Method for detecting thickness uniformity of P layer of EPE adhesive film based on particle swarm optimization

Abstract

The invention discloses a method for detecting thickness uniformity of a P layer of an EPE adhesive film based on particle swarm optimization, and particularly relates to the field of EPE adhesive film detection.The method comprises the steps that an optical interference detection system is built, and the optical interference detection system comprises a laser light source, an optical splitter, an interference detector and a data acquisition module; an EPE adhesive film sample is placed in a measurement area of an optical interference detection system, then light path parameters of a laser light source and an optical splitter are adjusted, interference images at different positions are captured through an interference detector, and the interference images comprise fringe displacement, fringe spacing and fringe intensity distribution and are used for reflecting change information of the thickness of a P layer of the EPE adhesive film. Through combination of optical interference detection and a particle swarm optimization algorithm, an adhesive film P layer thickness distribution model is established, the model integrates information of fringe displacement, fringe spacing and fringe intensity distribution, and the comprehensiveness of thickness uniformity detection is relatively improved, so that the detection requirement of thickness change in reality is met.

Description

Technical Field

[0001] The present invention relates to the technical field of EPE film detection, and more specifically, to a method for detecting the thickness uniformity of an EPE film P layer based on particle swarm optimization. Background Art

[0002] EPE film is a functional film based on polyethylene foam material. It has the characteristics of light weight, cushioning, shockproof and high light transmittance. It is widely used in the fields of optical components and photovoltaic packaging. The P layer of EPE film is a functional layer in its structure, which is usually used to provide optical performance, mechanical strength or protection. The uniformity of the thickness of the P layer has a relative influence on the overall performance of the film. The existing technology has limitations in the measurement method of the P layer thickness of EPE film. It mainly relies on a single measurement method, such as contact measurement or optical scanning, and cannot capture the thickness change characteristics of the sample. At the same time, there is a lack of mathematical models for thickness distribution, and it is difficult to comprehensively process characteristics such as fringe displacement, fringe spacing and intensity distribution, resulting in limitations in sample detection. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for detecting the thickness uniformity of the P layer of an EPE film based on particle swarm optimization. By combining optical interference detection with a particle swarm optimization algorithm, a thickness distribution model of the P layer of the film is established. The model integrates the information of fringe displacement, fringe spacing and fringe intensity distribution to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for detecting the thickness uniformity of the P layer of an EPE film based on particle swarm optimization, comprising: First, an optical interference detection system is built, which includes a laser light source, a spectrometer, an interference detector and a data acquisition module. The EPE film sample is placed in the measurement area of ​​the optical interference detection system, and then the optical path parameters of the laser light source and the spectrometer are adjusted. The interference images at different positions are captured by the interference detector. The interference images include fringe displacement, fringe spacing and fringe intensity distribution. The interference images are used to reflect the change information of the thickness of the P layer of the EPE film. The data acquisition module is used to record the interference images captured by the interference detector. The collected interference image is digitized to remove noise interference; then the characteristic parameters of the fringes are extracted through an image processing algorithm; the image processing algorithm includes an edge detection algorithm; According to the physical characteristics of optical interference, the characteristic parameters extracted from the interference image are correlated with the thickness distribution of the P layer of the adhesive film, and a thickness distribution model of the P layer of the adhesive film is established; The parameters of the film P layer thickness distribution model are iteratively adjusted through the particle swarm optimization algorithm to optimize the fitting accuracy of the film P layer thickness distribution model and the actual interference image data, and complete the analysis and verification of thickness uniformity.

[0005] In a preferred embodiment, the particle swarm optimization algorithm is initialized, the particle swarm size is set, and multiple initial solutions are randomly generated in the parameter space, each particle corresponds to a thickness distribution scheme; the speed and position of the particle are initialized at the same time, and a fitness function is defined; The particle swarm optimization algorithm is used for iterative calculation. In each iteration, the speed and position of the particles are updated according to the particle swarm optimization rules. After each iteration, the fitness value of each particle is calculated, and the fitness value is used to reflect the error between the predicted result of the film P layer thickness distribution model and the actual measured interference image data; In each round of iteration, the solution of the current individual particle is recorded and the global solution is continuously updated. When the fitness value is less than the preset threshold or the number of iterations reaches the upper limit, the optimization is stopped and the final film P layer thickness distribution model is output.

[0006] In a preferred embodiment, the parameters of the optimized film P layer thickness distribution model are used to generate a thickness distribution map of the EPE film sample, and the thickness distribution map is used to display the thickness variation of different regions of the EPE film sample; The thickness distribution diagram of the EPE film sample was compared with the actual test value to evaluate the fitting accuracy of the model.

[0007] In a preferred embodiment, the characteristic components of the interference image include fringe displacement, fringe spacing and fringe intensity distribution; The fringe displacement is expressed as: the fringe center is offset in a plane; ; in , The current stripe center is Axis and Coordinates on the axis; , The reference fringe centers are Axis and Coordinates on the axis; , are the displacement changes of the stripes in two directions respectively; The fringe spacing is expressed as: the distance between the centers of adjacent fringes; ; in is the Euclidean distance between the fringe centers; The fringe intensity distribution is expressed as follows: the intensity value of each point in the interference image is determined by the optical path difference; ; ; in Indicates at point The fringe intensity at is the initial intensity of the light source; is the optical path difference between the reference beam and the measurement beam; is the wavelength of the laser light source; Indicates that the EPE film sample is at point The refractive index at Indicates the thickness of the EPE film sample at a point.

[0008] In a preferred embodiment, the film P layer thickness distribution model is constructed by integrating the fringe displacement, fringe spacing and fringe intensity distribution; Indicates that the EPE film sample is at point The thickness at ; ; in A normalized parameter representing the fringe spacing; The Gaussian attenuation factor representing the variation of fringe intensity with fringe spacing; is the total number of stripes; For the Euclidean distance between stripe centers; is the reference fringe spacing.

[0009] In a preferred embodiment, the particle positions of the particle swarm optimization algorithm are initialized; in the parameter space of the film P layer thickness distribution model, randomly generate particles, each particle represents a thickness distribution scheme; For particles In the The thickness value at each position, and ; ; in For the A set of thickness parameters for each particle; is the total number of positions after discretization of the EPE film sample; Initialize the particle velocity of the particle swarm optimization algorithm; For particles In the The velocity value of each position, and ; The speed of each particle is randomly initialized and expressed as: ; in For the The velocity set of each particle; The fitness function is defined to evaluate the degree of fit between the particle solution and the actual interference image data: ; in For the The fitness value of each particle; is the interference image actually measured at point The strength value of Represents particles Predicted interferogram intensity values.

[0010] In a preferred embodiment, in each iteration, the speed is updated according to the particle swarm optimization rule: ; in For particles In the The velocity vector at the iteration; For particles In the The velocity vector at the iteration; is the inertia weight; is the acceleration factor, Control the weights of individual historical solutions and global solutions respectively; is a random factor, The value range of is [0, 1]; For particles The historical optimal solution of is the global optimal solution among all particles; For particles In the The position at the iteration; Update the particle's position based on its velocity: ; in For particles In the The position at the iteration; Establish the iteration stop condition; the iteration process of the particle swarm optimization algorithm is terminated when condition one or condition two is met; Condition 1 is: Less than a preset threshold; Condition 2: The number of iterations reaches the upper limit.

[0011] In a preferred embodiment, using the optimized The parameters in are used to generate the final thickness distribution diagram of the EPE film sample; ; in is the final optimized thickness distribution value; the thickness distribution diagram of the EPE film sample is obtained by The corresponding mark is presented at each position of the EPE film sample, intuitively showing the spatial variation of thickness; Compare the optimized thickness distribution value with the actual measured interference image data and calculate the final error: ; in is the final verification error; is the total number of sampling points; Represents the predicted strength based on the optimized thickness distribution.

[0012] Technical effects and advantages of the present invention: 1. Through optical interference detection combined with particle swarm optimization algorithm, a film P layer thickness distribution model was established. The model integrates the information of fringe displacement, fringe spacing and fringe intensity distribution, which relatively improves the comprehensiveness of thickness uniformity detection, making it suitable for the detection needs of thickness changes in reality; 2. The particle swarm optimization algorithm is used to converge to the optimal solution of thickness distribution. The iterative process controls the search path through the speed and position update formula, thereby reducing the time of parameter optimization and ensuring the global fitting accuracy of the model; 3. By marking the optimized thickness distribution value, the thickness distribution diagram of the EPE film sample is generated, which intuitively shows the thickness changes in different areas of the sample, providing a clear basis for quality control and process optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0014] 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.

[0015] Refer to the instruction manual Figure 1 According to an embodiment of the present invention, a method for detecting uniformity of thickness of an EPE film P layer based on particle swarm optimization includes: First, an optical interference detection system is built to form a detection environment for the thickness of the P layer of the EPE film. The optical interference detection system includes a laser light source, a spectrometer, an interference detector and a data acquisition module. The EPE film sample is placed in the measurement area of ​​the optical interference detection system, and then the optical path parameters of the laser light source and the spectrometer are adjusted. The interference images at different positions are captured by the interference detector. The interference images include fringe displacement, fringe spacing and fringe intensity distribution. The interference images are used to reflect the change information of the thickness of the P layer of the EPE film. The data acquisition module is used to record the interference images captured by the interference detector, and transmit them to the computing system after digital processing for subsequent analysis and processing. The laser light source is used to provide a stable and highly monochromatic light source, that is, to generate a coherent light beam required for interference; the beam splitter is used to divide the laser light source into a reference beam and a measurement beam to ensure that the two beams of light can interfere with each other on the sample surface and the reference surface; the interference detector is used to capture the interference image; in addition, the optical path parameters of the laser light source and the beam splitter are adjusted, including but not limited to: by adjusting the incident angle and output intensity of the laser light source, ensuring that the light beam enters the beam splitter at the best angle and matches the reflectivity of the sample surface, while avoiding the detector signal being too weak or saturated; secondly, optimizing the splitting ratio of the beam splitter, balancing the energy distribution of the reference beam and the measurement beam, and generating clear interference fringes; in addition, adjusting the beam path length to keep the optical path difference within the coherent range, and ensuring that the beam is parallel through a collimating lens to improve coherence; accurately focusing on the sample surface to avoid distortion of the fringes due to offset or tilt. By adjusting these optical path parameters, the quality and signal-to-noise ratio of the interference fringes can be improved, providing reliable data for subsequent thickness detection; The collected interference image is digitally processed to remove noise interference; then the characteristic parameters of the fringe are extracted by an image processing algorithm, and the characteristic parameters include fringe displacement, fringe spacing and fringe intensity distribution; the image processing algorithm includes an edge detection algorithm; According to the physical properties of optical interference, the characteristic parameters extracted from the interference image are correlated with the thickness distribution of the P layer of the film, and a thickness distribution model of the P layer of the film is established; the thickness distribution model of the P layer of the film is used to calculate the thickness variation of the P layer of the EPE film at different positions; The parameters of the film P layer thickness distribution model are iteratively adjusted through the particle swarm optimization algorithm to optimize the fitting accuracy of the film P layer thickness distribution model and the actual interference image data, and complete the analysis and verification of thickness uniformity.

[0016] What needs to be specifically explained about the above scheme is that the collected interference image is first digitized through the data acquisition module. The core of this step is to convert the analog signal into a discrete digital signal to facilitate subsequent analysis and processing; due to the influence of ambient light interference, light source fluctuations or electronic noise, the digitized image may have a quality degradation problem, so it is necessary to remove noise; commonly used methods include Gaussian filtering and median filtering; Gaussian filtering reduces high-frequency noise in the image through smoothing processing, while retaining the main edge features of the image, making the light and dark transition of the stripes more uniform; median filtering is more suitable for removing random pulse noise and helps to eliminate isolated strong noise points; after these processing steps, the clarity of the stripes can be enhanced, providing high-quality input for subsequent feature extraction; In the interference image after denoising, the stripes are alternating curves of light and dark formed by changes in light intensity, and their characteristic parameters include stripe displacement, stripe spacing and stripe intensity distribution; the core of feature extraction is to identify and locate the stripe center through image processing algorithm; the position of grayscale change in the image is calculated by edge detection algorithm, the edge of the stripe is identified, and then the center position of the stripe is located by fitting technology to calculate the displacement information of the stripe; the stripe spacing is the distance between the centers of adjacent stripes, and the stripe spacing distribution is obtained by calculating the Euclidean distance of adjacent stripes point by point; and the stripe intensity distribution extracts the light intensity value of each stripe by counting the light intensity change information in the stripe area to reflect the light intensity characteristics under interference conditions; The edge detection algorithm is a link in feature extraction, and its function is to identify the edge of the stripe and further extract the center position of the stripe. During the processing, the interference image is first smoothed to reduce the interference of noise on edge detection. Then, the gradient calculation method is used to detect the area with drastic grayscale changes in the image, and the redundant weak gradient information is filtered out through the non-maximum suppression technology, retaining only relatively significant edge features. Finally, the insignificant edge noise is filtered out by setting a threshold, and the stripe edge is fitted to extract the center position of the stripe. This process ensures the accuracy of the calculation of stripe displacement and stripe spacing, and provides an accurate basis for regional division for the statistics of stripe intensity distribution.

[0017] Initialize the particle swarm optimization algorithm, set the particle swarm size, randomly generate multiple initial solutions in the parameter space, and each particle corresponds to a possible thickness distribution scheme; initialize the particle speed and position at the same time, and define the fitness function, which is used to evaluate the quality of each particle's solution; The particle swarm optimization algorithm is used for iterative calculation. In each iteration, the speed and position of the particles are updated according to the particle swarm optimization rules, and the particles gradually approach the optimal solution in the search space. The update of the particles is affected by the historical best solution and the global best solution, so that the particle swarm optimization algorithm searches for the optimal solution of the thickness distribution model in the global scope. After each iteration, the fitness value of each particle is calculated. The fitness value is used to reflect the error between the predicted result of the film P layer thickness distribution model and the actual measured interference image data. The smaller the fitness value, the closer the particle solution is to the actual distribution. In each round of iteration, the optimal solution of the current individual particle is recorded, and the global optimal solution is continuously updated. When the fitness value is less than the preset threshold or the number of iterations reaches the upper limit, the optimization is stopped and the final film P layer thickness distribution model is output; The thickness distribution diagram of the EPE film sample is generated by using the parameters of the optimized film P layer thickness distribution model, and the thickness variation of different areas of the EPE film sample is displayed through the thickness distribution diagram; The thickness distribution diagram of the EPE film sample is compared with the actual detection value to evaluate the fitting accuracy of the model. In actual application, if the fitting accuracy does not meet the requirements, the parameters of the particle swarm optimization algorithm (such as particle swarm size, number of iterations, etc.) can be adjusted or the data acquisition and preprocessing methods can be improved. The above process can be repeated for different sample materials or process conditions to verify the applicability and robustness of the algorithm, ensuring that this method can be widely used in the detection of the thickness uniformity of the EPE film P layer.

[0018] The characteristic components of the interference image include fringe displacement, fringe spacing and fringe intensity distribution; The fringe displacement is expressed as: the fringe center is offset in a plane; ; in , The current stripe center is Axis and Coordinates on the axis; , The reference fringe centers are Axis and Coordinates on the axis; , They are the displacement changes of the stripes in two directions, which are used to reflect the local surface changes of the thickness distribution of the P layer of the EPE film; The fringe spacing is expressed as: the distance between the centers of adjacent fringes; ; in is the Euclidean distance between the fringe centers, which is used to reflect the uniformity of fringe distribution; The fringe intensity distribution is expressed as follows: the intensity value of each point in the interference image is determined by the optical path difference; ; ; in Indicates at point The fringe intensity at is the initial intensity of the light source; is the optical path difference between the reference beam and the measurement beam; is the wavelength of the laser light source; Indicates that the EPE film sample is at point The refractive index at Indicates that the EPE film sample is at point The thickness at The coefficients in the formula The wavelength of the laser source Convert to angular frequency to get; The coefficient in the formula Represents the two-way path of the beam through the sample surface.

[0019] The thickness distribution model of the film P layer is constructed by integrating the fringe displacement, fringe spacing and fringe intensity distribution; Indicates that the EPE film sample is at point The thickness at ; ; in The standardized parameter representing the fringe spacing is used to control the influence range of the fringe spacing on the intensity attenuation; The Gaussian attenuation factor representing the variation of fringe intensity with fringe spacing; is the total number of stripes; For the Euclidean distance between stripe centers; is the reference fringe spacing, Standard value used to calibrate spacing; Regarding the formation of the above formula, it should be noted that: Effect of fringe intensity distribution on thickness; Determined by the cosine relationship of the optical path difference, the basic formula is: ; The inverse cosine function can be used to infer ; Fringe displacement correction for thickness; Fringe displacement , Reflecting the inhomogeneity of the local area of ​​the sample surface, its change will lead to the asymmetry of the thickness distribution. The displacement is calculated through its components as: ; Modulation of intensity by fringe spacing; as the fringe spacing changes, the intensity of the interference fringe gradually decays, so the Gaussian attenuation factor of the fringe intensity due to the change of fringe spacing is introduced into the intensity distribution formula; ; Finally, the fringe intensity distribution, displacement correction and spacing modulation are combined to obtain the final thickness distribution model formula.

[0020] Initialize the particle positions of the particle swarm optimization algorithm; randomly generate particles, each particle represents a possible thickness distribution scheme; For particles In the The thickness value at each position indicates the thickness of the EPE film sample at each discrete point. The corresponding thickness parameter, and ; ; in For the A set of thickness parameters for each particle; is the total number of positions of the EPE film sample after discretization, that is, the number of grid points; Represents the position index after the sample is discretized; Initialize the particle velocity of the particle swarm optimization algorithm; For particles In the The velocity value at each position indicates the rate at which the thickness is adjusted, and ; The speed of each particle is randomly initialized and expressed as: ; in For the The velocity set of each particle; The fitness function is defined to evaluate the degree of fit between the particle solution and the actual interference image data: ; in For the The fitness value of each particle, the smaller the value, the higher the fitting accuracy; is the interference image actually measured at point The strength value of It is recorded by the interferometer detector and represents the optical characteristics of the position; Represents particles The predicted interference image intensity value is calculated by the film P layer thickness distribution model; Indicate point The square of the error between the actual measured intensity and the predicted intensity is used to reflect the local fitting deviation of the point; Further explanation of the fitness function: ; Combined with the actual measured values, the sum of squares of the differences between the two is calculated to obtain the fitting error.

[0021] In each iteration, the velocity is updated according to the particle swarm optimization rule: ; in For particles In the The velocity vector at the iteration; For particles In the The velocity vector at the iteration; is the inertia weight, which is used to control the particle search range; is the acceleration factor, Control the weights of individual historical solutions and global solutions respectively; is a random factor, The value range of is [0, 1]; For particles The best historical optimization solution; It is the global best optimization solution among all particles; For particles In the The position at the iteration; Update the particle's position based on its velocity: ; in For particles In the The position at the iteration; Establish the iteration stop condition; the iteration process of the particle swarm optimization algorithm is terminated when condition one or condition two is met; Condition 1 is: Less than a preset threshold; Condition 2: The number of iterations reaches the upper limit.

[0022] Using the optimized The parameters in are used to generate the final thickness distribution diagram of the EPE film sample; ; in is the final optimized thickness distribution value; the thickness distribution diagram of the EPE film sample is obtained by The corresponding mark is presented at each position of the EPE film sample, intuitively showing the spatial variation of thickness; Compare the optimized thickness distribution value with the actual measured interference image data and calculate the final error: ; in is the final verification error, the final verification error Represents the average error between the model prediction results and the actual data. is the total number of sampling points; represents the predicted strength based on the optimized thickness distribution; If the final verification error If the accuracy requirements are met, the model optimization is completed; otherwise, adjust the parameters and optimize again.

[0023] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting the thickness uniformity of the P layer of an EPE film based on particle swarm optimization, characterized in that: include: First, an optical interference detection system is built, which includes a laser light source, a spectrometer, an interference detector and a data acquisition module. The EPE film sample is placed in the measurement area of ​​the optical interference detection system, and then the optical path parameters of the laser light source and the spectrometer are adjusted. The interference images at different positions are captured by the interference detector. The interference images include fringe displacement, fringe spacing and fringe intensity distribution. The interference images are used to reflect the change information of the thickness of the P layer of the EPE film. The data acquisition module is used to record the interference images captured by the interference detector. Digitally process the collected interference image to remove noise interference; Then, the characteristic parameters of the stripes are extracted by an image processing algorithm; the image processing algorithm includes an edge detection algorithm; According to the physical characteristics of optical interference, the characteristic parameters extracted from the interference image are correlated with the thickness distribution of the P layer of the adhesive film, and a thickness distribution model of the P layer of the adhesive film is established; The parameters of the film P layer thickness distribution model are iteratively adjusted through the particle swarm optimization algorithm to optimize the fitting accuracy of the film P layer thickness distribution model and the actual interference image data, and complete the analysis and verification of thickness uniformity.

2. According to claim 1, a method for detecting uniformity of thickness of EPE film P layer based on particle swarm optimization, characterized in that: Initialize the particle swarm optimization algorithm, set the particle swarm size, randomly generate multiple initial solutions in the parameter space, and each particle corresponds to a thickness distribution scheme; initialize the particle speed and position at the same time, and define the fitness function; The particle swarm optimization algorithm is used for iterative calculation. In each iteration, the speed and position of the particles are updated according to the particle swarm optimization rules. After each iteration, the fitness value of each particle is calculated, and the fitness value is used to reflect the error between the predicted result of the film P layer thickness distribution model and the actual measured interference image data; In each round of iteration, the solution of the current individual particle is recorded and the global solution is continuously updated. When the fitness value is less than the preset threshold or the number of iterations reaches the upper limit, the optimization is stopped and the final film P layer thickness distribution model is output.

3. The method for detecting thickness uniformity of the EPE film P layer based on particle swarm optimization according to claim 2, characterized in that: The thickness distribution diagram of the EPE film sample is generated by using the parameters of the optimized film P layer thickness distribution model, and the thickness variation of different areas of the EPE film sample is displayed through the thickness distribution diagram; The thickness distribution diagram of the EPE film sample was compared with the actual test value to evaluate the fitting accuracy of the model.

4. The method for detecting thickness uniformity of the EPE film P layer based on particle swarm optimization according to claim 3, characterized in that: The characteristic components of the interference image include fringe displacement, fringe spacing and fringe intensity distribution; The fringe displacement is expressed as: the fringe center is offset in a plane; ； in , The current stripe center is Axis and Coordinates on the axis; , The reference fringe centers are Axis and Coordinates on the axis; , are the displacement changes of the stripes in two directions respectively; The fringe spacing is expressed as: the distance between the centers of adjacent fringes; ； in is the Euclidean distance between the fringe centers; The fringe intensity distribution is expressed as follows: the intensity value of each point in the interference image is determined by the optical path difference; ； in Indicates at point The fringe intensity at is the initial intensity of the light source; is the optical path difference between the reference beam and the measurement beam; is the wavelength of the laser light source; Indicates that the EPE film sample is at point The refractive index at Indicates that the EPE film sample is at point The thickness of the 5. The method for detecting thickness uniformity of the EPE film P layer based on particle swarm optimization according to claim 4, characterized in that: The thickness distribution model of the film P layer is constructed by integrating the fringe displacement, fringe spacing and fringe intensity distribution; Indicates that the EPE film sample is at point The thickness at ； ； in A normalized parameter representing the fringe spacing; The Gaussian attenuation factor representing the variation of fringe intensity with fringe spacing; is the total number of stripes; For the Euclidean distance between stripe centers; is the reference fringe spacing.

6. The method for detecting thickness uniformity of the EPE film P layer based on particle swarm optimization according to claim 5, characterized in that: Initialize the particle positions of the particle swarm optimization algorithm; randomly generate particles, each particle represents a thickness distribution scheme; For particles In the The thickness value at each position, and ; ； in For the A set of thickness parameters for each particle; is the total number of positions after discretization of the EPE film sample; Initialize the particle velocity of the particle swarm optimization algorithm; For particles In the The velocity value of each position, and ; The speed of each particle is randomly initialized and expressed as: ； in For the The velocity set of each particle; The fitness function is defined to evaluate the degree of fit between the particle solution and the actual interference image data: ； in For the The fitness value of each particle; is the interference image actually measured at point The strength value of Represents particles Predicted interferogram intensity values.

7. The method for detecting thickness uniformity of the EPE film P layer based on particle swarm optimization according to claim 6, characterized in that: In each iteration, the velocity is updated according to the particle swarm optimization rule: ； in For particles In the The velocity vector at the iteration; For particles In the The velocity vector at the iteration; is the inertia weight; is the acceleration factor, Control the weights of individual historical solutions and global solutions respectively; is a random factor, The value range of is [0, 1]; For particles The historical optimal solution of is the global optimal solution among all particles; For particles In the The position at the iteration; Update the particle's position based on its velocity: ； in For particles In the The position at the iteration; Establish the iteration stop condition; the iteration process of the particle swarm optimization algorithm is terminated when condition one or condition two is met; Condition 1 is: Less than a preset threshold; Condition 2: The number of iterations reaches the upper limit.

8. The method for detecting thickness uniformity of the EPE film P layer based on particle swarm optimization according to claim 7, characterized in that: Using the optimized The parameters in are used to generate the final thickness distribution diagram of the EPE film sample; ； in is the final optimized thickness distribution value; the thickness distribution diagram of the EPE film sample is obtained by The corresponding mark is presented at each position of the EPE film sample, intuitively showing the spatial variation of thickness; Compare the optimized thickness distribution value with the actual measured interference image data and calculate the final error: ； in is the final verification error; is the total number of sampling points; Represents the predicted strength based on the optimized thickness distribution.

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