Light guide film uniformity testing method, device and equipment

Abstract

The present invention discloses a method, device and equipment for testing the uniformity of a light-guiding film, wherein the method for testing the uniformity of a light-guiding film includes: dynamically adjusting the temperature of the light-guiding film to obtain multi-wavelength dynamic spectrum data of the edge region; extracting nonlinear optical response characteristics; generating a dynamic optical characteristic diagram and performing three-dimensional structural analysis; calculating a thermal-optical coupling complexity index; generating dynamic change data of optical performance in the edge region; performing multi-dimensional evaluation index processing and time series analysis to obtain dynamic and accurate evaluation results. The technical solution of the present invention can accurately evaluate the optical performance of the edge region of the light-guiding film under dynamic temperature conditions, effectively capture the impact of temperature changes on the uniformity of the light-guiding film, improve the accuracy and practicality of the test results, and provide a more reliable basis for the design optimization and quality control of the light-guiding film.

Description

Technical Field

[0001] The present invention relates to the field of optical testing technology, and in particular to a method, device and equipment for testing the uniformity of a light guide film. Background Art

[0002] Light guide film is a key optical component widely used in liquid crystal displays, flat panel lighting and various optoelectronic devices. Its main function is to evenly distribute light from the light source to the entire surface, thereby providing uniform lighting or backlighting effects. Light guide film is usually made of transparent polymer material with special microstructure design on the surface or inside to control the propagation and distribution of light. In modern display technology and lighting systems, the performance of light guide film directly affects the display quality, energy efficiency and user experience of the device.

[0003] Uniformity testing of light guide films is a key step in evaluating their performance, which aims to ensure that light is evenly distributed across the entire surface. This test is essential to ensure brightness consistency, color accuracy, and overall display effects of display devices. Traditional uniformity testing methods are usually performed under fixed temperature conditions, using a photometer to measure the brightness distribution on the surface of the light guide film. However, in actual applications, light guide films often experience different temperature environments, which may affect their optical performance, especially in the edge area. Temperature changes may cause slight deformations of the light guide film material or changes in optical properties, which in turn affect its uniformity. Existing test methods have difficulty simulating this dynamic temperature environment and lack the ability to accurately evaluate the optical performance of the edge area as the temperature changes. Therefore, it is particularly important to develop a test method that can accurately evaluate the optical performance of the edge area of ​​the light guide film under dynamic temperature conditions, which will help to better predict and optimize the performance of the light guide film in actual use environments. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing light guide film uniformity testing method is difficult to accurately evaluate the optical performance of the edge area under dynamic temperature conditions.

[0005] A first aspect of the present invention provides a method for testing uniformity of a light guide film, the method comprising:

[0006] Dynamically adjust the temperature of the light guide film and obtain multi-wavelength dynamic spectrum data of the edge area of ​​the light guide film within a preset temperature range;

[0007] According to the multi-wavelength dynamic spectrum data, extracting the nonlinear optical response characteristics of the edge region by data dimension reduction and decomposition technology;

[0008] Based on the nonlinear optical response characteristics, a dynamic optical characteristic diagram of the edge area of ​​the light guide film is generated, and a three-dimensional structural analysis is performed to determine the edge effect dynamic characteristic parameters;

[0009] Inputting the edge effect dynamic characteristic parameters into a preset temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge area;

[0010] Using the thermal-optical coupling complexity index and a preset edge performance evaluation algorithm, dynamically changing data of optical performance of edge regions are generated;

[0011] According to the dynamic change data of the optical performance of the edge area, multi-dimensional evaluation index processing and time series analysis are performed to obtain dynamic and accurate evaluation results of the optical performance of the edge area.

[0012] Optionally, dynamically adjusting the temperature of the light guide film to obtain multi-wavelength dynamic spectrum data of the edge area of ​​the light guide film within a preset temperature range includes:

[0013] According to a preset temperature curve, the light guide film is nonlinearly temperature-regulated to obtain multiple non-uniformly distributed temperature stages;

[0014] At each temperature stage, multi-wavelength optical excitation is performed on the edge region of the light guide film to obtain a multi-dimensional response signal of the edge region;

[0015] Adaptively sampling and wavelet transforming the multidimensional response signal of the edge region to obtain spectral characteristics in the time-frequency domain;

[0016] According to the spectral characteristics in the time-frequency domain, multi-wavelength dynamic spectral data of the edge area of ​​the light guide film is obtained through dynamic threshold segmentation and signal reconstruction.

[0017] Optionally, the extracting the nonlinear optical response characteristics of the edge region by data dimension reduction and decomposition technology according to the multi-wavelength dynamic spectrum data includes:

[0018] Performing tensor decomposition on the multi-wavelength dynamic spectrum data to obtain a feature matrix in three dimensions: time, space, and wavelength;

[0019] According to the characteristic matrix, a non-negative matrix decomposition algorithm is used to extract the main spectral components to obtain the basic spectral pattern of the edge area;

[0020] Applying nonlinear principal component analysis to the basic spectral pattern to extract high-order statistical features and obtain a nonlinear spectral variation pattern in the edge region;

[0021] According to the nonlinear spectral variation pattern, a feature dictionary is constructed by using sparse coding technology to obtain a spectral feature representation of the edge area;

[0022] The spectral feature representation is used in combination with a dynamic time warping algorithm to align the spectral features at different temperature stages and obtain the nonlinear optical response characteristics of the edge region.

[0023] Optionally, generating a dynamic optical characteristic diagram of the edge area of ​​the light guide film based on the nonlinear optical response characteristics, and performing a three-dimensional structure analysis to determine edge effect dynamic characteristic parameters includes:

[0024] Performing spatiotemporal mapping on the nonlinear optical response characteristics to obtain a two-dimensional optical characteristic distribution of the edge region of the light guide film;

[0025] According to the two-dimensional optical characteristic distribution, a feature point set is extracted by a topological data analysis method to obtain key optical nodes in the edge area;

[0026] Using the key optical nodes to construct a three-dimensional structure representation, and applying a non-rigid registration algorithm to obtain a dynamic deformation field at different temperature stages;

[0027] According to the dynamic deformation field and the two-dimensional optical characteristic distribution, edge effect dynamic characteristic parameters are obtained through multivariate analysis.

[0028] Optionally, the preset temperature gradient light propagation model includes a heat conduction layer, an optical propagation layer and a coupling calculation layer;

[0029] The step of inputting the edge effect dynamic characteristic parameters into a preset temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge region includes:

[0030] Performing wavelet transformation on the edge effect dynamic characteristic parameters to obtain a time-frequency domain feature map;

[0031] Inputting the time-frequency domain characteristic graph into the heat conduction layer, and calculating the temperature distribution field by a finite difference method;

[0032] According to the temperature distribution field, a non-sequential ray tracing algorithm is applied in the optical propagation layer to obtain a light intensity distribution prediction result;

[0033] Using the light intensity distribution prediction result, performing thermal-optical interaction iterative calculation in the coupled calculation layer to obtain a steady-state thermal-optical field distribution;

[0034] Performing fractal dimension analysis on the steady-state thermal-optical field distribution to obtain a field complexity index of the edge region;

[0035] According to the field complexity index and the temperature distribution field, a thermal-optical coupling complexity index of the edge region is obtained through multivariate nonlinear regression.

[0036] Optionally, obtaining the thermal-optical coupling complexity index of the edge region by multivariate nonlinear regression according to the field complexity index and the temperature distribution field includes:

[0037] Combining the field complexity index and the temperature distribution field into a multidimensional input variable, performing data standardization processing on the multidimensional input variable to obtain standardized multidimensional feature data;

[0038] Using a kernel function to perform nonlinear transformation on the standardized multidimensional feature data to obtain a high-dimensional feature representation;

[0039] According to the high-dimensional feature representation, a regression coefficient is calculated by a preset multivariate nonlinear regression algorithm to obtain a preliminary estimate of the thermal-optical coupling complexity;

[0040] Residual analysis and outlier detection are performed on the preliminary estimated value of the thermal-optical coupling complexity, and the thermal-optical coupling complexity index of the edge area is obtained by iterative weighted least squares optimization.

[0041] Optionally, the preset edge performance evaluation algorithm includes an optical transmission efficiency evaluation module, a color uniformity evaluation module and a dynamic response evaluation module;

[0042] The method of using the thermal-optical coupling complexity index and a preset edge performance evaluation algorithm to generate dynamic change data of optical performance of edge areas includes:

[0043] Inputting the thermal-optical coupling complexity index into the optical transmission efficiency evaluation module, and calculating the optical transmission efficiency curve of the edge area by using a Monte Carlo ray tracing method;

[0044] According to the optical transmission efficiency curve, chromaticity coordinate mapping is performed in the color uniformity evaluation module to obtain a color deviation distribution diagram of the edge area;

[0045] Using the color deviation distribution map in combination with the thermal-optical coupling complexity index, a state space model is applied in the dynamic response evaluation module to calculate the transient optical response characteristics of the edge area;

[0046] Performing wavelet multi-resolution analysis on the transient optical response characteristics to extract multi-scale optical performance characteristics of the edge region;

[0047] According to the multi-scale optical performance characteristics, dynamic change data of the optical performance of the edge area is comprehensively generated through an adaptive weighted fusion algorithm.

[0048] Optionally, performing multi-dimensional evaluation index processing and time series analysis based on the dynamic change data of the optical performance of the edge area to obtain a dynamic and accurate evaluation result of the optical performance of the edge area includes:

[0049] Decomposing the dynamic change data of the optical performance of the edge area in multiple dimensions to obtain feature sequences in three dimensions: time, space and spectrum;

[0050] Using the feature sequence, data of different dimensions are aligned through a dynamic time warping algorithm to obtain a standardized multidimensional time series;

[0051] applying empirical mode decomposition to the standardized multidimensional time series to extract intrinsic mode functions of optical properties of edge regions;

[0052] According to the intrinsic mode function, the instantaneous frequency and amplitude are calculated by Hilbert-Huang transform to obtain a time-frequency characteristic diagram of the optical performance of the edge area;

[0053] Using the time-frequency characteristic graph and combining a recursive quantitative analysis method, a dynamic complexity index of the optical performance of the edge region is calculated;

[0054] According to the dynamic complexity index, a dynamic and accurate evaluation result of the optical performance of the edge area is obtained through a fuzzy comprehensive evaluation method.

[0055] A second aspect of the present invention provides a light guide film uniformity testing device, the light guide film uniformity testing device comprising:

[0056] The temperature regulation and spectrum acquisition module is used to dynamically regulate the temperature of the light guide film and obtain multi-wavelength dynamic spectrum data of the edge area of ​​the light guide film within a preset temperature range;

[0057] A feature extraction module, used to extract the nonlinear optical response characteristics of the edge area through data dimension reduction and decomposition technology according to the multi-wavelength dynamic spectrum data;

[0058] An optical characteristic analysis module, used to generate a dynamic optical characteristic diagram of the edge area of ​​the light guide film based on the nonlinear optical response characteristics, and perform a three-dimensional structural analysis to determine the edge effect dynamic characteristic parameters;

[0059] A thermal-optical coupling analysis module, used to input the edge effect dynamic characteristic parameters into a preset temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge area;

[0060] A performance evaluation module, used to generate dynamic change data of optical performance of edge areas by using the thermal-optical coupling complexity index and a preset edge performance evaluation algorithm;

[0061] The result analysis module is used to perform multi-dimensional evaluation index processing and time series analysis according to the dynamic change data of the optical performance of the edge area, so as to obtain a dynamic and accurate evaluation result of the optical performance of the edge area.

[0062] The third aspect of the present invention provides a uniformity testing device for a light-guiding film, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via lines; the at least one processor calls the instructions in the memory so that the uniformity testing device for the light-guiding film executes the steps of the above-mentioned uniformity testing method for the light-guiding film.

[0063] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the above-mentioned method for testing the uniformity of a light-guiding film.

[0064] The technical solution provided by the embodiments of the present application has at least the following advantages:

[0065] First, the method dynamically adjusts the temperature of the light-guiding film to obtain multi-wavelength dynamic spectral data of the edge area within a preset temperature range. This step simulates the temperature changes that the light-guiding film may encounter in the actual use environment, thereby overcoming the limitations of traditional fixed temperature testing. By collecting multi-wavelength dynamic spectral data, the method can fully capture the impact of temperature changes on the optical properties of the light-guiding film, especially in the edge area. This dynamic acquisition method is based on the principle of spectroscopy and can provide richer information than single wavelength measurement, laying a solid foundation for subsequent analysis.

[0066] Secondly, the method uses data dimensionality reduction and decomposition techniques to extract the nonlinear optical response characteristics of the edge area. This step can extract the most critical information from complex multidimensional data. Through dimensionality reduction and decomposition, the method effectively reduces data redundancy while retaining the core characteristics of the impact of temperature changes on the optical properties of the light-guiding film. This processing not only improves the efficiency of analysis, but also reveals subtle nonlinear changes that may be overlooked by traditional methods.

[0067] Next, based on the extracted nonlinear optical response characteristics, the method generates a dynamic optical property map of the edge area of ​​the light-guiding film and performs a three-dimensional structural analysis. This step converts the two-dimensional spectral data into three-dimensional structural information, making the dynamic characteristics of the edge effect more intuitive. In this way, the method can accurately locate and quantify the changes in optical properties of the edge area at different temperatures, which is crucial for understanding and optimizing the overall performance of the light-guiding film.

[0068] At the same time, this method also introduces a temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge area. This step takes into account the impact of temperature changes on light propagation, filling the gap in traditional testing methods in this regard. By simulating the light propagation process under temperature gradient, the method can more accurately predict the performance of the light guide film in the actual use environment.

[0069] Finally, the method uses the thermal-optical coupling complexity index and the preset edge performance evaluation algorithm to generate dynamic change data of the optical performance of the edge area, and obtains dynamic and accurate evaluation results of the optical performance of the edge area through multi-dimensional evaluation index processing and time series analysis. This series of steps not only considers the instantaneous performance, but also pays attention to the performance change trend over time, thus providing a more comprehensive and reliable evaluation result.

[0070] In summary, the method of the present invention successfully achieves dynamic and accurate evaluation of the optical performance of the edge area of ​​the temperature-sensitive light-guiding film through innovative steps such as dynamic temperature adjustment, multi-wavelength spectrum analysis, nonlinear feature extraction, three-dimensional structure analysis, and thermal-optical coupling complexity calculation. This method not only overcomes the limitations of traditional fixed temperature testing, but also can more accurately reflect the performance of the light-guiding film in the actual use environment, providing a more reliable basis for the design optimization and quality control of the light-guiding film. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0072] Figure 1 A schematic diagram of an embodiment of a method for testing uniformity of a light guide film in an embodiment of the present invention;

[0073] Figure 2 A schematic diagram of an embodiment of a uniformity testing device for a light guide film according to an embodiment of the present invention;

[0074] Figure 3 A schematic diagram of an embodiment of a uniformity testing device for a light guide film according to an embodiment of the present invention;

[0075] Figure 4 An exploded schematic diagram of an embodiment of a light guide component.

[0076] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0079] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, which must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0080] Light guide components play a vital role in modern electronic devices, especially in the fields of lighting and display technology. Figure 4 Taking the light guide assembly shown in the figure as an example, the assembly is mainly composed of a light-homogenizing film 1 (which can be designed into films of different colors according to needs to filter specific light waves. For example, a slightly yellow film can be used to filter part of the blue light to reduce stimulation to the eyes; or a light green film can be used to enhance the green spectrum and improve the color saturation of the display. In addition, it can also be designed as a neutral gray film to balance the light of various wavelengths and improve the overall color reproduction), a light guide plate 2 (which can be made of transparent polycarbonate or acrylic material, and its surface can be designed with microstructures as needed, The optical fiber transmission device is composed of a plurality of components, such as a prism or a dot matrix to optimize the output angle and uniformity of light, a reflective film 3 (a metal film or a polymer film with high reflectivity, such as an enhanced polyester film, can be used to maximize the utilization efficiency of light and improve the overall brightness), a black film 4 (used to absorb stray light and improve contrast. In some applications, it can be replaced by a white film to increase reflectivity and improve overall brightness), and a protective film 5 (using wear-resistant and scratch-resistant materials, such as PET film, to protect other optical layers from damage by the external environment, and at the same time, it can have anti-glare or anti-fingerprint functions). These components work together to ensure uniform distribution and efficient transmission of light. It is worth noting that the "light-guiding film" mentioned in the present invention hereinafter should be understood in a broad sense. The light-guiding film can be Figure 4Any of the light homogenizing film 1, light guide plate 2, reflective film 3, black film 4 (or white film) and protective film 5 in the optical fiber. Because these structures are involved in the conduction, distribution and modulation of light. Whether it is the light homogenizing film 1, light guide plate 2 or other optical films, they all face similar edge effects and uniformity challenges, so the test method of the present invention is generally applicable to them.

[0081] exist Figure 4 In the example of , the ring structure adopted by the light uniformity film 1 meets the needs of specific application scenarios, such as camera backlight products. This ring-shaped design means that the edges of the entire component are key optical transmission areas, without the relatively simple straight edges in traditional rectangular light guide films. This complex edge structure makes the propagation and distribution of light more complicated, because the light needs to propagate evenly on a curved path. Considering this complexity of the edge area of ​​the light guide component and the interaction between the various optical film layers, it becomes particularly important to accurately test these areas. First, the edge area is often where light loss and uneven distribution are most likely to occur. In the ring-shaped structure, the light needs to propagate on a curved path, which increases the possibility of light scattering, reflection and attenuation. Secondly, the performance of the edge area directly affects the optical efficiency and uniformity of the entire light guide component. If the edge area has uneven brightness or color deviation, it may cause the quality of the entire display or lighting effect to deteriorate. In addition, in actual applications, the light guide component may experience the influence of external factors such as temperature changes and mechanical stress, which may be more obvious in the edge area. Especially considering the existence of multi-layer structures such as the light-homogenizing film 1, the light guide plate 2, and the reflective film 3, these external factors may cause interlayer stress, micro-deformation, or changes in optical properties, further complicating the optical behavior of the edge area. Therefore, comprehensive and accurate testing of the edge area can not only ensure the performance of a single optical film layer, but also evaluate the synergistic effect of the multi-layer structure, thereby providing valuable data for optimizing the overall design and manufacturing process. This comprehensive testing method will ultimately improve the quality, reliability, and optical performance of light guide components, ensuring excellent performance in a variety of application environments.

[0082] An embodiment of the present application provides a method for testing the uniformity of a light guide film. Figure 1 A flow chart of a method for testing uniformity of a light guide film provided in an embodiment of the present application. In this embodiment, the method includes:

[0083] See also Figure 1 , dynamically adjust the temperature of the light guide film, and obtain multi-wavelength dynamic spectrum data of the edge area of ​​the light guide film within a preset temperature range;

[0084] In one embodiment of the present invention, the dynamic temperature adjustment of the light-guiding film to obtain multi-wavelength dynamic spectral data of the edge region of the light-guiding film within a preset temperature range includes: performing nonlinear temperature adjustment on the light-guiding film according to a preset temperature curve to obtain a plurality of non-uniformly distributed temperature stages; performing multi-wavelength optical excitation on the edge region of the light-guiding film in each of the temperature stages to obtain a multi-dimensional response signal of the edge region; performing adaptive sampling and wavelet transformation processing on the multi-dimensional response signal of the edge region to obtain spectral characteristics in the time-frequency domain; and obtaining multi-wavelength dynamic spectral data of the edge region of the light-guiding film through dynamic threshold segmentation and signal reconstruction based on the spectral characteristics in the time-frequency domain.

[0085] Specifically, in the test method for uniformity of light-guiding film, the light-guiding film is first subjected to nonlinear temperature adjustment according to a preset temperature curve to obtain multiple non-uniformly distributed temperature stages. The preset temperature curve is a temperature change curve pre-set according to the material properties of the light-guiding film and the actual application environment, covering the temperature range experienced by the light-guiding film. For the light-guiding film made of polycarbonate (PC) material, the preset temperature curve starts from -20°C, heats up to 80°C at a rate of 2°C per minute, then cools to -20°C at a rate of 1°C per minute, and finally returns to room temperature at a rate of 1.5°C per minute. This nonlinear temperature adjustment simulates various temperature changes encountered by the light-guiding film in actual use, including scenes such as rapid heating and slow cooling. The non-uniformly distributed temperature stages include key temperature points such as -20°C, 0°C, 25°C, 50°C, and 80°C, and each temperature point is maintained for a certain time to ensure that the light-guiding film fully adapts to the temperature. The beneficial effect of this nonlinear temperature adjustment is that it can comprehensively evaluate the performance of the light-guiding film under various temperature conditions, especially the problems that occur when the temperature changes rapidly, and improve the accuracy and practicality of the test results.

[0086] At each temperature stage, the edge area of ​​the light guide film is subjected to multi-wavelength optical excitation to obtain a multi-dimensional response signal of the edge area. The edge area of ​​the light guide film is defined as an area 2-5 mm away from the edge of the light guide film. For the annular light guide film, this area includes an annular area 2-5 mm wide at the inner and outer edges. Multi-wavelength optical excitation uses a light source covering the visible spectrum, including three main bands of red (620-750nm), green (495-570nm), and blue (450-495nm), as well as some intermediate wavelengths. Light-emitting diode (LED) light sources of 450nm, 520nm, 590nm and 660nm are used for excitation. These light sources are evenly distributed along the inner and outer edges of the annular light guide film to ensure that the edge area is fully excited. The multi-dimensional response signal includes information such as light intensity at different wavelengths (unit: cd / m²), spectral distribution (relative intensity of each wavelength), and spatial distribution (changes in light intensity at different positions). The beneficial effect of this multi-dimensional data collection is that it can capture the complex optical behavior of the edge area of ​​the light-guiding film at different temperatures and wavelengths, providing a rich data basis for subsequent analysis and enhancing the comprehensiveness and accuracy of the test.

[0087] The multi-dimensional response signal of the edge area is adaptively sampled and processed by wavelet transform to obtain the spectral characteristics in the time-frequency domain. Adaptive sampling dynamically adjusts the sampling frequency according to the rate of change of the signal. When the temperature changes rapidly, a sampling rate of 1000Hz is used, and it is reduced to 100Hz in the temperature stabilization stage. The wavelet transform uses the discrete wavelet transform (DWT) algorithm and selects Daubechies Wavelet or Haar Wavelet as the basis function. For the annular light-guiding film, this step accurately captures the characteristics of the optical response of the edge area over time and temperature. It is observed that during the rapid increase of temperature from 25°C to 50°C, the intensity of the 520nm green light fluctuates briefly, while the intensity of the 660nm red light shows a slow upward trend. The beneficial effect of this processing method is that it can simultaneously analyze the optical response of the light-guiding film in both time and frequency dimensions, providing a more comprehensive and accurate performance evaluation, which helps to identify transient optical effects and long-term performance changes.

[0088] Finally, according to the spectral characteristics in the time-frequency domain, the multi-wavelength dynamic spectral data of the edge area of ​​the light-guiding film are obtained through dynamic threshold segmentation and signal reconstruction. The dynamic threshold segmentation uses the OTSU algorithm or the adaptive threshold method to dynamically adjust the segmentation threshold according to the local statistical characteristics of the signal. For the inner edge of the annular light-guiding film, a lower threshold (80% of the average intensity) is set to capture weak optical changes, while the outer edge uses a higher threshold (120% of the average intensity) to filter out ambient light interference. Signal reconstruction uses inverse wavelet transform or inverse Fourier transform to recombine the key frequency components to obtain clear spectral data. The reconstructed multi-wavelength dynamic spectral data includes the light intensity values ​​at each temperature stage and each wavelength, as well as the changing trends of these values ​​over time. The accurate data that the light intensity of the 450nm blue light at the inner edge of the annular light-guiding film at 80°C is 200cd / m², while that at the outer edge is 180cd / m². The beneficial effect of this processing method is that it can provide high-quality, low-noise spectral data, providing a reliable basis for subsequent performance evaluation and optimization, while being able to effectively distinguish optical changes from different sources and improve the accuracy and interpretability of test results.

[0089] Please continue reading Figure 1 , extracting the nonlinear optical response characteristics of the edge region through data dimension reduction and decomposition technology according to the multi-wavelength dynamic spectrum data;

[0090] In one embodiment of the present invention, the nonlinear optical response characteristics of the edge area are extracted through data dimensionality reduction and decomposition technology based on the multi-wavelength dynamic spectral data, including: performing tensor decomposition on the multi-wavelength dynamic spectral data to obtain a feature matrix in three dimensions of time, space and wavelength; based on the feature matrix, using a non-negative matrix decomposition algorithm to extract the main spectral components to obtain a basic spectral pattern of the edge area; applying nonlinear principal component analysis to the basic spectral pattern to extract high-order statistical features to obtain a nonlinear spectral change pattern of the edge area; based on the nonlinear spectral change pattern, constructing a feature dictionary through sparse coding technology to obtain a spectral feature representation of the edge area; using the spectral feature representation, combined with a dynamic time warping algorithm, to align the spectral features of different temperature stages to obtain the nonlinear optical response characteristics of the edge area.

[0091] Specifically, first, the multi-wavelength dynamic spectral data is tensor decomposed to obtain feature matrices of three dimensions: time, space, and wavelength. Tensor decomposition is a multidimensional data analysis technique that decomposes high-dimensional data into low-dimensional representations. For the annular light-guiding film, the multi-wavelength dynamic spectral data forms a three-dimensional tensor, in which the time dimension represents different temperature stages, the space dimension represents different positions in the edge area of ​​the light-guiding film, and the wavelength dimension represents different spectral components. Tensor decomposition is performed using methods such as Tucker decomposition or CANDECOMP / PARAFAC (CP) decomposition. For example, for a data tensor of size 100×200×50 (representing 100 time points, 200 spatial positions, and 50 wavelengths), it can be decomposed into three feature matrices, namely, matrices of 100×10, 200×15, and 50×5. The beneficial effect of this decomposition is to greatly reduce the data dimension and extract key features, while retaining the multidimensional structural information of the data, laying the foundation for subsequent analysis.

[0092] According to the characteristic matrix, the non-negative matrix factorization algorithm is used to extract the main spectral components and obtain the basic spectral pattern of the edge area. The non-negative matrix factorization (NMF) algorithm decomposes the non-negative matrix into the product of two non-negative matrices, which is suitable for spectral data analysis. For the annular light-guiding film, the characteristic matrix of the wavelength dimension (such as 50×5) is input into the NMF algorithm to obtain the basic spectral pattern. For example, 5 basic spectral patterns may be obtained, each representing a typical spectral distribution. These basic patterns include blue light dominant mode (450nm peak), green light dominant mode (520nm peak), etc. The beneficial effect of NMF is to extract physically meaningful spectral components, which facilitates the understanding of the nature of spectral changes while reducing the impact of noise and redundant information.

[0093] Nonlinear principal component analysis is applied to the basic spectral pattern to extract high-order statistical features and obtain the nonlinear spectral change pattern in the edge area. Nonlinear principal component analysis (NLPCA) is an extension of traditional principal component analysis and can capture nonlinear relationships in the data. NLPCA is implemented using methods such as kernel principal component analysis (Kernel PCA) or autoencoder. For annular light-guiding films, the basic spectral pattern is input into NLPCA to obtain a nonlinear spectral change pattern. For example, nonlinear drift of the spectral peak position caused by temperature changes, or complex interactions between different wavelengths may be found. The beneficial effect of this step is to reveal the complex nonlinear relationships in the spectral data, provide deeper physical insights, and help identify subtle optical changes of light-guiding films under different conditions.

[0094] According to the nonlinear spectral change pattern, a feature dictionary is constructed by sparse coding technology to obtain the spectral feature representation of the edge area. Sparse coding is a technology that represents the signal as a linear combination of a small number of basic elements. The feature dictionary is constructed using K-SVD (K-Singular Value Decomposition) or Online Dictionary Learning algorithm. For the annular light-guiding film, a dictionary containing 100 atoms is constructed based on the nonlinear spectral change pattern, and each atom represents a typical spectral feature. Then, the Orthogonal Matching Pursuit (OMP) algorithm is used to represent each spectral data as a combination of a small number of atoms in the dictionary. For example, a spectrum at a specific position and time may be represented as a linear combination of 3-5 dictionary atoms. The beneficial effect of sparse coding is to greatly compress the data representation, highlight the key features, and retain enough information to reconstruct the original signal, which helps to efficiently store and analyze a large amount of spectral data.

[0095] By using spectral feature representation and combining it with the dynamic time warping algorithm, the spectral features of different temperature stages are aligned to obtain the nonlinear optical response characteristics of the edge area. The dynamic time warping (DTW) algorithm is a method for measuring the similarity of time series, which can process sequences of different lengths and speeds. For the annular light-guiding film, the DTW algorithm is used to align the spectral feature sequences of different temperature stages. For example, the spectral feature sequence at 25°C is used as a reference, and the sequences at other temperatures are aligned to this reference sequence through nonlinear scaling. In this way, even if the duration of different temperature stages is different, they can be directly compared. After alignment, the characteristic change trend of each temperature stage is extracted to obtain the complete nonlinear optical response characteristics. The beneficial effect of this step is to eliminate the influence of time scale differences, realize direct comparison of spectral features under different conditions, reveal the influence of temperature changes on the optical properties of light-guiding films, and provide a reliable basis for subsequent performance evaluation and optimization.

[0096] Please continue reading Figure 1 , based on the nonlinear optical response characteristics, generating a dynamic optical characteristic diagram of the edge area of ​​the light guide film, and performing a three-dimensional structural analysis to determine the edge effect dynamic characteristic parameters;

[0097] In one embodiment of the present invention, a dynamic optical characteristic diagram of the edge area of ​​the light-guiding film is generated based on the nonlinear optical response characteristics, and a three-dimensional structural analysis is performed to determine the edge effect dynamic characteristic parameters, including: performing spatiotemporal mapping on the nonlinear optical response characteristics to obtain a two-dimensional optical characteristic distribution of the edge area of ​​the light-guiding film; based on the two-dimensional optical characteristic distribution, a feature point set is extracted by a topological data analysis method to obtain key optical nodes of the edge area; a three-dimensional structural representation is constructed using the key optical nodes, and a non-rigid registration algorithm is applied to obtain a dynamic deformation field at different temperature stages; based on the dynamic deformation field and the two-dimensional optical characteristic distribution, the edge effect dynamic characteristic parameters are obtained by multivariate analysis.

[0098] Specifically, firstly, the nonlinear optical response characteristics are spatiotemporally mapped to obtain the two-dimensional optical property distribution of the edge area of ​​the light-guiding film. Spatiotemporal mapping is a data processing technology that combines time and space information to show the changes of optical properties over time and space. For the annular light-guiding film, the nonlinear optical response characteristics are unfolded on a two-dimensional plane, the x-axis represents the angular position along the ring (0° to 360°), and the y-axis represents different time points or temperature stages. The value of each coordinate point represents the optical property at that position and time, such as light intensity or the response intensity of a specific wavelength. For example, for a ring light-guiding film with an outer diameter of 100mm and an inner diameter of 80mm, a 360×100 two-dimensional matrix can be generated, where 360 ​​represents an angular resolution of 1° and 100 represents a total of 100 time points or temperature stages. The beneficial effect of this spatiotemporal mapping is to convert complex three-dimensional data (spatial position, time, optical properties) into an intuitive two-dimensional representation, which facilitates the identification of patterns and anomalies in space and time, and provides a clear data basis for subsequent analysis.

[0099] According to the two-dimensional optical property distribution, the feature point set is extracted by the topological data analysis method to obtain the key optical nodes in the edge area. Topological Data Analysis (TDA) is a method to study the geometric and topological properties of data, which can capture the essential structural characteristics of the data. For the two-dimensional optical property distribution of the annular light-guiding film, the persistent homology algorithm is used for analysis. The algorithm studies the topological characteristics of the data by constructing simplicial complexes of different scales. For example, for a 360×100 two-dimensional distribution, 20 different threshold levels can be set, and a simplicial complex is constructed at each level, and then the topological features that appear continuously (such as connected components, rings, holes, etc.) are analyzed. Those features that persist at multiple scales are considered to be key optical nodes. This includes extreme points of light intensity, areas with significant gradient changes, or locations with special spectral features. For the annular light-guiding film, key points at the junction of the inner and outer edges, feature points near the injection position of the light source, etc. may be identified. The beneficial effect of using TDA is the ability to extract robust structural features from complex data, immune to noise and small perturbations, thereby accurately locating key areas that are critical to the performance of the light-guiding film.

[0100] The three-dimensional structure representation is constructed using key optical nodes, and the non-rigid registration algorithm is applied to obtain the dynamic deformation field at different temperature stages. The three-dimensional structure representation is to combine the two-dimensional optical property distribution and temperature information to construct a complete three-dimensional model. For the annular light-guiding film, each key optical node can be represented as a point in three-dimensional space, where the x and y coordinates represent the position of the node on the plane of the light-guiding film, and the z coordinate represents the optical property value (such as light intensity). The non-rigid registration algorithm is used to analyze the position changes of these three-dimensional points at different temperature stages. An improved version of the iterative closest point (ICP) algorithm, such as the coherent point drift (CPD) algorithm, is used to achieve non-rigid registration. For example, the three-dimensional structure at 25°C is selected as a reference, and the structures at other temperature stages (such as -20°C, 50°C, and 80°C) are registered with it. During the registration process, the position of the points is allowed to deform non-uniformly to simulate the deformation of the light-guiding film at different temperatures. By comparing the difference in the point positions before and after registration, the dynamic deformation field is obtained, which describes the displacement vector of each key node at different temperatures. The beneficial effect of this method is that it can accurately quantify the changes in the microstructure of the light-guiding film caused by temperature changes, reveal the distribution of thermal stress and material properties, and provide a direct physical explanation for understanding the impact of temperature on optical properties.

[0101] According to the dynamic deformation field and the two-dimensional optical characteristic distribution, the edge effect dynamic characteristic parameters are obtained through multivariate analysis. Multivariate analysis is a statistical method for studying the relationship between multiple variables and is suitable for the characteristic extraction of complex systems. For the annular light-guiding film, a comprehensive data set is constructed by combining the displacement information of the dynamic deformation field and the optical information of the two-dimensional optical characteristic distribution. Multivariate analysis is performed using methods such as principal component analysis (PCA) and partial least squares regression (PLSR). For example, the displacement of each key optical node, the change in light intensity, the change in spectral characteristics, etc. can be used as variables to construct a high-dimensional data matrix. PCA is used to reduce the dimension and extract the main change patterns; then PLSR is used to establish a relationship model between these patterns and temperature changes. The final edge effect dynamic characteristic parameters may include the thermal-optical coupling coefficient (describing the degree of influence of temperature changes on optical performance), the edge light intensity attenuation rate (characterizing the rate of change of light intensity in the edge area with temperature), the spectral drift coefficient (reflecting the influence of temperature on spectral characteristics), etc. The beneficial effect of this multivariate analysis is the ability to extract a small but highly informative number of parameters from complex data, comprehensively characterizing the dynamic optical behavior of the edge region of the light-guiding film, and providing direct quantitative indicators for product design and quality control.

[0102] Please continue reading Figure 1 , inputting the edge effect dynamic characteristic parameters into a preset temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge area;

[0103] In one embodiment of the present invention, the preset temperature gradient light propagation model includes a heat conduction layer, an optical propagation layer and a coupling calculation layer;

[0104] The method of inputting the edge effect dynamic characteristic parameters into a preset temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge area includes: performing wavelet transform on the edge effect dynamic characteristic parameters to obtain a time-frequency domain feature map; inputting the time-frequency domain feature map into the heat conduction layer to calculate the temperature distribution field by a finite difference method; applying a non-sequential ray tracing algorithm in the optical propagation layer to obtain a light intensity distribution prediction result based on the temperature distribution field; using the light intensity distribution prediction result, performing thermal-optical interaction iterative calculation in the coupling calculation layer to obtain a steady-state thermal-optical field distribution; performing fractal dimension analysis on the steady-state thermal-optical field distribution to obtain a field complexity index of the edge area; and obtaining a thermal-optical coupling complexity index of the edge area by multivariate nonlinear regression based on the field complexity index and the temperature distribution field.

[0105] Specifically, the edge effect dynamic characteristic parameters are first subjected to wavelet transform to obtain a time-frequency domain feature map. Wavelet transform is a time-frequency analysis method that can provide both time and frequency information of the signal. For the annular light-guiding film, the continuous wavelet transform (CWT) is selected for analysis, and the Morlet wavelet is used as the mother wavelet function. For example, for a time series data containing parameters such as the thermal-optical coupling coefficient, the edge light intensity attenuation rate, and the spectral drift coefficient (assuming there are 1000 time points), a 1000×64 time-frequency domain feature map is obtained after wavelet transform, where 64 represents the number of selected frequency scales. The x-axis of this time-frequency domain feature map represents time, the y-axis represents frequency, and the color depth represents energy intensity. The beneficial effect of this transformation is that it can reveal the law of change of the edge effect dynamic characteristic parameters over time, especially it can capture transient changes and periodic patterns, and provide comprehensive time-frequency information for subsequent thermal-optical coupling analysis.

[0106] The time-frequency domain characteristic graph is input into the heat conduction layer, and the temperature distribution field is calculated by the finite difference method. The heat conduction layer is the part of the preset temperature gradient light propagation model that simulates the heat conduction process. The finite difference method is a numerical method for solving partial differential equations, which is suitable for heat conduction problems of complex geometric shapes. For the annular light-guiding film, it is discretized into a two-dimensional grid, such as a 360×20 grid, where 360 ​​represents the resolution in the circumferential direction (one grid point per 1°) and 20 represents the radial resolution. The heat conduction equation is discretized using an explicit finite difference format, with a time step of 0.1 seconds and a spatial step of 0.5 mm. The time-frequency domain characteristic graph is input into the heat conduction equation as a heat source term to simulate the heat propagation process in the light-guiding film. After iterative calculation, the temperature value of each grid point that changes with time is obtained to form a temperature distribution field. The beneficial effect of this method is that it can accurately simulate the heat conduction process in the light-guiding film, taking into account the thermal properties and boundary conditions of the material, and providing an accurate temperature basis for subsequent optical analysis.

[0107] According to the temperature distribution field, the non-sequential ray tracing algorithm is applied in the optical propagation layer to obtain the light intensity distribution prediction result. The optical propagation layer simulates the propagation behavior of light in the light-guiding film. The non-sequential ray tracing algorithm does not restrict the propagation path of light and can simulate multiple reflections and scattering in complex optical systems. For the annular light-guiding film, the Monte Carlo ray tracing method is used to emit a large number of light rays from the light source position (for example, 1 million light rays from each light source). During the propagation of each light ray, the influence of the temperature distribution field on the optical properties of the material is considered, such as the change of refractive index with temperature. The interaction between light and the surface and internal structure of the light-guiding film follows the Fresnel equation and Snell's law. Each light ray is tracked until its energy drops below the threshold or leaves the light-guiding film. Finally, the number and energy of light rays reaching each point on the surface of the light-guiding film are counted to obtain the light intensity distribution prediction result. The beneficial effect of this method is that it can accurately simulate the light propagation process in the light-guiding film with complex geometry, taking into account the influence of temperature on optical properties, and providing reliable optical data for thermal-optical coupling analysis.

[0108] Using the predicted results of the light intensity distribution, the thermo-optical interaction iterative calculation is performed in the coupled calculation layer to obtain the steady-state thermo-optical field distribution. The coupled calculation layer is a key part of simulating the interaction between thermal and optical. The thermo-optical interaction iterative calculation takes into account the heat generation caused by light absorption and the influence of temperature changes on optical performance. For the annular light-guiding film, the initial conditions are set to the previously calculated temperature distribution field and light intensity distribution prediction results. Each iteration includes the following steps:

[0109] 1) Update the material optical parameters according to the current temperature field;

[0110] 2) Use the updated parameters to re-trace the rays and obtain a new light intensity distribution;

[0111] 3) Calculate the heat generated by light absorption;

[0112] 4) Solve the heat conduction equation and update the temperature distribution field.

[0113] This process is repeated until the change in temperature and light intensity distribution is less than a preset threshold (e.g. 0.1%), and a steady-state thermal light field distribution is obtained. The beneficial effect of this method is that it can accurately simulate the interaction process between heat and light, reveal the performance of the light-guiding film under actual working conditions, and provide an important basis for optimizing the design.

[0114] The fractal dimension analysis of the steady-state thermal-optical field distribution is performed to obtain the field complexity index of the edge area. Fractal dimension analysis is an effective method to evaluate the structural characteristics of complex systems. For the steady-state thermal-optical field distribution of the annular light-guiding film, the fractal dimension is calculated using the box-counting method. The edge area is divided into grids of different scales (for example, from 1mm to 0.1mm, a total of 10 scale levels), and the number of non-empty grids is calculated for each scale. The fractal dimension is obtained by linear fitting under logarithmic coordinates. In addition, the multifractal spectrum is calculated to fully describe the complexity of the field. The final field complexity index includes the characteristic parameters of the fractal dimension and the multifractal spectrum. The beneficial effect of this analysis method is that it can quantify the spatial complexity and inhomogeneity of the thermal-optical field distribution, providing a concise and informative indicator to describe the complexity of the edge effect.

[0115] According to the field complexity index and the temperature distribution field, the thermal-optical coupling complexity index of the edge area is obtained through multivariate nonlinear regression. Multivariate nonlinear regression is a powerful tool for establishing the input-output relationship of complex systems. For the annular light-guiding film, the field complexity index and the characteristic parameters of the temperature distribution field are used as input variables, and the thermal-optical coupling complexity index is used as the output variable. The support vector regression (SVR) method is used for modeling, and the radial basis function (RBF) is selected as the kernel function. The input variables include fractal dimension, multifractal spectrum parameters, temperature gradient, maximum temperature, etc., totaling about 20 variables. The model parameters, such as regularization coefficient and kernel function parameters, are optimized through cross-validation. The thermal-optical coupling complexity index finally obtained is a dimensionless number that comprehensively reflects the degree of thermal-optical interaction in the edge area. The beneficial effect of this method is that the complex thermal-optical coupling phenomenon is condensed into a quantitative index, which is convenient for the comparison and optimization of different design schemes, and provides a powerful tool for the performance evaluation and quality control of light-guiding films.

[0116] In one embodiment of the present invention, the thermal-optical coupling complexity index of the edge area is obtained through multivariate nonlinear regression based on the field complexity index and the temperature distribution field, including: combining the field complexity index and the temperature distribution field into multidimensional input variables, performing data standardization processing on the multidimensional input variables to obtain standardized multidimensional feature data; performing nonlinear transformation on the standardized multidimensional feature data using a kernel function to obtain a high-dimensional feature representation; calculating the regression coefficient based on the high-dimensional feature representation through a preset multivariate nonlinear regression algorithm to obtain a preliminary estimate of the thermal-optical coupling complexity; performing residual analysis and outlier detection on the preliminary estimate of the thermal-optical coupling complexity, and obtaining the thermal-optical coupling complexity index of the edge area through iterative weighted least squares optimization.

[0117] Specifically, the field complexity index and the temperature distribution field are first combined into multidimensional input variables, and these multidimensional input variables are subjected to data standardization to obtain standardized multidimensional feature data. For the annular light-guiding film, the field complexity index includes fractal dimension and multifractal spectrum parameters, and the temperature distribution field includes features such as temperature gradient, maximum temperature, and average temperature. Assume that there are 20 features in total, forming a 20-dimensional input vector. The data is standardized using the Z-score method, that is, each feature is subtracted from its mean value and divided by the standard deviation. For example, for the fractal dimension feature, if the original value is 1.8, the mean value is 1.5, and the standard deviation is 0.2, the standardized value is (1.8-1.5) / 0.2=1.5. The beneficial effect of this standardization process is to eliminate the dimensional differences between different features, so that all features have the same weight in subsequent analysis, and improve the stability and interpretability of the model.

[0118] The kernel function is used to perform nonlinear transformation on the standardized multidimensional feature data to obtain a high-dimensional feature representation. The kernel function is a technique for realizing the inner product calculation of a high-dimensional space in a low-dimensional space, which can effectively capture the nonlinear relationship in the data. For the 20-dimensional standardized feature data of the annular light-guiding film, the radial basis function (RBF) is selected as the kernel function. The expression of the RBF kernel function is K(x,y)=exp(-γ||xy||²), where x and y are two vectors in the input space, ||xy|| represents the Euclidean distance between the two vectors, and γ is a parameter that controls the width of the function. The optimal γ value is determined by grid search, for example, γ=0.1. The kernel function value is calculated for each 20-dimensional input vector and a pre-selected support vector (for example, 100 representative sample points are selected as support vectors) to obtain a 100-dimensional high-dimensional feature representation. The beneficial effect of this nonlinear transformation is to map the original feature to a higher-dimensional space, so that in the new feature space, complex nonlinear relationships may become linearly separable, providing a richer feature representation for subsequent regression analysis.

[0119] According to the high-dimensional feature representation, the regression coefficient is calculated by the preset multivariate nonlinear regression algorithm to obtain the preliminary estimate of the complexity of thermal-optical coupling. Support Vector Regression (SVR) is selected as the nonlinear regression algorithm. The goal of SVR is to find an optimal hyperplane in the high-dimensional feature space so that the distance from all sample points to this hyperplane is minimized. For the annular light-guiding film, the ε-SVR variant is used, and ε=0.1 is set to indicate the allowable error range. The regression coefficient is calculated by the quadratic programming solver to obtain a 100-dimensional weight vector w and a bias term b. The preliminary estimate of the complexity of thermal-optical coupling is calculated as f(x)=w·φ(x)+b, where x is the input vector, φ(x) is the representation of x in the high-dimensional feature space, w is the weight vector, and b is the bias term. For example, for a new input sample, the kernel function value of it and 100 support vectors is calculated to obtain a 100-dimensional feature vector, which is then inner-producted with the weight vector w, and then added with the bias b to obtain the estimated value of the thermal-optical coupling complexity of the sample. The beneficial effect of this approach is that it can handle highly nonlinear relationships while controlling the model complexity through regularization terms, avoiding overfitting and providing a robust estimate of the thermal-optical coupling complexity.

[0120] Residual analysis and outlier detection are performed on the preliminary estimate of the thermal-optical coupling complexity, and the thermal-optical coupling complexity index of the edge area is obtained by iterative weighted least squares optimization. Residual analysis calculates the difference between the predicted value and the actual value of each sample point, and outlier detection uses the modified Z-score method to mark points with a median absolute deviation (MAD) greater than 3.5 as potential outliers. For example, if the predicted value of a sample is 5.2, the actual value is 6.0, the residual is 0.8, and the MAD is 0.3, then the modified Z-score of the sample is 0.8 / 0.3≈2.67, which is not considered an outlier. Iteratively Reweighted Least Squares (IRLS) is used to optimize the regression model. In each iteration, a weight is assigned to each sample according to the current residual size. The larger the residual, the smaller the weight. For example, using the Huber weight function, for the residual r, if |r|≤k (k is a threshold, such as 1.345), the weight is 1; otherwise the weight is k / |r|. The regression coefficients are updated by minimizing the weighted residual sum of squares. This process is repeated until convergence, usually after 5-10 iterations. The resulting edge region thermal-optical coupling complexity index is a reliable estimate after outlier processing and model optimization. The beneficial effect of this method is that it improves the robustness of the model to outliers, while improving the accuracy of the estimate through iterative optimization, providing a more accurate and reliable quantitative indicator for the thermal-optical coupling characteristics of the edge region of the light-guiding film.

[0121] Please continue reading Figure 1 , using the thermal-optical coupling complexity index and a preset edge performance evaluation algorithm to generate dynamic change data of optical performance of the edge area;

[0122] In one embodiment of the present invention, the preset edge performance evaluation algorithm includes an optical transmission efficiency evaluation module, a color uniformity evaluation module and a dynamic response evaluation module;

[0123] The method of using the thermal-optical coupling complexity index and a preset edge performance evaluation algorithm to generate dynamic change data of optical performance of edge areas includes: inputting the thermal-optical coupling complexity index into the optical transmission efficiency evaluation module, and calculating the optical transmission efficiency curve of the edge area by using a Monte Carlo ray tracing method; performing chromaticity coordinate mapping in the color uniformity evaluation module according to the optical transmission efficiency curve to obtain a color deviation distribution diagram of the edge area; using the color deviation distribution diagram and combining it with the thermal-optical coupling complexity index, applying a state space model in the dynamic response evaluation module to calculate the transient optical response characteristics of the edge area; performing wavelet multi-resolution analysis on the transient optical response characteristics to extract multi-scale optical performance characteristics of the edge area; and comprehensively generating dynamic change data of optical performance of the edge area according to the multi-scale optical performance characteristics by using an adaptive weighted fusion algorithm.

[0124] Specifically, the thermal-optical coupling complexity index is first input into the optical transmission efficiency evaluation module, and the optical transmission efficiency curve of the edge area is calculated by the Monte Carlo ray tracing method. Monte Carlo ray tracing is a statistical method used to simulate the propagation of light in a complex optical system. For a circular light-guiding film, a light source is set at the edge of the inner circle to emit a large amount of light (e.g. The initial direction of each light ray is randomly generated according to the Lambertian distribution, and the initial energy value is 1. When the light propagates in the light-guiding film, reflection, refraction and absorption are taken into account, and the direction and energy of the light are updated after each interaction. When the energy of the light drops below a threshold (such as 0.01) or leaves the light-guiding film, the tracking is terminated. The number and energy of light rays reaching the edge of the outer circle are recorded, and the optical transmission efficiency η= (total energy reaching the edge of the outer circle) / (total energy of the emitted light) is calculated. By changing the complexity index of the thermal-optical coupling (such as from 0 to 1, with a step size of 0.01), a series of η values ​​are obtained, and the optical transmission efficiency curve is plotted. The beneficial effect of this method is that it can accurately simulate the light propagation process in light-guiding films with complex geometric shapes, taking into account the influence of thermal-optical coupling on optical performance, and providing a quantitative description of the optical transmission efficiency in the edge area.

[0125] According to the optical transmission efficiency curve, chromaticity coordinate mapping is performed in the color uniformity evaluation module to obtain the color deviation distribution diagram of the edge area. Chromaticity coordinate mapping is the process of converting spectral data into a standard color space. For annular light guide films, the CIE 1931 XYZ color space is selected. First, the optical transmission efficiency curve is multiplied by the spectrum of a standard light source (such as D65) to obtain the output spectrum. Then, the tristimulus values ​​X, Y, and Z are calculated using the CIE standard observer function (2° or 10°). The chromaticity coordinate calculation formula is:

[0126] x=X / (X+Y+Z), y=Y / (X+Y+Z).

[0127] Repeat this calculation for each point in the edge area of ​​the light guide film to obtain the chromaticity coordinate (x, y) distribution. The color deviation is defined as the Euclidean distance between the chromaticity coordinate of each point and the ideal chromaticity coordinate:

[0128] .

[0129] Finally, a color deviation distribution graph is generated, with the x-axis representing the edge position (0° to 360°) and the y-axis representing the color deviation Δ. The beneficial effect of this method is that it converts complex spectral data into intuitive color information, making it easier to evaluate the color uniformity of the edge area of ​​the light guide film, providing an important basis for product quality control.

[0130] Using the color deviation distribution map and the thermal-optical coupling complexity index, the state space model is applied in the dynamic response evaluation module to calculate the transient optical response characteristics of the edge area. The state space model is a mathematical model that describes the dynamic system, including the state equation and the observation equation. For the annular light guide film, the state variable is defined is the optical performance parameter (such as light intensity, chromaticity, etc.) at time t, and the observed variable is the actual measured value. State equation:

[0131] .

[0132] in is the state transfer matrix, is the control input matrix, is the control input (here is the thermal-optical coupling complexity index), is the process noise. The observation equation is:

[0133] .

[0134] Where C is the observation matrix, is the observation noise. The state variables are estimated using the Extended Kalman Filter (EKF). The EKF iterates through the prediction and update steps by linearizing the nonlinear system. Prediction step:

[0135] .

[0136] .

[0137] Update steps:

[0138] .

[0139] .

[0140] .

[0141] in, represents state estimation, P represents the error covariance matrix, K is the Kalman gain, Q and R are the covariance matrices of process noise and observation noise, respectively. The beneficial effect of this method is that it can estimate and predict the optical performance of the edge area of ​​the light-guiding film in real time, taking into account the dynamic influence of thermal-optical coupling, and providing a powerful tool for transient response analysis.

[0142] Wavelet multi-resolution analysis is performed on the transient optical response characteristics to extract the multi-scale optical performance characteristics of the edge area. Wavelet multi-resolution analysis is a time-frequency analysis method that can decompose signals at different scales. For the annular light-guiding film, discrete wavelet transform (DWT) is used for analysis. Daubechies wavelet (such as db4) is used as the basis function to decompose the transient response signal into 5 layers. Decomposition formula:

[0143] .

[0144] Where s is the original signal, is the fifth-layer approximation coefficient, arrive The detail coefficients for layers 1 to 5. Calculate the energy for each layer coefficient ,in is the wavelet coefficient of the i-th layer. The energy distribution reflects the distribution of the signal in different frequency bands. In addition, the entropy of each layer coefficient is calculated ,in is the normalized probability distribution of the coefficients of the i-th layer. The entropy value characterizes the complexity of the signal. Finally, the statistical features of the coefficients of each layer, such as mean, variance, skewness and kurtosis, are extracted. These multi-scale features together constitute the optical performance feature vector of the edge region. The beneficial effect of this method is that it can comprehensively capture the variation characteristics of optical performance at different time and frequency scales, providing rich information for subsequent analysis.

[0145] According to the multi-scale optical performance characteristics, the dynamic change data of the optical performance of the edge area is comprehensively generated through the adaptive weighted fusion algorithm. The adaptive weighted fusion algorithm is a data integration method that dynamically adjusts the weight according to the data characteristics. For the annular light-guiding film, the multi-scale feature vector is first standardized to eliminate the dimension effect. Then, the principal component analysis (PCA) is used for dimensionality reduction to retain the principal components that explain 90% of the variance. For each principal component p , calculate its corresponding weight ,in is the eigenvalue of the i-th principal component, indicating the variance explained by the principal component. Initial fusion result:

[0146] F= .

[0147] Next, an adaptive mechanism is introduced: according to the feature vector at each time point t , calculate the local correlation (t)=corr( , ). Update weights: (t)= (1+α (t)), where α is the learning rate (such as 0.1). Normalize the updated weights to get the final dynamic change data D(t)=Σ (t) (t). The beneficial effect of this method is that it can adaptively adjust the importance of each scale feature according to the local characteristics of the data, generate a comprehensive index that fully reflects the dynamic changes in the optical performance of the edge area, and provide strong data support for the performance evaluation and optimization of light guide films.

[0148] Please continue reading Figure 1 According to the dynamic change data of the optical performance of the edge area, multi-dimensional evaluation index processing and time series analysis are performed to obtain dynamic and accurate evaluation results of the optical performance of the edge area.

[0149] In one embodiment of the present invention, multi-dimensional evaluation index processing and time series analysis are performed based on the dynamic change data of the optical performance of the edge area to obtain a dynamic and accurate evaluation result of the optical performance of the edge area, including: multi-dimensional decomposition of the dynamic change data of the optical performance of the edge area to obtain a feature sequence of three dimensions of time, space and spectrum; using the feature sequence, aligning data of different dimensions through a dynamic time warping algorithm to obtain a standardized multi-dimensional time series; applying empirical mode decomposition to the standardized multi-dimensional time series to extract the intrinsic mode function of the optical performance of the edge area; according to the intrinsic mode function, calculating the instantaneous frequency and amplitude through the Hilbert-Huang transform to obtain a time-frequency feature map of the optical performance of the edge area; using the time-frequency feature map, combined with a recursive quantitative analysis method, to calculate the dynamic complexity index of the optical performance of the edge area; according to the dynamic complexity index, through a fuzzy comprehensive evaluation method, to obtain a dynamic and accurate evaluation result of the optical performance of the edge area.

[0150] Specifically, the dynamic change data of the optical performance of the edge area is first decomposed in multiple dimensions to obtain the characteristic sequences of time, space and spectrum. For the annular light-guiding film, it is assumed that the dynamic change data is a three-dimensional tensor D(t,θ,λ), where t represents time (such as 0 to 1000 seconds), θ represents the circular angle (0° to 360°), and λ represents the wavelength (such as 400nm to 700nm). Using tensor decomposition methods, such as Tucker decomposition, D is decomposed into three factor matrices and a core tensor: , where G is the core tensor, U, V, and W are the factor matrices of the time, space, and spectral dimensions, respectively. Represents the tensor product along the i-th dimension. For example, for a data tensor of size 1000×360×300, you may get a core tensor G of size 20×15×10, and factor matrices U, V, and W of size 1000×20, 360×15, and 300×10. The beneficial effect of this decomposition is to significantly reduce the data dimension, extract key features, and retain the multidimensional structural information of the data, laying the foundation for subsequent analysis.

[0151] Using the feature sequence, the data of different dimensions are aligned through the Dynamic Time Warping (DTW) algorithm to obtain a standardized multidimensional time series. The DTW algorithm can process time series of different lengths and speeds. For the annular light-guiding film, the feature sequences of time, space and spectrum dimensions are aligned respectively. Taking the time dimension as an example, the first column of the U matrix is ​​selected as the reference sequence r, and the other columns are the query sequence q. Calculate the distance matrix Dist, where Dist(i,j)=( - )². Then the optimal path W={ ,..., }, minimize the cumulative distance Σ(Dist( )). The length of the aligned sequence is k, which may be greater than the original sequence length. Repeat this process for the spatial and spectral dimensions. Finally, the aligned sequences are combined into a standardized multidimensional time series S. The beneficial effect of this alignment is that the time scale differences between data of different dimensions are eliminated, so that subsequent analysis can be carried out in a unified time frame, improving the comparability and consistency of the analysis results.

[0152] Apply Empirical Mode Decomposition (EMD) to the standardized multidimensional time series to extract the intrinsic mode functions (IMFs) of the optical performance of the edge area. EMD is an adaptive signal decomposition method that is particularly suitable for nonlinear and non-stationary signals. For each spatial position and wavelength of the annular light-guiding film, perform the following steps:

[0153] (1) Find all extreme points;

[0154] (2) Generate the upper envelope through spline interpolation and lower envelope ;

[0155] (3) Calculate the mean ;

[0156] (4) Subtract m(t) from the original signal to obtain h(t)=x(t)-m(t);

[0157] (5) Check whether h(t) satisfies the IMF condition. If not, repeat steps 1-4 until the condition is satisfied. (6) Subtract the obtained IMF from the original signal and repeat the above process for the remaining signal until the remaining signal becomes a monotonic function. Finally, the original signal is decomposed into M IMFs and a residual:

[0158] .

[0159] Where M is the total number of IMF, (t) is the ith IMF, (t) is the residual. The beneficial effect of this decomposition is that it can adaptively decompose complex signals into physically meaningful components, reveal different scale oscillation patterns in the signal, and provide a basis for subsequent time-frequency analysis.

[0160] According to the intrinsic mode function, the instantaneous frequency and amplitude are calculated by Hilbert-Huang transform to obtain the time-frequency characteristic diagram of the optical performance of the edge area. Perform the following steps:

[0161] (1) Calculate the Hilbert transform H[ ];

[0162] (2) Constructing analytical signals (t)= +jH[ ];

[0163] (3) Calculate the instantaneous amplitude:

[0164] (4) Calculate the phase ,in, represents the instantaneous phase of the i-th IMF;

[0165] (5) Calculate instantaneous frequency .

[0166] Finally, we get the Hilbert spectrum:

[0167] .

[0168] in (t) = 2π (t). H(ω,t) is plotted as a time-frequency characteristic diagram, with the x-axis representing time, the y-axis representing frequency, and the color depth representing amplitude. The beneficial effect of this analysis is that it can accurately characterize the time-frequency characteristics of the signal, reveal the subtle changes in the optical properties of the edge area over time, and provide a powerful tool for the analysis of complex dynamic systems.

[0169] The dynamic complexity index of the optical performance of the edge area is calculated by using the time-frequency feature map and the recurrence quantification analysis (RQA) method. RQA is a nonlinear analysis method for studying the complexity of dynamic systems. First, construct a recurrence graph:

[0170] .

[0171] in is the Heaviside function, is the threshold used to determine state similarity, and is the state vector of the time-frequency feature graph at different time points. Then calculate the following indicators:

[0172] (1) Recursion rate , represents the degree of repetition of the system state, where N is the total number of samples;

[0173] (2) Certainty ,in Is the length The probability distribution of the diagonal line indicates the predictability of the system;

[0174] (3) Average diagonal length , reflecting the stability of the system;

[0175] (4) Entropy ENTR = ,in It is normalized , represents the complexity of the system.

[0176] These indices together form the dynamic complexity index vector. The beneficial effect of this analysis is that it can quantify the dynamic complexity of optical performance in edge regions from multiple perspectives, providing a comprehensive numerical index for performance evaluation.

[0177] According to the dynamic complexity index, the dynamic and accurate evaluation results of the optical performance of the edge area are obtained through the fuzzy comprehensive evaluation method. The fuzzy comprehensive evaluation method is a multi-index decision-making method based on fuzzy mathematics. The steps are as follows: (1) Establish the factor set U = , including n dynamic complexity indicators; (2) establish the evaluation set V = , such as {excellent, good, average, poor}; (3) construct the fuzzy relationship matrix R, where represents the membership of the i-th indicator to the j-th evaluation level; (4) Determine the weight vector W = , which can be determined by the analytic hierarchy process (AHP) or the entropy weight method; (5) Calculate the comprehensive evaluation result B = W·R, where · represents the fuzzy synthesis operation; (6) Determine the final evaluation result according to the maximum membership principle or the weighted average method. For example, if B = (0.6, 0.3, 0.1, 0), the final evaluation is "excellent". The beneficial effect of this evaluation method is that it can comprehensively consider multiple complexity indicators and give an overall evaluation of the optical performance of the edge area, providing an intuitive and reliable decision-making basis for the quality control and optimization of light-guiding films.

[0178] The above describes the uniformity test method of the light guide film in the embodiment of the present invention. The following describes the uniformity test device of the light guide film in the embodiment of the present invention. Figure 2 , an embodiment of a uniformity testing device for a light guide film in an embodiment of the present invention comprises:

[0179] The temperature adjustment and spectrum acquisition module 201 is used to dynamically adjust the temperature of the light guide film and obtain multi-wavelength dynamic spectrum data of the edge area of ​​the light guide film within a preset temperature range;

[0180] A feature extraction module 202 is used to extract the nonlinear optical response characteristics of the edge area through data dimension reduction and decomposition technology according to the multi-wavelength dynamic spectrum data;

[0181] An optical characteristic analysis module 203 is used to generate a dynamic optical characteristic diagram of the edge area of ​​the light guide film based on the nonlinear optical response characteristics, and perform a three-dimensional structure analysis to determine the edge effect dynamic characteristic parameters;

[0182] Thermo-optical coupling analysis module 204 is used to input the edge effect dynamic characteristic parameters into a preset temperature gradient light propagation model to calculate the thermo-optical coupling complexity index of the edge area;

[0183] The performance evaluation module 205 is used to generate dynamic change data of optical performance of edge areas by using the thermal-optical coupling complexity index and a preset edge performance evaluation algorithm;

[0184] The result analysis module 206 is used to perform multi-dimensional evaluation index processing and time series analysis according to the dynamic change data of the optical performance of the edge area, so as to obtain a dynamic and accurate evaluation result of the optical performance of the edge area.

[0185] above Figure 2 The uniformity testing device for the light guide film in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The uniformity testing device for the light guide film in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0186] Figure 3 3 is a schematic diagram of the structure of a uniformity test device for a light-guiding film provided by an embodiment of the present invention. The uniformity test device 300 for a light-guiding film may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 may be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the uniformity test device 300 for a light-guiding film. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the uniformity test device 300 for a light-guiding film to implement the steps of the above-mentioned uniformity test method for a light-guiding film.

[0187] The light guide film uniformity test device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the light guide film uniformity testing device shown does not constitute a limitation on the light guide film uniformity testing device provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0188] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for testing the uniformity of the light-guiding film.

[0189] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0190] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0191] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for testing uniformity of a light guide film, characterized in that: include: Dynamically adjust the temperature of the light guide film and obtain multi-wavelength dynamic spectrum data of the edge area of ​​the light guide film within a preset temperature range; According to the multi-wavelength dynamic spectrum data, extracting the nonlinear optical response characteristics of the edge region by data dimension reduction and decomposition technology; Based on the nonlinear optical response characteristics, a dynamic optical characteristic diagram of the edge area of ​​the light guide film is generated, and a three-dimensional structural analysis is performed to determine the edge effect dynamic characteristic parameters; Inputting the edge effect dynamic characteristic parameters into a preset temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge area; the preset temperature gradient light propagation model includes a heat conduction layer, an optical propagation layer and a coupling calculation layer; The step of inputting the edge effect dynamic characteristic parameters into a preset temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge area comprises: performing wavelet transform on the edge effect dynamic characteristic parameters to obtain a time-frequency domain feature map; inputting the time-frequency domain feature map into the heat conduction layer to calculate the temperature distribution field by a finite difference method; applying a non-sequential ray tracing algorithm in the optical propagation layer to obtain a light intensity distribution prediction result based on the temperature distribution field; performing thermal-optical interaction iterative calculation in the coupling calculation layer using the light intensity distribution prediction result to obtain a steady-state thermal-optical field distribution; performing fractal dimension analysis on the steady-state thermal-optical field distribution to obtain a field complexity index of the edge area; and obtaining a thermal-optical coupling complexity index of the edge area by multivariate nonlinear regression based on the field complexity index and the temperature distribution field. The thermal-optical coupling complexity index and the preset edge performance evaluation algorithm are used to generate the dynamic change data of the optical performance of the edge area; the preset edge performance evaluation algorithm includes an optical transmission efficiency evaluation module, a color uniformity evaluation module and a dynamic response evaluation module; the thermal-optical coupling complexity index and the preset edge performance evaluation algorithm are used to generate the dynamic change data of the optical performance of the edge area, including: inputting the thermal-optical coupling complexity index into the optical transmission efficiency evaluation module, and calculating the optical transmission efficiency curve of the edge area by the Monte Carlo ray tracing method; according to the optical transmission efficiency curve, performing chromaticity coordinate mapping in the color uniformity evaluation module to obtain a color deviation distribution map of the edge area; using the color deviation distribution map, in combination with the thermal-optical coupling complexity index, applying a state space model in the dynamic response evaluation module to calculate the transient optical response characteristics of the edge area; performing wavelet multi-resolution analysis on the transient optical response characteristics to extract the multi-scale optical performance characteristics of the edge area; according to the multi-scale optical performance characteristics, using an adaptive weighted fusion algorithm, comprehensively generating the dynamic change data of the optical performance of the edge area; According to the dynamic change data of the optical performance of the edge area, multi-dimensional evaluation index processing and time series analysis are performed to obtain dynamic and accurate evaluation results of the optical performance of the edge area.

2. The method for testing uniformity of a light guide film according to claim 1, characterized in that: The method of dynamically adjusting the temperature of the light guide film and obtaining multi-wavelength dynamic spectrum data of the edge area of ​​the light guide film within a preset temperature range includes: According to a preset temperature curve, the light guide film is nonlinearly temperature-regulated to obtain multiple non-uniformly distributed temperature stages; At each temperature stage, multi-wavelength optical excitation is performed on the edge region of the light guide film to obtain a multi-dimensional response signal of the edge region; Adaptively sampling and wavelet transforming the multidimensional response signal of the edge region to obtain spectral characteristics in the time-frequency domain; According to the spectral characteristics in the time-frequency domain, multi-wavelength dynamic spectral data of the edge area of ​​the light guide film is obtained through dynamic threshold segmentation and signal reconstruction.

3. The method for testing uniformity of a light guide film according to claim 1, characterized in that: The method of extracting the nonlinear optical response characteristics of the edge region by using data dimension reduction and decomposition technology based on the multi-wavelength dynamic spectrum data includes: Performing tensor decomposition on the multi-wavelength dynamic spectrum data to obtain a feature matrix in three dimensions: time, space, and wavelength; According to the characteristic matrix, a non-negative matrix decomposition algorithm is used to extract the main spectral components to obtain the basic spectral pattern of the edge area; Applying nonlinear principal component analysis to the basic spectral pattern to extract high-order statistical features and obtain a nonlinear spectral variation pattern in the edge region; According to the nonlinear spectral variation pattern, a feature dictionary is constructed by using sparse coding technology to obtain a spectral feature representation of the edge area; The spectral feature representation is used in combination with a dynamic time warping algorithm to align the spectral features at different temperature stages and obtain the nonlinear optical response characteristics of the edge region.

4. The method for testing uniformity of a light guide film according to claim 1, characterized in that: The method of generating a dynamic optical characteristic diagram of the edge region of the light guide film based on the nonlinear optical response characteristics and performing a three-dimensional structural analysis to determine the edge effect dynamic characteristic parameters includes: Performing spatiotemporal mapping on the nonlinear optical response characteristics to obtain a two-dimensional optical characteristic distribution of the edge region of the light guide film; According to the two-dimensional optical characteristic distribution, a feature point set is extracted by a topological data analysis method to obtain key optical nodes in the edge area; Using the key optical nodes to construct a three-dimensional structure representation, and applying a non-rigid registration algorithm to obtain a dynamic deformation field at different temperature stages; According to the dynamic deformation field and the two-dimensional optical characteristic distribution, edge effect dynamic characteristic parameters are obtained through multivariate analysis.

5. The method for testing uniformity of a light guide film according to claim 1, characterized in that: The step of obtaining the thermal-optical coupling complexity index of the edge region by multivariate nonlinear regression based on the field complexity index and the temperature distribution field includes: Combining the field complexity index and the temperature distribution field into a multidimensional input variable, performing data standardization processing on the multidimensional input variable to obtain standardized multidimensional feature data; Using a kernel function to perform nonlinear transformation on the standardized multidimensional feature data to obtain a high-dimensional feature representation; According to the high-dimensional feature representation, a regression coefficient is calculated by a preset multivariate nonlinear regression algorithm to obtain a preliminary estimate of the thermal-optical coupling complexity; Residual analysis and outlier detection are performed on the preliminary estimated value of the thermal-optical coupling complexity, and the thermal-optical coupling complexity index of the edge area is obtained by iterative weighted least squares optimization.

6. The method for testing uniformity of a light guide film according to claim 1, characterized in that: The method of performing multi-dimensional evaluation index processing and time series analysis based on the dynamic change data of the optical performance of the edge area to obtain a dynamic and accurate evaluation result of the optical performance of the edge area includes: Decomposing the dynamic change data of the optical performance of the edge area in multiple dimensions to obtain feature sequences in three dimensions: time, space and spectrum; Using the feature sequence, data of different dimensions are aligned through a dynamic time warping algorithm to obtain a standardized multidimensional time series; applying empirical mode decomposition to the standardized multidimensional time series to extract intrinsic mode functions of optical properties of edge regions; According to the intrinsic mode function, the instantaneous frequency and amplitude are calculated by Hilbert-Huang transform to obtain a time-frequency characteristic diagram of the optical performance of the edge area; Using the time-frequency characteristic graph and combining a recursive quantitative analysis method, a dynamic complexity index of the optical performance of the edge region is calculated; According to the dynamic complexity index, a dynamic and accurate evaluation result of the optical performance of the edge area is obtained through a fuzzy comprehensive evaluation method.

7. A light guide film uniformity testing device, characterized in that: The uniformity testing device for the light guide film is applied to the uniformity testing method for the light guide film according to any one of claims 1 to 6, and the uniformity testing device for the light guide film comprises: The temperature regulation and spectrum acquisition module is used to dynamically regulate the temperature of the light guide film and obtain multi-wavelength dynamic spectrum data of the edge area of ​​the light guide film within a preset temperature range; A feature extraction module, used to extract the nonlinear optical response characteristics of the edge area through data dimension reduction and decomposition technology according to the multi-wavelength dynamic spectrum data; An optical characteristic analysis module, used to generate a dynamic optical characteristic diagram of the edge area of ​​the light guide film based on the nonlinear optical response characteristics, and perform a three-dimensional structural analysis to determine the edge effect dynamic characteristic parameters; A thermal-optical coupling analysis module is used to input the edge effect dynamic characteristic parameters into a preset temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge area; the preset temperature gradient light propagation model includes a heat conduction layer, an optical propagation layer and a coupling calculation layer; the inputting the edge effect dynamic characteristic parameters into the preset temperature gradient light propagation model to calculate the thermal-optical coupling complexity index of the edge area includes: performing wavelet transform on the edge effect dynamic characteristic parameters to obtain a time-frequency domain feature map; inputting the time-frequency domain feature map into the heat conduction layer to calculate the temperature distribution field by the finite difference method; according to the temperature distribution field, applying a non-sequential ray tracing algorithm in the optical propagation layer to obtain a light intensity distribution prediction result; using the light intensity distribution prediction result, performing thermal-optical interactive iterative calculation in the coupling calculation layer to obtain a steady-state thermal-optical field distribution; performing fractal dimension analysis on the steady-state thermal-optical field distribution to obtain a field complexity index of the edge area; according to the field complexity index and the temperature distribution field, obtaining the thermal-optical coupling complexity index of the edge area by multivariate nonlinear regression; A performance evaluation module, used to generate dynamic change data of optical performance of edge area by using the thermal-optical coupling complexity index and a preset edge performance evaluation algorithm; the preset edge performance evaluation algorithm includes an optical transmission efficiency evaluation module, a color uniformity evaluation module and a dynamic response evaluation module; the method of generating dynamic change data of optical performance of edge area by using the thermal-optical coupling complexity index and the preset edge performance evaluation algorithm includes: inputting the thermal-optical coupling complexity index into the optical transmission efficiency evaluation module, and calculating the optical transmission efficiency curve of edge area by Monte Carlo ray tracing method; performing chromaticity coordinate mapping in the color uniformity evaluation module according to the optical transmission efficiency curve to obtain a color deviation distribution map of edge area; using the color deviation distribution map, in combination with the thermal-optical coupling complexity index, applying a state space model in the dynamic response evaluation module to calculate the transient optical response characteristics of edge area; performing wavelet multi-resolution analysis on the transient optical response characteristics to extract multi-scale optical performance characteristics of edge area; and comprehensively generating dynamic change data of optical performance of edge area by using an adaptive weighted fusion algorithm according to the multi-scale optical performance characteristics; The result analysis module is used to perform multi-dimensional evaluation index processing and time series analysis according to the dynamic change data of the optical performance of the edge area, so as to obtain a dynamic and accurate evaluation result of the optical performance of the edge area.

8. A light guide film uniformity testing device, characterized in that: The uniformity testing device of the light guide film comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the light guide film uniformity testing device to perform the steps of the light guide film uniformity testing method according to any one of claims 1 to 6.

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