CN119438145A - Watch plasma nanofilm detection method and device based on image processing - Google Patents
Watch plasma nanofilm detection method and device based on image processing Download PDFInfo
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Abstract
本发明公开一种基于图像处理的手表等离子纳米膜的检测方法及装置，其中，基于图像处理的手表等离子纳米膜的检测方法通过光漂白效应激发处理和相位调制分析，对手表等离子纳米膜进行双偏振态光强数据采集，利用动态恢复特征和区域分割技术，选取光漂白深度最大点作为参考基准进行差分处理，实现了几何曲率效应与材料效应的精确解耦，从而准确评估纳米膜的本征性能。本发明技术方案能够有效解决手表曲面导致的非线性干涉问题，提供可靠的纳米膜质量检测结果。
The present invention discloses a detection method and device for plasma nanofilm of watches based on image processing, wherein the detection method for plasma nanofilm of watches based on image processing collects dual polarization light intensity data of plasma nanofilm of watches through photobleaching effect excitation processing and phase modulation analysis, uses dynamic recovery characteristics and regional segmentation technology, selects the maximum point of photobleaching depth as a reference benchmark for differential processing, realizes the precise decoupling of geometric curvature effect and material effect, and thus accurately evaluates the intrinsic performance of nanofilm. The technical solution of the present invention can effectively solve the nonlinear interference problem caused by the curved surface of watches and provide reliable nanofilm quality detection results.
Description
Technical Field
The invention relates to the technical field of watch surface treatment detection, in particular to a detection method and device of a watch plasma nano film based on image processing.
Background
Watch plasma nanomembranes are a new type of functional nanostructured coating that is typically formed by the deposition of precious metal (e.g., gold, silver, etc.) nanoparticles onto the watch surface under specific process conditions. The nano film structure not only can endow the watch surface with unique optical characteristics, but also can provide excellent waterproof, scratch-proof and other protection functions. At the nanoscale, metal particles in the plasma nanomembrane can generate a surface plasmon resonance effect, and the effect enables the nanomembrane to generate special selective absorption and reflection on incident light, so that unique optical response characteristics are formed, and the effect is an important foundation of the wristwatch plasma nanomembrane in the aspects of functionality and decoration.
In the watch production process, the quality of the plasma nano-film is directly related to the performance and appearance of the product, so that strict quality detection is required. The current detection equipment generally comprises a light source system, an image acquisition device, a sample fixing platform and a data processing system, wherein the detection method is to irradiate the surface of a watch through a fixed light source, and then acquire a reflected image of the nano film by a camera for analysis. However, this conventional detection method encounters a serious technical bottleneck in practical application. In particular, when the light source irradiates the watch surface, the incident light can generate different degrees of reflection and scattering in different areas due to the complex three-dimensional curvature (including spherical surface, cylindrical surface or irregular curved surface) of the watch surface. Meanwhile, the surface plasmon resonance effect of the plasma nano film can generate nonlinear optical interference phenomenon related to the observation angle in the edge area of the curved surface. This interference phenomenon is quite different from simple reflection of a common metal surface, which contains complex optical response information. For example, when viewing a nanomembrane at the junction of a wristwatch bezel and a scope, even if the nanomembrane masses are perfectly uniform, the difference in optical response due to the change in curvature can cause significant deviations in the measurement results. The conventional image processing method cannot accurately distinguish whether the differences are caused by geometric distortion caused by surface curvature or performance differences of the nano film, which seriously affects the accuracy and reliability of detection results.
Disclosure of Invention
The invention mainly aims to solve the technical problem that geometric distortion caused by a curved surface and the difference of the intrinsic performance of a nano film cannot be accurately distinguished in the existing watch plasma nano film detection technology.
The first aspect of the invention provides a method for detecting a watch plasma nano-film based on image processing, which comprises the following steps:
Collecting dual-polarization light intensity distribution data before and after photobleaching of the watch plasma nano film in a preset incidence angle range, wherein the dual-polarization light intensity distribution data comprises s-polarization and p-polarization intensity data before photobleaching and s-polarization and p-polarization intensity data after photobleaching;
Carrying out phase modulation on the dual-polarization light intensity distribution data, recording a phase-intensity response curve in a preset phase range, and carrying out Fourier transformation processing to obtain amplitude-phase characteristic data representing local plasma resonance characteristics;
Extracting dynamic recovery features of the photo-bleaching region based on the dual-polarization light intensity distribution data and the amplitude-phase feature data, and generating a region segmentation feature map according to a spatial distribution rule of the dynamic recovery features;
selecting the maximum point of the photo-bleaching depth in each region as a reference standard according to the region segmentation characteristic map, and performing differential processing on the optical response of other points in the region and the reference standard to obtain pure material effect distribution data for eliminating the influence of geometric curvature;
performing dimension reduction treatment on the characteristic parameters in the pure material effect distribution data, extracting main characteristic components representing the resonance characteristics of the plasma, and obtaining performance evaluation data;
And establishing a spatial distribution relation of regional performance parameters according to the performance evaluation data, and generating a quantitative detection result for characterizing the intrinsic performance of the nano film.
Optionally, the collecting the data of the dual polarized light intensity distribution before and after photobleaching of the watch plasma nano film in the preset incident angle range includes:
Forming a photo-bleaching area array with a preset interval on the surface of a sample, wherein the photo-bleaching area array comprises a plurality of independent photo-bleaching spots;
Scanning the photo-bleaching area array at an angle of 0-75 degrees, and collecting a group of polarized light intensity data at intervals of 5 degrees to obtain original light intensity data before photo-bleaching;
Performing photo-bleaching excitation on the photo-bleaching area array, scanning according to the same angle stepping mode as the 0-75 degree angle scanning, and collecting polarized light intensity data of the photo-bleaching area array to obtain excited light intensity data after photo-bleaching;
performing polarization state separation according to the original light intensity data and the excited light intensity data to obtain s-polarized intensity data and p-polarized intensity data before photobleaching;
performing polarization state separation according to the excited state light intensity data to obtain s-polarized intensity data and p-polarized intensity data after photobleaching;
And combining the s-polarized and p-polarized intensity data before the photo-bleaching and the s-polarized and p-polarized intensity data after the photo-bleaching into dual-polarized light intensity distribution data.
Optionally, the phase modulating the dual-polarized light intensity distribution data, recording a phase-intensity response curve in a preset phase range, and performing fourier transform processing to obtain amplitude-phase characteristic data representing local plasma resonance characteristics, where the phase modulating includes:
Carrying out phase scanning on the dual-polarization light intensity distribution data within the range of 0-2 pi according to the step length of 0.1 pi to obtain a phase scanning sequence before and after photobleaching;
calculating local polarization state ellipse parameters according to s-polarization and p-polarization intensity data of each phase point in the phase scanning sequence to obtain phase-intensity modulation data;
performing discrete Fourier transform on the phase-intensity modulation data, extracting first-order and second-order Fourier components, and obtaining initial amplitude-phase characteristic quantity;
Calculating a local plasma resonance intensity coefficient and a phase delay coefficient according to the initial amplitude-phase characteristic quantity to obtain resonance characteristic parameters;
normalizing the resonance characteristic parameters to generate normalized amplitude-phase characteristic data;
And performing correlation matching on the normalized amplitude-phase characteristic data and the phase-intensity modulation data at the corresponding position to obtain amplitude-phase characteristic data representing the resonance characteristic of the local plasma.
Optionally, the extracting the dynamic recovery feature of the photo-bleaching region based on the dual-polarization light intensity distribution data and the amplitude-phase feature data, and generating the region segmentation feature map according to the spatial distribution rule of the dynamic recovery feature includes:
continuously sampling the dual-polarization light intensity distribution data according to a preset time interval to obtain time sequence intensity change data of a photo-bleaching area;
extracting a recovery time constant and a recovery amplitude parameter of each sampling point according to the time sequence intensity change data to obtain initial dynamic characteristic data;
Performing cross-correlation analysis according to the amplitude-phase characteristic data and the initial dynamic characteristic data to obtain comprehensive dynamic parameters representing response characteristics of materials;
carrying out space gradient analysis on the comprehensive dynamic parameters to obtain a dynamic characteristic difference distribution map of a local area;
according to the spatial continuity of the dynamic characteristic difference distribution diagram, carrying out dynamic clustering on adjacent areas to obtain an area boundary characteristic point set;
and performing closed curve fitting on the regional boundary feature point set to generate a regional segmentation feature map.
Optionally, selecting a maximum point of the photobleaching depth in each region as a reference standard according to the region segmentation feature map, and performing differential processing on optical responses of other points in the region and the reference standard to obtain pure material effect distribution data for eliminating the influence of geometric curvature, including:
carrying out light intensity gradient calculation on each region in the region segmentation feature map to obtain photobleaching depth distribution data in the region;
Detecting extreme points of each region according to the photobleaching depth distribution data, and screening candidate point sets with the maximum photobleaching depth;
Carrying out local curvature calculation on each point in the candidate point set to obtain curvature characteristic data of the candidate points;
optimizing the candidate point set according to the curvature characteristic data, and determining a final reference datum point in each region;
Performing ratio operation on the light intensity data of other points in the area and the light intensity data of the reference point to obtain normalized light intensity distribution data;
and performing curvature compensation processing on the normalized light intensity distribution data to generate pure material effect distribution data for eliminating the influence of geometric curvature.
Optionally, the performing dimension reduction processing on the characteristic parameters in the pure material effect distribution data, extracting main characteristic components representing the resonance characteristics of the plasma, and obtaining performance evaluation data includes:
nonlinear transformation is carried out on the formant positions, the peak widths and the peak intensities in the pure material effect distribution data, so that a characteristic point set in a multidimensional characteristic space is obtained;
Performing principal component-extremum decomposition according to the feature point set, extracting a change trend in a principal feature direction, and obtaining an orthogonal feature vector group;
singular value decomposition is carried out on each component of the orthogonal feature vector group, so that feature weight distribution data are obtained;
reordering the characteristic weight distribution data according to weight size to obtain an ordered characteristic component sequence;
Reconstructing the characteristic components with the contribution rate exceeding a preset threshold value in the characteristic component sequence to obtain feature data with reduced dimensions;
And mapping and matching the feature data with the dimension reduced with the plasma resonance theoretical feature to generate performance evaluation data.
Optionally, the performing singular value decomposition on each component of the orthogonal feature vector set to obtain feature weight distribution data includes:
Constructing the orthogonal eigenvectors into an eigenvector matrix, and carrying out singular value decomposition to obtain a left singular matrix, a singular value matrix and a right singular matrix;
Carrying out orthogonality analysis on the left singular matrix and the right singular matrix to obtain a basis vector group of a feature space;
Calculating energy contribution coefficients of each characteristic component according to the size of diagonal elements of the singular value matrix;
Performing hierarchical clustering analysis based on the energy contribution coefficient to obtain a characteristic hierarchical tree structure;
Pruning optimization is carried out on the characteristic hierarchical tree structure, and a key characteristic node sequence is obtained;
and recalculating a weight coefficient according to the key characteristic node sequence to generate characteristic weight distribution data.
Optionally, the establishing a spatial distribution relation of regional performance parameters according to the performance evaluation data, and generating a quantitative detection result for characterizing the intrinsic performance of the nano-film, includes:
Carrying out local response characteristic analysis on the performance evaluation data to obtain a plasma resonance intensity index, a dynamic stability index and a uniformity index of each region;
Combining the plasma resonance intensity index, the dynamic stability index and the uniformity index according to a preset weighting coefficient to obtain a region comprehensive performance index;
Performing Delaue triangulation on the test area according to the area comprehensive performance index to obtain a spatial interpolation grid of the performance parameter;
Performing thin-plate spline interpolation on node data of the spatial interpolation grid to obtain a continuous performance distribution function;
Calculating local gradient and curvature based on the performance distribution function, and generating a performance change feature map;
and carrying out fusion superposition on the performance change characteristic diagram and the performance distribution function to obtain a quantitative detection result for characterizing the intrinsic performance of the nano film.
The second aspect of the invention provides a detection device of a watch plasma nano-film based on image processing, which comprises:
The data acquisition module is used for acquiring dual-polarization state light intensity distribution data of the watch plasma nano film before and after photobleaching in a preset incidence angle range, wherein the dual-polarization state light intensity distribution data comprise s-polarization and p-polarization intensity data before photobleaching and s-polarization and p-polarization intensity data after photobleaching;
The phase modulation module is used for carrying out phase modulation on the dual-polarization light intensity distribution data, recording a phase-intensity response curve in a preset phase range, and obtaining amplitude-phase characteristic data representing the resonance characteristic of local plasma through Fourier transformation;
The dynamic characteristic extraction module is used for extracting dynamic recovery characteristics of the photo-bleaching region based on the dual-polarization state light intensity distribution data and the amplitude-phase characteristic data, and generating a region segmentation characteristic map according to the spatial distribution rule of the dynamic recovery characteristics;
The decoupling analysis module is used for selecting the maximum point of the photobleaching depth in each region as a reference standard according to the region segmentation characteristic map, and carrying out differential processing on the optical responses of other points in the region and the reference standard to obtain pure material effect distribution data for eliminating the influence of geometric curvature;
The dimension reduction processing module is used for carrying out dimension reduction processing on the characteristic parameters in the pure material effect distribution data, extracting main characteristic components representing the resonance characteristics of the plasma and obtaining performance evaluation data;
And the performance evaluation module is used for establishing a spatial distribution relation of regional performance parameters according to the performance evaluation data and generating a quantitative detection result for characterizing the intrinsic performance of the nano film.
The technical scheme provided by the embodiment of the application has at least the following advantages:
Firstly, a mode of combining photo-bleaching effect excitation treatment and dual-polarization light intensity distribution data acquisition is adopted, so that a material effect is obviously different before and after photo-bleaching, and a geometric curvature effect is kept unchanged, thereby providing a physical basis for subsequent decoupling analysis. And secondly, amplitude-phase characteristic data are obtained through phase modulation and Fourier transformation, and region segmentation is carried out by combining the spatial distribution rule of dynamic recovery characteristics, so that regions with similar optical response characteristics are effectively identified. Finally, a local reference standard method based on the maximum point of the photo-bleaching depth is introduced, the influence of geometric curvature is eliminated through differential processing, and the accurate extraction of the pure material effect is realized. The multi-layer decoupling analysis method not only solves the problem that curvature distortion and material performance are difficult to separate in traditional detection, but also provides a reliable technical means for quantitative evaluation of the performance of the nano-film.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an embodiment of a method for detecting a plasma nanomembrane of a wristwatch based on image processing according to an embodiment of the invention;
fig. 2 is a schematic diagram of an embodiment of a detection device for a plasma nanomembrane of a wristwatch based on image processing according to an embodiment of the invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, if directional indications (such as up, down, left, right, front, and rear are referred to in the embodiments of the present invention), the directional indications are merely used to explain the relative positional relationship, movement conditions, and the like between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between the embodiments can be mutually combined, and the technical solutions must be based on the realization that one of ordinary skill in the art can realize, when the combination of the technical solutions contradicts or cannot realize, the combination of the technical solutions is considered to be absent and is not within the protection scope required by the invention.
The embodiment of the application provides a detection method of a watch plasma nano film based on image processing. Fig. 1 is a flowchart of a method for detecting a wristwatch plasma nanomembrane based on image processing according to an embodiment of the application. In this embodiment, the method includes:
Referring to fig. 1, dual-polarization light intensity distribution data of a wristwatch plasma nano-film before and after photobleaching is collected within a preset incident angle range, wherein the dual-polarization light intensity distribution data comprises s-polarization and p-polarization intensity data before photobleaching and s-polarization and p-polarization intensity data after photobleaching;
In one embodiment of the invention, the method for acquiring the dual-polarization light intensity distribution data before and after the photo-bleaching of the watch plasma nano film in the preset incidence angle range comprises the steps of forming a photo-bleaching area array with a preset interval value on the surface of a sample, wherein the photo-bleaching area array comprises a plurality of independent photo-bleaching spots, carrying out 0-75-degree angle scanning on the photo-bleaching area array, acquiring a group of polarization light intensity data at intervals of 5 degrees to obtain original light intensity data before the photo-bleaching, carrying out photo-bleaching excitation on the photo-bleaching area array, carrying out scanning according to the same angle stepping mode as the 0-75-degree angle scanning, acquiring polarization light intensity data of the photo-bleaching area array to obtain excited light intensity data after the photo-bleaching, carrying out polarization state separation according to the original light intensity data and the excited light intensity data to obtain s-polarization intensity data before the photo-bleaching and p-polarization intensity data, carrying out polarization state separation according to the excited light intensity data to obtain s-polarization intensity data after the photo-bleaching and p-polarization intensity data, and combining the s-polarization light intensity data after the photo-polarization distribution data with the dual-polarization light intensity data.
Specifically, in the detection process of the watch plasma nanomembrane, the first step is to construct a photobleaching area array on the sample surface. The construction of the photo-bleaching area array adopts an array laser irradiation method, and regularly distributed photo-bleaching areas are formed on the surface of the watch by controlling the irradiation time and energy density of a laser. The photobleaching region is a region in which plasmon resonance effect in the nanomaterial is temporarily suppressed by strong light irradiation. The size and spacing of each photobleaching area is designed to take into account two factors, one to ensure that individual areas are fully reflective of local material properties and one to avoid interference between adjacent areas. The preset value of the spacing of the photobleaching areas is selected mainly based on two key factors of the spatial attenuation characteristic of the plasma resonance effect and the gradient of the change of the curvature of the watch surface. Specifically, experiments prove that when the size of the photo-bleaching area is set to be 50-100 micrometers and the interval is set to be 200 micrometers, the photo-bleaching effects of adjacent areas can be ensured not to interfere with each other, and the sampling density can be ensured to sufficiently reflect the change characteristics of the surface curvature. For example, in detecting nanomembranes of a wristwatch bezel, if the photobleaching area is too large, it may span areas of different curvature, resulting in distortion of the measurement results, and if the spacing is too small, the photobleaching effects of adjacent areas may affect each other. This array design has the obvious advantage of not only ensuring the spatial resolution of the detection, but also providing enough reference points for subsequent decoupling analysis.
After the array of photobleaching areas is obtained, angular scanning data acquisition is required. The angular scan starts at 0 degrees (perpendicular to the sample surface) and a set of data is acquired every 5 degrees up to 75 degrees. At each angular position, light intensity data of two polarization states of s polarization (vibration direction perpendicular to the incident surface) and p polarization (vibration direction parallel to the incident surface) are acquired respectively. The s-polarized intensity data and p-polarized intensity data here reflect the response characteristics of the material to light of different polarization states, respectively. For example, when detecting nanomembranes at the edges of a wristwatch, p-polarized light will exhibit greater sensitivity to plasmon resonance at 45 degrees incidence, while s-polarized light will be primarily affected by geometric curvature. The full-angle and double-polarization data acquisition mode can provide rich material response information, and lays a foundation for subsequent characteristic decoupling.
Next, the array of labeled photobleaching regions is subjected to photobleaching excitation. The term "photobleaching excitation" refers to irradiation of a material with a laser of higher intensity to bring the material into a photobleaching state. In this state, the plasmon resonance effect of the material is significantly suppressed, while the geometry of the surface remains unchanged. The data must then be re-acquired in exactly the same angular scan as before, and this requirement for angular step consistency stems from the need for decoupling analysis. Only under the same observation angle, the change of the material response can be accurately compared, and the data before and after the photo-bleaching has a strict spatial correspondence. For example, for curved areas of the watch dial edge, the pre-photobleaching data contains both curvature scattering and material resonance information, while the post-photobleaching data reflects mainly the effects of geometric curvature. This front-to-back contrast mode of acquisition has the important advantage of being able to selectively modulate the optical response of the material without changing the geometry of the sample.
When polarization state separation processing is performed on the acquired data, a polarization optical system is used to decompose the mixed optical signal into two independent components of s polarization and p polarization. The physical basis of this separation process is that p-polarized light undergoes significant reflectivity suppression under plasmon resonance conditions, while s-polarized light is primarily affected by geometric scattering. For example, when detecting the concave-convex edges of a watch surface, by comparing the difference in response of the two polarization states, it is possible to effectively distinguish which signal changes are caused by material properties and which are caused by surface topography. The polarization analysis method has the advantage that two independent but mutually complementary information channels are provided, so that the reliability of feature extraction is enhanced.
The final step is data combination, which is to construct a data matrix according to three dimensions of a space position, a polarization state and a photo-bleaching state, and combine s-polarization and p-polarization intensity data before and after photo-bleaching into a complete dual-polarization light intensity distribution data set according to a time sequence and the space position. For each detection point, a four-dimensional dataset is finally formed, which contains spatial coordinates (x-y), polarization states (s-p), and states before and after photobleaching. This structured data organization needs to ensure accurate correspondence between the different data. For example, for each detection point of the watch surface, a complete data set is formed containing both the polarization states before and after photobleaching. The multidimensional data combination has the advantages of providing sufficient information redundancy for subsequent decoupling analysis and improving the accuracy of feature extraction. Through the data acquisition and combination method of the system, the accurate evaluation of the performance of the plasma nano-film of the watch can be finally realized.
With continued reference to fig. 1, the dual-polarized light intensity distribution data is subjected to phase modulation, a phase-intensity response curve is recorded in a preset phase range, and amplitude-phase characteristic data representing local plasma resonance characteristics is obtained through fourier transform processing;
In one embodiment of the invention, the method comprises the steps of carrying out phase modulation on the dual-polarization-state light intensity distribution data, recording a phase-intensity response curve in a preset phase range, carrying out Fourier transform processing to obtain amplitude-phase characteristic data representing local plasma resonance characteristics, carrying out phase scanning on the dual-polarization-state light intensity distribution data in a range of 0-2 pi according to a step length of 0.1 pi to obtain a phase scanning sequence before and after photo-bleaching, calculating local polarization-state ellipse parameters according to s-polarization and p-polarization intensity data of each phase point in the phase scanning sequence to obtain phase-intensity modulation data, carrying out discrete Fourier transform on the phase-intensity modulation data to extract first-order and second-order Fourier components to obtain initial amplitude-phase characteristic parameters, calculating local plasma resonance intensity coefficients and phase delay coefficients according to the initial amplitude-phase characteristic parameters to obtain resonance characteristic parameters, carrying out normalization processing on the resonance characteristic parameters to generate normalized amplitude-phase characteristic data, and carrying out correlation matching on the normalized amplitude-phase characteristic data and the phase-intensity modulation data of corresponding positions to obtain the amplitude-phase characteristic resonance characteristic data representing local plasma resonance characteristics.
Specifically, the process of performing phase characteristic analysis on the dual-polarization light intensity distribution data first requires phase scanning. Phase scanning refers to modulating the phase of incident light in steps of 0.1 pi in the range of 0-2 pi. This fine step setting is to accurately capture the response characteristics of the material to phase changes. At each phase point, the light intensity response before and after photobleaching needs to be recorded, forming a complete phase scanning sequence. The physical meaning of phase scanning is that by changing the phase of the incident light, the optical response of the material under different phase conditions can be excited, which contains information about the intrinsic properties of the material. For example, when a certain area of the watch surface is scanned, if there is a plasmon resonance effect there, a significant light intensity modulation phenomenon occurs at a specific phase point. The advantage of this phase scanning method is that the phase response characteristics of the material can be comprehensively obtained.
From the obtained phase scan sequence, local polarization state ellipse parameters need to be calculated. The polarization state ellipse parameters are important indexes for describing the change of the polarization state of the light wave in the propagation process, and include parameters such as ellipticity, azimuth angle and the like. In the specific calculation, the s-polarization and p-polarization intensity data of each phase point are utilized to construct a polarization state ellipse by a Stokes parametric method. These parameters are directly related to the optical anisotropy and plasmon resonance properties of the material. For example, in detecting uniformity of a nanofilm on a watch surface, changes in the elliptical parameters of polarization state may reflect localized differences in material structure. The phase-intensity modulation data obtained by this method contains rich material property information.
Next, discrete fourier transform processing is performed on the phase-intensity modulated data. In the fourier transform process, the first and second order fourier components are extracted with emphasis, because these two components correspond to the linear response and nonlinear response characteristics of the material, respectively. The first order component reflects mainly the basic periodic variation, while the second order component contains the nonlinear optical effect information of the material. For example, in analyzing the plasmon resonance effect of a nanomembrane on a watch surface, the intensity of the second order fourier component may directly reflect the resonance intensity. The advantage of this fourier analysis method is the ability to convert complex time domain signals into frequency domain features that are easy to analyze.
Based on the obtained initial amplitude-phase characteristic quantity, local plasma resonance characteristics are calculated. This calculation process includes two key parameters, the resonant intensity coefficient and the phase delay coefficient. The resonance intensity coefficient reflects the intensity of the plasma resonance effect of the material, and the phase delay coefficient characterizes the phase response characteristic in the resonance process. For example, when defects of the nanomembrane of the watch surface are detected, abnormal changes in these coefficients may indicate the location and nature of the defects. The advantage of such parameterized characterization is the conversion of complex physical processes into quantifiable indicators.
In order to eliminate the measurement difference between different regions, normalization processing is required for the resonance characteristic parameters. The normalization process takes into account the reflection characteristics of the local substrate and the response function of the measurement system so that the data of the different regions are comparable. This step is critical to ensure the reliability of the measurement results. For example, the normalization process may eliminate the effects of curvature differences when analyzing nanomembrane properties of different curvature regions of the watch surface. The advantage of this normalization process is that it provides a uniform evaluation criterion.
The final step is correlation matching, and the normalized amplitude-phase characteristic data and the original phase-intensity modulation data are spatially corresponding and characteristic correlated. The process needs to establish an accurate position mapping relation, so that the characteristic data of each spatial position can accurately reflect the local plasma resonance characteristic. For example, such correlation matching ensures the accuracy of feature extraction when generating performance profiles of the watch surface nanomembranes. An important advantage of this step is that the multi-dimensional measurement data is integrated into a complete characterization, providing a reliable data basis for subsequent performance evaluation. The phase analysis method of the system finally realizes the accurate characterization of the plasma resonance characteristics of the nano film.
With continued reference to fig. 1, based on the dual-polarization light intensity distribution data and the amplitude-phase characteristic data, extracting dynamic recovery characteristics of the photo-bleaching region, and generating a region segmentation characteristic map according to a spatial distribution rule of the dynamic recovery characteristics;
In one embodiment of the invention, the method for extracting dynamic recovery characteristics of a photo-bleaching region based on the dual-polarization light intensity distribution data and the amplitude-phase characteristic data comprises the steps of continuously sampling the dual-polarization light intensity distribution data according to a preset time interval to obtain time sequence intensity variation data of the photo-bleaching region, extracting recovery time constant and recovery amplitude parameters of each sampling point according to the time sequence intensity variation data to obtain initial dynamic characteristic data, performing cross-correlation analysis according to the amplitude-phase characteristic data and the initial dynamic characteristic data to obtain comprehensive dynamic parameters representing response characteristics of materials, performing spatial gradient analysis on the comprehensive dynamic parameters to obtain a dynamic characteristic difference distribution map of a local region, performing dynamic clustering on adjacent regions according to spatial continuity of the dynamic characteristic difference distribution map to obtain a region boundary characteristic point set, and performing closed curve fitting on the region boundary characteristic point set to generate the region segmentation characteristic map.
Specifically, it is first necessary to perform time series analysis on the dual-polarization light intensity distribution data. The time sequence analysis adopts a continuous sampling mode, and dynamically monitors the photobleaching area according to preset time intervals (typically 100 milliseconds). The purpose of this continuous sampling is to capture the complete process of the material gradually returning from the photobleaching state to the original state. The time sequence intensity change data records the optical response intensity of the material at different time points, and the data comprises the dynamic recovery characteristic information of the material. For example, when monitoring a certain photobleaching area of the nanomembrane of the watch surface, a gradual process of the light intensity over time can be observed, which reflects the optical relaxation properties of the material. The advantage of this time-series sampling method is that the dynamic response process of the material can be completely recorded.
When the obtained time sequence intensity change data is subjected to feature extraction, two key parameters, namely a recovery time constant and a recovery amplitude parameter, are focused. The recovery time constant describes the characteristic time required for the material to recover from the photobleaching state to the original state, while the recovery amplitude parameter reflects the amplitude of the light intensity change during this process. These two parameters directly reflect the optical stability and response characteristics of the material. For example, in analyzing nanomembranes in different areas of the watch surface, if the recovery time is significantly longer or the recovery amplitude is abnormal in a certain area, this means that there is a difference in material properties. The initial dynamic characteristic data obtained through the parameterized description provides a basis for subsequent material characteristic analysis.
Next, a cross-correlation analysis of the dynamic signature with previously obtained amplitude-phase signature data is required. This analysis is actually a correlation between the transient response characteristics and the steady state optical characteristics of the investigated materials. By calculating the correlation coefficient between different characteristic quantities, dynamic parameters for representing the comprehensive response characteristics of the material can be obtained. For example, when analyzing complex curved areas of the watch surface, such cross-correlation analysis may help identify which optical response changes are due to intrinsic properties of the material and which are due to geometric effects. This multidimensional feature correlation analysis method has the important advantage of being able to verify the performance characteristics of a material from multiple angles.
And when the obtained comprehensive dynamic parameters are subjected to spatial gradient analysis, calculating the parameter change rate between adjacent areas, so as to obtain the dynamic characteristic difference distribution of the local areas. This analysis is actually performed in constructing a spatial distribution map of the material properties. Gradient analysis can highlight areas where abrupt changes in material properties occur, which areas are often boundaries where there is a significant difference in material properties. For example, when analyzing the uniformity of the nanomembrane on the surface of the watch, the boundary of the area with uneven performance can be clearly identified through the characteristic difference distribution diagram. The advantage of this gradient-based analysis method is that the regions of variation in the material properties can be accurately located.
Based on the dynamic characteristic difference distribution diagram, dynamic cluster analysis is required for the adjacent areas. The clustering process takes into account spatial continuity constraints, i.e. the areas formed by the clusters are required to remain spatially continuous. This process uses a clustering algorithm with adaptive thresholds to combine adjacent regions together according to the similarity of the feature parameters. For example, when analyzing the nanomembrane of the transition region of the watch bezel and the watch mirror, the clustering method can accurately identify the boundary position where the performance characteristics change. The regional boundary feature point set obtained in this way accurately reflects the spatial distribution characteristics of the material performance.
And finally, performing closed curve fitting on the regional boundary feature point set to generate a complete regional segmentation feature map. The fitting process adopts a spline interpolation method to ensure the smoothness and continuity of the boundary curve. The closed curve description not only can accurately express the spatial distribution of the material performance, but also is convenient for subsequent quantitative analysis. For example, in generating a performance map of a nanomembrane of a watch surface, a closed curve clearly defines the range of different performance regions. The method for dividing the region has the important advantages of ensuring the accuracy of the division result and providing visual expression which is convenient to understand and analyze. By the dynamic analysis method of the system, the accurate characterization of the spatial distribution of the performance of the nano film is finally realized.
The innovation of the whole analysis process is that the time sequence dynamic analysis and the space distribution analysis are combined, and the accurate characterization of the material performance is finally realized through multi-dimensional feature extraction and association analysis. The method not only can identify the fine differences of the material performance, but also can accurately position the spatial distribution of the differences, thereby providing reliable technical support for the quality control of the nano film.
With continued reference to fig. 1, according to the region segmentation feature map, selecting a maximum point of the photobleaching depth in each region as a reference standard, and performing differential processing on optical responses of other points in the region and the reference standard to obtain pure material effect distribution data for eliminating the influence of geometric curvature;
In one embodiment of the invention, the method comprises the steps of selecting the maximum point of the photo-bleaching depth in each region according to the region segmentation feature map, taking the maximum point of the photo-bleaching depth in each region as a reference standard, carrying out differential processing on optical responses of other points in each region and the reference standard to obtain pure material effect distribution data for eliminating the influence of geometric curvature, carrying out light intensity gradient calculation on each region in the region segmentation feature map to obtain photo-bleaching depth distribution data in the region, carrying out extreme point detection on each region according to the photo-bleaching depth distribution data, screening a candidate point set with the maximum photo-bleaching depth, carrying out local curvature calculation on each point in the candidate point set to obtain curvature feature data of the candidate point, carrying out optimization on the candidate point set according to the curvature feature data, determining a final reference standard in each region, carrying out specific operation on light intensity data of other points in the region and light intensity data of the reference standard to obtain normalized light intensity distribution data, and carrying out curvature compensation processing on the normalized light intensity distribution data to generate pure material effect distribution data for eliminating the influence of geometric curvature.
Specifically, first, it is necessary to perform light intensity gradient calculation for each region in the region-segmentation feature map. The light intensity gradient reflects the spatial change rate of the light intensity, and the spatial distribution characteristic of the photobleaching depth in the region can be obtained by calculating the light intensity difference between adjacent measuring points. And calculating the light intensity gradient by adopting a two-dimensional gradient operator, and calculating the change rate of the light intensity in the x and y directions respectively to finally obtain complete photobleaching depth distribution data. For example, when analyzing a certain curvature change region of the watch surface, the light intensity gradient data can reflect the spatial non-uniformity of the photobleaching effect, which includes both the intrinsic response differences of the material and the curvature-induced geometric effects. The advantage of this gradient analysis method is that the spatially varying character of the photobleaching effect can be clearly shown.
Based on the obtained photobleaching depth distribution data, extreme point detection is required to screen the candidate point set. The extreme point detection adopts a local maximum search algorithm to search the position point with the maximum photobleaching depth in each region. These extreme points represent the locations where the photobleaching effect is most pronounced, often corresponding to the areas where the material response is most sensitive. For example, when analyzing the nanomembrane at the edge of the watch glass, several location points with the strongest response to the photobleaching effect can be found by extreme point detection. These candidate points will serve as the basis for the subsequent selection of the reference datum. The screening method based on extremum detection has the advantage of being capable of automatically identifying the most representative measuring points.
And (3) carrying out local curvature calculation on the screened candidate point set. The curvature calculation adopts a local quadric fitting method, specifically, each candidate point is taken as a center, a measuring point with the surrounding radius of R (generally taking 2-3 mm) is selected, and a local coordinate system is constructed. In this local coordinate system, the quadratic polynomial z=ax2+by2+cxy+dx+ey+f is used to fit the spatial distribution of the points. In the equation, z represents the height value of the measurement point, x and y are plane coordinates of the measurement point in a local coordinate system, coefficients a, b and c represent the second-order curvature terms in the x2 direction and the y2 direction and the intersecting terms of xy respectively, coefficients d and e represent the first-order slope terms in the x direction and the y direction, and f represents the local translation amount as a constant term. These coefficients are solved by a least square method to minimize the sum of squares of the errors of the fitted surface and the actual measurement points. Based on the obtained curved surface equation, the principal curvature of the point can be calculated、) And gaussian curvature (k=)×) Etc. characteristic parameters, whereinThe maximum principal curvature is indicated as such,Representing the minimum principal curvature, the directions of the two are perpendicular to each other. These curvature characteristic data directly reflect the geometric properties of the sample surface. For example, in analyzing the candidate points of the transition region of the bezel and dial of a wristwatch, if the calculated principal curvature=0.5,=0.2This indicates an elliptical transition curve where the degree of curvature in one direction is 2.5 times that in the other direction. The advantage of such local curvature analysis is that the geometric characteristics at each candidate point can be quantitatively characterized, providing an accurate mathematical basis for subsequent curvature effect compensation.
And according to the obtained curvature characteristic data, the candidate point set is optimized to determine a final reference datum point. The preferred process considers two aspects in combination, namely that the photobleaching depth of the point location is large enough to ensure high signal-to-noise ratio, and that the curvature characteristic at the point location is typical and can represent the geometric characteristics of the region. For example, when selecting a reference point for a certain section of the watch surface, the location in that section where the photobleaching effect is most pronounced and the curvature characteristics are most representative should be selected. An advantage of this multi-dimensional preferred strategy is that it ensures that the selected reference point is well representative.
After the reference point is selected, the ratio operation is needed to be carried out on the light intensity data of other points in the area and the light intensity data of the reference point. This ratio operation is effectively a normalization process, which aims to eliminate the influence of the response function of the measurement system and to make the data of different locations comparable. For example, when analyzing the performance of nanomembranes at different locations on the watch surface, the relative response intensities at the different locations can be directly compared by this normalization process. The normalization method based on the reference point has the advantage of providing a uniform evaluation standard.
And performing curvature compensation processing on the normalized light intensity distribution data. The compensation process performs compensation calculation by modeling the relationship between the light intensity response and curvature based on the previously obtained local curvature characteristics. Specifically, for the light intensity response I at any observation point P, it can be expressed as i=×f(,) WhereinIndicating the intrinsic light intensity response of the material, f #,) Representing the geometric modulation function caused by the curvature,AndThe maximum and minimum principal curvatures at that point, respectively. Geometric modulation function f # -,)=exp[-α(+)-β|-I ] where α represents the influence coefficient of the curvature magnitude and β represents the influence coefficient of curvature anisotropy, both coefficients can be determined from calibration data of the reference region. By solving the equation, one can obtain=I/f(,) Thereby eliminating the effect of geometric curvature. For example, when analyzing nanomembranes of complex curvature areas of the watch surface, if the principal curvature at a point=0.5,=0.2The light intensity i=100 (normalized unit) is measured, and f is calculated to obtain,) Material intrinsic response of this point, =0.85This value represents the pure material response after removal of the geometric effect =117.6. The decoupling analysis method has the advantages that a clear mathematical relationship is established, and pure material characteristic data which is not influenced by geometric morphology can be obtained quantitatively.
The accurate extraction of the intrinsic characteristics of the material is finally realized by the reference point selection and decoupling analysis method of the system. The innovation of the whole analysis process is that a multi-level screening strategy is adopted to ensure the representativeness of the reference datum point, the precise separation of the geometric effect and the material effect is realized through strict mathematical treatment, and a reliable technical basis is provided for the quantitative evaluation of the performance of the nano-film.
With continued reference to fig. 1, performing dimension reduction on the characteristic parameters in the pure material effect distribution data, and extracting main characteristic components representing the resonance characteristics of the plasma to obtain performance evaluation data;
In one embodiment of the invention, the method comprises the steps of performing dimension reduction processing on characteristic parameters in the pure material effect distribution data, extracting main characteristic components representing plasma resonance characteristics, obtaining performance evaluation data, including performing nonlinear transformation on formant positions, peak widths and peak intensities in the pure material effect distribution data, obtaining a characteristic point set in a multidimensional characteristic space, performing main component-extremum decomposition according to the characteristic point set, extracting a variation trend in a main characteristic direction, obtaining an orthogonal characteristic vector group, performing singular value decomposition on each component of the orthogonal characteristic vector group, obtaining characteristic weight distribution data, reordering the characteristic weight distribution data according to weight, obtaining a characteristic component sequence after sequencing, reconstructing characteristic components with contribution rates exceeding a preset threshold in the characteristic component sequence, obtaining characteristic data after dimension reduction, performing mapping matching on the characteristic data after dimension reduction and plasma resonance theoretical characteristics, and generating the performance evaluation data.
Specifically, it is first necessary to perform a feature transformation on pure material effect distribution data. This transformation process focuses mainly on three key parameters, formant location, peak width and peak intensity. Wherein the resonance peak position reflects the resonance frequency characteristic of the material, the peak width reflects the energy loss characteristic of the material, and the peak intensity reflects the resonance intensity of the material. These original parameters can be converted into data points in the feature space by nonlinear transformation. For example, when analyzing the plasma resonance characteristics of the nanomembrane on the surface of the watch, a formant in a certain area may appear at a specific wavelength position and have a certain peak width and intensity, and these three parameters form a point in the feature space after nonlinear transformation. The advantage of such a nonlinear transformation is that it is able to highlight key features of the material response and reduce the effects of noise.
And carrying out principal component-extremum decomposition analysis on the formed characteristic point set. The decomposition method combines the advantages of principal component analysis and extremum analysis, and can identify key change characteristics while retaining the main information of data. Specifically, the most significant directions of data change are first found in the feature space, and these directions constitute a set of orthogonal feature vectors. For example, when analyzing nanomembrane properties of different areas of the watch surface, certain characteristic directions correspond to major trends in material properties, such as systematic changes in resonance intensity. The decomposition method has the advantage of extracting the most representative characteristic direction.
Next, singular value decomposition is performed on the obtained orthogonal feature vector group. Singular value decomposition is capable of decomposing a complex data structure into products of three matrices, where singular values directly reflect the importance of each feature component. By this decomposition, a weight distribution of each feature component can be obtained. For example, in analyzing the performance profile of a nanofilm on a watch surface, certain feature components may have larger singular values, indicating that these features have more significance for the characterization of material properties. The advantage of this singular value based weight analysis method is that the importance of the individual features can be quantitatively assessed.
And according to the obtained characteristic weight distribution data, reordering processing is needed. The reordering process rearranges the feature components according to weight size to form an ordered sequence of feature components. The purpose of this process is to highlight the main features, providing a basis for subsequent dimension reduction. For example, when analyzing a large amount of measured data on the watch surface, it is clear by reordering which feature components contribute most to the characterization of the material properties. An advantage of this weight-based ranking approach is that key features can be efficiently identified.
And selecting components with contribution rates exceeding a preset threshold value from the sequenced characteristic component sequences to reconstruct. This preset threshold is typically set at a level that retains 85% -95% of the original information. By this dimension reduction, the complexity of the data can be significantly reduced while retaining the primary information. For example, when processing large scale measurements of the watch surface, the raw data may contain tens of dimensional features, but after dimension reduction, only 3-5 major dimensions may be required to accurately characterize the material properties. An advantage of this dimension reduction method is that it can increase the efficiency of data processing while maintaining the accuracy of the characterization.
The final step is to perform feature map matching. The process compares the feature data after dimension reduction with the standard feature parameters of plasma resonance. The standard characteristic parameter includes the resonant frequencyThree key indexes of resonance bandwidth delta omega and resonance intensity Q. Resonant frequency ofThe resonance bandwidth delta omega reflects the energy loss corresponding to the collective oscillation frequency of electrons in the material, and the resonance intensity Q characterizes the sharpness of resonance. In the actual matching process, firstly, the measured data is normalized to a standard parameter space, namely, the characteristic value of each measured point is normalized, wherein ω=ω +.,Δω'=Δω/,Q'=Q/WhereinAndIs a standard reference value. Then the feature matching degree m=exp < - > ((ω -1) 2 +.+(Δω'-1)2/+(Q'-1)2/) In which、、Is a tolerance parameter for the corresponding feature dimension. For example, in evaluating the performance of a nanomembrane on a watch surface, if the normalized parameters for a region are ω=0.95, Δω '=1.1, q' =0.9, the tolerance parameters are taken==If=0.1, the feature matching degree m=0.86 of the region can be calculated, which indicates that the performance of the region reaches a better level. The mapping matching method has the advantage of providing an objective and quantitative performance evaluation standard, so that measurement results of different batches and different areas are comparable.
The innovation of the whole characteristic analysis process is that a multi-level characteristic extraction and dimension reduction strategy is adopted, the dimension reduction and characteristic extraction of data are realized through strict mathematical treatment, and finally a reliable performance evaluation system is established. The method not only can accurately reflect the performance characteristics of the material, but also can effectively process large-scale measurement data, and provides powerful technical support for quality control of the performance of the nano-film.
In one embodiment of the invention, singular value decomposition is carried out on each component of the orthogonal feature vector group to obtain feature weight distribution data, the method comprises the steps of constructing a feature matrix by the orthogonal feature vector group, carrying out singular value decomposition to obtain a left singular matrix, a singular value matrix and a right singular matrix, carrying out orthogonality analysis on the left singular matrix and the right singular matrix to obtain a base vector group of a feature space, calculating energy contribution coefficients of each feature component according to the size of diagonal elements of the singular value matrix, carrying out hierarchical clustering analysis based on the energy contribution coefficients to obtain a feature level tree structure, carrying out pruning optimization on the feature level tree structure to obtain a key feature node sequence, and recalculating weight coefficients according to the key feature node sequence to generate feature weight distribution data.
Specifically, orthogonal feature vectors first need to be constructed into a feature matrix. The construction process is to arrange and combine a plurality of feature vectors obtained before and independent of each other in a column manner into a complete data matrix. The singular value decomposition is carried out on the feature matrix, namely the feature matrix is decomposed into three independent matrices, wherein the left singular matrix reflects the distribution features of the features in the original data space, the singular value matrix contains the importance degree information of each feature component, and the right singular matrix reflects the interrelationship among the features. For example, when analyzing the characteristics of a nanomembrane in a certain area of the surface of a wristwatch, if the raw data contains multiple parameters such as reflectivity, resonant frequency, peak width, etc., after the parameters are decomposed by a matrix, it can be clearly seen which combinations of parameters are most important for the characterization of the material properties. The advantage of this decomposition method is that the underlying structural features in the data can be fully revealed.
And carrying out orthogonality analysis on the left singular matrix and the right singular matrix obtained by decomposition, namely checking whether vectors in the matrices are mutually perpendicular and normalizing the length. Such analysis may ensure that the sets of feature space basis vectors obtained are independent of each other, each basis vector reflecting a unique dimension of the data. For example, in analyzing the multidimensional features of a wristwatch nanomembrane, one basis vector may reflect mainly the resonance intensity features of the material, and another basis vector may reflect mainly the variation features of the resonance frequency, and there is no information overlap between these basis vectors. The advantage of this orthogonality analysis is that the accuracy and independence of feature extraction is guaranteed.
The energy contribution coefficient of each feature component can be calculated based on the element size on the diagonal of the singular value matrix. These coefficients actually reflect the specific gravity of each feature component in the overall feature. And dividing the square of each singular value by the sum of squares of all singular values in calculation to obtain the energy contribution ratio of the corresponding characteristic component. For example, when analyzing the characteristics of a nanomembrane on the surface of a wristwatch, it may be found that the first characteristic component contributed 70% of the information, the second contributed 20%, and the remaining characteristic components contributed 10% in combination. The advantage of this energy analysis method is that the main features can be clearly identified.
Hierarchical cluster analysis is then performed, which is a step-wise categorization of feature components with similar energy contributions. Specifically, each feature component is first treated as an independent class, then the similar classes are gradually combined according to the similarity of their energy contribution coefficients, and finally a tree-like hierarchical structure is formed. For example, when analyzing the characteristics of a wristwatch nanomembrane, three main layers are formed, the uppermost layer comprising those characteristics that are decisive for the properties of the material, the intermediate layer comprising those characteristics that have a significant impact, and the lowermost layer being the relatively less impact. The hierarchical clustering method has the advantage of clearly showing the importance level relation among the features.
Pruning optimization is performed on the formed feature level tree, namely, a threshold value of energy contribution is set, feature nodes with accumulated energy contribution exceeding the threshold value are reserved, and other nodes are pruned. Typically this threshold is set around 90%, which means that the retained feature nodes can express more than 90% of the information of the original data. For example, in analyzing the characteristics of the watch nanomembrane, if the cumulative contribution of the first three characteristic nodes is found to have reached 92%, only the three key nodes can be retained, thereby greatly simplifying the data structure. The pruning optimization has the advantages of maintaining main information and reducing the complexity of data processing.
And finally, recalculating the weight coefficient according to the reserved key characteristic node sequence. This calculation divides the energy contribution of each retention node by the sum of the energy contributions of all retention nodes to obtain a new weight distribution. For example, if three feature nodes are retained, their weights in the final performance characterization may be 60%, 30% and 10%, respectively, which directly reflect the importance of the individual features in the material performance assessment. The advantage of this weight distribution method is that it provides a simplified but accurate characterization system.
The innovation of the whole characteristic weight analysis process is that the screening and weight distribution of the characteristics are realized by a strict mathematical analysis method, and a characteristic characterization system which not only keeps the data essence but also simplifies the calculation complexity is established. The method provides reliable technical support for quantitative evaluation of the performance of the nano-film.
With continued reference to fig. 1, a spatial distribution relationship of regional performance parameters is established according to the performance evaluation data, so as to generate a quantitative detection result for characterizing the intrinsic performance of the nano-film.
In one embodiment of the invention, the method for generating the quantitative detection result for characterizing the intrinsic performance of the nano film by establishing the spatial distribution relation of regional performance parameters according to the performance evaluation data comprises the steps of carrying out local response characteristic analysis on the performance evaluation data to obtain a plasma resonance intensity index, a dynamic stability index and a uniformity index of each region, combining the plasma resonance intensity index, the dynamic stability index and the uniformity index according to preset weighting coefficients to obtain a regional comprehensive performance index, carrying out Delaue triangulation on a test region according to the regional comprehensive performance index to obtain a spatial interpolation grid of the performance parameters, carrying out thin plate spline interpolation on node data of the spatial interpolation grid to obtain a continuous performance distribution function, calculating local gradients and curvatures based on the performance distribution function to generate a performance change feature map, and carrying out fusion superposition on the performance change feature map and the performance distribution function to obtain the quantitative detection result for characterizing the intrinsic performance of the nano film.
Specifically, the performance evaluation data first needs to be subjected to local response characteristic analysis. The analysis process focuses on three core indexes, namely, a plasma resonance intensity index reflects the resonance capability of the material, namely, the response intensity of the material to incident light, a dynamic stability index represents the stability of the material from a photobleaching state to an initial state, and a uniformity index reflects the uniformity level of the performance distribution in a local area. For example, when analyzing nanomembranes in a region of the watch surface, if the region exhibits a higher resonance intensity (greater than 0.8, normalized value), good recovery stability (recovery time deviation less than 5%) and excellent uniformity (local performance fluctuation less than 3%), it is shown that the nanomembrane performance in this region reaches a higher level. The multi-index analysis method has the advantage of being capable of comprehensively reflecting the performance characteristics of the material.
When the three indexes are weighted and combined, a weighting coefficient is set according to the actual application requirement of the nano film. Typically, the weight of the plasma resonance intensity is greatest (about 0.5) because it directly reflects the core function of the material, inferior in kinetic stability (about 0.3) because it relates to the life of the material, and least in uniformity index (about 0.2), but is also important for product quality control. Through the weighted combination, the obtained regional comprehensive performance index can comprehensively reflect the comprehensive performance level of the material. For example, when the nanomembranes of different regions of the watch surface are evaluated, if the overall performance index of a region is above 0.9, it is shown that the overall performance of that region is very excellent. The weighted evaluation method has the advantage of being capable of flexibly adjusting the evaluation standard according to actual application requirements.
Based on the region comprehensive performance index, the whole test region is subjected to grid division by adopting a Delaunay triangulation method. The Delay triangulation is characterized in that the generated triangles are as close to equilateral triangles as possible, so that excessively long triangles can be avoided, and the interpolation accuracy is improved. In the splitting process, points with similar performance indexes form denser triangular grids, and areas with larger performance changes form looser grids. For example, in analyzing nanomembranes in the transition region of a wristwatch bezel and a scope, the triangulated triangular mesh may become correspondingly denser due to the gradient of performance that may exist here. The adaptive mesh generation method has the advantage of being capable of automatically adjusting analysis accuracy according to performance distribution characteristics.
And performing thin-plate spline interpolation on the spatial interpolation grid obtained by subdivision, wherein the purpose is to obtain a continuous performance distribution function. The performance distribution function F (x, y) here represents the value of the performance index at any position (x, y) on the watch surface, and consists of a first part, the base term B (x, y), reflecting the overall trend of the performance, a second part, the correction term C (x, y), for compensating the local performance fluctuations, and a third part, the smoothing term S (x, y), ensuring a spatially continuous variation of the performance. Thus, F (x, y) =b (x, y) +c (x, y) +s (x, y). The thin plate spline interpolation simulates the deformation characteristic of the thin elastic plate, and can minimize the bending energy of the curved surface while ensuring the smooth and continuous curved surface. This interpolation method is particularly suitable for processing performance profile data with slowly varying features. For example, when analyzing the overall performance profile of a watch surface, a smooth performance profile surface can be obtained by thin-plate spline interpolation, clearly showing the gradual nature of the performance. The interpolation method has the advantage of accurately reflecting the continuously-changing characteristics of the performance.
Based on the obtained continuous performance distribution function, local gradients and curvature characteristics are calculated. The gradient reflects the direction and rate of change of performance, while the curvature reflects the severity of the change of performance. These feature parameters are of great significance for identifying areas of performance mutation and defect locations. For example, in detecting uniformity of a nanofilm on a watch surface, if a large gradient value or an abnormal curvature value occurs somewhere, it may mean that there is a performance defect. Such a feature analysis method is advantageous in that a performance abnormality region can be effectively identified.
The final step is to fuse and superimpose the performance change feature map and the performance distribution function. This process effectively superimposes the local variation features on the overall distribution to form a comprehensive performance characterization result. The superposition adopts a characteristic enhancement mode, and the original performance distribution function F (x, y) is corrected through a modulation function T (x, y), namely a final performance characterization result R (x, y) =F (x, y) ×T (x, y), wherein the modulation function T (x, y) is dynamically adjusted according to the local gradient and curvature characteristics. For example, when the final detection report of the nano film on the surface of the watch is generated, the overall distribution state of the performance can be clearly displayed, and the region with abnormal performance or significant gradient change is highlighted. The feature fusion method has the advantages that the overall performance distribution information is reserved, and the locally important features are highlighted.
The innovation of the whole performance evaluation process is that a multi-dimensional index system is adopted for performance characterization, continuous expression of performance is realized through self-adaptive mesh subdivision and high-precision interpolation, and finally a comprehensive performance evaluation system is established through feature fusion. The method not only can accurately evaluate the material performance, but also can effectively identify the abnormal performance area, thereby providing reliable technical support for product quality control.
The method for detecting a wristwatch plasma nanomembrane based on image processing in the embodiment of the invention is described above, and the following describes a device for detecting a wristwatch plasma nanomembrane based on image processing in the embodiment of the invention, please refer to fig. 2, and one embodiment of the device for detecting a wristwatch plasma nanomembrane based on image processing in the embodiment of the invention includes:
The data acquisition module 201 is configured to acquire dual-polarization state light intensity distribution data before and after photobleaching of the watch plasma nano-film within a preset incident angle range, where the dual-polarization state light intensity distribution data includes s-polarization and p-polarization intensity data before photobleaching and s-polarization and p-polarization intensity data after photobleaching;
the phase modulation module 202 is configured to perform phase modulation on the dual-polarization light intensity distribution data, record a phase-intensity response curve in a preset phase range, and obtain amplitude-phase characteristic data representing local plasma resonance characteristics through fourier transform processing;
The dynamic feature extraction module 203 is configured to extract dynamic recovery features of a photobleaching area based on the dual-polarization light intensity distribution data and the amplitude-phase feature data, and generate an area segmentation feature map according to a spatial distribution rule of the dynamic recovery features;
The decoupling analysis module 204 is configured to select a maximum point of the photobleaching depth in each region as a reference standard according to the region segmentation feature map, and perform differential processing on optical responses of other points in the region and the reference standard to obtain pure material effect distribution data for eliminating the influence of geometric curvature;
The dimension reduction processing module 205 is configured to perform dimension reduction processing on the feature parameters in the pure material effect distribution data, extract a main feature component representing the resonance characteristic of the plasma, and obtain performance evaluation data;
the performance evaluation module 206 is configured to establish a spatial distribution relationship of regional performance parameters according to the performance evaluation data, and generate a quantitative detection result for characterizing the intrinsic performance of the nano-film.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the description of the present invention and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the invention.
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