A quantitative analysis method for the distribution characteristics of steel inclusions

Abstract

This application relates to the field of statistical analysis technology for non-metallic inclusions in steel, and particularly to a quantitative analysis method for the distribution characteristics of inclusions in steel. The method includes: acquiring a binary image of inclusions and background; calculating the average areal density and average equivalent spacing of inclusions within the binary image; dividing the binary image into several partitions, and calculating the areal density and equivalent spacing of inclusions within each partition; plotting a density distribution curve of inclusions within each partition based on the areal density of inclusions within that partition, and calculating the ratio of the half-width at half-maximum (WHM) of the curve to the average areal density of inclusions within the binary image; plotting an equivalent spacing distribution curve of inclusions within each partition based on the equivalent spacing of inclusions within that partition, and calculating the ratio of the WHM of the curve to the average equivalent spacing of inclusions within the binary image. This application solves the technical problem of the difficulty in quantitatively analyzing the distribution of inclusions.

Description

Technical Field

[0001] This application relates to the field of statistical analysis technology of non-metallic inclusions in steel, and in particular to a quantitative analysis method for the distribution characteristics of inclusions in steel. Background Technology

[0002] Inclusions in steel have a significant impact on the material's service performance, and researchers are particularly interested in information such as their composition, quantity, size, and distribution. An automated inclusion analysis system based on scanning electron microscopy and characteristic X-ray energy dispersive spectroscopy can collect large-scale information on the composition, shape, size, and location of each inclusion within a specified area on the polished surface of a sample. Then, the information on all inclusions is statistically analyzed according to the analytical requirements. To understand the distribution of inclusions on a two-dimensional observation surface, researchers need to know their density and uniformity.

[0003] However, conventional statistical methods can only provide the average density within a scanned surface using data on quantity and scanned area. While the location of each inclusion is related to the distribution, it cannot directly reflect the overall distribution characteristics. Therefore, existing technologies lack quantitative analysis methods for inclusion distribution. Summary of the Invention

[0004] This application provides a quantitative analysis method for the distribution characteristics of inclusions in steel, in order to solve the existing technical problem that it is difficult to quantitatively analyze the distribution of inclusions.

[0005] In a first aspect, this application provides a quantitative analysis method for the distribution characteristics of inclusions in steel, the method comprising:

[0006] Obtain a binary image of the inclusions and background;

[0007] Calculate the average surface density and average equivalent spacing of the inclusions in the binary image, respectively.

[0008] The binary image is segmented into several partitions, and the areal density and equivalent spacing of the inclusions in each partition are calculated respectively.

[0009] Based on the areal density of inclusions in each partition, a density distribution curve of the inclusions in each partition is plotted, and the ratio of the half-width at half-maximum of the density distribution curve to the average areal density of the inclusions in the binary image is calculated to analyze the uniformity of the inclusion distribution.

[0010] Based on the equivalent spacing of inclusions within each partition, an equivalent spacing distribution curve of inclusions within each partition is plotted, and the ratio of the half-width at half-maximum (WHM) of the spacing distribution curve to the average equivalent spacing of inclusions within the binary image is calculated to analyze the uniformity of inclusion distribution.

[0011] Optionally, acquiring the binary image of the inclusion-background includes:

[0012] Images of the distribution and morphology of inclusions in the target region of steel samples were captured using a scanning electron microscope.

[0013] The image information is read and image processing is performed to obtain a binary image of inclusions and background.

[0014] Optionally, the operating parameters of the scanning electron microscope include: operating mode: backscatter; magnification: 200x to 1000x.

[0015] Optionally, the step of reading image information and performing image processing on the image to obtain a binary image of the inclusions-background includes:

[0016] The image is read using a scale and its dimensions to obtain the correspondence between pixels and image dimensions;

[0017] The image is processed to obtain a binary image of the inclusions and background.

[0018] Optionally, the image is processed to obtain a binary image of the inclusions and background, including:

[0019] The image is inverted and median filtered to obtain a smoothed image;

[0020] Based on the gray value corresponding to each pixel in the smoothed image, the binary segmentation point is determined by the maximum inter-class variance method.

[0021] The smooth image is binarized based on the binary segmentation points to obtain a binary image of the inclusions and background.

[0022] Optionally, the method for calculating the equivalent spacing of the inclusions includes:

[0023] The area is filled with regular hexagonal grids such that the number of grids is close to the number of inclusions in the area, and the total area of ​​the regular hexagonal grids is equal to the area of ​​the area. Then the side length of the hexagons is the equivalent spacing of the inclusions.

[0024] Optionally, the inclusions in the region are uniformly distributed in a hexagonal close-packed manner.

[0025] Optionally, calculating the average surface density and average equivalent spacing of inclusions within the binary image includes:

[0026] Based on the number of all inclusions in the binary image, calculate the average surface density of the inclusions;

[0027] The average equivalent spacing of the inclusions is calculated based on the area and number of all inclusions in the binary image.

[0028] Optionally, the binary image is segmented into several partitions, and the areal density and equivalent spacing of inclusions in each partition are calculated, including:

[0029] The binary image is divided into several partitions, and the areal density and equivalent spacing of the inclusions in each partition are calculated respectively.

[0030] Optionally, the step of segmenting the binary image into several partitions based on the binary segmentation points includes:

[0031] The binary image is divided into several equal parts in rows and columns so that the size of the partitions is the same; wherein, the number of inclusions in each partition is ≥1, and the number of partitions with 1 inclusion should be as small as possible.

[0032] The technical solutions provided in this application have the following advantages compared with the prior art:

[0033] The quantitative analysis method for the distribution characteristics of steel inclusions provided in this application, by partitioning a binary image, can reflect the uneven distribution of local micro-regions through statistical results; and by developing an equivalent spacing calculation method, the quantification of the two-dimensional distribution can be achieved with minimal computation. This solves the technical problem of the difficulty in quantitatively analyzing the distribution of inclusions. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0036] Figure 1 A flowchart illustrating a quantitative analysis method for the distribution characteristics of steel inclusions provided in an embodiment of this application;

[0037] Figure 2 A diagram showing the uniform distribution of inclusions in a hexagonal close-packed arrangement within a partitioned area, provided in the embodiments of this application;

[0038] Figure 3 This is a backscattering topography image of the MnS inclusions in region 1 of Embodiment 1 of this application;

[0039] Figure 4 This is a distribution diagram of the number of inclusions in region 1 of embodiment 1 of this application, divided into 6×6 sections;

[0040] Figure 5 This is a surface density distribution diagram of inclusions in region 1 of Embodiment 1 of this application;

[0041] Figure 6 This is an equivalent spacing distribution diagram of inclusions in region 1 in Embodiment 1 of this application;

[0042] Figure 7 This is a backscattering topography image of the MnS inclusions in region 2 of Embodiment 1 of this application;

[0043] Figure 8 This is a distribution diagram of the number of inclusions in region 2 divided into 5×5 sections in Embodiment 1 of this application;

[0044] Figure 9 This is a surface density distribution diagram of inclusions in region 2 of Embodiment 1 of this application;

[0045] Figure 10 This is a diagram showing the equivalent spacing distribution of inclusions in region 2 in Embodiment 1 of this application. Detailed Implementation

[0046] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] Various embodiments of this application may exist in the form of a range; it should be understood that the description in the form of a range is merely for convenience and brevity and should not be construed as a hard limitation on the scope of this application; therefore, it should be considered that the range description has specifically disclosed all possible sub-ranges and single numerical values ​​within that range. For example, it should be considered that the range description from 1 to 6 has specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and single numbers within the range, such as 1, 2, 3, 4, 5, and 6, regardless of the range. Furthermore, whenever a numerical range is referred to herein, it means including any referenced number (fraction or integer) within the referred range.

[0048] In this application, unless otherwise stated, directional terms such as "upper" and "lower" specifically refer to the drawing directions in the accompanying drawings. Furthermore, in the description of this application, terms such as "comprising" and "including" mean "including but not limited to." In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In this document, "and / or" describes the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. In this document, "at least one" means one or more, and "more than one" means two or more. "At least one," "at least one of the following," or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" or "at least one of a, b, and c" can both mean: a, b, c, ab (i.e., a and b), ac, bc, or abc, where a, b, and c can be a single or multiple.

[0049] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this application can be purchased from the market or prepared by existing methods.

[0050] Firstly, this application provides a quantitative analysis method for the distribution characteristics of inclusions in steel. Please refer to [link to relevant documentation]. Figure 1 The method includes:

[0051] S1. Obtain the binary image of the inclusions and the background;

[0052] S2. Calculate the average surface density and average equivalent spacing of the inclusions in the binary image respectively;

[0053] S3. Divide the binary image into several partitions, and calculate the areal density and equivalent spacing of the inclusions in each partition respectively;

[0054] S4. Draw the density distribution curve of the inclusions in each partition based on the areal density of the inclusions in each partition, and calculate the ratio of the half-width at half-maximum of the density distribution curve to the average areal density of the inclusions in the binary image, so as to analyze the uniformity of the inclusion distribution.

[0055] S5. Based on the equivalent spacing of inclusions in each partition, draw the equivalent spacing distribution curve of the inclusions in the partition, and calculate the ratio of the half-width at half-height of the spacing distribution curve to the average equivalent spacing of the inclusions in the binary image, so as to analyze the uniformity of the inclusion distribution.

[0056] In this embodiment of the application, a binary image of inclusions-background of steel is first obtained, which can comprehensively observe the distribution of inclusions; the average areal density and average equivalent spacing of all inclusions in the binary image are calculated to obtain an overall average distribution data;

[0057] The binary image is segmented into several partitions because the distribution of the number of inclusions is uneven. Segmentation is for local observation, thereby better realizing quantitative analysis. The areal density and equivalent spacing of the inclusions in each partition are calculated to obtain local data.

[0058] Based on the areal density data of inclusions within the aforementioned local partitions, a density distribution curve of the inclusions within the partitions is plotted, and the ratio of the half-width at half-maximum (WHM) of the density distribution curve to the average areal density of the inclusions in the binary image is calculated to analyze the uniformity of the inclusion distribution. Based on the equivalent spacing data of inclusions within the aforementioned local partitions, an equivalent spacing distribution curve of the inclusions within the partitions is plotted, and the ratio of the WHM of the density distribution curve to the average areal density of the inclusions in the binary image is calculated to analyze the uniformity of the inclusion distribution. The above two ratios represent the uniformity of the inclusion distribution; the smaller the ratio, the more uniform the distribution, and vice versa.

[0059] In some embodiments, acquiring the binary image of the inclusion-background includes:

[0060] Images of the distribution morphology of the target region in a steel sample were captured using a scanning electron microscope.

[0061] The image information is read and image processing is performed to obtain a binary image of inclusions and background.

[0062] Images of the distribution morphology of steel inclusions obtained by scanning electron microscopy, such as Figure 3 , Figure 7 This allows observation of the size and distribution of inclusions. Target area processing of the image provides a more intuitive and comprehensive view of the inclusion distribution. The inclusion distribution morphology image needs to clearly show the inclusions and have a significant contrast difference with the background. Besides the inclusions, there should be no other microstructural features. The field of view should be as large as possible to show the distribution pattern of the inclusions, and the inclusion morphology should be clear. Based on these requirements, the surface of the steel sample perpendicular to the rolling direction is first polished to obtain a mirror finish. The processed sample is then placed in a scanning electron microscope, and representative micro-areas are selected, with brightness and contrast adjusted.

[0063] In some embodiments, the operating parameters of the scanning electron microscope include: operating mode: backscatter; magnification: 200x to 1000x.

[0064] The positive effects of setting the operating mode to backscatter include increased imaging clarity of nanoscale precipitates. The positive effects of setting the magnification to 200x to 1000x include: appropriate scanning electron microscope magnification facilitates clear observation of the area and quantity of inclusions. This magnification can be 200x, 400x, 600x, 800x, 1000x, etc.

[0065] In some embodiments, the step of reading image information and performing image processing on the image to obtain a binary image of the inclusion-background includes:

[0066] The image is read using a scale and its dimensions to obtain the correspondence between pixels and image dimensions;

[0067] The image is processed to obtain a binary image of the inclusions and background.

[0068] First, the computer reads the image data, reads the image size and scale from the image, and calculates the correspondence between pixels and actual size.

[0069] In some embodiments, image processing is performed on the image to obtain a binary image of the inclusions and background, including:

[0070] The image is inverted and median filtered to obtain a smoothed image;

[0071] Based on the gray value corresponding to each pixel in the smoothed image, the binary segmentation point is determined by the maximum inter-class variance method.

[0072] The smooth image is binarized based on the binary segmentation points to obtain a binary image of the inclusions and background.

[0073] In this embodiment, the image processing sequence includes color inversion, filtering, and binarization. Binarization requires calculating binary segmentation points based on the smoothed image obtained after filtering. Color inversion of the image, making the inclusions light-colored and the background dark-colored, facilitates the quantitative analysis of the inclusions.

[0074] Median filtering is a non-linear smoothing technique that sets the gray value of each pixel to the median of the gray values ​​of all pixels within a neighborhood window of that pixel. To filter and reduce noise in an image, the Otsu's method is used to calculate binary segmentation points, and then the image is segmented into binary segments.

[0075] "Grayscale value" refers to the color depth of a point in a black and white image. The "maximum inter-class variance" method is an automatic thresholding method adapted to bimodal cases. Based on the image's grayscale characteristics, it is divided into background and target parts. The larger the inter-class variance between the background and target, the greater the difference between the two parts of the image. Misclassifying part of the target as background or vice versa will reduce the difference between the two parts. Therefore, segmentation that maximizes the inter-class variance means minimizing the probability of misclassification. "Binary segmentation points" are used as the benchmark for generating binary images; grayscale values ​​above the binary segmentation point are set to 1, and those below are set to 0.

[0076] In some embodiments, the method for calculating the equivalent spacing of the inclusions includes:

[0077] The area is filled with regular hexagonal grids such that the number of grids is close to the number of inclusions in the area, and the total area of ​​the regular hexagonal grids is equal to the area of ​​the area. Then the side length of the hexagons is the equivalent spacing of the inclusions in the partition.

[0078] The concept of equivalent spacing is based on the assumption that there are several inclusions within a given area, and that the inclusions are uniformly distributed, with each inclusion having an equal distance from its nearest neighbor. The distance between the two nearest neighbors is then the equivalent spacing. The calculation method is as follows: The given area is filled with a regular hexagonal grid, the number of grids being closest to the number of inclusions in the area, and the total area of ​​the hexagonal grid being equal to the area of ​​the given area. The side length of the hexagon is the equivalent spacing of the inclusions within the given area. The equivalent spacing D of the inclusions is then calculated for the entire field of view. ave The equivalent spacing D between each partition i (i = 1, 2, 3...). The distribution of equivalent spacing of inclusions in all zones is statistically analyzed to obtain a histogram of equivalent spacing distribution. A smoothing curve is then calculated from the histogram using an interpolation function, and the half-width at half-maximum (WHM) is calculated. The ratio of the WHM to the equivalent spacing of inclusions in the entire market is denoted as h. h reflects the density non-uniformity; the larger h is, the more significant the non-uniformity.

[0079] In some embodiments, the inclusions in the region are uniformly distributed in a hexagonal close-packed manner.

[0080] When inclusions are uniformly distributed in a plane, they are arranged in a hexagonal close-packed manner, meaning each point has six equidistant nearest neighbors, distributed at a 60-degree angle. (See [reference needed]). Figure 2 .

[0081] In some implementations, the average surface density and average equivalent spacing of inclusions within the binary image are calculated, including:

[0082] Based on the number of all inclusions in the binary image, calculate the average surface density of the inclusions;

[0083] The average equivalent spacing of the inclusions is calculated based on the area and number of all inclusions in the binary image.

[0084] The inclusion surface density and average inclusion spacing in the field of view represent the overall density of inclusions within the entire observation area. For details, please refer to... Figures 3-10 Distribution map of inclusions.

[0085] In some implementations, the binary image is segmented into several partitions, and the areal density and equivalent spacing of inclusions within each partition are calculated, including:

[0086] The binary image is divided into several equal parts in rows and columns so that the size of the partitions is the same; wherein, the number of inclusions in each partition is ≥1, and the number of partitions with 1 inclusion should be as small as possible.

[0087] In this embodiment, the number of inclusion features in the binary image is counted, and the ratio of this number to the actual area of ​​the image's field of view is the average surface density Nave of the inclusions. The binary image is divided into several partitions, each with the same size, the specific size depending on the density of the inclusions: each partition contains at least one inclusion, and the number of partitions containing only one inclusion should be as small as possible. Each partition is labeled, and the number of inclusions in each partition is counted. Then, the surface density Ni (i = 1, 2, 3...) of the inclusions in that partition is calculated based on the partition size and the image scale. The distribution of surface density values ​​for all partitions is statistically analyzed to obtain a surface density distribution histogram. A smooth distribution curve is then calculated from the histogram using an interpolation function, and the half-width at half-maximum (WHM) is calculated. The ratio of the WHM to the average surface density is denoted as g, where g reflects the density non-uniformity; the larger the g value, the more significant the non-uniformity.

[0088] The present application is further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the application. Experimental methods in the following embodiments that do not specify specific conditions are generally determined according to national standards. If there is no corresponding national standard, then general international standards, conventional conditions, or conditions recommended by the manufacturer are followed.

[0089] Example 1

[0090] Hot-rolled free-cutting steel bars were sampled along the direction perpendicular to the rolling process. The sample cross-section was ground and polished to a mirror finish. The sample was placed in a scanning electron microscope (SEM) and observed in backscatter mode under an accelerating voltage of 15 kV. Under these conditions, the MnS contrast was high, while the steel matrix contrast was low. Two representative regions were selected, and images of the MnS inclusion morphology were captured at 500x magnification. In region 1, the MnS inclusions were small and clustered; in region 2, the MnS inclusions were larger and dispersed. The image files were then output.

[0091] The image processing program developed in this invention reads the image file of region 1, reads the scale from the image, and calculates the correspondence between pixels and actual size as 0.1786 μm / pixel. Then, the information bars on the image are cropped, retaining the image portion, which has 960 rows and 1278 columns. The image portion is then inverted to make the inclusions white and the background black. After median filtering for noise reduction, the Otsu's method is used to calculate binary segmentation points for binary segmentation of the image. The number of inclusions is determined by statistically analyzing the white features on the binary image, resulting in 758 inclusions. Based on the scale information, the average surface density of inclusions in region 1 can be calculated as 19345 inclusions / mm². 2 By filling the field of view with 26 rows and 29 columns, totaling 754 regular hexagonal grids, the average spacing between inclusions in region 1 can be calculated to be 8.8 μm.

[0092] The binary image is then divided into 6 equal parts in both rows and columns, resulting in 36 partitions, each with 130 rows and 128 columns. Since the original image's row and column counts are both divisible by 6, image expansion is unnecessary. Each partition is labeled, and the number of inclusions in each partition is counted. Each partition contains at least one inclusion, and exactly one partition contains only one inclusion. The areal density and equivalent spacing of inclusions within each partition are then calculated based on the partition size and scale, yielding 36 density values ​​and 36 equivalent spacing values. The distribution of these data is statistically analyzed to obtain an areal density distribution histogram and an equivalent spacing distribution histogram. An interpolation function is then used to calculate a smooth distribution curve from the histograms, and the half-width at half-height (WHM) is calculated. The calculated peak areal density ranges from 6400 to 12800 inclusions / mm. 2 Half-height and width is 25600 pieces / mm 2 The half-width at half maximum (HWHM) / average density (g) is 1.32. The peak value of the equivalent spacing of inclusions is in the range of 3.0–7.1 μm, the HWHM is 7.2 μm, and the HWHM / equivalent spacing (h) is 0.82.

[0093] The image file for region 2 was processed using the same method. The image size and magnification of region 2 are the same as those of region 1, therefore the scale is the same. After cropping, the image portion consists of 960 rows and 1280 columns. The image was then inverted, denoised, and binarized to obtain a binary image of MnS inclusions. White features were statistically analyzed on the binary image, yielding 163 inclusions. Based on the scale information, the average surface density of inclusions in region 2 can be calculated to be 3868 inclusions / mm². 2 By filling the field of view with 165 regular hexagonal grids arranged in 11 rows and 15 columns, the average spacing between inclusions in region 2 can be calculated to be 18.2 μm.

[0094] The binary image is then divided into 5 equal parts by rows and columns, resulting in 25 partitions, each with 192 rows and 256 columns. Since the original image's row and column counts are both divisible by 5, image expansion is unnecessary. Each partition is labeled, and the number of inclusions in each partition is counted. Each partition contains at least one inclusion, and exactly one partition contains only one inclusion. The areal density and equivalent spacing of inclusions within each partition are then calculated based on the partition size and scale, yielding 25 density values ​​and 25 equivalent spacing values. The distribution of these data is statistically analyzed to obtain an areal density distribution histogram and an equivalent spacing distribution histogram. An interpolation function is then used to calculate a smooth distribution curve from the histograms, and the half-width at half-maximum (FWHM) is calculated. The calculated peak areal density ranges from 3900 to 4680 inclusions / mm. 2 Half-height and width is 2900 pieces / mm 2 The half-width at half maximum (HWHM) / average density (g) is 0.75. The peak value of the equivalent spacing of inclusions is in the range of 17.4–22.1 μm, the HWHM is 6.0 μm, and the HWHM / equivalent spacing (h) is 0.33.

[0095] The g and h values ​​of region 1 are significantly greater than those of region 2, indicating that the uneven distribution of MnS inclusions is more pronounced in region 1.

[0096] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A quantitative analysis method for the distribution characteristics of inclusions in steel, characterized in that, The method includes: Obtain a binary image of the inclusions and background; Calculate the average surface density and average equivalent spacing of the inclusions in the binary image, respectively. The binary image is segmented into several partitions, and the areal density and equivalent spacing of the inclusions in each partition are calculated respectively. Based on the areal density of inclusions in each partition, a density distribution curve of the inclusions in each partition is plotted, and the ratio of the half-width at half-maximum of the density distribution curve to the average areal density of the inclusions in the binary image is calculated to analyze the uniformity of the inclusion distribution. Based on the equivalent spacing of inclusions within each partition, an equivalent spacing distribution curve of inclusions in each partition is plotted, and the ratio of the half-width at half-maximum of the spacing distribution curve to the average equivalent spacing of inclusions in the binary image is calculated to analyze the uniformity of inclusion distribution. The acquisition of the binary image of the inclusion-background includes: Images of the distribution and morphology of inclusions in the target region of steel samples were captured using a scanning electron microscope. The image information is read and image processing is performed on the image to obtain a binary image of inclusions and background. The method for calculating the equivalent spacing of the inclusions includes: The area is filled with a regular hexagonal grid, such that the number of grids corresponds to the number of inclusions within the area. If the quantities are similar and the total area of ​​the regular hexagonal grid is equal to the area of ​​the region, then the side length of the hexagon is the equivalent spacing of the inclusions within the partition.

2. The method according to claim 1, characterized in that, The operating parameters of the scanning electron microscope include: operating mode: backscatter; magnification: 200x to 1000x.

3. The method according to claim 1, characterized in that, The image information is read and image processing is performed to obtain a binary image of inclusions and background, including: The image is read using a scale and its dimensions to obtain the correspondence between pixels and image dimensions; The image is processed to obtain a binary image of the inclusions and background.

4. The method according to claim 3, characterized in that, Image processing is performed on the image to obtain a binary image of inclusions and background, including: The image is inverted and median filtered to obtain a smoothed image; Based on the gray value corresponding to each pixel in the smoothed image, the binary segmentation point is determined by the maximum inter-class variance method. The smooth image is binarized based on the binary segmentation points to obtain a binary image of the inclusions and background.

5. The method according to claim 1, characterized in that, The inclusions in the area are evenly distributed in a hexagonal close-packed pattern.

6. The method according to claim 1, characterized in that, Calculate the average surface density and average equivalent spacing of inclusions in the binary image, including: Based on the number of all inclusions in the binary image, calculate the average surface density of the inclusions; The average equivalent spacing of the inclusions is calculated based on the area and number of all inclusions in the binary image.

7. The method according to claim 1, characterized in that, The binary image is segmented into several partitions, and the areal density and equivalent spacing of inclusions within each partition are calculated, including: The binary image is divided into several equal parts in rows and columns so that the size of the partitions is the same; wherein, the number of inclusions in each partition is ≥1, and the number of partitions with 1 inclusion should be as small as possible.

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