acquires actual optical data by confocal microscopic scanning after the whole processing is completed, and matches and analyzes the actual optical data with the target value to generate a quantitative evaluation atlas, so that the consistency of high-precision realization of the phase change dot pattern and the optical effect is ensured.

Description

Technical Field

[0001] This invention relates to a laser processing method for achieving anti-counterfeiting effects on holographic images. Background Technology

[0002] Holographic imaging anti-counterfeiting technology achieves visual effects that are difficult to replicate through complex optical structures, playing a crucial role in high-end product anti-counterfeiting and security document applications. Its core lies in using microstructures to precisely control light and generate unique images. Laser processing methods, due to their high precision and flexibility, have become an important means of fabricating such microstructures. However, existing technologies still face significant challenges in achieving stable and reliable holographic imaging effects. Many processing methods rely on engraving fixed physical relief structures on the material surface. Once formed, these structures are difficult to adjust, and their optical performance depends primarily on the pre-set geometry. This makes the processing overly dependent on the inherent properties of the material itself. When dealing with diverse materials or achieving more dynamic optical responses, the adaptability and effect control capabilities of such methods become insufficient.

[0003] From the perspective of material interaction, the limitations of existing methods often stem from insufficient precision in controlling the internal changes of materials during processing. When a laser interacts with a material, the energy injection triggers a change in the material's state, such as melting or compositional changes. These changes directly determine the optical properties of the final formed region, such as its ability to reflect light. The current technical challenge lies in how to precisely guide and solidify this change. Specifically, the first core factor is the highly instantaneous and localized nature of the laser-induced phase transition process. A phase transition refers to a material changing from one stable state to another, such as from a solid to a liquid and then re-solidifying. If this process is not properly controlled, the optical properties of the region will be non-uniform. The second related factor is the cooling behavior of the phase transition region. The material cools rapidly after laser treatment, and the rate and uniformity of cooling profoundly affect the stability and consistency of the microstructure after the phase transition. These two factors are closely linked because the instantaneous phase transition process directly sets the initial cooling conditions, while uncontrollable cooling will cause the expected optical properties to drift or become unrepeatable.

[0004] Therefore, in specific processing operations, how to precisely control the laser action and material cooling to form an optically stable phase transition matrix distributed according to a preset pattern on the material surface, thereby constructing a high-definition holographic image, has become a key issue in improving the quality and reliability of anti-counterfeiting labels prepared by laser processing. Summary of the Invention

[0005] This invention proposes a laser processing method to achieve holographic anti-counterfeiting effects, realizing high-precision realization of phase change dot matrix patterns and consistency with optical effects, thereby improving the quality and reliability of the prepared anti-counterfeiting labels.

[0006] The technical solution of this invention is implemented as follows:

[0007] A laser processing method for achieving anti-counterfeiting effects of holographic images, the method comprising:

[0008] Obtain the digital design file of the target holographic image, and parse the preset phase transition dot matrix pattern and its corresponding optical attribute target value to achieve specific optical effects from the file;

[0009] Based on the inherent optical and thermophysical parameters of the material to be processed, a mapping model is established between laser energy density, interaction time, expected phase transition depth of the material, and changes in regional optical properties.

[0010] For each processing unit in the preset phase transition lattice pattern, the mapping relationship model is used to calculate and generate a laser processing parameter sequence that matches the target optical properties of the unit. This sequence includes energy, pulse width, and scanning path.

[0011] During laser processing, a high-precision sensor synchronously collects the transient thermal radiation signal of the processing area after the laser pulse. This signal characterizes the instantaneous energy state of the material phase transition process.

[0012] The acquired transient thermal radiation signal is compared in real time with the theoretical thermal radiation curve predicted based on the material's thermophysical parameters and the current laser parameters to determine the deviation between the actual phase transition process and the expected state.

[0013] If a deviation is detected, the energy or position of the subsequent laser pulse is dynamically adjusted using a preset feedback control algorithm based on the direction and magnitude of the deviation, in order to correct the morphology of the phase transition region.

[0014] After a single processing unit is completed, the active temperature control module for that area is activated. Based on the material phase change volume of that unit and the surrounding thermal diffusion conditions, a gradient cooling field is applied to control the cooling rate and direction of that area.

[0015] After all the preset dot matrix patterns have been processed, the entire processing area is scanned using a confocal microscope system to obtain the actual surface morphology and optical reflectivity distribution data of each phase transition unit.

[0016] The actual optical reflectivity distribution data obtained are matched point by point with the initially resolved optical attribute target values ​​to generate a quantitative evaluation map that identifies the consistency of processing and the uniformity of optical performance.

[0017] This invention parses the preset phase transition lattice pattern and optical property target values ​​from the digital design file of holographic images, and establishes a mapping model between laser parameters and changes in material phase transition depth and optical properties. Based on this model, a matching laser processing parameter sequence is calculated and generated for each processing unit. During processing, transient thermal radiation signals are collected and compared with theoretical curves in real time to dynamically judge the deviation of the phase transition process, and the subsequent laser pulse parameters are adjusted accordingly to correct the morphology of the phase transition region. After unit processing, a gradient cooling field is applied to control the cooling process. After all processing is completed, actual optical data is obtained through confocal microscopy and matched with target values ​​to generate a quantitative evaluation spectrum, thereby ensuring the high-precision realization of the phase transition lattice pattern and the consistency of optical effect. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] A laser processing method for achieving anti-counterfeiting effects of holographic images may specifically include:

[0020] Step S101: Obtain the digital design file of the target holographic image, and parse the preset phase transition dot matrix pattern and its corresponding optical attribute target value from the file to achieve specific optical effects.

[0021] The digital design file of the target holographic image is obtained from the repository through a secure transmission protocol, resulting in a complete data package of the design file. A multi-layer pattern decomposition method is used to separate optical layer data from the complete data package layer by layer, parsing out a preset phase transition dot matrix pattern to obtain its structural distribution. Based on the structural distribution of the phase transition dot matrix pattern, corresponding optical attribute target values ​​are extracted. Phase transition point coordinates are matched from the structural distribution to determine the quantification index of the optical attribute target value. The quantification index of the optical attribute target value is simulated using virtual rendering testing to generate a virtual holographic projection. Deviation data of the simulated effect is obtained. If the deviation data exceeds a preset threshold, a real-time calibration mechanism is used to adjust the optical attribute target value. The quantification index is gradually corrected by iteratively comparing the deviation data with the target effect, and the adjusted value is determined to meet the specific optical effect requirements, thereby obtaining the preset phase transition dot matrix pattern and its corresponding optical attribute target value for achieving the specific optical effect.

[0022] In one implementation, a digital design file of the target holographic image is acquired. This file is typically stored in a standard format, such as binary or text format generated by computer-aided design software, and contains the geometric and optical data of the holographic image. A preset phase-change lattice pattern for achieving specific optical effects is parsed from this digital design file. The phase-change lattice pattern refers to a structure composed of multiple lattice units, each capable of modulating the phase change of light to produce the desired diffraction or interference effect. This pattern may be designed as a grid arrangement to simulate light wave modulation on an object's surface. The parsing process includes scanning data blocks in the file to identify the coordinate positions and unit sizes of the pattern. A data decoding algorithm can be used to first extract the boundary parameters of the pattern, and then read the phase change values ​​point by point to ensure complete pattern reconstruction. After parsing the preset phase-change lattice pattern, its corresponding optical attribute target values ​​are further extracted. These target values ​​include quantitative indicators such as phase retardation, refractive index, or transmittance, used to define the optical behavior of each lattice unit.

[0023] When parsing the phase transition dot matrix pattern from the file, the file structure is first decomposed into header information and data body. The header information contains the overall specifications of the pattern, such as the dot matrix density and size range; the data body stores the phase transition parameters of each dot matrix unit. The pattern is read and assembled line by line through a layer-by-layer parsing algorithm to ensure that the optical effect is realized like a three-dimensional relief. Then, the target values ​​of optical properties are extracted.

[0024] Step S102: Based on the inherent optical and thermophysical parameters of the material to be processed, establish a mapping relationship model between laser energy density, interaction time, expected phase transition depth of the material, and changes in regional optical properties.

[0025] The refractive index distribution data and thermal diffusivity values ​​of the material to be processed are retrieved from the material database. A multi-dimensional feature space mapping is performed on the refractive index distribution data and the thermal diffusivity values ​​to obtain a feature vector of the material's inherent properties. This feature vector is then input into a pre-constructed energy absorption field simulation model to determine the initial absorption rate of the laser energy density on the material surface. A time-domain convolution operation is performed based on the initial absorption rate and a preset laser pulse waveform to obtain a transient heat flux density distribution sequence. If a local value in the transient heat flux density distribution sequence exceeds the material's melting point threshold, the evolution process of the transient heat flux density distribution sequence within the material is analyzed using the heat conduction differential equation to obtain three-dimensional temperature field dynamic evolution data. This three-dimensional temperature field dynamic evolution data is then used to match the phase transition kinetic equation to determine the critical isothermal surface position where the lattice structure transformation occurs within the material, thus obtaining the expected phase transition depth. The change in atomic packing density in the corresponding region is extracted by the expected phase transition depth, and then substituted into the electromagnetic wave scattering model to reconstruct the dielectric constant, thereby determining the change in the region's optical properties. The change in the region's optical properties is then correlated with the expected phase transition depth to establish a mapping model between laser energy density, interaction time, the expected phase transition depth of the material, and the change in the region's optical properties.

[0026] In one implementation, the core of establishing the mapping model lies in quantifying the heat accumulation and phase transition process triggered by laser energy injection into the material. The laser energy density determines the amount of energy absorbed per unit area on the material surface, while the interaction time affects the depth and uniformity of heat diffusion into the material. The inherent optical parameters of the material, such as the absorption coefficient and reflectivity for a specific laser wavelength, directly determine how much incident energy is absorbed by the material and converted into heat energy. The thermophysical parameters of the material, including thermal conductivity, specific heat capacity, density, and latent heat of phase transition, collectively determine the heat conduction rate, temperature rise rate, and energy threshold required for phase transitions (such as melting, vaporization, or crystal transformation) within the material. Based on the above physical foundations, the mapping relationship model is constructed by combining numerical simulation and experimental calibration. First, a partial differential equation for heat conduction describing the laser heating process is established. This equation takes the laser energy density and the interaction time as input variables and the temperature field inside the material that changes with time and space as the solution objective. The equation includes the aforementioned thermophysical parameters of the material as coefficients. By solving this equation using numerical methods such as finite element or finite difference, the distribution of the region where the temperature inside the material exceeds its phase transition critical temperature (such as melting point) under given laser parameters can be obtained. The depth of this region can be preliminarily predicted as the "expected phase transition depth".

[0027] The temperature field model described above can only predict regions where phase transitions may occur. To further correlate these changes with changes in regional optical properties, a correspondence between material phase transitions and their optical properties needs to be introduced. When a local temperature exceeds a certain value and undergoes rapid cooling, its internal structure may transform from a crystalline to an amorphous state, or microcracks and density changes may occur. These changes in microstructure directly lead to changes in optical properties such as refractive index and extinction coefficient in that region. By consulting material handbooks or conducting specialized experiments, a lookup table or empirical formula can be established to correlate "phase transition states" with "optical property values." By combining the phase transition region information predicted by the temperature field model with this lookup table, the expected changes in the optical properties of the processed region can be mapped.

[0028] To improve the practicality and accuracy of the model, the basic thermal model can be modified to account for dynamic changes in the reflectivity of the material surface during laser treatment (caused by temperature rise or phase transition) or nonlinear changes in the material's thermal properties with temperature. These factors are introduced as variables in the numerical model. Furthermore, for pulsed lasers, the relative relationship between the pulse width and the material's thermal diffusion time must be considered to determine whether the processing is dominated by heat conduction or ablation, thus allowing for the selection of a more suitable physical model.

[0029] In the context of fabricating microstructures for holographic optical elements, the material to be processed is a phase-change thin film sensitive to a specific wavelength. Material characterization was used to obtain the film's absorption coefficient, thermal conductivity, specific heat capacity, and the phase transition temperature and enthalpy from amorphous to crystalline states at room temperature. The goal was then to form a periodic lattice structure with a depth of 100 nanometers and a refractive index change of 0.2 on the film. Based on a heat conduction model, parametric scanning calculations were performed using laser energy density and interaction time as variables to find a laser parameter combination that could bring the temperature within approximately 100 nanometers of the film surface to a level slightly exceeding the phase transition point. Experimental verification was then conducted by actually processing samples using the selected laser parameters, and the refractive index change in the processed area was measured using an ellipsometry. The measured data were compared with the model predictions, and certain empirical coefficients in the model (such as the effective absorption coefficient) were iteratively adjusted to control the errors between the model's predicted phase transition depth and refractive index change values ​​and the experimental results within acceptable limits, thus completing the calibration and establishment of the mapping model for this specific material system.

[0030] The established mapping model can be encapsulated as a software module and integrated into the control system of laser processing equipment. Before actual processing, the operator inputs the parameters of the target material and the desired phase transition depth and optical effect. The model can then reverse-calculate the recommended combination of laser energy density and interaction time parameters, or display the predicted processing results under different parameter combinations. This guides process development, reduces trial-and-error costs, and ensures the repeatability and accuracy of processing holographic optical elements or other functional microstructures.

[0031] Step S103: For each processing unit in the preset phase transition lattice pattern, the mapping relationship model is used to calculate and generate a laser processing parameter sequence that matches the target optical properties of the unit. The sequence includes energy, pulse width and scanning path.

[0032] The target refractive gradient of the processing unit is extracted from the preset phase transition lattice pattern, and the degree of lattice distortion corresponding to the target refractive gradient is retrieved in the multidimensional mapping relationship model. The phase transition volume of the processing unit is calculated based on the degree of lattice distortion, and the transient heat flux vector distribution within the phase transition volume is determined by combining the dielectric tensor evolution law. The transient heat flux vector distribution is matched with the laser pulse envelope to determine the energy peak value and pulse width duration in the laser processing parameter sequence. The pulse width duration is used to analyze the thermal overlap ratio between adjacent processing units, and the scanning vector in the laser processing parameter sequence is spatially compensated according to the cooling rate. Path planning is performed on the compensated scanning vector to generate a complete laser processing parameter sequence containing energy, pulse width, and scanning path, thereby realizing the generation of a laser processing parameter sequence that matches the target optical properties of the processing unit.

[0033] In one implementation, for each processing unit in a preset phase transition lattice pattern, the target optical properties of that unit must first be defined, such as the refractive index change or extinction coefficient adjustment target. These properties are set based on the design requirements of the holographic optical element, such as the microstructure used to create a specific diffraction pattern. Using the mapping model, the target optical properties of the unit are taken as input, and a corresponding laser processing parameter sequence is generated through reverse calculation. This sequence includes laser energy, pulse width, and scanning path, ensuring that the processed material achieves the expected phase transition depth and optical property changes in the unit region.

[0034] Specifically, the model utilizes previously established heat conduction equations and phase transition relationships to simulate the effect distribution under different parameter combinations. The calculation process begins with the overall layout of the lattice pattern. The preset phase transition lattice pattern typically consists of multiple processing units, each corresponding to a tiny region, such as a rectangular or circular grid. For each unit, the model extracts its position coordinates and target optical property values. For example, one unit requires a refractive index increase of 0.15, while adjacent units might require 0.10. Through mapping relationships, the model queries or calculates energy density thresholds, such as setting the energy in the range of 5-10 J / cm² to match the phase transition depth of approximately 50-200 nanometers. The pulse width is adjusted according to the material's thermal diffusion time. The scanning path is designed as a dotted or linear path to ensure that the energy is uniformly distributed within the unit.

[0035] For example, assuming the lattice pattern is a periodic array, the goal of each cell is to form a phase transition region of varying depths. The model first inputs the target phase transition depth of the cell, for example, 100 nanometers, and then calculates the required energy, such as by solving the thermal model using an iterative algorithm, yielding an energy value of 6 J / cm². Next, the pulse width is determined, for example, 50 nanoseconds, to control heat accumulation. The scanning path can be a grating-style path, extending from the center of the cell to the edge, ensuring uniformity of the phase transition region. It should be noted that this calculation takes into account material parameters, such as an absorption coefficient of 0.8, thus generating a sequence with an energy of 6 J / cm², a pulse width of 50 nanoseconds, and a helical scanning path.

[0036] Step S104: During the laser processing, a high-precision sensor synchronously collects the transient thermal radiation signal of the processing area after the laser pulse. This signal characterizes the instantaneous energy state of the material phase transition process.

[0037] The transient thermal radiation signal of the processing area at the moment of laser pulse application is captured by a high-precision sensor, and the radiation intensity distribution characteristics in the transient thermal radiation signal are extracted. Discrete sampling is performed based on the radiation intensity distribution characteristics to obtain grayscale mapping data corresponding to the spatial coordinates of the processing unit. The peak wavelength matching the grayscale mapping data is retrieved in a preset mapping matrix to determine the evolution trajectory of the material in the instantaneous energy state. If the evolution trajectory deviates from a preset energy threshold, the deviation vector between the evolution trajectory and the standard path is calculated. The attenuation rate of the processing area is analyzed using the deviation vector, and the correction pulse of the laser is adjusted in real time according to the attenuation rate. The instantaneous energy state of the processing area is closed-loop compensated by the correction pulse to achieve accurate characterization of the transient thermal radiation signal of the material phase transition process.

[0038] In one embodiment, the laser processing system is equipped with a high-precision sensor to monitor changes in the material under the action of laser pulses. This sensor, which can be an infrared thermal imager or a photodetector, is installed near the processing platform to enable real-time observation of the processing area. When a laser pulse is applied to the material surface, the sensor immediately activates its acquisition mode to capture thermal radiation signals. These signals originate from the energy release caused by temperature gradients and phase transitions within the material. In phase transition lattice pattern processing, the signals reflect localized heat accumulation and structural transformations. The acquisition process is synchronized with the laser pulse to avoid data deviations caused by delays. Simultaneously, the laser emits an electrical signal to the sensor controller to activate the acquisition window. The duration of this window is typically matched to the pulse width; for example, nanosecond-level pulses correspond to millisecond-level acquisition to cover transient responses.

[0039] It should be noted that transient thermal radiation signals refer to electromagnetic radiation waves emitted by a material over a short period of time, primarily in the infrared band, with intensity proportional to temperature. The acquisition process for this signal involves photoelectric conversion and amplification. First, the sensor receives the radiated photons and converts them into electrical signals. Then, a filtering circuit removes background noise to ensure signal purity. For example, signals acquired by high-precision sensors are used to characterize the depth of phase transition. The signal intensity curve shows a sharp rise at the onset of the phase transition, indicating the energy release during the material's transition from an amorphous to a crystalline state.

[0040] Step S105: The acquired transient thermal radiation signal is compared in real time with the theoretical thermal radiation curve predicted based on the material's thermophysical parameters and the current laser parameters to determine the deviation between the actual phase transition process and the expected state.

[0041] The transient waveform of the processing area under laser pulse is acquired, and the spectral brightness evolving over time in the transient waveform is extracted. Based on the variation characteristics of the spectral brightness in the time domain, the latent heat of phase change corresponding to the current processing unit is matched. A dynamic model of thermal diffusivity reflecting the internal heat conduction process of the material is constructed using the latent heat of phase change and the real-time input energy density. A preset sampling frequency is introduced into the dynamic model of thermal diffusivity to calculate the theoretical radiation intensity characterizing the evolution process of the molten pool morphology. It is determined whether the deviation vector between the theoretical radiation intensity and the actual radiation intensity in the transient waveform exceeds a preset fluctuation range. If the deviation vector exceeds the preset fluctuation range, it is determined that the actual phase change process deviates from the expected state.

[0042] In one implementation, laser processing utilizes a dedicated processor to achieve real-time comparison of transient thermal radiation signals. The system constructs a theoretical thermal radiation curve based on material thermophysical parameters such as thermal conductivity, specific heat capacity, and phase transition temperature, as well as current laser parameters including pulse energy, wavelength, and focal size. This curve simulates the temperature distribution and radiation intensity changes of the material under laser irradiation using heat transport equations, for example, by calculating the temperature field evolution using the finite difference method, and then deriving the radiation curve according to Planck's law. This prediction process ensures that the theoretical curve reflects the expected phase transition state, such as the energy transition from solid to liquid. The acquired transient thermal radiation signals are digitized and compared with the theoretical curve using a time-series matching algorithm. For example, the Euclidean distance or correlation coefficient between the signal curve and the theoretical curve is calculated to quantify the deviation. First, the signal sampling points are mapped to the corresponding times on the theoretical curve, and then the intensity values ​​are compared point by point. If the deviation exceeds a preset threshold, it is determined that the actual phase transition process deviates from the expected state. The system monitors the deviation in real time to adjust parameters. When the acquired signal shows a peak delay, it indicates that the phase transition started later than expected. This time-shift deviation is detected by the comparison algorithm, thereby triggering an increase in laser energy. Deviation judgment involves spectral analysis, which involves extracting the Fourier components of the signal and comparing them with the spectrum of the theoretical curve. A small deviation in a narrow spectral line indicates that the process is stable.

[0043] Step S106: If a deviation is determined, the energy or position of the subsequent laser pulse is dynamically adjusted according to the direction and magnitude of the deviation using a preset feedback control algorithm to correct the morphology of the phase transition region.

[0044] The geometric distortion characteristics of the phase transition region are determined based on the deviation vector, and these characteristics are used to generate a displacement component at the boundary of the molten pool. The displacement component is input into a preset proportional-integral-derivative controller to calculate the energy compensation value of the laser pulse. The energy compensation value is then superimposed with the current laser output power reference to obtain a corrected pulse energy command. If the displacement component indicates a shift in the center of the molten pool, the incident coordinates of the laser beam on the processing plane are changed by the galvanometer control system according to the pulse energy command. The real-time updating of the incident coordinates enables precise guidance of the position of the subsequent laser pulse's point of action. This precise guidance, in conjunction with the pulse energy command, changes the heat flux density distribution on the surface of the material to be processed. The rearrangement of the heat flux density distribution guides the hydrodynamic behavior inside the molten pool back to the expected trajectory, thereby correcting the morphology of the phase transition region.

[0045] In one implementation, upon determining that the transient thermal radiation signal deviates beyond a threshold from the theoretically predicted curve, the system immediately initiates a feedback control process. This process involves a pre-defined feedback control algorithm module. This module receives quantified deviation data from the comparison unit and outputs energy adjustment commands for the laser or position offset commands for the scanning galvanometer. The direction and magnitude of the deviation need to be converted into input variables that the control algorithm can process. The deviation direction can be defined by comparing the time difference between the actual signal peak and the theoretical peak: if the actual peak is later than the theoretical peak, it is defined as the "delay" direction; if it is earlier, it is defined as the "lead" direction. The deviation magnitude can be quantified as the absolute value of the time difference, or the root mean square error value between the actual signal curve and the theoretical curve within a critical time window.

[0046] One basic implementation of feedback control algorithms employs a proportional-integral-derivative (PI-DE) control strategy. This algorithm uses the real-time calculated deviation amplitude as the process variable and zero deviation as the setpoint. Internally, the algorithm calculates proportional, integral, and derivative terms, and its output is directly converted into an adjustment amount for the laser pulse energy after being mapped by a preset gain coefficient. When a significant "delay" in the phase transition process is detected, the proportional term outputs a large positive value, the integral term accumulates positive errors, and the algorithm outputs a positive "energy increment" command after comprehensive calculation. Based on this, the system proportionally increases the energy of the next pulse or the next set of pulses to accelerate material heating.

[0047] To address the common nonlinear and hysteresis characteristics in laser processing, a fuzzy logic control algorithm can be employed. This algorithm uses the direction and magnitude of the deviation as fuzzy input variables. Specifically, the input is first fuzzified, for example, the deviation magnitude is divided into fuzzy sets such as "small," "medium," and "large," and the deviation direction is divided into fuzzy sets such as "negative" (e.g., insufficient intensity), "zero," and "positive" (e.g., overheating). Then, inference is performed based on a preset fuzzy rule base, with rules in the form of: "If the deviation magnitude is 'large' and the direction is 'negative,' then the energy adjustment amount is 'significantly increased.'" Finally, through a defuzzification step, the output fuzzy adjustment amount is converted into a precise percentage of laser energy adjustment or a position offset in micrometers.

[0048] This feedback control not only adjusts the energy but also involves fine-tuning the position of the action point. When processing a periodic optical array, if the transient thermal radiation signal at a certain point continuously shows incomplete phase transition (the deviation direction is negative), and the feedback control algorithm fails to correct this after trying to increase the energy several times, it may determine that there is a local material defect or contamination at that point. At this time, the algorithm can output a command to make the scanning galvanometer make a small random shift in the position of the subsequent processing point near that coordinate, for example, reselecting a point within the original planned coordinate range for processing, in order to avoid the defect area and ensure the uniformity of the phase transition morphology.

[0049] For processing continuous pulse sequences, feedback control needs to possess memory and predictive capabilities. One implementation approach is to employ a model predictive control framework. This framework maintains a simplified dynamic model of the processing process, which predicts the thermal radiation response after the next pulse based on the current laser parameters and material state. After each comparison generates a deviation, the algorithm not only calculates the adjustment amount for the current pulse but also performs rolling optimization based on the model for the processing effect of several future pulses, calculating a series of optimal laser parameter adjustment sequences and executing only the first adjustment instruction in the sequence. For example, when processing a microlens array on a phase change material thin film, the system compares the thermal radiation signal at the center point of each lens in real time. When it finds that the peak signal intensity at the center of a lens is lower than the theoretical value by 10% (negative deviation), the feedback control algorithm calculates, based on a preset proportional coefficient, that the energy of subsequent pulses processing that region needs to be increased by 8%. After the system executes this adjustment, it continues to monitor the signal at that point. If the deviation is reduced to within 2%, the energy level is maintained; if the deviation still exists, the integral control term begins to take effect, further slowly increasing the energy until the deviation is eliminated. Through this dynamic adjustment, the phase change depth and optical characteristics of each microlens in the array are kept consistent. Furthermore, the parameters of the feedback control algorithm, such as proportional gain, integral time constant, or fuzzy rule base, are not fixed. The system can select from multiple pre-stored parameter sets based on the type of material being processed and the complexity of the pattern. For processing high thermal conductivity materials, parameters with faster response are used; for processing intricate patterns, more conservative parameters are used to avoid overshoot. For non-uniform materials or scenarios with substrate thermal influence, the feedback control algorithm can be combined with spatial weights. Since the system has already obtained the deviation distribution map of different spatial locations during the comparison stage, when calculating the adjustment amount, the feedback algorithm will assign higher adjustment weights to processing points in high energy density regions or regions with large historical deviations, meaning that the parameter adjustments for these points will be more aggressive and larger in magnitude.

[0050] Step S107: After a single processing unit is completed, the active temperature control module for that region is activated. Based on the material phase change volume of the unit and the surrounding thermal diffusion conditions, a gradient cooling field is applied to control the cooling rate and direction of that region.

[0051] The phase change volume of the processing unit at the instant of solidification and the thermal conductivity of the surrounding matrix are obtained. The total latent heat release is determined based on the phase change volume and the thermal conductivity. The heat dissipation flux is calculated using the distribution characteristics of the total latent heat release in a three-dimensional spatial coordinate system. The gradient field strength is obtained by comparing the heat dissipation flux with a preset boundary temperature difference. If the gradient field strength exceeds a preset stability threshold, the cooling vector inside the processing unit is extracted. The cooling vector is used to drive the active temperature control module to adjust the cooling rate and direction, thereby achieving the final correction of the phase change region morphology.

[0052] In one implementation, after applying a laser pulse to a predetermined processing point (i.e., a single processing unit), the system does not immediately move to the next point. Instead, it initiates an independent active temperature control phase. The system needs to apply an externally controllable cooling intervention based on the actual thermal state of the current processing point to guide the material's transformation from a molten or high-temperature phase transition state to a solid state, thereby precisely controlling the final microstructure. The active temperature control module first needs to obtain two key input parameters: the volume of the material phase transition induced by the current processing unit, and the local thermal diffusion conditions of that unit.

[0053] One feasible approach to estimating phase transition volume is to establish an empirical or semi-empirical model relating laser energy, pulse width, spot size, and material absorption characteristics. This model can quickly calculate the approximate volume of the material heated above the phase transition temperature based on the parameters of the laser processing just completed. For example, when processing vanadium dioxide phase transition lattices on a silicon substrate, the system pre-stores a table showing the correspondence between the molten pool depth and diameter of the material at different laser energy densities. After using a pulse with energy E and a spot radius r, the module can obtain an approximate hemispherical phase transition region volume V by looking up the table and interpolating. In another, more precise implementation, the system can integrate a high-speed confocal thermometry or optical coherence tomography (OCT) device to perform a rapid three-dimensional topographic scan of the processing point within a very short time (on the order of microseconds) after the laser pulse ends, directly measuring the size of the molten or high-temperature region, thereby calculating the actual phase transition volume.

[0054] The assessment of surrounding thermal diffusion conditions is equally crucial, as it determines the primary path and rate of heat loss from the phase transition region. This involves a comprehensive analysis of the thermophysical properties (such as thermal conductivity and heat capacity) and geometry of the materials surrounding the processing point. When processing a new point at the center of an array of already processed dense points, the module identifies that the point is surrounded by other high-temperature points or phase transition regions with altered thermal conductivity. The thermal diffusion conditions there are significantly different from those when processing a single point on a completely cold substrate. The module estimates the direction in which heat diffuses faster (e.g., vertically downwards into the substrate, or horizontally towards adjacent points) and the overall equivalent thermal diffusivity by solving simplified one-dimensional or two-dimensional heat conduction equations or by querying a pre-calculated thermally affected zone database. Based on the obtained phase transition volume V and thermal diffusion condition assessment results, the core task of the active temperature control module is to generate and apply a gradient cooling field. A gradient cooling field refers to a cooling method with non-uniform intensity or action in space. A basic physical implementation utilizes a multi-channel, independently temperature-controlled micro Peltier element array, arranged in a ring around the processing point. Based on the assessment of thermal diffusion conditions, the module determines that heat is primarily directed to one side (e.g., because that side is closer to a metal wire with high thermal conductivity, where heat diffuses faster). To prevent excessive cooling on that side from causing cracks or excessive internal stress, the Peltier element in that direction is controlled to receive a weaker cooling power (or even slight heating), while the element in the opposite direction receives a stronger cooling power. In this way, a gradient field of cooling intensity from "weak" to "strong" is artificially created around the processing point, guiding the material to solidify sequentially from the slower-cooling side to the faster-cooling side.

[0055] For scenarios requiring extremely high cooling rates or specific cooling directions, gas microjets can be used as actuators for gradient cooling fields. Specifically, multiple micro-nozzles with apertures on the order of tens of micrometers are arranged around the processing point, each nozzle connected to an independent gas path and piezoelectric valve. The module calculates and outputs a set of different gas pressure and flow commands based on the control objective. For example, when directional rapid solidification along a specific direction is required to form slender columnar crystals, the module instructs one or more nozzles in the target direction to inject highly efficient cooling gases such as helium at high pressure, while nozzles in other directions inject argon at lower pressure or are even shut off, thus creating a strong unidirectional cooling airflow. The cooling rate is controlled by adjusting the gas pressure, flow rate, and injection duration in the dominant cooling direction. The injection is typically initiated within hundreds of nanoseconds to several microseconds after the laser pulse ends, capturing the critical time window when the material is in a supercooled liquid state.

[0056] The cooling rate and direction settings are not fixed, but closely related to the phase transformation volume V. The system has a pre-built process knowledge base, which defines empirical relationships between the required cooling rate R, phase transformation volume V, and thermal diffusivity D for different materials and target microstructures (e.g., equiaxed crystals, columnar crystals, amorphous). For example, R = k * D / V^α, where k and α are material-related constants. After calculating V and evaluating D, the module can use this relationship to solve for the target cooling rate R_target. Subsequently, the gradient cooling field generation unit adjusts its output parameters (e.g., Peltier current, gas jet pressure) and uses built-in calibration curves to make the slope of the measured or simulated cooling curve at the center of the processing point approximate R_target. The cooling direction is determined comprehensively based on the "easiest heat dissipation direction" identified in the thermal diffusivity assessment and process requirements. If the process requires suppressing this natural heat dissipation direction to achieve a uniform microstructure, the gradient direction of the cooling field can be set to the opposite direction.

[0057] Step S108: After all the preset dot matrix patterns have been processed, a confocal microscope system is used to scan the entire processing area to obtain the actual surface morphology and optical reflectivity distribution data of each phase transition unit.

[0058] The original light intensity signal of the processing area is acquired by a confocal microscope system to obtain the surface morphology and optical reflectivity distribution data of each phase transition unit. A three-dimensional spatial topology is constructed using the surface morphology and optical reflectivity distribution data, and the phase distortion of the phase transition unit in the solidified state is calculated. Based on the grayscale gradient change of the phase distortion at the pixel level, the geometric deviation between the spatial topology and the preset dot matrix pattern is determined. The geometric deviation is input into a preset mapping matrix for coordinate transformation to obtain feature vectors characterizing the inconsistency of the phase transition region's morphology. The feature vectors are used to inversely fit the initial processing path to generate a reconstruction model for correcting the contour of the phase transition unit. The deviation field strength of the local area is calculated using the reconstruction model, and a compensation operator is called to calibrate subsequent processing pulses in real time, thereby acquiring the actual surface morphology and optical reflectivity distribution data of each phase transition unit.

[0059] In one implementation, after completing laser processing of all preset dot matrix patterns, the system activates a confocal microscopy system to perform a comprehensive scan of the entire processing area. This system utilizes a laser source and pinhole structure to achieve high-resolution imaging, capturing the three-dimensional morphological details of the processed surface. By scanning the sample surface layer by layer to eliminate interference from scattered light, an accurate surface height map and reflection intensity distribution are generated. This scanning process ensures that the actual surface morphology data of each phase transition unit is accurately recorded, and the system can distinguish surface undulations and edge contours at the micrometer level.

[0060] After scanning, the system processes the acquired data to extract surface morphology parameters for each phase transition unit. Surface morphology includes height deviation, roughness, and volume change, which are calculated from a 3D height map using an algorithm. For an array consisting of multiple phase transition units, the system uses image segmentation to isolate individual units and then calculates their average height and peak-to-valley difference.

[0061] Step S109: The actual optical reflectance distribution data obtained is matched point by point with the initially resolved optical attribute target values ​​to generate a quantitative evaluation map that identifies the consistency of processing and the uniformity of optical performance.

[0062] The actual reflectance matrix is ​​extracted using a confocal microscope system. This matrix is ​​then subjected to point-by-point difference operations with preset target attributes to determine the deviation vector. Based on the spatial weight distribution of the deviation vector within a local region, a correlation operator is used to perform cluster analysis on the topological features of adjacent phase transition units to obtain a grayscale distribution. This grayscale distribution is then convolved with a preset mapping function to calculate the deviation field strength, which reflects the differences in the microstructure of the phase transition region. If the deviation field strength exceeds a preset fitting residual threshold, the characteristic frequency of that region is extracted and converted into a uniformity value. This uniformity value is used to hierarchically label the reflectance consistency of the entire processing area, ultimately generating a quantitative evaluation map that identifies processing consistency and optical performance uniformity.

[0063] In one implementation, the system first organizes the actual optical reflectivity distribution data acquired by the confocal microscopy system into a two-dimensional array, aligning it with the target optical property values ​​initially resolved from the design file. These target values ​​include the preset reflectivity threshold and distribution pattern for each phase transition unit, and the resolution process is based on the initial parameters of the lattice pattern, such as the expected reflectivity curve corresponding to the laser energy density. Through this alignment, the system ensures that the data points correspond in position, with each pixel's actual value mapped one-to-one with the target value. The core of point-by-point matching analysis lies in calculating the deviation value of each data point. A difference calculation method is used; for each coordinate position, the difference between the actual reflectivity and the target value is subtracted, and the absolute value or the difference of squares is applied to quantify the difference. This method is widely used in laser processing of phase transition lattices. For example, when processing large-area arrays, the system first divides the area and then matches it region by region to reduce computational load. Through this step, the deviation distribution reveals the non-uniformity in the processing, such as reflectivity shifts in edge regions.

[0064] The process of generating a quantitative evaluation map is based on data from matching analysis, mapping deviation values ​​onto color gradients to form a visual map where red represents high-deviation areas and blue represents low-deviation areas. This map not only identifies fabrication consistency but also quantifies optical performance uniformity, for example, by calculating the standard deviation of the deviation across the entire region to represent uniformity. In embodiments of phase-change lattice fabrication, this map covers evaluations from individual cells to the entire array.

[0065] For complex lattice patterns, the system performs multi-scale matching analysis. First, it compares the average reflectance of regions at a macroscopic scale, then refines the matching to the micrometer level, point-by-point. This hierarchical method is particularly suitable for phase transition structures formed by laser direct writing. For example, when processing patterns with gradient reflectance, the system first extracts the target values ​​for sub-regions and then calculates the local consistency index. In this way, the generated map provides a hierarchical view, helping to identify performance problems in specific regions.

Claims

1. A laser processing method for achieving anti-counterfeiting effects of holographic images, characterized in that, The method includes: Obtain the digital design file of the target holographic image, and parse the preset phase transition dot matrix pattern and its corresponding optical attribute target value to achieve specific optical effects from the file; Based on the inherent optical and thermophysical parameters of the material to be processed, a mapping model is established between laser energy density, interaction time, expected phase transition depth of the material, and changes in regional optical properties. For each processing unit in the preset phase transition lattice pattern, the mapping relationship model is used to calculate and generate a laser processing parameter sequence that matches the target optical properties of the unit. This sequence includes energy, pulse width, and scanning path. During laser processing, a high-precision sensor synchronously collects the transient thermal radiation signal of the processing area after the laser pulse. This signal characterizes the instantaneous energy state of the material phase transition process. The acquired transient thermal radiation signal is compared in real time with the theoretical thermal radiation curve predicted based on the material's thermophysical parameters and the current laser parameters to determine the deviation between the actual phase transition process and the expected state. If a deviation is detected, the energy or position of the subsequent laser pulse is dynamically adjusted using a preset feedback control algorithm based on the direction and magnitude of the deviation, in order to correct the morphology of the phase transition region. After a single processing unit is completed, the active temperature control module for that area is activated. Based on the material phase change volume of that unit and the surrounding thermal diffusion conditions, a gradient cooling field is applied to control the cooling rate and direction of that area. After all the preset dot matrix patterns have been processed, the entire processing area is scanned using a confocal microscope system to obtain the actual surface morphology and optical reflectivity distribution data of each phase transition unit. The actual optical reflectivity distribution data obtained are matched point by point with the initially resolved optical attribute target values ​​to generate a quantitative evaluation map that identifies the consistency of processing and the uniformity of optical performance.

2. The laser processing method for achieving holographic image anti-counterfeiting effect according to claim 1, characterized in that, The digital design file for acquiring the target holographic image is used to parse a preset phase transition dot matrix pattern and its corresponding optical attribute target values ​​for achieving specific optical effects, including: The digital design file of the target holographic image is obtained from the repository through a secure transmission protocol, resulting in a complete data packet of the design file; The design file employs a multi-layer pattern decomposition method to separate optical layer data layer by layer from the complete data package, and parses out the preset phase transition lattice pattern to obtain the structural distribution of the phase transition lattice pattern. Based on the structural distribution of the phase transition lattice pattern, extract the corresponding optical attribute target value, match the phase transition point coordinates from the structural distribution, and determine the quantitative index of the optical attribute target value; The optical effect is simulated by quantifying the target value of the optical attribute through virtual rendering test, and the virtual holographic projection is generated by inputting the quantified index into the rendering engine to obtain the deviation data of the simulation effect. If the deviation data exceeds the preset threshold, a real-time calibration mechanism is used to adjust the target value of the optical properties. The quantitative index is gradually corrected by iteratively comparing the deviation data with the target effect. It is determined that the adjusted value meets the specific optical effect requirements, thereby obtaining the preset phase transition lattice pattern and its corresponding target value of optical properties used to achieve the specific optical effect.

3. The laser processing method for achieving holographic image anti-counterfeiting effect according to claim 2, characterized in that, The process involves establishing a mapping model between laser energy density, interaction time, and the expected phase transition depth and regional optical property changes of the material, based on the inherent optical and thermophysical parameters of the material to be processed. The refractive index distribution data and thermal diffusivity values ​​of the material to be processed are retrieved from the material database, and a multi-dimensional feature space mapping is performed on the refractive index distribution data and the thermal diffusivity values ​​to obtain the material's inherent property feature vector. The material's inherent property feature vector is input into a pre-constructed energy absorption field simulation model to determine the initial absorption rate of laser energy density on the material's surface. A transient heat flux density distribution sequence is obtained by performing a time-domain convolution operation on the initial absorptivity and the preset laser pulse waveform. If a local value in the transient heat flux density distribution sequence exceeds the melting point threshold of the material, the evolution process of the transient heat flux density distribution sequence inside the material is analyzed by the heat conduction differential equation to obtain three-dimensional temperature field dynamic evolution data. By matching the phase transition kinetic equation with the dynamic evolution data of the three-dimensional temperature field, the position of the critical isothermal surface where the crystal structure transformation occurs inside the material is determined, and the expected phase transition depth is obtained. The change in atomic packing density in the corresponding region is extracted by the expected phase transition depth, and then substituted into the electromagnetic wave scattering model to reconstruct the dielectric constant, thereby determining the change in the region's optical properties. By performing correlation analysis between the changes in the optical properties of the region and the expected phase transition depth, a mapping model is established between laser energy density and interaction time and the expected phase transition depth of the material and the changes in the optical properties of the region.

4. The laser processing method for achieving holographic image anti-counterfeiting effect according to claim 3, characterized in that, For each processing unit in the preset phase transition lattice pattern, the mapping relationship model is used to calculate and generate a laser processing parameter sequence that matches the target optical properties of that unit. This sequence includes energy, pulse width, and scanning path, including: The target refractive gradient of the processing unit is extracted from the preset phase transition lattice pattern, and the degree of lattice distortion corresponding to the target refractive gradient is retrieved in the multidimensional mapping relationship model. The phase transition volume of the processing unit is calculated based on the degree of lattice distortion, and the transient heat flux vector distribution within the phase transition volume is determined in conjunction with the dielectric tensor evolution law. By matching the transient heat flux vector distribution with the laser pulse envelope, the energy peak value and pulse width duration in the laser processing parameter sequence can be determined. The pulse width duration is used to analyze the thermal overlap ratio between adjacent processing units, and spatial compensation is performed on the scan vector in the laser processing parameter sequence based on the cooling rate. Path planning is performed on the compensated scanning vector to generate a complete laser processing parameter sequence containing energy, pulse width, and scanning path, thereby achieving the generation of a laser processing parameter sequence that matches the optical properties of the processing unit target.

5. The laser processing method for achieving holographic image anti-counterfeiting effect according to claim 4, characterized in that, During laser processing, a high-precision sensor synchronously acquires the transient thermal radiation signal of the processing area after the laser pulse. This signal characterizes the instantaneous energy state of the material phase transition process, including: The transient thermal radiation signal of the processing area at the moment of laser pulse action is captured by a high-precision sensor, and the radiation intensity distribution characteristics in the transient thermal radiation signal are extracted. Discrete sampling is performed based on the radiation intensity distribution characteristics to obtain grayscale mapping data corresponding to the spatial coordinates of the processing unit; The peak wavelength that matches the grayscale mapping data is retrieved from the preset mapping matrix to determine the evolution trajectory of the material in the instantaneous energy state; If the evolutionary trajectory deviates from a preset energy threshold, then the deviation vector between the evolutionary trajectory and the standard path is calculated; The attenuation rate of the processing area is analyzed using the deviation vector, and the correction pulse of the laser is adjusted in real time according to the attenuation rate; By performing closed-loop compensation on the instantaneous energy state of the processing area using the correction pulse, the transient thermal radiation signal of the material phase transition process can be accurately characterized.

6. The laser processing method for achieving holographic image anti-counterfeiting effect according to claim 5, characterized in that, The step of comparing the acquired transient thermal radiation signal with the theoretical thermal radiation curve predicted based on the material's thermophysical parameters and current laser parameters in real time to determine the deviation between the actual phase transition process and the expected state includes: The transient waveform of the processing area under the action of laser pulse is obtained, and the spectral brightness of the transient waveform evolving over time is extracted. Based on the variation characteristics of the spectral brightness in the time domain, the latent heat of phase change corresponding to the current processing unit is obtained; A dynamic model of thermal diffusivity reflecting the internal heat conduction process of the material is constructed using the latent heat of phase change and the real-time input energy density. A preset sampling frequency is introduced into the dynamic model of thermal diffusivity to calculate the theoretical radiation intensity characterizing the evolution process of the molten pool morphology. Determine whether the deviation vector between the theoretical radiation intensity and the actual radiation intensity in the transient waveform exceeds a preset fluctuation range; If the deviation vector exceeds the preset fluctuation range, it is determined that the actual phase transition process deviates from the expected state.

7. The laser processing method for achieving holographic image anti-counterfeiting effect according to claim 6, characterized in that, If a deviation is detected, a preset feedback control algorithm is used to dynamically adjust the energy or position of the subsequent laser pulse based on the direction and magnitude of the deviation, in order to correct the morphology of the phase transition region, including: The geometric distortion characteristics of the phase transition region are determined based on the deviation vector, and these geometric distortion characteristics are used to generate displacement components at the molten pool boundary. The displacement component is input into a preset proportional-integral-derivative controller to calculate the energy compensation value of the laser pulse; The corrected pulse energy command is obtained by superimposing the energy compensation value with the current laser output power reference. If the displacement component indicates that the center of the molten pool has shifted, the incident coordinates of the laser beam on the processing plane are changed by the galvanometer control system according to the pulse energy command. The real-time update of the incident coordinates enables precise guidance of the position of the subsequent laser pulse application point. This precise guidance, together with the pulse energy command, changes the heat flux density distribution on the surface of the material to be processed. The rearrangement of the heat flux density distribution guides the hydrodynamic behavior inside the molten pool back to the expected trajectory, thereby correcting the morphology of the phase transition region.

8. The laser processing method for achieving holographic image anti-counterfeiting effect according to claim 7, characterized in that, After a single processing unit is completed, an active temperature control module for that region is activated. Based on the material phase change volume of that unit and the surrounding thermal diffusion conditions, a gradient cooling field is applied to control the cooling rate and direction of that region, including: The phase change volume of the processing unit at the instant of solidification and the thermal conductivity of the surrounding matrix are obtained, and the total amount of latent heat released is determined based on the phase change volume and the thermal conductivity. The heat dissipation flux is calculated using the distribution characteristics of the total latent heat release in a three-dimensional spatial coordinate system, and the gradient field strength is obtained by comparing the heat dissipation flux with the preset boundary temperature difference. If the gradient field strength exceeds a preset stability threshold, then the cooling vector inside the processing unit is extracted; The cooling vector drive active temperature control module is used to adjust the cooling speed and direction, thereby achieving the final correction of the phase transition region morphology.

9. A laser processing method for achieving holographic image anti-counterfeiting effect according to claim 8, characterized in that, After all the preset dot matrix patterns have been processed, a confocal microscope system is used to scan the entire processing area to obtain the actual surface morphology and optical reflectivity distribution data of each phase transition unit, including: The original light intensity signal of the processing area was acquired by a confocal microscope system to obtain the surface morphology and optical reflectivity distribution data of each phase transition unit. A three-dimensional spatial topology is constructed using the surface morphology and optical reflectivity distribution data, and the phase distortion of the phase transition unit in the solidification state is calculated. Based on the grayscale gradient change of the phase distortion at the pixel level, the geometric deviation between the spatial topology and the preset dot matrix pattern is determined; The geometric deviation is input into a preset mapping matrix for coordinate transformation to obtain a feature vector characterizing the inconsistency of the phase transition region morphology. The initial processing path is backfitted using the feature vectors to generate a reconstructed model for correcting the contour of the phase transition unit; The reconstructed model is used to calculate the deviation field strength in the local area, and the compensation operator is called to calibrate the subsequent processing pulses in real time, thereby obtaining the actual surface morphology and optical reflectivity distribution data of each phase transition unit.

10. A laser processing method for achieving holographic image anti-counterfeiting effect according to claim 9, characterized in that, The process involves performing a point-by-point matching analysis between the acquired actual optical reflectivity distribution data and the initially resolved optical attribute target values ​​to generate a quantitative evaluation map that identifies processing consistency and optical performance uniformity, including: The actual reflectance matrix is ​​extracted by a confocal microscope system, and the actual reflectance matrix is ​​then subjected to point-by-point difference operation with the preset target attribute to determine the deviation vector. Based on the spatial weight distribution of the deviation vector in the local region, the correlation operator is invoked to perform cluster analysis on the topological features of adjacent phase transition units to obtain the gray-scale distribution. The deviation field strength, which reflects the microstructural differences in the phase transition region, is calculated by convolving the gray-scale distribution with a preset mapping function. If the deviation field strength exceeds the preset fitting residual threshold, the feature frequency of the region is extracted and converted into a uniformity value. The uniformity value is used to classify and label the reflectivity consistency of the entire processing area, and finally a quantitative evaluation map is generated to identify the processing consistency and optical performance uniformity.