Ionic polymer pretreatment analysis method and system based on interface fractal characteristics and storage medium

Abstract

The invention provides an ionic polymer preprocessing analysis method and system based on interface fractal features and a storage medium, relates to the technical field of ionic polymer interface feature analysis, realizes automatic edge detection and feature extraction of a microscopic image through an advanced image processing technology, and improves the analysis efficiency and precision. Complex interface characteristics of an electrode layer and a basement membrane are quantified by using a fractal dimension and a Hurst index system, and the capability of distinguishing the influence of a pretreatment mode is enhanced. A total feature coefficient is generated through multi-feature weighting processing, and comprehensive and accurate judgment of a preprocessing mode is achieved. According to the method, the precision, stability and efficiency of ionic electroactive polymer interface characteristic analysis are improved, the method is suitable for performance optimization and quality control of flexible electronic equipment in different scenes, and powerful technical support is provided for new material research.

Description

Technical Field

[0001] The present invention relates to the technical field of ionic polymer interface characteristic analysis, and in particular to an ionic polymer pretreatment analysis method, system and storage medium based on interface fractal characteristics. Background Art

[0002] Ionic electroactive polymers (IPMCs) are widely used in sensors, actuators and flexible electronic devices due to their excellent electroactive properties. However, the complexity and irregularity of the interface between the electrode layer and the substrate membrane pose challenges to performance analysis. At present, there is a lack of systematic analysis of the effects of pretreatment methods (such as vertical grinding, rotary grinding, and free grinding) on ​​interface morphology and performance. Traditional methods rely on manual experience and simple statistics, which are difficult to accurately judge. Edge detection and feature extraction of microscopic images are key steps, but the existing technology lacks accuracy and stability when dealing with complex structures. In addition, the traditional Euclidean geometry model is difficult to effectively describe complex interfaces and lacks parameters for comprehensive quantitative features, which affects the reliability of the analysis results. Faced with a large number of microscopic images, the existing methods have low processing efficiency and are difficult to meet the needs of efficient analysis in production and research. Therefore, there is an urgent need for an automated, precise, and efficient analysis method to systematically extract and quantify the interface properties of ionic polymers, especially the effects of pretreatment methods on the interface.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0004] The purpose of the present invention is to provide an ionic polymer pretreatment analysis method, system and storage medium based on interface fractal characteristics to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: 1. An ionic polymer pretreatment analysis method based on interface fractal characteristics, characterized in that the specific steps include: S1: scanning the ionic polymer, collecting at least two sets of microscopic images of the electrode layer and the basement membrane at the cross section thereof, and preprocessing the microscopic images; S2: performing edge recognition on the preprocessed microscopic image to obtain an edge image between the electrode layer and the basement membrane, performing amplitude analysis on the edge image, and generating a first characteristic coefficient according to the analysis result; S3: Analyze the edge image based on the fractal method, calculate the fractal dimension and Hurst index of the edge image, and generate the second characteristic coefficient and the third characteristic coefficient according to the fractal dimension and the Hurst index respectively; S4: performing weighted processing on the first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient to generate a total characteristic coefficient, determining a preprocessing method of the electrode layer and the basement membrane according to the total characteristic coefficient, and calculating a corresponding capacitance range.

[0006] Preferably, the image preprocessing includes size cropping and grayscale conversion, which are used to unify the image size of the microscopic image and convert the microscopic image into a corresponding grayscale image; The logic of amplitude analysis of the grayscale image corresponding to the preprocessed microscopic image is: Collect the grayscale value of each pixel in the grayscale image, and build a global threshold based on the grayscale value of each pixel; Binarize the grayscale image based on the global threshold to generate the corresponding binary image, and use the edge detection algorithm to identify the edge of the binary image; The edge amplitude in the binary image corresponding to each group of microscopic images is calculated and the amplitude mean is calculated, and the first characteristic coefficient is generated according to the amplitude mean.

[0007] Preferably, the generation logic of the global threshold is: According to the gray value of each pixel, a gray histogram is constructed with the gray value as the horizontal coordinate and the number of pixels as the vertical coordinate. The distribution probability of each pixel in the gray value is calculated according to the gray histogram. The calculation method is: ; In the formula Indicates the gray value of the pixel The distribution probability when Indicates the gray value is The number of pixels when Indicates the total number of pixels; Then calculate the probability ratio and grayscale mean below the global threshold, as well as the total grayscale mean, and the calculation methods are: ; ; ; In the formula represents the global threshold, , Respectively represent the probability proportion and grayscale mean below the global threshold, represents the total grayscale mean, Indicates the maximum value of the gray value; Then calculate the between-class variance , the calculation method is: ; From the gray value range Iterate over all global thresholds The possible values ​​of will make the between-class variance The largest value The value is used as the final global threshold.

[0008] Preferably, the generation logic of the first characteristic coefficient is: The Sobel operator is used to calculate the horizontal and vertical gradients of each pixel, and then the gradient amplitude and gradient direction are calculated. The calculation methods are: ; ; In the formula , , Respectively represent the horizontal gradient, vertical gradient and gradient amplitude of the pixel point, Indicates the gradient direction; Taking each pixel as the center, detect whether its gradient amplitude is the local maximum in the 3*3 area along the gradient direction. If it is the local maximum, keep the pixel, otherwise set it to 0, so as to complete the edge recognition of the binary image and obtain the corresponding binary edge image; The amplitude mean of multiple groups of microscopic images is calculated based on the placement direction of the ionomer. The calculation method is: ; In the formula , Represent the amplitude mean and The edge amplitude corresponding to the group of microscopic images, represents the total number of microscopic images, where the edge amplitude is calculated as: ; In the formula , They represent the maximum and minimum values ​​of the horizontal coordinates of edge pixels in the binary edge image, respectively. , Respectively represent the maximum and minimum values ​​of the vertical coordinates of edge pixels in the binary edge image; Then generate the first characteristic coefficient based on the amplitude mean , the calculation method is: ; In the formula , They respectively represent the maximum and minimum values ​​of the amplitude mean in multiple groups of microscopic images.

[0009] Preferably, the generation logic of the second characteristic coefficient is: Divide the binary edge image into several groups Each grid is represented by ,in , Represents the index of the grid in the horizontal and vertical directions respectively; Calculate the number of grids containing edge pixels as follows: ; In the formula Indicates the number of grids containing edge pixels, Represents the indicator function, when the grid When there is at least one edge pixel in ,otherwise ; The number of grids and fractal dimension satisfy: ; In the formula Represents the preset scale factor, represents the fractal dimension; Take the logarithm of both sides of the above equation and use the double logarithmic coordinates Regression analysis is performed in the regression line to obtain the slope of the regression line, which is the fractal dimension. ; Calculate the dimension mean according to the fractal dimension of multiple groups of microscopic images, and calculate the second characteristic coefficient according to the dimension mean , the calculation method is: ; ; In the formula , Represent the dimension mean and The fractal dimension corresponding to the group of microscopic images, , They respectively represent the maximum and minimum values ​​of the fractal dimension in multiple groups of microscopic images.

[0010] Preferably, the generation logic of the third characteristic coefficient is: The edge contour composed of edge pixels in the binary edge image is regarded as a time series and divided into intervals, the intervals in the time series are marked as , subscript represents the interval number, and , calculate the cumulative deviation, the calculation method is: ; In the formula represents the cumulative deviation, represents the mean of the time series; Then, the range of the cumulative deviation in the time series and the standard deviation of the time series are calculated based on the cumulative deviation. The calculation methods are: ; ; Take the logarithm of the ratio of the range to the standard deviation and plot it on a double logarithmic coordinate system. Regression analysis is performed in , and the slope of the regression line is used as the corresponding Hurst index. The logarithmic expression is expressed as: ; in is the Hurst index; The exponential mean is calculated based on the fractal dimension of multiple groups of microscopic images, and the third characteristic coefficient is calculated based on the exponential mean , the calculation method is: ; ; In the formula , Represent the index mean and The Hurst index corresponding to the group of microscopic images, , They represent the maximum and minimum values ​​of the Hurst index in multiple groups of microscopic images respectively.

[0011] Preferably, the first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient are weighted to generate a total characteristic coefficient. , calculated as: ; In the formula , , represent the preset first weight, second weight, and third weight respectively, and ; Compare the total feature coefficient to the preset coefficient threshold: when When , it is considered that the pretreatment method of the electrode layer and the base film is vertical grinding; when When , it is considered that the pretreatment method of the electrode layer and the basement membrane is rotary grinding; when When , it is considered that the pretreatment method of the electrode layer and the base film is free grinding; In the formula , denote the first coefficient threshold and the second coefficient threshold, respectively, and .

[0012] Preferably, the logic for calculating the capacitance range is: The time series composed of edge pixels in the binary edge image is simulated in three dimensions using image analysis technology, and the contact area of ​​the electrode plate is calculated based on the three-dimensional simulation. The calculation method is: ; in , Respectively represent the width and length of the electrode plate, Represents the unit area in a three-dimensional simulation; Then use the differential element method to calculate the upper limit of the capacitance range. The calculation method is: ; In the formula represents the angle between the two electrode plates, represents the dielectric constant of the ionic polymer; The final capacitance range is expressed as .

[0013] An interface fractal characteristic analysis system based on ionic electroactive polymers, the analysis system is used to perform the above analysis method, comprising: An image acquisition module, which is electrically connected to the image analysis module and is used to acquire microscopic images of the electrode layer and the basement membrane at the cross section of the ionomer; An image analysis module, which is electrically connected to the comprehensive processing module and is used to perform image processing and edge recognition on the microscopic image; A comprehensive processing module, the comprehensive processing module is electrically connected to the data analysis module, and is used to analyze the microscopic image after edge recognition to generate a first characteristic coefficient, a second characteristic coefficient, and a third characteristic coefficient; The data analysis module is electrically connected to the comprehensive judgment module and is used to perform weighted processing on the first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient to generate a total characteristic coefficient; A comprehensive judgment module is used to compare the total characteristic coefficient with a preset coefficient threshold, judge the pretreatment method of the electrode layer and the basement membrane according to the comparison result, and calculate the corresponding capacitance range.

[0014] A computer storage medium is used to store a computer program, and the computer program implements the above-mentioned analysis method when executed by a processor.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes automatic edge detection and feature extraction of microscopic images through advanced image processing technology, thereby improving analysis efficiency and accuracy. The complex interface characteristics of the electrode layer and the basement membrane are systematically quantified using fractal dimension and Hurst index, enhancing the ability to distinguish the influence of pretreatment methods. Multi-feature weighted processing generates a total characteristic coefficient, which enables a comprehensive and accurate judgment of the pretreatment method. This method improves the accuracy, stability and efficiency of the analysis of the interface characteristics of ionic electroactive polymers, is suitable for performance optimization and quality control of flexible electronic devices in different scenarios, and provides strong technical support for new material research. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 It is a schematic diagram of the module structure of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example: See also Figure 1 , the present invention provides a technical solution: The ionic polymer pretreatment analysis method based on interface fractal characteristics includes the following specific steps: S1: Scan the ionic polymer, collect at least two sets of microscopic images of the electrode layer and the basement membrane at the cross section, and preprocess the microscopic images. The image preprocessing includes size cropping and grayscale conversion, which are used to unify the image size of the microscopic images and convert the microscopic images into corresponding grayscale images.

[0020] In this step, the microscopic images of the electrode layer and the base film at the cross section of the ionic polymer are collected in order to extract the characteristic parameters of interest to the user from the microscopic images and use the images for analysis. The electrode layer is generally prepared on the surface of the base film by chemical plating technology. The corresponding pretreatment methods generally include: 1. vertical grinding; 2. rotary grinding; 3. free grinding. Different pretreatment methods will not only affect the thickness of the electrode layer deposited on the surface of the base film, but also affect the electrode morphology formed by the deposition of metal particles. It can be seen that under the same preparation process, different pretreatment methods play a vital role in the final interface formation of the ionic polymer and will also have a great impact on its properties. Therefore, for factories or laboratories, after getting a new material, it is very necessary to use the pretreatment method as a reference for the subsequent material property analysis.

[0021] S2: Perform edge recognition on the preprocessed microscopic image to obtain an edge image between the electrode layer and the basement membrane, perform amplitude analysis on the edge image, and generate a first characteristic coefficient based on the analysis result.

[0022] The logic of amplitude analysis of the grayscale image corresponding to the preprocessed microscopic image is: Collect the grayscale value of each pixel in the grayscale image, and build a global threshold based on the grayscale value of each pixel; Binarize the grayscale image based on the global threshold to generate the corresponding binary image, and use the edge detection algorithm to identify the edge of the binary image; The edge amplitude in the binary image corresponding to each group of microscopic images is calculated and the amplitude mean is calculated, and the first characteristic coefficient is generated according to the amplitude mean.

[0023] The logic for generating the global threshold is: According to the gray value of each pixel, a gray histogram is constructed with the gray value as the horizontal coordinate and the number of pixels as the vertical coordinate. The distribution probability of each pixel in the gray value is calculated according to the gray histogram. The calculation method is: ; In the formula Indicates the gray value of the pixel The distribution probability when Indicates the gray value is The number of pixels when Indicates the total number of pixels; Then calculate the probability ratio and grayscale mean below the global threshold, as well as the total grayscale mean, and the calculation methods are: ; ; ; In the formula represents the global threshold, , Respectively represent the probability proportion and grayscale mean below the global threshold, represents the total grayscale mean, Indicates the maximum value of the gray value; Then calculate the between-class variance , the calculation method is: ; From the gray value range Iterate over all global thresholds The possible values ​​of will make the between-class variance The largest value The value is used as the final global threshold.

[0024] In this step, the global thresholding method can automatically determine the threshold without manual intervention, which is very efficient when processing a large number of microscopic images and performs well under uniform lighting conditions. It can stably process different batches of images. At the same time, through histogram analysis and between-class variance calculation, the appropriate threshold can be quickly determined. The computational complexity is low, which is suitable for large-scale image processing and can significantly improve the accuracy and stability of feature extraction.

[0025] The generation logic of the first characteristic coefficient is: The Sobel operator is used to calculate the horizontal and vertical gradients of each pixel, and then the gradient amplitude and gradient direction are calculated. The calculation methods are: ; ; In the formula , , Respectively represent the horizontal gradient, vertical gradient and gradient amplitude of the pixel point, Indicates the gradient direction; Taking each pixel as the center, detect whether its gradient amplitude is the local maximum in the 3*3 area along the gradient direction. If it is the local maximum, keep the pixel, otherwise set it to 0, so as to complete the edge recognition of the binary image and obtain the corresponding binary edge image; The amplitude mean of multiple groups of microscopic images is calculated based on the placement direction of the ionomer. The calculation method is: ; In the formula , Represent the amplitude mean and The edge amplitude corresponding to the group of microscopic images, represents the total number of microscopic images, where the edge amplitude is calculated as: ; In the formula , They represent the maximum and minimum values ​​of the horizontal coordinates of edge pixels in the binary edge image, respectively. , They represent the maximum and minimum values ​​of the vertical coordinates of the edge pixels in the binary edge image. The placement direction of the ionomer is consistent with the direction of the cross-section boundary, that is, when the ionomer is placed horizontally, the cross-section boundary is also oriented horizontally, so its edge amplitude is determined by the maximum and minimum values ​​of the vertical coordinates of the edge pixels. Conversely, when the ionomer is placed vertically, its edge amplitude is determined by the maximum and minimum values ​​of the horizontal coordinates of the edge pixels.

[0026] Then generate the first characteristic coefficient based on the amplitude mean , the calculation method is: ; In the formula , They respectively represent the maximum and minimum values ​​of the amplitude mean in multiple groups of microscopic images.

[0027] It can be seen from the formula of the first characteristic coefficient that the first characteristic coefficient reflects the maximum fluctuation range of the edge contour of the ion polymer electrode layer and the substrate membrane. The logarithmic function is constructed to weaken the impact of the edge contour growth on the first characteristic coefficient, so that the change trend of the first characteristic coefficient is more stable. From the research on ion polymers in the prior art, the maximum fluctuation ranges of the edge contours of the electrode layer and the substrate membrane of the ion polymer prepared by three different pretreatment methods are as follows: vertical grinding: about 10.7um; rotary grinding: about 8.7um; free grinding: about 4.0um. It can be seen that the maximum fluctuation range of vertical grinding is about 23% higher than that of rotary grinding, and the maximum fluctuation range of rotary grinding is about 101% higher than that of free grinding. Therefore, the first characteristic coefficient constructed by the maximum fluctuation range can be used as a characteristic parameter with reference significance to judge the type of pretreatment method.

[0028] In this step, the complete algorithm process can automatically calculate the edge amplitude and amplitude mean, and finally generate the first characteristic coefficient without manual intervention, which is suitable for batch processing. The generation logic of the first characteristic coefficient extracts the fluctuation range of the edge contour of the ion polymer electrode layer and the basement membrane through edge detection and amplitude analysis. This method has significant advantages in accuracy, automation and robustness, and provides a scientific and reasonable feature extraction method compared with existing technologies.

[0029] S3: Analyze the edge image based on the fractal method, calculate the fractal dimension and Hurst index of the edge image, and generate the second characteristic coefficient and the third characteristic coefficient according to the fractal dimension and the Hurst index respectively.

[0030] The generation logic of the second characteristic coefficient is: Divide the binary edge image into several groups Each grid is represented by ,in , Represents the index of the grid in the horizontal and vertical directions respectively; Calculate the number of grids containing edge pixels as follows: ; In the formula Indicates the number of grids containing edge pixels, Represents the indicator function, when the grid When there is at least one edge pixel in ,otherwise ; The number of grids and fractal dimension satisfy: ; In the formula Represents the preset scale factor, represents the fractal dimension; Take the logarithm of both sides of the above equation and use the double logarithmic coordinates Regression analysis is performed in the regression line to obtain the slope of the regression line, which is the fractal dimension. ; Calculate the dimension mean according to the fractal dimension of multiple groups of microscopic images, and calculate the second characteristic coefficient according to the dimension mean , the calculation method is: ; ; In the formula , Represent the dimension mean and The fractal dimension corresponding to the group of microscopic images, , They respectively represent the maximum and minimum values ​​of the fractal dimension in multiple groups of microscopic images.

[0031] It is understandable that most surface structure analyses currently use Euclidean geometry models, and traditional surface roughness characterization parameters are also based on statistics. However, many complex surface structures are difficult to describe clearly using Euclidean geometry, and traditional parameters show instability with changes in measurement intervals and measurement scales, that is, they are scale-related. Fractal parameters are scale-independent and can reflect the intrinsic characteristics of rough surfaces. Characterizing surfaces with fractal parameters can more effectively reflect the complexity, irregularity, and roughness of the surface, and to a certain extent overcome the shortcomings of scale correlation of traditional roughness parameters. Among them, the fractal dimension is a scalar that describes the complexity of fractal geometric structures. For a fractal structure, the fractal dimension reflects its self-similar properties when scaling. The higher the fractal dimension, the greater the complexity and irregularity of the local area of ​​the image. Regions with high fractal dimensions usually correspond to regions with steep edges. From the research on ionomers in the prior art, the fractal dimensions of the edge contours of the electrode layer and the basement membrane of ionomers prepared by three different pretreatment methods are the largest in vertical grinding, about 1.66, followed by free grinding, about 1.65, and the smallest in rotary grinding, about 1.60. It can be seen that the fractal dimensions of the three pretreatment methods are slightly different, and the difference percentage does not exceed 1%. Therefore, the second characteristic coefficient constructed by the fractal dimension can also be used as a characteristic parameter with reference significance to judge the type of pretreatment method.

[0032] The generation logic of the third characteristic coefficient is: The edge contour composed of edge pixels in the binary edge image is regarded as a time series and divided into intervals, the intervals in the time series are marked as , subscript represents the interval number, and Calculate the cumulative deviation as: ; In the formula represents the cumulative deviation, represents the mean of the time series; Then, the range of the cumulative deviation in the time series and the standard deviation of the time series are calculated based on the cumulative deviation. The calculation methods are: ; ; Take the logarithm of the ratio of the range to the standard deviation and plot it on a double logarithmic coordinate system. Regression analysis is performed in , and the slope of the regression line is used as the corresponding Hurst index. The logarithmic expression is expressed as: ; in is the Hurst index; The exponential mean is calculated based on the fractal dimension of multiple groups of microscopic images, and the third characteristic coefficient is calculated based on the exponential mean , the calculation method is: ; ; In the formula , Represent the index mean and The Hurst index corresponding to the group of microscopic images, , They represent the maximum and minimum values ​​of the Hurst index in multiple groups of microscopic images respectively.

[0033] It is understandable that the degree of change of the interface curve is not strictly positively correlated with its fractal dimension. For example, the free grinding curve has the smallest change, while its fractal dimension is close to that of vertical grinding, that is, the pretreatment of the free grinding method makes the interface form a structure with small fluctuations but complex internal structure. Therefore, the degree of change of the surface cannot be simply equated to roughness, but the whole (undulation) and details (fine structure) should be combined and characterized by multiple parameters. Therefore, the Hurst index is used to supplement the overall characteristics. When the Hurst index is between 0 and 0.5, it means that the time series has anti-persistence, that is, negative correlation, and the trend will reverse; when the Hurst index is between 0.5 and 1, it means that the time series has long-term dependence, that is, positive correlation, and the trend will continue. From the research on ionic polymers in the prior art, the Hurst index of the electrode layer and the edge profile of the basement membrane of the ionic polymers prepared by three different pretreatment methods is the largest under the free grinding method, which is 0.7367, and its value is closer to 1, indicating that the continuity of this change is strong, while the H of vertical grinding and rotation grinding is basically slightly greater than 0.5, so the continuity of the change trend is weaker. Therefore, the third characteristic coefficient constructed by the Hurst index can also be used as a characteristic parameter with reference significance to judge the type of pretreatment method.

[0034] S4: performing weighted processing on the first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient to generate a total characteristic coefficient, determining the preprocessing method of the electrode layer and the basement membrane according to the total characteristic coefficient, and calculating the corresponding capacitance range.

[0035] The first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient are weighted to generate a total characteristic coefficient. , calculated as: ; In the formula , , Respectively represent the preset first weight, second weight, and third weight; Compare the total feature coefficient to the preset coefficient threshold: when When , it is considered that the pretreatment method of the electrode layer and the base film is vertical grinding; when When , it is considered that the pretreatment method of the electrode layer and the basement membrane is rotary grinding; when When , it is considered that the pretreatment method of the electrode layer and the base film is free grinding; In the formula , denote the first coefficient threshold and the second coefficient threshold, respectively, and .

[0036] The logic for calculating the capacitance range is: The time series composed of edge pixels in the binary edge image is simulated in three dimensions using image analysis technology. The image analysis technology used here can use the Weierstrass Mandelbrot function, which is a relatively simple mathematical model with self-similar characteristics. It has a definite analytical expression and can effectively simulate the time series more accurately. The contact area of ​​the electrode plate is then calculated based on the three-dimensional simulation. The calculation method is: ; in , Respectively represent the width and length of the electrode plate, Represents the unit area in a three-dimensional simulation; Then use the differential element method to calculate the upper limit of the capacitance range. The calculation method is: ; In the formula represents the angle between the two electrode plates, represents the dielectric constant of the ionic polymer; The final capacitance range is expressed as .

[0037] The width and length of the electrode plate can be set according to the user's expectations. It is understandable that in the process of forming the ionic polymer electrode layer, the resistance of the surface electrode is formed along with the formation of the double-layer capacitor, that is, a double-layer capacitor is formed at the interface between the two surface electrodes and the basement membrane. Different interface characteristics will result in different relative areas and positions of capacitor formation, which will inevitably cause an angle between the upper and lower plate planes. By considering the differences in interface characteristics caused by different pretreatment methods between the electrode layer and the basement membrane, and exploring the impact of this difference in interface characteristics on capacitance, users can better understand the characteristics of the ionic polymer.

[0038] In this step, the second weight is greater than the third weight and greater than the first weight. This is because among the three preprocessing methods, the difference of fractal dimension is the smallest, so a larger second weight is needed to amplify this difference. Similarly, the difference of Hurst index is medium, so a medium third weight is used to maintain it. The difference of edge amplitude is the largest, so a smaller first weight is needed to scale it, so as to obtain a parameter that can reflect the overall characteristics of ionic polymers. Since the total characteristic coefficient is most affected by the first characteristic coefficient, the judgment method is also mainly based on the law of edge amplitude, that is, vertical grinding, rotational grinding, and free grinding are used to distinguish from large to small, and the corresponding first coefficient threshold and second coefficient threshold can be obtained by professionals through experience or experiments.

[0039] In summary, the present invention realizes automatic edge detection and feature extraction of microscopic images through advanced image processing technology, thereby improving analysis efficiency and accuracy. The complex interface characteristics of the electrode layer and the basement membrane are systematically quantified using fractal dimension and Hurst index, thereby enhancing the ability to distinguish the effects of pretreatment methods. Multi-feature weighted processing generates a total feature coefficient, thereby achieving a comprehensive and accurate judgment of the pretreatment method. This method improves the accuracy, stability and efficiency of the analysis of the interface characteristics of ionic electroactive polymers, is suitable for performance optimization and quality control of flexible electronic devices in different scenarios, and provides strong technical support for new material research.

[0040] See also Figure 2 The present invention also provides an interface fractal feature analysis system based on ionic electroactive polymers, which is used to perform the above-mentioned analysis method, including: An image acquisition module, which is electrically connected to the image analysis module and is used to acquire microscopic images of the electrode layer and the basement membrane at the cross section of the ionomer; An image analysis module, which is electrically connected to the comprehensive processing module and is used to process the microscopic image and perform edge recognition; A comprehensive processing module, which is electrically connected to the data analysis module and is used to analyze the microscopic image after edge recognition to generate a first characteristic coefficient, a second characteristic coefficient, and a third characteristic coefficient; The data analysis module is electrically connected to the comprehensive judgment module and is used to perform weighted processing on the first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient to generate a total characteristic coefficient; The comprehensive judgment module is used to compare the total characteristic coefficient with the preset coefficient threshold, judge the pretreatment method of the electrode layer and the basement membrane according to the comparison result, and calculate the corresponding capacitance range.

[0041] The present invention also provides a computer storage medium for storing a computer program, and the computer program implements the above-mentioned analysis method when executed by a processor.

[0042] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0043] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0044] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0045] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An ionic polymer pretreatment analysis method based on interface fractal characteristics, characterized in that: The specific steps include: S1: scanning the ionic polymer, collecting at least two sets of microscopic images of the electrode layer and the basement membrane at the cross section thereof, and preprocessing the microscopic images; S2: performing edge recognition on the preprocessed microscopic image to obtain an edge image between the electrode layer and the basement membrane, performing amplitude analysis on the edge image, and generating a first characteristic coefficient according to the analysis result; S3: Analyze the edge image based on the fractal method, calculate the fractal dimension and Hurst index of the edge image, and generate the second characteristic coefficient and the third characteristic coefficient according to the fractal dimension and the Hurst index respectively; S4: performing weighted processing on the first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient to generate a total characteristic coefficient, determining a preprocessing method of the electrode layer and the basement membrane according to the total characteristic coefficient, and calculating a corresponding capacitance range.

2. The ionic polymer pretreatment analysis method based on interface fractal characteristics according to claim 1, characterized in that: The image preprocessing includes size cropping and grayscale conversion, which are used to unify the image size of the microscopic image and convert the microscopic image into a corresponding grayscale image; The logic of amplitude analysis of the grayscale image corresponding to the preprocessed microscopic image is: Collect the grayscale value of each pixel in the grayscale image, and build a global threshold based on the grayscale value of each pixel; Binarize the grayscale image based on the global threshold to generate the corresponding binary image, and use the edge detection algorithm to identify the edge of the binary image; The edge amplitude in the binary image corresponding to each group of microscopic images is calculated and the amplitude mean is calculated, and the first characteristic coefficient is generated according to the amplitude mean.

3. The ionic polymer pretreatment analysis method based on interface fractal characteristics according to claim 2, characterized in that: The generation logic of the global threshold is: According to the gray value of each pixel, a gray histogram is constructed with the gray value as the horizontal coordinate and the number of pixels as the vertical coordinate. The distribution probability of each pixel in the gray value is calculated according to the gray histogram. The calculation method is: ； In the formula Indicates the gray value of the pixel The distribution probability when Indicates the gray value is The number of pixels when Indicates the total number of pixels; Then calculate the probability ratio and grayscale mean below the global threshold, as well as the total grayscale mean, and the calculation methods are: ； ； ； In the formula represents the global threshold, , Respectively represent the probability proportion and grayscale mean below the global threshold, represents the total grayscale mean, Indicates the maximum value of the gray value; Then calculate the between-class variance , the calculation method is: ； From the gray value range Iterate over all global thresholds The possible values ​​of will make the between-class variance The largest value The value is used as the final global threshold.

4. The ionic polymer pretreatment analysis method based on interface fractal characteristics according to claim 2, characterized in that: The generation logic of the first characteristic coefficient is: The Sobel operator is used to calculate the horizontal and vertical gradients of each pixel, and then the gradient amplitude and gradient direction are calculated. The calculation methods are: ； ； In the formula , , Respectively represent the horizontal gradient, vertical gradient and gradient amplitude of the pixel point, Indicates the gradient direction; Taking each pixel as the center, detect whether its gradient amplitude is the local maximum in the 3*3 area along the gradient direction. If it is the local maximum, keep the pixel, otherwise set it to 0, so as to complete the edge recognition of the binary image and obtain the corresponding binary edge image; The amplitude mean of multiple groups of microscopic images is calculated based on the placement direction of the ionomer. The calculation method is: ； In the formula , Represent the amplitude mean and The edge amplitude corresponding to the group of microscopic images, represents the total number of microscopic images, where the edge amplitude is calculated as: ； In the formula , They represent the maximum and minimum values ​​of the horizontal coordinates of edge pixels in the binary edge image, respectively. , Respectively represent the maximum and minimum values ​​of the vertical coordinates of edge pixels in the binary edge image; Then generate the first characteristic coefficient based on the amplitude mean , the calculation method is: ； In the formula , They respectively represent the maximum and minimum values ​​of the amplitude mean in multiple groups of microscopic images.

5. The ionic polymer pretreatment analysis method based on interface fractal characteristics according to claim 4 is characterized in that: The generation logic of the second characteristic coefficient is: Divide the binary edge image into several groups Each grid is represented by ,in , Represents the index of the grid in the horizontal and vertical directions respectively; Calculate the number of grids containing edge pixels as follows: ； In the formula Indicates the number of grids containing edge pixels, Represents the indicator function, when the grid When there is at least one edge pixel in ,otherwise ; The number of grids and fractal dimension satisfy: ； In the formula Represents the preset scale factor, represents the fractal dimension; Take the logarithm of both sides of the above equation and use the double logarithmic coordinates Regression analysis is performed in the regression line to obtain the slope of the regression line, which is the fractal dimension. ; Calculate the dimension mean according to the fractal dimension of multiple groups of microscopic images, and calculate the second characteristic coefficient according to the dimension mean , the calculation method is: ； ； In the formula , Represent the dimension mean and The fractal dimension corresponding to the group of microscopic images, , They respectively represent the maximum and minimum values ​​of the fractal dimension in multiple groups of microscopic images.

6. The ionic polymer pretreatment analysis method based on interface fractal characteristics according to claim 5, characterized in that: The generation logic of the third characteristic coefficient is: The edge contour composed of edge pixels in the binary edge image is regarded as a time series and divided into intervals, the intervals in the time series are marked as , subscript represents the interval number, and , calculate the cumulative deviation, the calculation method is: ； In the formula represents the cumulative deviation, represents the mean of the time series; Then, the range of the cumulative deviation in the time series and the standard deviation of the time series are calculated based on the cumulative deviation. The calculation methods are: ； ； Take the logarithm of the ratio of the range to the standard deviation and plot it on a double logarithmic coordinate system. Regression analysis is performed in , and the slope of the regression line is used as the corresponding Hurst index. The logarithmic expression is expressed as: ； in is the Hurst index; The exponential mean is calculated based on the fractal dimension of multiple groups of microscopic images, and the third characteristic coefficient is calculated based on the exponential mean , the calculation method is: ； ； In the formula , Represent the index mean and The Hurst index corresponding to the group of microscopic images, , They represent the maximum and minimum values ​​of the Hurst index in multiple groups of microscopic images respectively.

7. The ionic polymer pretreatment analysis method based on interface fractal characteristics according to claim 6, characterized in that: The first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient are weighted to generate a total characteristic coefficient. , calculated as: ； In the formula , , represent the preset first weight, second weight, and third weight respectively, and ; Compare the total feature coefficient to the preset coefficient threshold: when When , it is considered that the pretreatment method of the electrode layer and the base film is vertical grinding; when When , it is considered that the pretreatment method of the electrode layer and the basement membrane is rotary grinding; when When , it is considered that the pretreatment method of the electrode layer and the base film is free grinding; In the formula , denote the first coefficient threshold and the second coefficient threshold, respectively, and .

8. The ionic polymer pretreatment analysis method based on interface fractal characteristics according to claim 7, characterized in that: The logic for calculating the capacitance range is: The time series composed of edge pixels in the binary edge image is simulated in three dimensions using image analysis technology, and the contact area of ​​the electrode plate is calculated based on the three-dimensional simulation. The calculation method is: ； in , Respectively represent the width and length of the electrode plate, Represents the unit area in three-dimensional simulation; Then use the differential element method to calculate the upper limit of the capacitance range. The calculation method is: ； In the formula represents the angle between the two electrode plates, represents the dielectric constant of the ionic polymer; The final capacitance range is expressed as .

9. The interface fractal feature analysis system based on ionic electroactive polymer is characterized by: The analysis system is used to perform the analysis method according to any one of claims 1 to 8, comprising: An image acquisition module, which is electrically connected to the image analysis module and is used to acquire microscopic images of the electrode layer and the basement membrane at the cross section of the ionomer; An image analysis module, which is electrically connected to the comprehensive processing module and is used to perform image processing and edge recognition on the microscopic image; A comprehensive processing module, the comprehensive processing module is electrically connected to the data analysis module, and is used to analyze the microscopic image after edge recognition to generate a first characteristic coefficient, a second characteristic coefficient, and a third characteristic coefficient; The data analysis module is electrically connected to the comprehensive judgment module and is used to perform weighted processing on the first characteristic coefficient, the second characteristic coefficient, and the third characteristic coefficient to generate a total characteristic coefficient; A comprehensive judgment module is used to compare the total characteristic coefficient with a preset coefficient threshold, judge the pretreatment method of the electrode layer and the basement membrane according to the comparison result, and calculate the corresponding capacitance range.

10. A computer storage medium, characterized in that: The storage medium is used to store a computer program, and when the computer program is executed by a processor, the analysis method according to any one of claims 1 to 8 is implemented.

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