A performance evaluation method and system for aerogel foam

Abstract

The present invention relates to the technical field of material performance evaluation, and discloses a performance evaluation method and system for aerogel foam, the method comprising obtaining a foam macroscopic image and a foam microscopic image; performing boundary extraction according to the foam microscopic image to obtain a pore image; performing pore analysis according to the pore image to obtain pore characteristic parameters; performing shape analysis according to the pore characteristic parameters to obtain pore shape; performing flatness analysis according to the foam macroscopic image to obtain flatness parameters; performing thermal insulation performance evaluation according to the flatness parameters, the pore shape and the pore characteristic parameters to obtain foam thermal insulation performance. The method has the following effects: The method can improve the accuracy of thermal insulation performance evaluation of aerogel foam.

Description

Technical Field

[0001] The present invention relates to the technical field of material performance evaluation, and in particular to a method and system for evaluating the performance of an aerogel foam. Background Art

[0002] At present, with the rapid development of microelectronic information technology, various electrical and electronic equipment will generate a lot of heat during operation. This heat will not only accelerate the aging of the equipment and shorten its service life, but also reduce the efficiency of the equipment, and even generate high heat to cause fire and loss. Therefore, the heat dissipation problem of electronic equipment cannot be ignored. Aerogel has been widely used in the field of electronic equipment in recent years due to its very low thermal conductivity and excellent thermal insulation performance, especially in the fields of thermal management such as photovoltaics, 5G communications, and new energy vehicles. Aerogel foam is a polymer aerogel composite material developed on the basis of aerogel materials. It inherits the excellent thermal insulation performance, high and low temperature resistance, corrosion resistance and flame retardant properties of aerogel, and can solve the defect of aerogel being fragile. It is very suitable for thermal insulation materials for electronic products; therefore, the thermal insulation performance of aerogel foam has become a relatively important indicator.

[0003] In one prior art, when evaluating the porosity of an aerogel material, a small sample is first selected from the material, processed to maintain its original morphology, and the sample surface is metal-sprayed to improve conductivity and image quality, and then a scanning electron microscope (SEM) is used to capture a high-resolution microstructure image. The color SEM image is then converted into a grayscale image and binarized by setting a threshold to distinguish between the pores and the solid part; connected component markers are applied to identify independent pore units and calculate their geometric features. Based on this information, the thermal insulation performance is evaluated by estimating the overall porosity by calculating the proportion of the pore area in the binarized image, that is, the proportion of the pore volume to the total sample volume.

[0004] The prior art does not analyze in detail the shape, size and distribution of the pores, which are important indicators of the thermal insulation performance of foam. There is no uniform measurement standard for the porosity of foam. In addition, the foam is formed by the combined action of two components, so the thermal insulation performance evaluation lacks accuracy. Summary of the invention

[0005] The present invention provides a method and system for evaluating the performance of an aerogel foam, so as to improve the accuracy of evaluating the thermal insulation performance of the aerogel foam.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a method for evaluating the performance of an aerogel foam, comprising:

[0007] Acquire foam macroscopic images and foam microscopic images;

[0008] Extracting boundaries according to the foam microscopic image to obtain a cell image;

[0009] Performing cell analysis according to the cell image to obtain cell characteristic parameters;

[0010] Performing shape analysis according to the cell characteristic parameters to obtain the cell shape;

[0011] Performing a flatness analysis based on the foam macroscopic image to obtain a flatness parameter;

[0012] The thermal insulation performance is evaluated according to the flatness parameter, the pore shape and the pore characteristic parameter to obtain the thermal insulation performance of the foam.

[0013] In an optional embodiment, the step of performing boundary extraction according to the foam microscopic image to obtain a cell image includes:

[0014] grayscale the foam microscopic image to obtain a microscopic grayscale image;

[0015] Performing noise reduction processing on the microscopic grayscale image to obtain a noise-reduced grayscale image;

[0016] Calculating pixel gradients according to the denoised grayscale image;

[0017] Performing double threshold detection according to the pixel gradient to obtain strong edge points, weak edge points and exclusion points;

[0018] Perform edge tracking analysis according to the strong edge points and the weak edge points to obtain a first boundary image;

[0019] A morphological operation is performed on the first boundary image to obtain a cell image.

[0020] In an optional embodiment, the performing cell analysis according to the cell image to obtain cell characteristic parameters includes:

[0021] Binarizing the cell image to obtain a binary cell image;

[0022] Performing connectivity analysis on the binary cell image to obtain cell units, and numbering each of the cell units;

[0023] Counting the number of the cell units to obtain the total number of cells;

[0024] Calculating the cell area and cell perimeter of each cell unit according to the binary cell image;

[0025] Dividing the total number of cells by the area of ​​the cell image to obtain the cell density;

[0026] Performing morphological analysis on the cell units to obtain the number of cell corners;

[0027] The cell characteristic parameters include the cell area and the cell density.

[0028] In an optional embodiment, performing shape analysis according to the cell characteristic parameters to obtain the cell shape comprises:

[0029] The cell roundness is calculated by the following formula:

[0030]

[0031] in, is the cell roundness, is the circumference of a circle, is the cell area, is the cell perimeter;

[0032] When the cell roundness is greater than a preset first roundness threshold, determining that the cell shape corresponding to the cell roundness is a circle;

[0033] When the cell roundness is less than the first roundness threshold but greater than a preset second roundness threshold, determining that the cell shape corresponding to the cell roundness is an ellipse;

[0034] When the cell roundness is less than the second roundness threshold, and the number of cell sharp corners is less than the preset first sharp corner threshold, the cell shape is determined to be a rhombus;

[0035] When the number of cell sharp corners is greater than a first sharp corner threshold, the cell shape is determined to be a triangle.

[0036] In an optional embodiment, the flatness analysis is performed according to the foam macroscopic image to obtain the flatness parameter, including:

[0037] graying the foam macro image to obtain a grayscale macro image;

[0038] The flatness parameters are calculated by the following formula:

[0039]

[0040] in, is the flatness parameter, is the total number of pixels of the grayscale macro image, is the pixel number, For the The gray value of pixel number, is the average grayscale value of the pixels in the grayscale macro image.

[0041] In an optional embodiment, the thermal insulation performance is evaluated according to the flatness parameter, the pore shape and the pore characteristic parameter to obtain the thermal insulation performance of the foam, including:

[0042] The foam insulation performance score is calculated using the following formula:

[0043]

[0044]

[0045]

[0046] in, Score the thermal insulation performance of the foam. is the thermal conductivity of static air, is the change in the thermal conductivity of the structure, is the change of radiation thermal conductivity, The flatness is The change in surface thermal conductivity under the condition of is the foam material constant, is the cell density, is the average cell diameter, is the shape factor, is the first index, is the second index, is the effective emissivity of the material, is the Boltzmann constant, is the material temperature;

[0047] When the thermal insulation performance score of the foam is less than a preset score threshold, it is determined that the thermal insulation performance of the foam is excellent;

[0048] When the thermal insulation performance score of the foam is greater than a preset score threshold, it is determined that the thermal insulation performance of the foam is good.

[0049] In an optional embodiment, the performing connectivity analysis on the binary cell image to obtain cell units and numbering each of the cell units comprises:

[0050] Inputting the binary cell image into a pre-trained cell recognition model, and outputting the cell unit number and cell shape;

[0051] The training process of the cell recognition model includes:

[0052] The model is trained based on historical binary cell images and pre-labeled cell labels. The training is considered complete when the preset upper limit of training times is reached or the loss function of the model is detected to meet the conditions, and the trained cell recognition model is obtained.

[0053] In a second aspect, the present invention provides a performance evaluation system for aerogel foam, comprising:

[0054] a data acquisition module for acquiring a macroscopic image and a microscopic image of the foam;

[0055] a boundary extraction module for extracting boundaries based on the microscopic image of the foam to obtain a pore image;

[0056] a pore analysis module for analyzing pores based on the pore image to obtain pore characteristic parameters;

[0057] a shape analysis module for performing shape analysis based on the pore characteristic parameters to obtain a pore shape;

[0058] a macroscopic analysis module for performing flatness analysis based on the macroscopic image of the foam to obtain a flatness parameter;

[0059] a score calculation module for evaluating the heat insulation performance based on the flatness parameter, the pore shape, and the pore characteristic parameters to obtain the heat insulation performance of the foam.

[0060] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the performance evaluation method of the aerogel foam described in any one of the above is implemented.

[0061] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the performance evaluation method of the aerogel foam described in any one of the above.

[0062] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a performance evaluation method and system for aerogel foam. The method includes acquiring a macroscopic image and a microscopic image of the foam; extracting boundaries based on the microscopic image of the foam to obtain a pore image; analyzing pores based on the pore image to obtain pore characteristic parameters; performing shape analysis based on the pore characteristic parameters to obtain a pore shape; performing flatness analysis based on the macroscopic image of the foam to obtain a flatness parameter; and evaluating the heat insulation performance based on the flatness parameter, the pore shape, and the pore characteristic parameters to obtain the heat insulation performance of the foam. The present method has the following effects: The present method can improve the accuracy of the heat insulation performance evaluation of aerogel foam.

[0063] Specifically, in the face of irregular pores, this method introduces a roundness calculation formula for distinguishing pore shapes. The mathematical and physical basis of this formula is that it compares the actual area of ​​a geometric shape with the square of its perimeter to quantify the similarity between the shape and the ideal circle. For a perfect circle, its roundness is equal to 1, because there is a fixed proportional relationship between the area and the perimeter of the circle. In terms of technical implementation, this method provides an objective and quantitative means to describe the pore morphology. By setting the first roundness threshold and the second roundness threshold, circular, elliptical and non-circular pores can be distinguished. Furthermore, when the pore roundness is lower than the second roundness threshold, the number of pore sharp corners is introduced as an auxiliary criterion to distinguish between rhombus and triangular pores. This hierarchical judgment strategy ensures the accurate identification of pores of different shapes and improves the efficiency and accuracy of pore shape analysis. Compared with the traditional pore shape analysis that relies on visual estimation or simple geometric measurement, the method based on the above formula provides a more scientific and consistent standard, which can effectively reduce the deviation caused by subjective factors.

[0064] Furthermore, in order to calculate the thermal insulation performance score of the foam, this method introduces a formula for calculating the thermal insulation performance score of the foam. Compared with the traditional method of estimating thermal insulation performance by relying on a single factor (such as material thickness or density), the mathematical and physical basis of this formula is that it combines multiple factors that affect the thermal conductivity, including the effects of structure, radiation, and surface flatness on the thermal insulation effect, thereby providing a comprehensive and quantitative method to evaluate the thermal insulation performance of aerogel foam. In addition, by setting a preset score threshold to distinguish between excellent and good thermal insulation performance, it helps to distinguish the foam effect and improve the accuracy of the foam thermal insulation performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic flow chart of a method for evaluating the performance of an aerogel foam provided by the first embodiment of the present invention;

[0066] Figure 2 Schematic diagram of the structure of an aerogel foam performance evaluation system provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0067] 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.

[0068] At present, with the rapid development of microelectronic information technology, various electrical and electronic equipment will generate a lot of heat during operation. This heat will not only accelerate the aging of the equipment and shorten its service life, but also reduce the efficiency of the equipment, and even generate high heat to cause fire and loss. Therefore, the heat dissipation problem of electronic equipment cannot be ignored. Aerogel has been widely used in the field of electronic equipment in recent years due to its very low thermal conductivity and excellent thermal insulation performance, especially in the fields of thermal management such as photovoltaics, 5G communications, and new energy vehicles. Aerogel foam is a polymer aerogel composite material developed on the basis of aerogel materials. It inherits the excellent thermal insulation performance, high and low temperature resistance, corrosion resistance and flame retardant properties of aerogel, and can solve the defect of aerogel being fragile. It is very suitable for thermal insulation materials for electronic products; therefore, the thermal insulation performance of aerogel foam has become a relatively important indicator.

[0069] In one prior art, when evaluating the porosity of an aerogel material, a small sample is first selected from the material, processed to maintain its original morphology, and the sample surface is metal-sprayed to improve conductivity and image quality, and then a scanning electron microscope (SEM) is used to capture a high-resolution microstructure image. The color SEM image is then converted into a grayscale image and binarized by setting a threshold to distinguish between the pores and the solid part; connected component markers are applied to identify independent pore units and calculate their geometric features. Based on this information, the thermal insulation performance is evaluated by estimating the overall porosity by calculating the proportion of the pore area in the binarized image, that is, the proportion of the pore volume to the total sample volume.

[0070] The prior art does not analyze in detail the shape, size and distribution of the pores, which are important indicators of the thermal insulation performance of foam. There is no uniform measurement standard for the porosity of foam. In addition, the foam is formed by the combined action of two components, so the thermal insulation performance evaluation lacks accuracy.

[0071] To solve the above problems, refer to Figure 1 The first embodiment of the present invention provides a method for evaluating the performance of an aerogel foam, comprising the following steps:

[0072] S11, acquiring a foam macroscopic image and a foam microscopic image;

[0073] S12, performing boundary extraction according to the foam microscopic image to obtain a cell image;

[0074] S13, performing cell analysis according to the cell image to obtain cell characteristic parameters;

[0075] S14, performing shape analysis according to the cell characteristic parameters to obtain the cell shape;

[0076] S15, performing a flatness analysis according to the foam macroscopic image to obtain a flatness parameter;

[0077] S16, evaluating the thermal insulation performance according to the flatness parameter, the pore shape and the pore characteristic parameter to obtain the thermal insulation performance of the foam.

[0078] In step S11 , a foam macroscopic image and a foam microscopic image are acquired.

[0079] In one embodiment, a digital camera or an industrial camera is used to capture a macroscopic image of the foam. For analysis requiring higher precision, an industrial-grade camera equipped with a high-resolution lens can be selected. To ensure image quality, uniform lighting conditions should be provided and shadows or reflections should be avoided from affecting the image. The foam is placed on a flat, undisturbed background for shooting to ensure that the image clearly reflects the surface features of the foam. A scanning electron microscope (SEM) is used to obtain a microscopic image of the foam. Prior to acquisition, the foam sample is cut into thin slices, fixed, dehydrated, and the like to adapt to the requirements of the microscope.

[0080] It is worth noting that in order to ensure that the collected macro and micro images are of the same size, after obtaining the foam macro and micro images, the images are cropped using image processing software to ensure that the size of all sample images is 2048. 2048 pixels, which is not limited in the present invention.

[0081] In step S12, boundary extraction is performed based on the foam microscopic image to obtain a cell image.

[0082] In one embodiment, the foam microscopic image is grayed to obtain a microscopic grayscale image; the microscopic grayscale image is denoised to obtain a denoised grayscale image; pixel gradients are calculated based on the denoised grayscale image; double threshold detection is performed based on the pixel gradients to obtain strong edge points, weak edge points and exclusion points; edge tracking analysis is performed based on the strong edge points and the weak edge points to obtain a first boundary image; and morphological operations are performed on the first boundary image to obtain a bubble image.

[0083] It is worth noting that the grayscale processing formula used in this method is as follows:

[0084]

[0085] in, is the pixel brightness value, R, G, and B represent the intensity of the red, green, and blue channels of each pixel in the image respectively.

[0086] It is worth noting that this method uses a Gaussian filter for noise reduction. Dual threshold detection sets two thresholds, a high threshold and a low threshold. Points above the high threshold are considered strong edge points, and points below the low threshold are excluded; points between the two are considered weak edge points and are retained only when they are connected to strong edge points. This method is derived from the Canny edge detection algorithm and can effectively capture continuous edges while suppressing noise interference. The purpose of edge tracking analysis is to connect scattered edge points to form a complete boundary. Based on the results of dual threshold detection, a depth-first search is used to explore weak edge points in the vicinity of strong edge points and construct a complete boundary path. This process ensures that even broken edges can be correctly connected.

[0087] In step S13, a cell analysis is performed based on the cell image to obtain cell characteristic parameters.

[0088] In one embodiment, the cell image is binarized to obtain a binary cell image; connectivity analysis is performed on the binary cell image to obtain cell units, and each cell unit is numbered; the cell units are counted to obtain the total number of cells; the cell area and cell perimeter of each cell unit are calculated based on the binary cell image; the total number of cells is divided by the area of ​​the cell image to obtain the cell density; a morphological analysis is performed on the cell units to obtain the number of cell corners; the cell characteristic parameters include the cell area and the cell density.

[0089] In one embodiment, the binary cell image is input into a pre-trained cell recognition model, and the cell unit number and cell shape are output; wherein the training process of the cell recognition model includes: training the model based on historical binary cell images and pre-labeled cell labels, and determining that the training is completed after reaching a preset upper limit of the number of training times or detecting that the loss function of the model meets the conditions, thereby obtaining a trained cell recognition model.

[0090] It should be noted that the purpose of binarization is to convert the grayscale or denoised pore image into a black-and-white (binary) image to more easily identify the pore area. The pixels in the image are divided into two parts, pores and background, by the global threshold method. The pixels inside the pores and those outside the pores are separated. The pore pixels are set to white, and by using the Connected Component Labeling (CCL) algorithm, the entire binarized pore image is scanned to find all the connected white pixels and label them with the same number. Each number corresponds to a unique pore unit. For each pore unit with a specific number, the number of occupied pixels is calculated to determine the pore area; the contour tracking algorithm is used to calculate the perimeter of the pore. The purpose of morphological analysis and sharp corner number calculation is to further analyze the shape characteristics of the pores and determine whether there are non-circular features such as sharp corners. Specifically, the skeletonization operation in morphology is applied to detect the complex shape characteristics of the pores. The number of sharp corners can be obtained by analyzing the branch points on the skeleton line. Generally speaking, the pore area represents the number of pixels occupied by each pore unit, reflecting the pore size. The pore density represents the number of pores per unit area, indicating the pore distribution of the foam material.

[0091] In step S14, shape analysis is performed based on the pore feature parameters to obtain the pore shape.

[0092] In one implementation, the pore roundness is calculated by the following formula:

[0093]

[0094] where, is the pore roundness, is the pi, is the pore area, is the pore perimeter;

[0095] When the pore roundness is greater than a preset first roundness threshold, it is determined that the pore shape corresponding to the pore roundness is circular;

[0096] When the pore roundness is less than the first roundness threshold but greater than a preset second roundness threshold, it is determined that the pore shape corresponding to the pore roundness is oval;

[0097] When the pore roundness is less than the second roundness threshold and the number of pore sharp corners is less than a preset first sharp corner threshold, it is determined that the pore shape is rhombic;

[0098] When the number of pore sharp corners is greater than the first sharp corner threshold, it is determined that the pore shape is triangular.

[0099] It is worth noting that the shape of the pores describes the morphological characteristics of the pores through indicators such as area, perimeter, and the number of roundness and sharp corners. The roundness of the pore is a dimensionless number used to measure the degree to which the pore is close to the ideal circle. The mathematical basis of this formula is that it compares the actual area of ​​a geometric shape with the square of its perimeter to quantify the similarity between the shape and the ideal circle. For a perfect circle, its roundness is equal to 1, because there is a fixed proportional relationship between the area and the perimeter of the circle. When the shape deviates from the circle, the value of the roundness decreases. When the roundness of the pore is greater than the preset first roundness threshold, the pore shape is considered to be circular. This shows that the pore has a high degree of symmetry and uniformity, which is very close to the ideal circular structure. The first roundness threshold can be taken as 0.9, which is not limited by this method. For pores slightly smaller than the first roundness threshold, although such pores are not completely round, they still maintain a relatively smooth and continuous boundary, but are stretched or compressed in certain directions. For those pores with lower roundness and fewer sharp corners, they show more diamond characteristics. A rhombus has four right-angle intersections, but the overall outline is relatively regular. Finally, if the cell not only has a low roundness but also has many sharp corners (exceeding the first sharp angle threshold), it is classified as a triangle. This type of cell is formed due to special conditions in the manufacturing process and has multiple obvious vertices and sharp angles. Cells of different shapes will affect the physical properties of the material, such as the air flow pattern and heat conduction path, and thus affect the thermal insulation effect of the entire material.

[0100] In step S15, a flatness analysis is performed based on the foam macroscopic image to obtain a flatness parameter.

[0101] In one embodiment, the foam macro image is grayed to obtain a gray macro image;

[0102] The flatness parameters are calculated by the following formula:

[0103]

[0104] in, is the flatness parameter, is the total number of pixels of the grayscale macro image, is the pixel number, For the The gray value of pixel number, is the average grayscale value of the pixels in the grayscale macro image.

[0105] It is worth mentioning that the flatness parameter, also known as the Root Mean Square Deviation (RMS), is a standard indicator for measuring surface roughness. The total number of pixels in a grayscale macro image is the number of all pixels in the entire image. For a grayscale macro image, the average grayscale value of all pixels is calculated, which is obtained by adding up the grayscale values ​​of all pixels and dividing by the total number of pixels. The flatness parameter represents the standard deviation of the grayscale change on the foam surface, which can reflect the degree of surface undulation. A smaller flatness parameter means that the grayscale distribution of the image is more uniform, indicating that the surface is relatively flat; while a larger flatness parameter indicates that there are more uneven areas on the surface, that is, the roughness is higher. In the performance evaluation of aerogel foam, the flatness parameter is very important to ensure that the material has good thermal insulation properties. A flat surface helps reduce obstacles in the heat conduction path, thereby improving the thermal insulation effect.

[0106] In step S16, thermal insulation performance is evaluated according to the flatness parameter, the cell shape and the cell characteristic parameter to obtain the thermal insulation performance of the foam.

[0107] In one embodiment, the foam thermal insulation performance score is calculated by the following formula:

[0108]

[0109]

[0110]

[0111] in, Score the thermal insulation performance of the foam. is the thermal conductivity of static air, is the change in the thermal conductivity of the structure, is the change in radiation thermal conductivity, The flatness is The change in surface thermal conductivity under the condition of is the foam material constant, is the cell density, is the average cell diameter, is the shape factor, is the first index, is the second index, is the effective emissivity of the material, is the Boltzmann constant, is the material temperature;

[0112] When the thermal insulation performance score of the foam is less than a preset score threshold, it is determined that the thermal insulation performance of the foam is excellent;

[0113] When the thermal insulation performance score of the foam is greater than a preset score threshold, it is determined that the thermal insulation performance of the foam is good.

[0114] It is worth noting that the foam insulation performance score is a comprehensive indicator that reflects the overall insulation capacity of the material. The thermal conductivity of static air is a benchmark value of 0.026 W / m·K (under standard atmospheric pressure and room temperature) for comparing the thermal insulation performance of different materials. The foam material constant, which depends on the specific material composition and manufacturing process, reflects the inherent influence of the material itself on thermal conductivity. Cell density, that is, the number of cells per unit volume, higher cell density means better thermal insulation performance. Average cell diameter, smaller cell diameter helps to reduce the heat conduction path and improve the thermal insulation effect. They are the first index and the second index, respectively. These parameters describe the degree of influence of cell density and diameter on thermal conductivity. Their specific values ​​depend on experimental determination. The first index is 1.1 and the second index is 0.7. The shape factor reflects the influence of cell shape on thermal conduction. Circular cells provide better thermal insulation than other shapes. Circular cells and elliptical cells take their roundness, and triangular and diamond cells take 0.3 and 0.5 respectively. The present invention is not limited to this. The effective emissivity of a material is an indicator of the ability of the material surface to radiate heat, ranging from 0 to 1. The closer it is to 0, the less likely the material is to radiate heat. The value of the Stefan-Boltzmann constant is . Material temperature, in Kelvin, the higher the temperature, the more significant the radiation heat transfer. Flatness parameter, as mentioned earlier, is a measure of the roughness of the foam surface, quantified by the root mean square deviation. A flat surface helps reduce obstacles in the heat conduction path, thereby improving the thermal insulation effect. The change in surface thermal conductivity is recorded in a public experimental data table and obtained by looking up the table during calculation. For example, for a flatness parameter of 10 μm, the corresponding change in surface thermal conductivity is 0.005 W / m·K. The score threshold is 0.2 W / m·K.

[0115] In another embodiment, by setting the second score threshold to 0.1 W / m·K, aerogel foams having a foam thermal insulation performance score lower than the second score threshold are determined to be unqualified aerogel foams.

[0116] In summary, the present invention discloses a method for evaluating the performance of aerogel foam, which aims to provide a more accurate method for evaluating thermal insulation performance through a series of image processing and analysis techniques. Specifically, the method first obtains macroscopic and microscopic images of aerogel foam materials, and these two types of images are used to evaluate the flatness of the foam surface and the characteristics of the internal pore structure, respectively. When processing the microscopic image, the boundary extraction technology is used, and a clear pore image is finally obtained through graying, noise reduction, pixel gradient calculation, dual threshold detection, and edge tracking. These operations ensure that each pore can be accurately identified even in a complex pore structure.

[0117] Next, based on the obtained cell images, in-depth cell analysis was performed to determine the cell characteristic parameters, including but not limited to cell area, density, and shape. In this process, binarization processing converted the cell images into black and white to make it easier to distinguish the cell areas; connectivity analysis further helped identify each independent cell unit and numbered and counted them. The cell density was calculated by counting the number of cell units, and combined with morphological analysis, information about the number of cell corners was obtained.

[0118] For the classification of cell shapes, this method introduces a mathematical model based on the roundness calculation formula. This formula quantifies the similarity between the cell and the ideal circle by using the proportional relationship between the actual area of ​​the cell and the square of its perimeter. When the cell roundness exceeds the preset first roundness threshold, it can be considered that its shape is close to a circle; when the roundness is between the first and second roundness thresholds, it is judged as an ellipse; if the roundness is lower than the second roundness threshold and the number of sharp corners is limited, it is considered a rhombus; conversely, if the number of sharp corners is large, it is classified as a triangle. This hierarchical judgment strategy not only improves the accuracy of cell shape recognition, but also reduces the deviation caused by subjective factors.

[0119] In addition, in order to comprehensively evaluate the thermal insulation performance of the foam, this method also takes into account the flatness of the foam surface. The flatness parameter of the entire foam surface is calculated through the macroscopic image after grayscale processing. This parameter reflects the degree of undulation on the foam surface. A smaller flatness parameter means a flatter surface, which is conducive to reducing obstacles on the heat conduction path, thereby improving the thermal insulation effect.

[0120] Finally, combining all the above analysis results, namely the flatness parameter, cell shape and cell characteristic parameters, this method proposes a comprehensive thermal insulation performance score calculation formula. This formula integrates multiple influencing factors such as static air thermal conductivity, structural thermal conductivity change, radiation thermal conductivity change, surface thermal conductivity change, foam material constant, cell density, average cell diameter, shape factor, etc., to form an indicator that can comprehensively reflect the thermal insulation performance of aerogel foam. Depending on whether the obtained score is lower than the preset score threshold, it can be distinguished whether the thermal insulation performance of the foam is excellent or good.

[0121] In summary, the aerogel foam performance evaluation method provided by the present invention realizes a multi-dimensional evaluation of the foam microstructure and macroscopic properties through systematic image processing and data analysis, thereby improving the accuracy of the thermal insulation performance evaluation of the aerogel foam.

[0122] Reference Figure 2 The second embodiment of the present invention provides a performance evaluation system for aerogel foam, comprising:

[0123] A data acquisition module, used for acquiring a foam macroscopic image and a foam microscopic image;

[0124] A boundary extraction module, used for performing boundary extraction based on the foam microscopic image to obtain a cell image;

[0125] A cell analysis module, used to perform cell analysis based on the cell image to obtain cell characteristic parameters;

[0126] A shape analysis module, used to perform shape analysis according to the cell characteristic parameters to obtain the cell shape;

[0127] A macroscopic analysis module, used for performing a flatness analysis based on the foam macroscopic image to obtain a flatness parameter;

[0128] The score calculation module is used to evaluate the thermal insulation performance according to the flatness parameter, the pore shape and the pore characteristic parameter to obtain the thermal insulation performance of the foam.

[0129] Preferably, the data acquisition module is used to:

[0130] Acquire foam macro and foam micro images.

[0131] Preferably, the boundary extraction module is used to:

[0132] Boundary extraction is performed according to the foam microscopic image to obtain a cell image, including:

[0133] grayscale the foam microscopic image to obtain a microscopic grayscale image;

[0134] Performing noise reduction processing on the microscopic grayscale image to obtain a noise-reduced grayscale image;

[0135] Calculating pixel gradients according to the denoised grayscale image;

[0136] Performing double threshold detection according to the pixel gradient to obtain strong edge points, weak edge points and exclusion points;

[0137] Perform edge tracking analysis according to the strong edge points and the weak edge points to obtain a first boundary image;

[0138] A morphological operation is performed on the first boundary image to obtain a cell image.

[0139] Preferably, the cell analysis module is used to:

[0140] Performing cell analysis according to the cell image to obtain cell characteristic parameters includes:

[0141] Binarizing the cell image to obtain a binary cell image;

[0142] Performing connectivity analysis on the binary cell image to obtain cell units, and numbering each of the cell units;

[0143] Counting the number of the cell units to obtain the total number of cells;

[0144] Calculating the cell area and cell perimeter of each cell unit according to the binary cell image;

[0145] Dividing the total number of cells by the area of ​​the cell image to obtain the cell density;

[0146] Performing morphological analysis on the cell units to obtain the number of cell corners;

[0147] The cell characteristic parameters include the cell area and the cell density.

[0148] Preferably, the shape analysis module is used to:

[0149] Performing shape analysis according to the cell characteristic parameters to obtain the cell shape includes:

[0150] The cell roundness is calculated by the following formula:

[0151]

[0152] in, is the cell roundness, is the circumference of a circle, is the cell area, is the cell perimeter;

[0153] When the cell roundness is greater than a preset first roundness threshold, determining that the cell shape corresponding to the cell roundness is a circle;

[0154] When the cell roundness is less than the first roundness threshold but greater than a preset second roundness threshold, determining that the cell shape corresponding to the cell roundness is an ellipse;

[0155] When the cell roundness is less than the second roundness threshold, and the number of cell sharp corners is less than the preset first sharp corner threshold, the cell shape is determined to be a rhombus;

[0156] When the number of cell sharp corners is greater than a first sharp corner threshold, the cell shape is determined to be a triangle.

[0157] Preferably, the macro analysis module is used to:

[0158] The flatness analysis is performed according to the foam macroscopic image to obtain flatness parameters, including:

[0159] graying the foam macro image to obtain a grayscale macro image;

[0160] The flatness parameters are calculated by the following formula:

[0161]

[0162] in, is the flatness parameter, is the total number of pixels of the grayscale macro image, is the pixel number, For the The gray value of pixel number, is the average grayscale value of the pixels in the grayscale macro image.

[0163] Preferably, the score calculation module is used to evaluate the thermal insulation performance according to the flatness parameter, the cell shape and the cell characteristic parameter to obtain the thermal insulation performance of the foam, including:

[0164] The foam insulation performance score is calculated using the following formula:

[0165]

[0166]

[0167]

[0168] in, Score the thermal insulation performance of the foam. is the thermal conductivity of static air, is the change in the thermal conductivity of the structure, is the change amount of radiative thermal conductivity, is the surface flatness of and the change amount of surface thermal conductivity under this condition, is the foam material constant, is the cell density, is the average cell diameter, is the shape factor, is the first exponent, is the second exponent, is the effective emissivity of the material, is the Boltzmann constant, is the material temperature;

[0169] When the heat insulation performance score of the foam is less than the preset score threshold, it is determined that the heat insulation performance of the foam is excellent;

[0170] When the heat insulation performance score of the foam is greater than the preset score threshold, it is determined that the heat insulation performance of the foam is good.

[0171] Preferably, the connectivity analysis is performed on the binary cell image to obtain cell units, and each of the cell units is numbered, including:

[0172] Input the binary cell image into a pre-trained cell recognition model, and output the cell unit number and cell shape;

[0173] The training process of the cell recognition model includes:

[0174] Based on the historical binary cell image and the pre-labeled cell tags, the model is trained. After reaching the upper limit of the preset training times or detecting that the loss function of the model meets the conditions, the training is determined to be completed, and the trained cell recognition model is obtained.

[0175] It should be noted that a performance evaluation system for aerogel foam provided in an embodiment of the present invention is used to execute all the process steps of a performance evaluation method for aerogel foam in the above embodiment. The working principles and beneficial effects of the two correspond one by one, so they will not be repeated here.

[0176] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, the steps in the embodiments of the above performance evaluation method for aerogel foam are implemented, such as Figure 1 the step S11 shown. Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiments are implemented, such as the data acquisition module.

[0177] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0178] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0179] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0180] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0181] Wherein, if the module / unit integrated in the electronic device 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 such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0182] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0183] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the performance of an aerogel foam, characterized in that: include: Acquire foam macroscopic images and foam microscopic images; Extracting boundaries according to the foam microscopic image to obtain a cell image; Performing cell analysis according to the cell image to obtain cell characteristic parameters; Performing shape analysis according to the cell characteristic parameters to obtain the cell shape; Performing a flatness analysis based on the foam macroscopic image to obtain a flatness parameter; The thermal insulation performance is evaluated according to the flatness parameter, the cell shape and the cell characteristic parameter to obtain the thermal insulation performance of the foam, including: The foam insulation performance score is calculated using the following formula: in, Score the thermal insulation performance of the foam. is the thermal conductivity of static air, is the change in the thermal conductivity of the structure, is the change of radiation thermal conductivity, The flatness is The change in surface thermal conductivity under the condition of is the foam material constant, is the cell density, is the average cell diameter, is the shape factor, is the first index, is the second index, is the effective emissivity of the material, is the Boltzmann constant, is the material temperature; When the thermal insulation performance score of the foam is less than a preset score threshold, it is determined that the thermal insulation performance of the foam is excellent; When the thermal insulation performance score of the foam is greater than a preset score threshold, it is determined that the thermal insulation performance of the foam is good; Wherein, the cell characteristic parameters include the cell area and the cell density.

2. The method for evaluating the performance of aerogel foam according to claim 1, characterized in that: The step of extracting boundaries according to the foam microscopic image to obtain a cell image comprises: grayscale the foam microscopic image to obtain a microscopic grayscale image; Performing noise reduction processing on the microscopic grayscale image to obtain a noise-reduced grayscale image; Calculating pixel gradients according to the denoised grayscale image; Performing double threshold detection according to the pixel gradient to obtain strong edge points, weak edge points and exclusion points; Perform edge tracking analysis according to the strong edge points and the weak edge points to obtain a first boundary image; A morphological operation is performed on the first boundary image to obtain a cell image.

3. The performance evaluation method of aerogel foam according to claim 1, characterized in that: The performing of cell analysis according to the cell image to obtain cell characteristic parameters includes: Binarizing the cell image to obtain a binary cell image; Performing connectivity analysis on the binary cell image to obtain cell units, and numbering each of the cell units; Counting the number of the cell units to obtain the total number of cells; Calculating the cell area and cell perimeter of each cell unit according to the binary cell image; Dividing the total number of cells by the area of ​​the cell image to obtain the cell density; The morphological analysis is performed on the cell units to obtain the number of cell corners.

4. The method for evaluating the performance of aerogel foam according to claim 1, characterized in that: The step of performing shape analysis according to the cell characteristic parameters to obtain the cell shape comprises: The cell roundness is calculated by the following formula: in, is the cell roundness, is the circumference of a circle, is the cell area, is the cell perimeter; When the cell roundness is greater than a preset first roundness threshold, determining that the cell shape corresponding to the cell roundness is a circle; When the cell roundness is less than the first roundness threshold but greater than a preset second roundness threshold, determining that the cell shape corresponding to the cell roundness is an ellipse; When the cell roundness is less than the second roundness threshold, and the number of cell sharp corners is less than the preset first sharp corner threshold, the cell shape is determined to be a rhombus; When the number of cell sharp corners is greater than a first sharp corner threshold, the cell shape is determined to be a triangle.

5. The method for evaluating the performance of aerogel foam according to claim 1, characterized in that: The flatness analysis is performed according to the foam macroscopic image to obtain the flatness parameters, including: graying the foam macro image to obtain a grayscale macro image; The flatness parameter is calculated by the following formula: in, is the flatness parameter, is the total number of pixels in the grayscale macro image, is the pixel number, For the The gray value of pixel number, is the average grayscale value of the pixels in the grayscale macro image.

6. The method for evaluating the performance of aerogel foam according to claim 3, characterized in that: The performing connectivity analysis on the binary cell image to obtain cell units and numbering each of the cell units comprises: Inputting the binary cell image into a pre-trained cell recognition model, and outputting the cell unit number and cell shape; The training process of the cell recognition model includes: The model is trained based on historical binary cell images and pre-labeled cell labels. The training is considered complete when the preset upper limit of training times is reached or the loss function of the model is detected to meet the conditions, and the trained cell recognition model is obtained.

7. A performance evaluation system for aerogel foam, used to implement the performance evaluation method for aerogel foam according to any one of claims 1 to 6, characterized in that: include: A data acquisition module, used for acquiring a foam macroscopic image and a foam microscopic image; A boundary extraction module, used for performing boundary extraction based on the foam microscopic image to obtain a cell image; A cell analysis module, used to perform cell analysis based on the cell image to obtain cell characteristic parameters; A shape analysis module, used to perform shape analysis according to the cell characteristic parameters to obtain the cell shape; A macroscopic analysis module, used for performing a flatness analysis based on the foam macroscopic image to obtain a flatness parameter; The score calculation module is used to evaluate the thermal insulation performance according to the flatness parameter, the pore shape and the pore characteristic parameter to obtain the thermal insulation performance of the foam.

8. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the aerogel foam performance evaluation method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the aerogel foam performance evaluation method according to any one of claims 1 to 6.

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