Detection device and detection method for double-sided optical imaging film

Abstract

The invention relates to the technical field of optical film detection, and provides a double-sided optical imaging film detection device and method, and the method comprises the steps: obtaining first optical reflection data and second optical reflection data of a double-sided optical imaging film, carrying out the comparison, obtaining global reflection difference data, and carrying out the analysis; and obtaining optical reflection performance data and transmission performance data, inputting the optical reflection performance data and the transmission performance data into a preset analysis model, and outputting an optical performance evaluation report. The first optical reflection data and the second optical reflection data of the double-sided optical imaging film are obtained, comparison and statistical analysis are performed on the first optical reflection data and the second optical reflection data to obtain accurate global reflection difference data, and an optical performance evaluation report is output through a preset analysis model, so that more comprehensive performance evaluation is provided; the method is suitable for quality control and performance analysis of various high-precision optical devices, and the problem that comprehensive evaluation of optical uniformity and reflection characteristics is difficult to consider in a high-precision application scene is solved.

Description

Technical Field

[0001] This application relates to the technical field of optical film detection, and particularly to a detection device and method for a double-sided optical imaging film. Background Art

[0002] Optical imaging films play a crucial role in fields such as display devices, imaging devices, and optical instruments. With the rapid development of technology, the demand for high resolution, low reflectivity, and high optical uniformity is increasing continuously, which has promoted the continuous innovation and progress of optical imaging film technology. Double-sided optical imaging films are widely used in various high-precision optical devices due to their excellent transmission and reflection characteristics.

[0003] In related technical means, the detection method of double-sided optical imaging films mainly uses separate optical detection. By separately detecting the two surfaces of the double-sided optical imaging film to obtain optical reflection and transmission data, and using precise optical sensors and data processing software, the detection and evaluation of double-sided films are realized, effectively evaluating the double-sided optical performance of the optical imaging film.

[0004] Regarding the above technical solution, although the optical reflection and transmission data can be obtained by separately detecting the two surfaces of the double-sided optical imaging film for the detection and evaluation of the double-sided film, in high-precision application scenarios, such as in medical imaging devices, high-precision microscopes, and scientific experimental instruments, it is difficult to comprehensively evaluate both optical uniformity and reflection characteristics, resulting in difficulty in meeting the comprehensive optical performance evaluation requirements in some high-precision application scenarios. Summary of the Invention

[0005] In order to improve the problem that it is difficult to comprehensively evaluate both optical uniformity and reflection characteristics in high-precision application scenarios, this application provides a detection device and method for a double-sided optical imaging film.

[0006] The present invention provides a method for detecting a double-sided optical imaging film, including: obtaining first optical reflection data of the first surface and second optical reflection data of the second surface of the double-sided optical imaging film, comparing the first optical reflection data with the second optical reflection data to obtain global reflection difference data; statistically analyzing the global reflection difference data to obtain surface optical uniformity data, and analyzing the optical uniformity of the double-sided optical imaging film by using the surface optical uniformity data to obtain an optical uniformity evaluation value; comprehensively analyzing the optical uniformity of the first surface and the second surface of the double-sided optical imaging film according to the optical uniformity evaluation value to obtain an optical uniformity analysis result; performing cluster analysis on the optical uniformity analysis result to generate an optical uniformity index and an optical deviation parameter, analyzing the optical reflection characteristics of the double-sided optical imaging film by using the optical uniformity index to obtain optical reflection performance data, and analyzing the transmission performance of the double-sided optical imaging film by using the optical deviation parameter to obtain transmission performance data; inputting the optical reflection performance data and the transmission performance data into a preset analysis model, outputting an optical performance comprehensive score, and generating an optical performance evaluation report according to the optical performance comprehensive score.

[0007] As a preferred solution, the step of obtaining first optical reflection data of the first surface and second optical reflection data of the second surface of the double-sided optical imaging film, comparing the first optical reflection data with the second optical reflection data to obtain global reflection difference data includes: performing multi-point optical reflection tests on the first surface and the second surface of the double-sided optical imaging film by using a spectral reflection measurement device, and obtaining first surface original optical reflection data and second surface original optical reflection data through hyperspectral imaging technology; inputting the first surface original optical reflection data and the second surface original optical reflection data into a preset reflection comparison model to generate initial reflection difference data and a full-surface optical reflection difference distribution map; performing multi-scale decomposition on the full-surface optical reflection difference distribution map through discrete wavelet transform, and generating reflection difference spatial feature data based on the decomposition result; fusing the initial reflection difference data with the reflection difference spatial feature data to obtain the global reflection difference data of the double-sided optical imaging film.

[0008] As a preferred solution, the step of statistically analyzing the global reflection difference data to obtain surface optical uniformity data and using the surface optical uniformity data to analyze the optical uniformity of the double-sided optical imaging film to obtain an optical uniformity evaluation value includes: clustering the global reflection difference data through the K-means clustering algorithm to generate an optical uniformity clustering map, and based on the optical uniformity clustering map, calculating the overall deviation value of the optical uniformity of the film surface using the standard deviation method and calculating the local deviation value using the locally weighted regression method; performing frequency domain decomposition on the overall deviation value and the local deviation value using the fast Fourier transform to generate surface optical uniformity data; performing frequency domain analysis on the surface optical uniformity data using power spectral density analysis to obtain uniformity spectrum data, performing dimensionality reduction analysis on the uniformity spectrum data and the overall deviation value according to principal component analysis, and generating an optical uniformity characteristic index and an optical deviation distribution parameter based on the analysis results; performing spatial statistical analysis on the optical uniformity characteristic index based on spatial autocorrelation to generate a global uniformity index, and performing regression analysis on the global uniformity index and the optical deviation distribution parameter using a linear regression model to obtain an optical uniformity evaluation value.

[0009] As a preferred solution, the step of comprehensively analyzing the optical uniformity of the first surface and the second surface of the double-sided optical imaging film according to the optical uniformity evaluation value to obtain an optical uniformity analysis result includes: decomposing the optical uniformity evaluation value into a regional uniformity index and a global uniformity index, and generating a heat map of the optical uniformity of the double-sided optical imaging film based on the regional uniformity index; performing trend analysis on the global uniformity index and the heat map of the optical uniformity using the least squares fitting method to obtain an optical uniformity change curve, and applying a Bessel curve fitting algorithm to extract the optical characteristic trend of the optical uniformity change curve to obtain an optical uniformity analysis result.

[0010] As a preferred solution, the step of decomposing the optical uniformity evaluation value into a regional uniformity index and a global uniformity index includes: decomposing the optical uniformity evaluation value using a weighted average decomposition algorithm to obtain a global uniformity deviation value and a local uniformity change value; performing regional division on the global uniformity deviation value through a region growing method to generate a regional uniformity index; and fitting the local uniformity change value through a radial basis function interpolation method to generate a global uniformity index.

[0011] As a preferred solution, the steps of performing clustering analysis on the optical uniformity analysis results to generate an optical uniformity index and an optical deviation parameter, analyzing the optical reflection characteristics of the double-sided optical imaging film using the optical uniformity index to obtain optical reflection performance data, and analyzing the transmission performance of the double-sided optical imaging film using the optical deviation parameter to obtain transmission performance data include: performing clustering processing on the optical uniformity analysis results through the DBSCAN algorithm, identifying the regions with changes in the optical characteristics on the film surface, generating an optical uniformity region division map, extracting the optical uniformity mean value and the maximum deviation value of each characteristic change region based on the optical uniformity region division map, and generating an optical uniformity index through the optical uniformity mean value and the maximum deviation value; performing classification analysis on the optical uniformity index using linear discriminant analysis to obtain an optical uniformity feature data set, performing regression analysis on the optical uniformity feature data set through a support vector machine regression model to obtain parameters related to optical reflection characteristics and optical deviation parameters; performing optical reflection performance fitting calculation on the parameters related to optical reflection characteristics according to the transfer matrix method to obtain the optical reflection performance data of the double-sided optical imaging film; and calculating the transmission performance using the Fresnel equation for the optical deviation parameters to obtain the transmission performance data of the double-sided optical imaging film.

[0012] As a preferred solution, the steps of inputting the optical reflection performance data and the transmission performance data into a preset analysis model, outputting an optical performance comprehensive score, and generating an optical performance evaluation report based on the optical performance comprehensive score include: performing weighted processing on the optical reflection performance data and the transmission performance data through the entropy weight method to generate an optical performance comprehensive value, inputting the optical performance comprehensive value into the preset analysis model to obtain an optical performance comprehensive score; applying the fuzzy comprehensive evaluation method to classify and evaluate the optical performance comprehensive score, and generating an optical performance evaluation report including reflection performance, transmission performance, and deviation trend based on the evaluation results.

[0013] The present application also provides a detection device for a double-sided optical imaging film, comprising: a comparison module, configured to obtain first optical reflection data of a first surface and second optical reflection data of a second surface of the double-sided optical imaging film, compare the first optical reflection data with the second optical reflection data, and obtain global reflection difference data; a first analysis module, configured to perform statistical analysis on the global reflection difference data to obtain surface optical uniformity data, analyze the optical uniformity of the double-sided optical imaging film by using the surface optical uniformity data, and obtain an optical uniformity evaluation value; a second analysis module, configured to comprehensively analyze the optical uniformity of the first surface and the second surface of the double-sided optical imaging film according to the optical uniformity evaluation value to obtain an optical uniformity analysis result; a third analysis module, configured to perform cluster analysis on the optical uniformity analysis result to generate an optical uniformity index and an optical deviation parameter, analyze the optical reflection characteristics of the double-sided optical imaging film by using the optical uniformity index to obtain optical reflection performance data, and analyze the transmission performance of the double-sided optical imaging film by using the optical deviation parameter to obtain transmission performance data; an evaluation module, configured to input the optical reflection performance data and the transmission performance data into a preset analysis model, output an optical performance comprehensive score, and generate an optical performance evaluation report according to the optical performance comprehensive score.

[0014] Compared with the prior art, the present application has the following beneficial effects: high detection accuracy and comprehensive evaluation. By obtaining the first optical reflection data and the second optical reflection data of the double-sided optical imaging film, comparing and statistically analyzing them, accurate global reflection difference data can be obtained, so as to effectively evaluate the surface optical uniformity of the double-sided optical imaging film. Based on the optical uniformity evaluation value, comprehensive analysis is performed on the two surfaces to obtain an optical uniformity analysis result. Cluster analysis is used to generate an optical uniformity index and an optical deviation parameter based on the optical uniformity analysis result, and the optical reflection performance and the transmission performance are respectively analyzed to obtain comprehensive optical performance data. Then, through a preset analysis model, the optical reflection performance data and the transmission performance data are comprehensively evaluated. After outputting the optical performance comprehensive score, an optical performance evaluation report is generated, providing a more comprehensive and accurate performance evaluation of the double-sided optical imaging film, applicable to the quality control and performance analysis of various high-precision optical devices, and improving the problem that it is difficult to comprehensively evaluate the optical uniformity and reflection characteristics in high-precision application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] The structures, proportions, sizes, etc. shown in the accompanying drawings of this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Therefore, they do not have any substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the efficacy that the present invention can produce and the purpose that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0017] Figure 1 It is a schematic flow chart of a detection method for a double-sided optical imaging film provided by an embodiment of the present invention; Figure 2 It is a schematic structural block diagram of a detection device for a double-sided optical imaging film provided by an embodiment of the present invention.

[0018] Explanation of reference numerals: 10. Detection device for double-sided optical imaging film; 11. Comparison module; 12. First analysis module; 13. Second analysis module; 14. Third analysis module; 15. Evaluation module. Detailed implementation manners

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] The flow chart shown in the accompanying drawings is only an example for illustration, and does not necessarily include all the contents and operations / steps, nor does it necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, combined, or partially merged. Therefore, the actual execution order may be changed according to the actual situation.

[0021] It should also be understood that the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification of this application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0022] It should be further understood that the term " / and" as used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings and through specific embodiments.

[0024] Embodiment 1: As Figure 1 shown, the present application provides a detection method for a double-sided optical imaging film, including step S100 to step S500.

[0025] Step S100, obtain the first optical reflection data of the first surface and the second optical reflection data of the second surface of the double-sided optical imaging film, compare the first optical reflection data with the second optical reflection data, and obtain global reflection difference data.

[0026] In this step, a high-precision optical sensor is used to obtain the first optical reflection data of the first surface and the second optical reflection data of the second surface of the double-sided optical imaging film. Specifically, reflection spectroscopy analysis technology is adopted to perform reflection spectroscopy measurements on the two surfaces and record their reflection data. Then, the first optical reflection data is compared with the second optical reflection data, and the global reflection difference data is calculated to characterize the reflection difference between the two surfaces.

[0027] For example, the reflection spectroscopy data of the two surfaces are measured simultaneously on a high-precision optical detection instrument, and comparison and analysis are performed through data processing software to obtain the global reflection difference data.

[0028] Step S200, perform statistical analysis on the global reflection difference data to obtain surface optical uniformity data, and use the surface optical uniformity data to analyze the optical uniformity of the double-sided optical imaging film to obtain an optical uniformity evaluation value.

[0029] In this step, a statistical analysis tool is used to analyze the global reflection difference data to obtain the surface optical uniformity data of the double-sided optical imaging film. Specifically, statistical methods such as mean and variance are used to perform statistical analysis on the reflection difference data to quantify the surface optical uniformity.

[0030] For example, statistical analysis software is used to process the global reflection difference data to obtain the surface optical uniformity data. These data are further analyzed to evaluate the optical uniformity of the double-sided optical imaging film to obtain an optical uniformity evaluation value.

[0031] Step S300, comprehensively analyze the optical uniformity of the first surface and the second surface of the double-sided optical imaging film according to the optical uniformity evaluation value to obtain an optical uniformity analysis result.

[0032] In this step, a comprehensive analysis of the optical uniformity of the first and second surfaces of the double-sided optical imaging film is performed based on the optical uniformity evaluation value. Specifically, by comparing and comprehensively analyzing the evaluation value, the optical uniformity and its consistency of the two surfaces are evaluated.

[0033] For example, a chart tool is used to visually display the optical uniformity evaluation value to intuitively evaluate the overall optical uniformity of the double-sided optical imaging film and obtain the optical uniformity analysis result.

[0034] Step S400: Perform cluster analysis on the optical uniformity analysis result to generate an optical uniformity index and an optical deviation parameter. Use the optical uniformity index to analyze the optical reflection characteristics of the double-sided optical imaging film to obtain optical reflection performance data, and use the optical deviation parameter to analyze the transmission performance of the double-sided optical imaging film to obtain transmission performance data.

[0035] In this step, the optical uniformity analysis result is grouped and classified by the cluster analysis method to generate specific optical uniformity index and optical deviation parameter. Specifically, the K-means clustering algorithm is used to classify the analysis result to identify regions with different optical characteristics. The generated optical uniformity index is used to detailedly analyze the optical reflection characteristics of the double-sided optical imaging film to obtain optical reflection performance data; the optical deviation parameter is used to analyze its transmission performance to obtain transmission performance data.

[0036] For example, the K-means clustering algorithm is used to process the optical uniformity analysis result to generate an optical uniformity index, and the optical characteristics are analyzed in combination with the optical parameter database.

[0037] Step S500: Input the optical reflection performance data and the transmission performance data into a preset analysis model, output an optical performance comprehensive score, and generate an optical performance evaluation report according to the optical performance comprehensive score.

[0038] In this step, a preset analysis model is used to comprehensively evaluate the optical reflection performance data and the transmission performance data, and output an optical performance comprehensive score. Specifically, the obtained optical reflection performance data and transmission performance data are input into an analysis model based on artificial intelligence for multi-dimensional comprehensive scoring. An elaborate optical performance evaluation report is generated according to the comprehensive scoring result.

[0039] For example, a deep learning model is used to process the input data to obtain an optical performance comprehensive score and automatically generate an optical performance evaluation report including multiple evaluation indicators.

[0040] In this embodiment, by obtaining the first optical reflection data of the first surface and the second optical reflection data of the second surface of the double-sided optical imaging film, and then comparing the first optical reflection data with the second optical reflection data, global reflection difference data is obtained. Then, statistical analysis is performed on the global reflection difference data to obtain surface optical uniformity data, and the surface optical uniformity data is used to analyze the optical uniformity of the double-sided optical imaging film to obtain an optical uniformity evaluation value. Subsequently, based on the optical uniformity evaluation value, comprehensive analysis is performed on the optical uniformity of the first surface and the second surface of the double-sided optical imaging film to obtain an optical uniformity analysis result. Finally, cluster analysis is performed on the optical uniformity analysis result to generate an optical uniformity index and an optical deviation parameter. The optical reflection characteristics of the double-sided optical imaging film are analyzed using the optical uniformity index to obtain optical reflection performance data, and the transmission performance of the double-sided optical imaging film is analyzed using the optical deviation parameter to obtain transmission performance data. The optical reflection performance data and the transmission performance data are input into a preset analysis model to output an optical performance comprehensive score, and an optical performance evaluation report is generated based on the optical performance comprehensive score, realizing comprehensive and accurate detection and evaluation of the double-sided optical imaging film, and overcoming the shortcomings in the prior art that it is difficult to balance optical uniformity and reflection characteristics in high-precision application scenarios. It not only improves the accuracy and consistency of detection, but also provides more comprehensive optical performance data, providing reliable data support and evaluation basis for the use of the double-sided optical imaging film in application scenarios with high-precision requirements.

[0041] Embodiment 2: In step S100, a multi-point optical reflection test is performed on the first surface and the second surface of the double-sided optical imaging film using a spectral reflection measurement device, and the first surface original optical reflection data and the second surface original optical reflection data are obtained through hyperspectral imaging technology.

[0042] By performing multiple measurements at different positions, it is ensured that the obtained data is representative and of high precision; specifically, a high-precision spectral reflection measurement device is used to perform reflection tests at multiple predetermined positions on the double-sided optical imaging film to obtain optical reflection data at different positions, and hyperspectral imaging technology is used to obtain more comprehensive optical information, including the reflectivity at each wavelength.

[0043] For example, at least 25 measurement points are selected on a double-sided optical imaging film with a diameter of 10 cm, and the optical reflection data of each measurement point at different wavelengths is recorded through a hyperspectral imaging device to ensure that the data covers the entire surface and has sufficient details.

[0044] The first surface original optical reflection data and the second surface original optical reflection data are input into a preset reflection comparison model to generate initial reflection difference data and a full-surface optical reflection difference distribution map.

[0045] Preprocess the original optical reflection data to remove noise and outliers; specifically, use a reflection ratio model to compare the preprocessed data point by point, calculate the reflection difference at each measurement point, generate initial reflection difference data, and generate a full-surface optical reflection difference distribution map through an interpolation algorithm.

[0046] For example, use the data processing tools in Matlab to preprocess the optical reflection data. After removing the noise, write a comparison algorithm to analyze each measurement point, generate initial reflection difference data, and use the interpolation method to generate an optical reflection difference distribution map.

[0047] Perform multi-scale decomposition on the full-surface optical reflection difference distribution map through discrete wavelet transform, and generate reflection difference spatial feature data based on the decomposition results.

[0048] Decompose the optical reflection difference distribution map into components of different scales by applying discrete wavelet transform to capture features at different scales; specifically, select appropriate wavelet basis functions and decomposition levels, perform multi-scale decomposition on the distribution map, and extract spatial feature data at each scale.

[0049] For example, use the Haar wavelet basis function to decompose the optical reflection difference distribution map into three layers, obtain high-frequency and low-frequency components, analyze the image features after each layer of decomposition, and obtain reflection difference spatial feature data at different scales.

[0050] Fuse the initial reflection difference data with the reflection difference spatial feature data to obtain the global reflection difference data of the double-sided optical imaging film.

[0051] By performing weighted fusion on the spatial feature data obtained from multi-scale decomposition and the initial reflection difference data, more comprehensive reflection difference information can be obtained; specifically, use the weighted average algorithm to perform weighted processing on the feature data at each scale and combine it with the initial reflection difference data to generate global reflection difference data.

[0052] For example, use the weighted average function in Python to perform weighted fusion on the initial reflection difference data and the feature data obtained from three-layer wavelet decomposition to generate reflection difference data containing more details and global information.

[0053] In step S200, perform clustering processing on the global reflection difference data through the K-means clustering algorithm to generate an optical uniformity clustering map. Based on the optical uniformity clustering map, use the standard deviation method to calculate the overall deviation value of the optical uniformity of the film surface, and use the locally weighted regression method to calculate the local deviation value.

[0054] Perform unsupervised classification on the global reflection difference data through the K-means clustering algorithm to identify different clusters in the data; specifically, select an appropriate K value (such as 3 to 5), perform clustering on the global reflection difference data to generate an optical uniformity clustering map, and further calculate the overall and local optical uniformity deviation values on the film surface through the standard deviation method and the locally weighted regression method.

[0055] For example, use the K-means clustering function in R language to perform clustering analysis on the reflection difference data, select K = 4 for classification, generate an optical uniformity clustering map, calculate the overall deviation value through the standard deviation formula, and use the loess function for locally weighted regression to calculate the local deviation value.

[0056] Perform frequency domain decomposition on the overall deviation value and the local deviation value using the fast Fourier transform to generate surface optical uniformity data.

[0057] Through fast Fourier transform analysis of the deviation value, convert the deviation information in the spatial domain into frequency domain information; specifically, select an appropriate sampling frequency, perform Fourier transform on the overall and local deviation values to generate optical uniformity data in the frequency domain.

[0058] For example, use the fft function in Matlab to perform Fourier transform on the overall and local deviation values to generate a spectrogram, and analyze the contribution of different frequency components to the optical uniformity to obtain surface optical uniformity data.

[0059] Apply power spectral density analysis to perform frequency domain analysis on the surface optical uniformity data to obtain uniformity spectral data, perform dimensionality reduction analysis on the uniformity spectral data and the overall deviation value according to principal component analysis, and generate optical uniformity characteristic indicators and optical deviation distribution parameters based on the analysis results.

[0060] Identify the main components and characteristics in the frequency domain through power spectral density analysis; specifically, calculate the power spectral density of the optical uniformity data, extract the main spectral characteristics, and perform dimensionality reduction processing on the data through principal component analysis to generate optical uniformity characteristic indicators and optical deviation distribution parameters.

[0061] For example, use the welch method in Python to calculate the power spectral density of the optical uniformity data, extract the first two main components through principal component analysis to obtain the dimensionality-reduced characteristic indicators, and generate optical deviation distribution parameters based on the analysis results.

[0062] Perform spatial statistical analysis on the optical uniformity characteristic indicators based on spatial autocorrelation to generate a global uniformity index, and use a linear regression model to perform regression analysis on the global uniformity index and the optical deviation distribution parameters to obtain an optical uniformity evaluation value.

[0063] By applying the spatial autocorrelation analysis method, the spatial distribution characteristics of the optical uniformity characteristic index are evaluated; specifically, the global and local spatial autocorrelation indexes are calculated, the global uniformity index is generated, and regression analysis is performed on the uniformity index and the optical deviation distribution parameters through a linear regression model to obtain the optical uniformity evaluation value.

[0064] For example, the Moran's I method in GeoDa software is used to calculate the spatial autocorrelation of the optical uniformity characteristic index, generate the global uniformity index, and the regression analysis function in SPSS is used to perform regression analysis on the uniformity index and the deviation parameters to obtain the optical uniformity evaluation value.

[0065] In step S300, the optical uniformity evaluation value is decomposed into a regional uniformity index and a global uniformity index, and based on the regional uniformity index, a thermal map of the optical uniformity of the double-sided optical imaging film is generated.

[0066] By decomposing the optical uniformity evaluation value, the optical uniformity conditions in different regions are identified; specifically, the weighted average algorithm is used to decompose the evaluation value into a regional uniformity index and a global uniformity index, and a thermal map reflecting the regional uniformity is generated.

[0067] For example, the data pivot table function in Excel is used to perform weighted decomposition on the optical uniformity evaluation value, generate the uniformity indexes of different regions, and generate a thermal map of the optical uniformity of the double-sided optical imaging film through a thermal map plug-in.

[0068] The least squares fitting method is used to perform trend analysis on the global uniformity index and the optical uniformity thermal map to obtain the optical uniformity change curve, and the Bezier curve fitting algorithm is applied to extract the optical characteristic trend from the optical uniformity change curve to obtain the optical uniformity analysis result.

[0069] The least squares fitting method is used to fit the global uniformity index and the thermal map data to identify the uniformity change trend; specifically, an appropriate fitting function is selected to perform fitting analysis on the optical uniformity change curve, and the Bezier curve is applied to extract the optical characteristic trend to obtain the analysis result.

[0070] For example, the least squares fitting tool in Origin software is used to perform fitting analysis on the optical uniformity index, plot the change curve, and use the Bezier curve algorithm to smooth the change curve and extract the optical characteristic trend to obtain the uniformity analysis result.

[0071] Among them, the step of decomposing the optical uniformity evaluation value into a regional uniformity index and a global uniformity index includes: using a weighted average decomposition algorithm to decompose the optical uniformity evaluation value to obtain a global uniformity deviation value and a local uniformity change value.

[0072] By performing weighted average decomposition on the optical uniformity evaluation values of each region, the relevance between the indicators of each region and the overall indicators is ensured; specifically, the weighted average decomposition algorithm is used to perform weighted processing on the optical uniformity evaluation value of each region, and the global uniformity deviation value and the local uniformity change value are calculated.

[0073] For example, write a formula in Excel to decompose the optical uniformity data through the weighted average algorithm, generate the uniformity deviation value and change value of each region, and ensure the consistency and accuracy of data processing.

[0074] The global uniformity deviation value is divided into regions by the region growing method to generate regional uniformity indicators.

[0075] The global uniformity deviation value is divided into regions by applying the region growing algorithm to identify regions with different optical characteristics; specifically, an appropriate initial point for region growing is selected, and the deviation value is expanded recursively through the recursive algorithm to generate regional uniformity indicators.

[0076] For example, use the region growing algorithm in the scikit-image library in Python to process the optical uniformity data, set the initial growth point and threshold, automatically identify regions with similar optical characteristics, and generate regional uniformity indicators.

[0077] The local uniformity change value is fitted by the radial basis function interpolation method to generate the global uniformity indicator.

[0078] By applying the radial basis function interpolation method to perform smooth fitting on the local uniformity change value, a continuous global uniformity indicator is generated; specifically, an appropriate radial basis function and control parameters are selected to perform interpolation calculation on the local uniformity change value to generate a smooth global uniformity indicator.

[0079] For example, use the radial basis function interpolation tool in Matlab to fit the local uniformity data, select appropriate functions and parameters, and generate a global uniformity indicator with high fitting accuracy.

[0080] In step S400, the optical uniformity analysis results are clustered by the DBSCAN algorithm to identify the regions where the optical characteristics of the film surface change, generate an optical uniformity region division map, extract the optical uniformity mean value and the maximum deviation value of each characteristic change region based on the optical uniformity region division map, and generate an optical uniformity indicator through the optical uniformity mean value and the maximum deviation value.

[0081] Perform density clustering analysis on the optical uniformity data through the DBSCAN algorithm to identify the regions where the optical properties change; specifically, select appropriate ε values and the minimum number of samples, perform clustering on the optical uniformity analysis results, generate an optical uniformity region division map, and extract the average value and maximum deviation value of the optical uniformity for each region.

[0082] For example, use the DBSCAN function in R language to perform density clustering analysis on the optical uniformity data, select ε = 0.5 and the minimum number of samples as 5, generate an optical uniformity region division map, and extract the mean value and maximum deviation value of each region to generate optical uniformity indicators.

[0083] Perform classification analysis on the optical uniformity indicators using linear discriminant analysis to obtain an optical uniformity feature data set, and perform regression analysis on the optical uniformity feature data set through a support vector machine regression model to obtain parameters related to optical reflection characteristics and optical deviation parameters.

[0084] Classify the optical uniformity indicators through linear discriminant analysis to form an optical uniformity feature data set; specifically, use the linear discriminant analysis method to classify the regional uniformity indicators into different categories, generate a feature data set, and perform regression analysis on the data set through a support vector machine regression model to obtain parameters related to optical reflection characteristics and optical deviation parameters.

[0085] For example, use the LinearDiscriminantAnalysis and SVR modules in Python to perform classification and regression analysis on the optical uniformity data, extract relevant parameters, and generate optical reflection characteristics and deviation parameters.

[0086] Perform optical reflection performance fitting calculation on the parameters related to optical reflection characteristics according to the transfer matrix method to obtain the optical reflection performance data of the double-sided optical imaging film.

[0087] Perform precise optical reflection performance fitting on the parameters related to optical reflection characteristics by applying the transfer matrix method; specifically, use the transfer matrix model to perform fitting calculation on the relevant parameters to obtain the optical reflection performance data of the double-sided optical imaging film.

[0088] For example, use the transfer matrix calculation tool in Matlab to perform fitting calculation on the obtained optical reflection characteristic parameters to obtain the specific optical reflection performance data of the double-sided optical imaging film.

[0089] Perform transmission performance calculation on the optical deviation parameters using the Fresnel equation to obtain the transmission performance data of the double-sided optical imaging film.

[0090] By using the Fresnel equation to calculate the optical deviation parameters, the transmission performance data is obtained; specifically, the Fresnel equation is combined with the deviation parameters to perform transmission performance calculations, generating the transmission performance data of the double-sided optical imaging film.

[0091] For example, using the Fresnel equation calculation function in Python, substituting the optical deviation parameters into the equation to calculate the transmission performance of the film, and obtaining the transmission performance data of the double-sided optical imaging film.

[0092] In step S500, the optical reflection performance data and the transmission performance data are weighted by the entropy weight method to generate an optical performance comprehensive value, and the optical performance comprehensive value is input into a preset analysis model to obtain an optical performance comprehensive score.

[0093] By applying the entropy weight method to weight different types of optical performance data, a comprehensive performance value is generated; specifically, the entropy weight method is used to weight the reflection performance data and the transmission performance data, calculate the optical performance comprehensive value, and input it into a preset analysis model to obtain an optical performance comprehensive score.

[0094] For example, using the entropy weight method calculation tool in Excel to weight the optical reflection performance and transmission performance data, generate the optical performance comprehensive value, and input this comprehensive value into a machine learning model to obtain a detailed optical performance comprehensive score.

[0095] Apply the fuzzy comprehensive evaluation method to classify and evaluate the optical performance comprehensive score, and generate an optical performance evaluation report including reflection performance, transmission performance, and deviation trend based on the evaluation results.

[0096] By using the fuzzy comprehensive evaluation method to conduct a detailed classification and evaluation of the optical performance comprehensive score, a comprehensive report is generated; specifically, the fuzzy comprehensive evaluation method is used to perform hierarchical processing on the scoring results, evaluate the optical performance, and generate a detailed evaluation report including multiple indicators.

[0097] For example, using the fuzzy logic library in Python to conduct a fuzzy comprehensive evaluation of the optical performance comprehensive score, classify it according to predetermined rules, and generate a detailed optical performance evaluation report including information such as reflection performance, transmission performance, and optical deviation trend.

[0098] In this embodiment, by using a spectral reflection measurement device to perform multi-point optical reflection tests on a double-sided optical imaging film, and combining hyperspectral imaging technology to obtain original data, global reflection difference data is generated through processing by a preset reflection ratio model and discrete wavelet transform. Subsequently, through K-means clustering and fast Fourier transform analysis, surface optical uniformity data is obtained, and an optical uniformity characteristic index is generated. The region with optical property changes is identified by the DBSCAN algorithm, and the optical performance is calculated using a support vector machine regression model, the transfer matrix method, and the Fresnel equation to obtain a comprehensive score, and finally a detailed optical performance evaluation report is generated, providing a comprehensive and accurate detection and evaluation method.

[0099] Embodiment 3: This application also provides a detection device 10 for a double-sided optical imaging film, including a comparison module 11, a first analysis module 12, a second analysis module 13, a third analysis module 14, and an evaluation module 15.

[0100] The comparison module 11 is mainly used to obtain the first optical reflection data of the first surface and the second optical reflection data of the second surface of the double-sided optical imaging film, compare the first optical reflection data with the second optical reflection data, and obtain global reflection difference data.

[0101] The first analysis module 12 is mainly used to perform statistical analysis on the global reflection difference data to obtain surface optical uniformity data, and analyze the optical uniformity of the double-sided optical imaging film using the surface optical uniformity data to obtain an optical uniformity evaluation value.

[0102] The second analysis module 13 is mainly used to comprehensively analyze the optical uniformity of the first surface and the second surface of the double-sided optical imaging film according to the optical uniformity evaluation value to obtain an optical uniformity analysis result.

[0103] The third analysis module 14 is mainly used to perform clustering analysis on the optical uniformity analysis result to generate an optical uniformity index and an optical deviation parameter, analyze the optical reflection characteristics of the double-sided optical imaging film using the optical uniformity index to obtain optical reflection performance data, and analyze the transmission performance of the double-sided optical imaging film using the optical deviation parameter to obtain transmission performance data.

[0104] The evaluation module 15 is mainly used to input the optical reflection performance data and the transmission performance data into a preset analysis model, output an optical performance comprehensive score, and generate an optical performance evaluation report according to the optical performance comprehensive score.

[0105] In this embodiment, the comparison module 11 obtains the first optical reflection data of the first surface and the second optical reflection data of the second surface of the double-sided optical imaging film, and compares them to obtain the global reflection difference data. The first analysis module 12 statistically analyzes the global reflection difference data to obtain the surface optical uniformity data, and further analyzes the optical uniformity of the double-sided optical imaging film using these data to generate an optical uniformity evaluation value. Through the second analysis module 13, based on the optical uniformity evaluation value, a comprehensive analysis of the optical uniformity of the first surface and the second surface of the double-sided optical imaging film is performed to generate an optical uniformity analysis result. The third analysis module 14 performs a clustering analysis on the optical uniformity analysis result to generate an optical uniformity index and an optical deviation parameter, and analyzes the optical reflection characteristics and transmission performance of the double-sided optical imaging film respectively to obtain the optical reflection performance data and the transmission performance data. Finally, the evaluation module 15 inputs the optical reflection performance data and the transmission performance data into a preset analysis model, outputs a comprehensive optical performance score, and generates an optical performance evaluation report. This detection device effectively improves the detection accuracy and data consistency of the double-sided optical imaging film, and provides a reliable basis for quality control and performance evaluation in high-precision application scenarios.

[0106] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described device and each module can refer to the corresponding processes in the foregoing embodiment of the detection method for a double-sided optical imaging film, and will not be described in detail here.

[0107] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A detection method for a double-sided optical imaging film, characterized in that, Including: Obtain the first optical reflection data of the first surface and the second optical reflection data of the second surface of the double-sided optical imaging film, compare the first optical reflection data with the second optical reflection data, and obtain global reflection difference data; Perform statistical analysis on the global reflection difference data to obtain surface optical uniformity data, and use the surface optical uniformity data to analyze the optical uniformity of the double-sided optical imaging film to obtain an optical uniformity evaluation value; Comprehensively analyze the optical uniformity of the first surface and the second surface of the double-sided optical imaging film according to the optical uniformity evaluation value to obtain an optical uniformity analysis result; Perform clustering analysis on the optical uniformity analysis result to generate an optical uniformity index and an optical deviation parameter, use the optical uniformity index to analyze the optical reflection characteristics of the double-sided optical imaging film to obtain optical reflection performance data, and use the optical deviation parameter to analyze the transmission performance of the double-sided optical imaging film to obtain transmission performance data; Input the optical reflection performance data and the transmission performance data into a preset analysis model, output an optical performance comprehensive score, and generate an optical performance evaluation report according to the optical performance comprehensive score.

2. The detection method of the double-sided optical imaging film according to claim 1, characterized in that, The step of obtaining the first optical reflection data of the first surface and the second optical reflection data of the second surface of the double-sided optical imaging film, comparing the first optical reflection data with the second optical reflection data, and obtaining global reflection difference data includes: Use a spectral reflection measurement device to perform multi-point optical reflection tests on the first surface and the second surface of the double-sided optical imaging film, and obtain the first surface original optical reflection data and the second surface original optical reflection data through hyperspectral imaging technology; Input the first surface original optical reflection data and the second surface original optical reflection data into a preset reflection comparison model to generate initial reflection difference data and a full-surface optical reflection difference distribution map; Perform multi-scale decomposition on the full-surface optical reflection difference distribution map through discrete wavelet transform, and generate reflection difference spatial feature data based on the decomposition result; Fuse the initial reflection difference data with the reflection difference spatial feature data to obtain the global reflection difference data of the double-sided optical imaging film.

3. The detection method of the double-sided optical imaging film according to claim 1, wherein The step of performing statistical analysis on the global reflection difference data to obtain surface optical uniformity data, and using the surface optical uniformity data to analyze the optical uniformity of the double-sided optical imaging film to obtain an optical uniformity evaluation value includes: Perform clustering processing on the global reflection difference data through the K-means clustering algorithm to generate an optical uniformity clustering map. Based on the optical uniformity clustering map, use the standard deviation method to calculate the overall deviation value of the optical uniformity of the film surface, and use the locally weighted regression method to calculate the local deviation value; Perform frequency domain decomposition on the overall deviation value and the local deviation value through fast Fourier transform to generate surface optical uniformity data; Perform frequency-domain analysis on the surface optical uniformity data using power spectral density analysis to obtain uniformity spectrum data. Conduct dimensionality reduction analysis on the uniformity spectrum data and the overall deviation value according to principal component analysis, and generate optical uniformity characteristic indexes and optical deviation distribution parameters based on the analysis results; Perform spatial statistical analysis on the optical uniformity characteristic indexes based on spatial autocorrelation to generate a global uniformity index, and use a linear regression model to perform regression analysis on the global uniformity index and the optical deviation distribution parameters to obtain an optical uniformity evaluation value.

4. The detection method of the double-sided optical imaging film according to claim 1, wherein The step of comprehensively analyzing the optical uniformity of the first surface and the second surface of the double-sided optical imaging film according to the optical uniformity evaluation value, includes: Decompose the optical uniformity evaluation value into a regional uniformity index and a global uniformity index, and generate a heat map of the optical uniformity of the double-sided optical imaging film based on the regional uniformity index; Use the least squares fitting method to perform trend analysis on the global uniformity index and the optical uniformity heat map to obtain an optical uniformity change curve, and apply the Bessel curve fitting algorithm to extract the optical characteristic trend of the optical uniformity change curve to obtain the optical uniformity analysis result.

5. The detection method of the double-sided optical imaging film according to claim 4, characterized in that, The step of decomposing the optical uniformity evaluation value into a regional uniformity index and a global uniformity index, includes: Decompose the optical uniformity evaluation value using a weighted average decomposition algorithm to obtain a global uniformity deviation value and a local uniformity change value; Perform regional division on the global uniformity deviation value through a region growing method to generate a regional uniformity index; Fit the local uniformity change value through a radial basis function interpolation method to generate a global uniformity index.

6. The detection method of the double-sided optical imaging film according to claim 1, characterized in that, The step of performing cluster analysis on the optical uniformity analysis result to generate optical uniformity indexes and optical deviation parameters, using the optical uniformity indexes to analyze the optical reflection characteristics of the double-sided optical imaging film to obtain optical reflection performance data, and using the optical deviation parameters to analyze the transmission performance of the double-sided optical imaging film to obtain transmission performance data, includes: Perform clustering processing on the optical uniformity analysis result through the DBSCAN algorithm to identify the regions with changes in the optical characteristics of the film surface, generate an optical uniformity regional division map, extract the optical uniformity mean value and the maximum deviation value of each characteristic change region based on the optical uniformity regional division map, and generate optical uniformity indexes through the optical uniformity mean value and the maximum deviation value; Perform classification analysis on the optical uniformity indexes using linear discriminant analysis to obtain an optical uniformity characteristic data set, and perform regression analysis on the optical uniformity characteristic data set through a support vector machine regression model to obtain optical reflection characteristic-related parameters and optical deviation parameters; Perform optical reflection performance fitting calculation on the optical reflection characteristic-related parameters according to the transfer matrix method to obtain the optical reflection performance data of the double-sided optical imaging film; Perform transmission performance calculation on the optical deviation parameters using the Fresnel equation to obtain the transmission performance data of the double-sided optical imaging film.

7. The detection method of the double-sided optical imaging film according to claim 1, characterized in that The step of inputting the optical reflection performance data and the transmission performance data into a preset analysis model, outputting a comprehensive optical performance score, and generating an optical performance evaluation report based on the comprehensive optical performance score includes: Performing weighted processing on the optical reflection performance data and the transmission performance data by the entropy weight method to generate a comprehensive optical performance value, and inputting the comprehensive optical performance value into a preset analysis model to obtain a comprehensive optical performance score; Applying the fuzzy comprehensive evaluation method to classify and evaluate the comprehensive optical performance score, and generating an optical performance evaluation report including reflection performance, transmission performance, and deviation trend based on the evaluation results.

8. A detection device for a double-sided optical imaging film, characterized in that, Including: A comparison module for obtaining first optical reflection data of the first surface of the double-sided optical imaging film and second optical reflection data of the second surface, comparing the first optical reflection data with the second optical reflection data to obtain global reflection difference data; A first analysis module for statistically analyzing the global reflection difference data to obtain surface optical uniformity data, and analyzing the optical uniformity of the double-sided optical imaging film by using the surface optical uniformity data to obtain an optical uniformity evaluation value; A second analysis module for comprehensively analyzing the optical uniformity of the first surface and the second surface of the double-sided optical imaging film according to the optical uniformity evaluation value to obtain an optical uniformity analysis result; A third analysis module for performing cluster analysis on the optical uniformity analysis result to generate an optical uniformity index and an optical deviation parameter, analyzing the optical reflection characteristics of the double-sided optical imaging film by using the optical uniformity index to obtain optical reflection performance data, and analyzing the transmission performance of the double-sided optical imaging film by using the optical deviation parameter to obtain transmission performance data; An evaluation module for inputting the optical reflection performance data and the transmission performance data into a preset analysis model, outputting a comprehensive optical performance score, and generating an optical performance evaluation report based on the comprehensive optical performance score.

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