Method and device for identifying and calculating pore characteristics of porous material and readable storage medium

Abstract

The invention relates to the field of feature measurement of porous materials, in particular to an identification and calculation method and device for pore features of a porous material and a readable storage medium, and the method comprises the steps: obtaining a gray image of the porous material; carrying out binarization operation on the grayscale image by adopting a multi-scale mixed threshold segmentation method driven by dynamic fuzzy logic to obtain a binarized image; performing refined identification and segmentation on the binarized image by using a multi-stage morphological communication refined segmentation method, and performing calculation to obtain a first pore feature parameter; performing refined identification and segmentation on the binarized image by using a Morse-Smell complex topological feature segmentation method based on morphology, and performing calculation to obtain a second pore feature parameter; calculating the percentage difference between the first and second pore characteristic parameters; if the absolute value of the percentage difference is greater than the preset value, recalculating the first and second pore characteristic parameters; otherwise, recording the average value as a final result. According to the method, the recognition precision and adaptability of the complex pore image are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of characteristic measurement of porous materials, and in particular to a method, device and readable storage medium for identifying and calculating pore characteristics of porous materials. Background Art

[0002] Porous materials have many ideal physical properties and can be widely used in the medical field, chemical engineering, civil engineering, etc. The pore characteristics are the key parameters to characterize the performance of porous materials, which can directly affect the application effect of materials in filtration, catalysis, energy storage and other fields.

[0003] Traditional pore measurement methods mainly include gas adsorption method, mercury pressure method, etc. These methods analyze pore characteristics through physical experimental means and can provide overall parameters such as porosity. However, these methods generally have shortcomings such as long test cycle, complex experimental conditions, and strong destructiveness to samples. In addition, due to the limitations of experimental conditions, the measurement results often lack an accurate description of the pore geometric characteristics, making it difficult to meet the diverse needs of porous material characterization.

[0004] In recent years, with the rapid development of computer vision and artificial intelligence technology, image recognition technology has made significant progress in pore recognition. Common recognition methods include threshold segmentation, edge detection, watershed algorithm, etc. These algorithms have the advantages of high computational efficiency, simple implementation and strong intuitiveness, and are particularly suitable for scenes with high image quality, simple background and obvious pore features. At the same time, these methods usually do not require a large amount of data support and have good adaptability to small-scale tasks and real-time processing requirements.

[0005] However, the commonly used image processing algorithms still face challenges in practical applications. On the one hand, there is a lot of noise and uneven contrast in the images of porous materials, which can easily affect the segmentation accuracy of pores and background areas. For example, the threshold segmentation method is sensitive to noise and difficult to handle complex backgrounds. The threshold selection is subjective and has low robustness. On the other hand, a single algorithm is often difficult to balance recognition accuracy and robustness when dealing with images with complex pore morphology, uneven feature distribution, and uneven illumination. For complex connected pores, the recognition and refinement effects of traditional methods (such as threshold segmentation, edge detection, watershed algorithm, etc.) are limited. In addition, the lack of a collaborative optimization mechanism between different algorithms makes them show certain instability when extracting multidimensional pore features (such as porosity, geometric morphology, connectivity, etc.). Therefore, it is of great significance to develop a comprehensive method that combines the advantages of multiple algorithms and can achieve high-precision recognition through differential analysis and parameter optimization. Summary of the invention

[0006] Features and advantages of the invention are set forth in part in the following description, or may be obvious from the description, or may be learned through practice of the invention.

[0007] In order to overcome the problems of the prior art, the present invention provides a method for calculating the pore characteristics of porous materials, comprising the steps of: Acquire a two-dimensional image of the porous material, and preprocess the two-dimensional image to obtain a grayscale image; A multi-scale hybrid threshold segmentation method driven by dynamic fuzzy logic is used to perform a binarization operation on the grayscale image to obtain a binary image; Using a multi-stage morphological connectivity refinement segmentation method, the binary image is refinedly identified and segmented, and pore characteristic parameters are calculated, which are recorded as first pore characteristic parameters; Using a morphologically based Morse-Smale complex topological feature segmentation method, the binary image is refinedly identified and segmented, and pore characteristic parameters are calculated, which are recorded as second pore characteristic parameters; Calculating a percentage difference between the first pore characteristic parameter and the second pore characteristic parameter; If the absolute value of the percentage difference is greater than a preset value, the algorithm characteristic parameters in the multi-stage morphological connectivity refinement segmentation method and the morphology-based Morse-Smale complex topological feature segmentation method are optimized, and the first pore characteristic parameter and the second pore characteristic parameter are recalculated; otherwise, the average value of the first pore characteristic parameter and the second pore characteristic parameter is recorded as the final result.

[0008] In one embodiment of the present invention, the multi-scale hybrid threshold segmentation method driven by dynamic fuzzy logic is used to perform a binarization operation on the grayscale image to obtain a binarized image, which includes: S21, performing global grayscale analysis on the grayscale image, and determining an initial threshold using a mean shift clustering method; S22, dividing the image into multiple local areas, and capturing the characteristics of each local area in real time, wherein the local area characteristics include contrast and texture characteristics; S23, dynamically adjusting the local threshold according to the local area characteristics combined with fuzzy logic rules; S24, fusing the initial threshold and the local threshold using a region growing weight model to generate a fused final threshold; S25, iteratively optimizing the threshold value through the expectation maximization algorithm to obtain the final optimized threshold value; S26. Binarize the grayscale image according to the final optimized threshold.

[0009] In one embodiment of the present invention, the method of using a multi-stage morphological connectivity refinement segmentation method to perform fine identification and segmentation on the binary image and calculate pore characteristic parameters includes: S31, preprocessing the binary image for pore extraction; S32, marking the connected areas formed by all foreground pixels according to the processed binary image; S33, performing morphological reconstruction based on the marked connected regions; S34. Refine and distinguish the pores and the background of the image after morphological reconstruction.

[0010] In one embodiment of the present invention, step S34 includes: performing distance transformation on the morphologically reconstructed image, and performing watershed segmentation through extreme points on the distance transformation image, refining the boundary between the connected pores and the background, and accurately segmenting the connected area; wherein the calculation expression of the distance transformation is as follows:

[0011] In the expression Represents pixels The distance transformation value of Represents the Euclidean distance between two pixels.

[0012] In one embodiment of the present invention, the method of using the morphological-based Morse-Smale complex topological feature segmentation method to perform fine identification and segmentation on the binary image and calculate the pore feature parameters specifically includes: S41, generating a gradient field by gradient calculation of the grayscale image for scalar field construction; S42. Construct a scalar field based on the preprocessed gradient field , define key points; S43, constructing a Morse-Smale complex segmentation unit according to the key points through streamline tracing to obtain a pore area.

[0013] In one implementation of the present invention, the key points in step S42 include maximum points, minimum points and saddle points; the classification of the key points is determined by the eigenvalues ​​of the Hessian matrix 𝐻(𝑓):

[0014] when When taking the extreme point, Take the saddle point.

[0015] In one embodiment of the present invention, the Morse-Smale complex segmentation unit in step S43 is formed by the intersection of an upper-manifold and a lower-manifold; wherein the upper-manifold is a set of pixels whose gradient rises to a maximum point; and the lower-manifold is a set of pixels whose gradient descends to a minimum point.

[0016] In one embodiment of the present invention, the preset value is no greater than 1.0%.

[0017] The present invention also provides a porous material pore feature identification and calculation device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the porous material pore feature identification and calculation method described in any one of the present invention are implemented.

[0018] The present invention provides a computer-readable storage medium, on which is stored a porous material pore feature recognition calculation program, which, when executed by a processor, implements the steps of any porous material pore feature recognition calculation method described in the present invention.

[0019] The present invention provides a calculation method, device and computer-readable storage medium for pore feature recognition of porous materials. Through the innovative combination of dynamic fuzzy logic, multi-stage morphological refinement segmentation method and improved Morse-Smale complex segmentation algorithm, a high-precision and high-robustness pore feature recognition method is constructed.

[0020] The present invention overcomes the limitations of traditional physical testing and single image recognition algorithms. It can not only quickly and automatically extract the geometric and topological characteristics of porous materials, but also significantly improve the recognition accuracy and adaptability of complex pore images, providing strong technical support for the performance research and application design of porous materials.

[0021] By reading the specification, ordinary technicians in this field will have a better understanding of the characteristics and contents of these technical solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention will be described in detail below with reference to the accompanying drawings and in combination with examples, and the advantages and implementation methods of the present invention will become more obvious. The contents shown in the accompanying drawings are only used to explain the present invention and do not constitute any limitation to the present invention in any sense. In the accompanying drawings: Figure 1 A schematic diagram of a flow chart of a porous material pore feature recognition calculation method according to an embodiment of the present invention; Figure 2 is the grayscale histogram of the grayscale image in this embodiment; Figure 3 It is a flowchart of a multi-scale hybrid threshold segmentation method driven by dynamic fuzzy logic; Figure 4 It is a flowchart of the multi-stage morphological connectivity thinning segmentation method; Figure 5 It is a schematic diagram of the process of the topological feature segmentation method of the Morse-Smale complex based on morphology; Figure 6 3 is a comparison diagram before and after image processing in this embodiment. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0024] Embodiment 1 like Figure 1 As shown, this embodiment provides a method for identifying and calculating pore characteristics of porous materials, comprising the steps of: S10, acquiring a two-dimensional image of the porous material, and preprocessing the two-dimensional image to obtain a grayscale image; Generally, MATLAB (MATrix LABoratary, Matrix Laboratory) can be used to preprocess the two-dimensional images; S20, performing a binarization operation on the grayscale image using a multi-scale hybrid threshold segmentation method driven by dynamic fuzzy logic to obtain a binary image; Through the binarization operation, the grayscale image can be converted into a black and white image, and the pixels can be classified into "foreground" (usually pore area) and "background" (usually solid area) through a threshold. In the present invention, the pore area includes independent pore area and connected area.

[0025] S30, using a multi-stage morphological connectivity refinement segmentation method to perform fine recognition and segmentation on the binary image and calculate pore characteristic parameters, which are recorded as first pore characteristic parameters; S40, using a morphologically based Morse-Smale complex topological feature segmentation method to finely identify and segment the binary image and calculate pore characteristic parameters, which are recorded as second pore characteristic parameters; It should be noted that there is no order relationship between steps S30 and S40, and both can be performed simultaneously. The processing range of steps S30 and S40 is the entire binary image, but the ultimate goal of these two methods is to identify and segment the pore area. Among them, the multi-stage morphological connectivity refinement segmentation method of step S30 focuses on geometric feature extraction, and the Morse-Smale complex segmentation algorithm of step S40 focuses on topological structure analysis.

[0026] S50, calculating the percentage difference between the first pore characteristic parameter and the second pore characteristic parameter; judging whether the absolute value of the percentage difference is greater than a preset value, if so, optimizing the algorithm characteristic parameters in steps S30 and S40, and returning to step S30; if not, proceeding to step S60; In specific implementation, the algorithm characteristic parameters can be reversely optimized according to the calculation results of the two segmentation algorithms, and the difference can be continuously iterated to reduce. By automatically adjusting the parameters of the multi-stage morphological connectivity refinement segmentation method and the morphologically improved Morse-Smale complex topological feature segmentation algorithm, S30 and S40 are re-performed until the absolute value of the percentage difference is less than the preset value. Generally, the preset value is not greater than 1.0%, for example, 1.0%.

[0027] The optimization of the above algorithm feature parameters can be achieved by adjusting the size of the structural element in the morphological operation, the gradient threshold in the topological analysis, and other common operations in the field of computer vision and image processing. Among them, the algorithm feature parameters that can be changed in step S30 include at least one of the shape and size of the structural element (such as a circle, a rectangle and its size), the determination rule of the connected area (such as an 8-neighborhood or a 4-neighborhood), and the number of iterations of the opening operation and the closing operation. The algorithm feature parameters that can be changed in step S40 include at least one of the gradient threshold (affecting the accuracy of streamline tracing), the construction rule of the scalar field (such as the degree of smoothness, the threshold of the key point definition), and the segmentation scale of the upper-manifold and the lower-manifold.

[0028] The present invention combines two segmentation algorithms (morphological connectivity refinement segmentation method and Morse-Smale complex topological segmentation method) and iteratively optimizes feature parameters through differential analysis. This optimization is better than a single optimization result and has significant technical effects (improving the accuracy of complex pore identification).

[0029] S60. Record an average value of the first pore characteristic parameter and the second pore characteristic parameter as a final result.

[0030] In this embodiment, a pore feature recognition system with accuracy, efficiency and robustness is constructed through the global robustness of dynamic fuzzy logic, the geometric refinement segmentation capability of multi-stage morphology and the complex structure analysis capability of the Morse-Smale topological algorithm. The collaborative work overcomes the shortcomings of a single algorithm and shows significant advantages in adapting to complex image characteristics, accurately identifying complex pore structures and comprehensively extracting multi-dimensional features.

[0031] Embodiment 2 On the basis of the above-mentioned first embodiment, step S10 in the method for identifying and calculating pore characteristics of porous materials provided in this embodiment specifically includes the following steps: S11, collecting a two-dimensional image of the porous material by a digital image processing device, and recording it as an image to be processed; The two-dimensional image is collected by digital image processing equipment such as SEM, CT or industrial high-precision camera, preferably with dpi ≥ 300 and image size ≥ 1000 × 1000 pixels. The image format supports common formats such as bmp, png, jpg, tiff, etc., and tiff format is preferred.

[0032] S12, convert the two-dimensional image into a grayscale image with a grayscale value range of [0,255], and display it as follows Figure 2 Grayscale histogram of the grayscale image shown.

[0033] In specific implementation, the original image can also be cut by the coordinate value of the BoundingBox before step S12 to obtain the interested region of the image to be processed. At this time, it is only necessary to convert the interested region of the image to be processed into a grayscale image.

[0034] S13. Select different image enhancement methods according to the usage scenario to enhance the image and obtain the final grayscale image.

[0035] Among them, the image enhancement methods can be adaptive histogram equalization (suitable for pore images with poor contrast), Gaussian filtering (suitable for pore images with more noise), bilateral filtering (suitable for images that need to remove noise but retain pore boundaries), and unsupervised image segmentation (suitable for images with certain differences between pores and background).

[0036] Embodiment 3 Based on the above embodiment 1, Figure 3 As shown, step S20 in the method for identifying and calculating pore characteristics of porous materials provided in this embodiment specifically includes the following steps: S21, dynamic threshold initialization: perform global grayscale analysis on the input grayscale image and use the mean shift clustering method to determine the initial threshold , enhancing the robustness to images with uneven brightness. According to the image grayscale histogram characteristics, the mean shift clustering calculation expression is as follows:

[0037] In the expression is the gray value of the image pixel; is the cluster center; is the mean shift bandwidth parameter, which is used to control the clustering window size.

[0038] S22, real-time characteristic monitoring: Divide the grayscale image into multiple local areas, and capture the contrast and texture characteristics of each local area in real time as the input basis for subsequent dynamic threshold adjustment. The calculation expressions of local contrast and local texture complexity are as follows:

[0039]

[0040] In the expression is the local contrast; and are the maximum and minimum grayscale values ​​of the local area respectively; is the local texture complexity; and For images in x and y The second derivative of direction; is the number of pixels in the local area.

[0041] S23, local threshold adjustment: According to the local area characteristics of real-time monitoring, the local threshold is adjusted by fuzzy logic rules. Dynamic adjustment is performed to avoid the limitations of fixed rules and ensure that the characteristics of different image regions are adapted. The local threshold is adjusted based on the fuzzy logic method through the following formula:

[0042] In the expression, 𝑘 is the adjustment coefficient of the fuzzy function.

[0043] S24, global and local threshold fusion: Combine the global initial threshold and the local dynamic threshold, use the regional growing weight model to fuse them, and generate the final threshold after fusion , the calculation expression is as follows:

[0044] In the expression is the pixel gradient amplitude, is the saliency mask (the salient area is 1, the rest is 0).

[0045] S25, iterative optimization: Iteratively optimize the threshold through the expectation maximization algorithm to further improve the segmentation accuracy. The expectation and maximization calculation expressions are as follows:

[0046]

[0047] and Represent the probabilities of foreground and background respectively; is a Gaussian distribution; and are the mean values ​​of foreground and background respectively. Or the iteration is terminated when the maximum number of iterations is reached. The maximum number of iterations is recommended to be 10 to 15 times, preferably 12 times. It is recommended to take 0.5.

[0048] S26, final binarization: according to the final optimized threshold The image is binarized, and the binarization calculation expression is as follows:

[0049] In the expression is a grayscale image; is a binary image; Represents the grayscale threshold.

[0050] This embodiment adopts a multi-scale hybrid threshold segmentation method driven by dynamic fuzzy logic, which can dynamically adjust the threshold according to the contrast and texture characteristics of the local area of ​​the image, and achieve accurate binarization through global and local fusion. Compared with the traditional fixed threshold segmentation method, this method has higher robustness and segmentation accuracy for images with uneven lighting, complex grayscale distribution and background noise, and effectively avoids the problem of false detection or missed detection.

[0051] Embodiment 4 Based on any of the above embodiments, Figure 4 As shown, step S30 in the method for identifying and calculating pore characteristics of porous materials provided in this embodiment specifically includes the following steps: S31, preprocessing the binary image for pore extraction; On the basis of preliminary binarization, the opening operation is first used to eliminate small noise, and then the closing operation is used to fill the small holes inside the pores. The calculation expressions of the opening operation and the closing operation are as follows:

[0052]

[0053] In the expression A is the input image; 𝐵 is the structural element, using a circle with a radius of 1 / 3 of the average pore diameter; Indicates corrosion operation; Represents an expansion operation.

[0054] S32, marking the connected areas formed by all foreground pixels according to the processed binary image; Perform connected region analysis on the processed binary image, select 8-neighborhood connectivity to detect all connected parts, mark each connected region, and determine whether it belongs to a pore. The calculation expression is as follows:

[0055] In the expression is the position in the image The label value at is the label of a connected region.

[0056] The connected regions in the present invention are preliminary markings of all foreground pixels (i.e., pore regions) in the processed binary image. These regions include at least one of the independent regions of pores (single pores) and the connected regions of pores (regions formed by multiple pores connected to each other). Among them, the connected regions of pores are a special case of the connected regions, which refer to regions composed of multiple interconnected pores. By definition, the pore connected regions are part of the connected regions, but the connected regions may also contain other noise or non-pore features (such as possible misjudgment regions).

[0057] S33, performing morphological reconstruction based on the marked connected regions to obtain a complete pore region; The specific calculation expression is as follows:

[0058] in, is the image to be reconstructed (i.e. the preliminarily marked connected regions); It is a structural element, using a circle with a radius of 1 / 3 of the average pore diameter; It is the result of morphological reconstruction.

[0059] S34, performing fine distinction between pores and background on the image after morphological reconstruction; In this embodiment, a watershed algorithm is used to refine the distinction between pores and background. More specifically, a distance transform is first performed on the image after morphological reconstruction, and then a watershed segmentation is performed through the extreme points on the distance transform image to refine the boundary between the connected pores and the background and accurately segment the connected area. The calculation expression of distance transformation is as follows:

[0060] In the expression Represents pixels The distance transformation value of Represents the Euclidean distance between two pixels.

[0061] It can be seen that the connected areas marked in step S32 include independent pores and connected areas of pores, which will be further refined and distinguished in subsequent steps (such as morphological reconstruction or watershed segmentation) to accurately extract the connected areas of pores.

[0062] S35: Calculate the pore geometric characteristic parameters and record them as the first pore geometric characteristic parameters.

[0063] The calculation of pore geometric characteristic parameters can be performed based on the refined binary image.

[0064] In this embodiment, step S30 focuses on geometric feature extraction, and uses a multi-stage morphological method to perform fine identification and segmentation of all pores in the entire binary image, including refinement of pore boundaries, connectivity detection, etc.

[0065] Embodiment 5 Based on any of the above embodiments, Figure 5 As shown, step S40 in the method for identifying and calculating pore characteristics of porous materials provided in this embodiment specifically includes the following steps: S41: Generate a gradient field by calculating the gradient of the grayscale image for use in scalar field construction; The formula for constructing a scalar field is as follows:

[0066] and For images in x and y The gradient component in the direction.

[0067] Generally, before generating the gradient field, the pore extraction can be preprocessed, that is, on the basis of preliminary binarization, the opening operation is first used to eliminate small noise, and then the closing operation is used to fill the small holes inside the pores. The calculation expressions of the opening operation and the closing operation are as follows:

[0068]

[0069] In the expression A is the input image; 𝐵 is the structural element, using a circle with a radius of 1 / 3 of the average pore diameter; Indicates corrosion operation; Represents an expansion operation.

[0070] S42. Construct a scalar field based on the preprocessed gradient field , define key points; In this embodiment, the key points include maximum points, minimum points and saddle points; among them, the maximum point (maximum value) is the position where the gradient is zero and the maximum value in the local area; the minimum point (minimum value) is the position where the gradient is zero and the minimum value in the local area; the saddle point is the position where the gradient is zero but is neither a maximum value nor a minimum value.

[0071] The classification of key points is determined by the eigenvalues ​​of the Hessian matrix 𝐻(𝑓):

[0072] when When taking the extreme point, Take the saddle point.

[0073] S43: constructing a Morse-Smale complex segmentation unit according to the key points through streamline tracing to obtain a pore area.

[0074] It should be noted that the segmentation unit in this step is based on the topological features of the grayscale image and has no direct correlation with the connected regions in the binary image.

[0075] When performing streamline tracing, we can start from each pixel in the image and trace it along the gradient descent direction to the nearest minimum point to form an attractive streamline; trace it along the gradient ascending direction to the nearest maximum point to form a repulsive streamline: (Attractive flow line) (Repel streamlines) In the expression is the current pixel coordinate, is the streamline step length.

[0076] The Morse-Smale complex segmentation unit constructs an upper-manifold and a lower-manifold through attracting streamlines and repelling streamlines, and generates them through the intersection of the two. Specifically, the upper-manifold is a set of pixels where the gradient rises to the maximum point; the lower-manifold is a set of pixels where the gradient falls to the minimum point. The intersection of the upper-manifold and the lower-manifold can form a Morse-Smale complex segmentation unit. The construction of the Morse-Smale complex segmentation unit can refine the segmentation of the pore area and ensure accurate topological structure analysis.

[0077]

[0078] in and They are upper-manifold and lower-manifold respectively.

[0079] S44: extracting pore geometric features through the pore region and recording them as second pore feature parameters.

[0080] The Morse-Smale complex segmentation algorithm in this embodiment focuses on topological structure analysis (such as gradient field, scalar field, key points and streamline tracking), which is a global topological analysis method that can further identify complex pore topological structures (such as connectivity and overall distribution), while the multi-stage morphological connectivity refinement segmentation rule focuses on geometric feature extraction. The combination of geometric and topological features can take into account the connectivity, boundary fineness and morphological complexity of pores at the same time, thereby significantly improving the recognition ability of complex pores.

[0081] Embodiment 6 In this embodiment, the porous material pore feature recognition calculation method provided in any of the above embodiments is used to calculate the surface porosity of a specimen. The results are shown in Table 1 below. The images before and after processing are as follows: Figure 6 shown.

[0082] Table 1 Calculation results of concrete surface pore image feature recognition

[0083] The porosity of the same surface of the specimen was measured using the traditional physical experimental gas adsorption method and the conclusion was 8.19%.

[0084] The above-mentioned traditional physical experiment gas adsorption method is specifically implemented by the following steps: S91. Seal the remaining five surfaces of the test piece of the embodiment, expose only the surface to be tested, and measure the total area of ​​the exposed surface ; S92, drying the specimen at 100-105°C to remove the residual moisture in the pores to prevent the influence on gas adsorption; S93. Use nitrogen as the adsorption gas and place the dry specimen into the sample chamber of the gas adsorption instrument to ensure that the pores on the exposed surface are in direct contact with the gas: S94, adjust the gas pressure, record at different relative pressures ( , i.e. the ratio of actual pressure to saturated vapor pressure) of gas adsorption; S95, measure the adsorption equilibrium and record the isothermal adsorption data (gas adsorption amount and relationship); S96, use BET equation to fit the isothermal adsorption curve and calculate the surface specific area :

[0085] In the expression is the monolayer adsorption amount; is Avogadro's constant ( ); is the effective area occupied by the adsorbed gas molecules, nitrogen is ; The mass of the sample.

[0086] S97. Assuming that the adsorbed gas acts only on the pore area, then , based on the total exposed area and pore area The porosity can be obtained for:

[0087] By comparing the test results, the porosity measured by the method of the present invention is 8.37%, compared with 8.19% measured by the traditional gas adsorption method, the error is only 2.2%, indicating that the two have high consistency in measurement accuracy. This slight error mainly comes from the differences between the two methods in pore structure definition and test conditions. Based on image recognition and calculation algorithms, the present invention uses digital means to directly characterize pore characteristics, avoiding the uncertainty of traditional physical methods caused by experimental environment and gas molecular behavior, and showing high reliability and stability. Therefore, this method is suitable for pore measurement of porous materials and has more flexible application potential.

[0088] Compared with traditional physical testing methods, the present invention can efficiently extract all-round data including pore area, perimeter, length, width, curvature and other geometric features as well as porosity through only one image. This not only significantly improves the dimension of pore feature analysis, but also avoids the limitation that traditional methods can only obtain limited data in one experiment. In addition, traditional methods often require complex equipment operation and long testing cycles, while the present invention greatly simplifies the experimental process through automated processing and digital analysis, greatly improves test efficiency and data utilization, and is particularly suitable for rapid analysis of large batches of samples.

[0089] Embodiment 7 Based on the above embodiments, the present invention also proposes a porous material pore feature identification and calculation method device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the porous material pore feature identification and calculation method as described in any one of the above items are implemented.

[0090] It should be noted that the above-mentioned device embodiment and method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, and the technical features in the method embodiment are correspondingly applicable in the device embodiment, which will not be repeated here.

[0091] Embodiment 8 Based on the above embodiments, the present invention also proposes a computer-readable storage medium, which stores a program for a calculation method for identifying pore characteristics of porous materials. When the program for a calculation method for identifying pore characteristics of porous materials is executed by a processor, the steps of the method for identifying pore characteristics of porous materials as described in any of the above items are implemented.

[0092] It should be noted that the above-mentioned medium embodiment and method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, and the technical features in the method embodiment are correspondingly applicable in the medium embodiment, which will not be repeated here.

[0093] The porous material pore feature recognition calculation method, device and computer-readable storage medium provided by the present invention realize multi-scale adaptive binarization of images in complex scenes by means of a multi-scale hybrid threshold segmentation method driven by dynamic fuzzy logic, thus overcoming the shortcomings of the threshold segmentation method; the pore feature parameters calculated by the multi-stage morphological connectivity refinement segmentation method and the improved Morse-Smale complex segmentation algorithm based on morphology are used to reversely optimize the algorithm parameters through differential analysis, and the optimization process adapts the algorithm to the diverse pore structures and image characteristics through multiple rounds of iterations, thereby improving the adaptability and robustness to complex, multi-scale and diverse pores. The method overcomes the problems that traditional algorithms (such as simple threshold segmentation and single morphological methods) often fail to take into account both geometric and topological features when dealing with complex or morphologically variable pores, and are prone to omissions, false detections, blurred boundaries, poor adaptability and insufficient recognition of complex structures, thus providing strong technical support for the efficient characterization of the pore features of porous materials.

[0094] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0095] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0096] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0097] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for calculating pore characteristics of porous materials, characterized in that: Includes steps: Acquire a two-dimensional image of the porous material, and preprocess the two-dimensional image to obtain a grayscale image; A multi-scale hybrid threshold segmentation method driven by dynamic fuzzy logic is used to perform a binarization operation on the grayscale image to obtain a binary image; Using a multi-stage morphological connectivity refinement segmentation method, the binary image is refinedly identified and segmented, and pore characteristic parameters are calculated, which are recorded as first pore characteristic parameters; Using a morphologically based Morse-Smale complex topological feature segmentation method, the binary image is refinedly identified and segmented, and pore characteristic parameters are calculated, which are recorded as second pore characteristic parameters; Calculating a percentage difference between the first pore characteristic parameter and the second pore characteristic parameter; If the absolute value of the percentage difference is greater than a preset value, the algorithm characteristic parameters in the multi-stage morphological connectivity refinement segmentation method and the morphology-based Morse-Smale complex topological feature segmentation method are optimized, and the first pore characteristic parameter and the second pore characteristic parameter are recalculated; otherwise, the average value of the first pore characteristic parameter and the second pore characteristic parameter is recorded as the final result.

2. The porous material pore feature recognition and calculation method according to claim 1, characterized in that: The multi-scale hybrid threshold segmentation method driven by dynamic fuzzy logic is used to perform a binarization operation on the grayscale image to obtain a binarized image, which includes: S21, performing global grayscale analysis on the grayscale image, and determining an initial threshold using a mean shift clustering method; S22, dividing the image into multiple local areas, and capturing the characteristics of each local area in real time, wherein the local area characteristics include contrast and texture characteristics; S23, dynamically adjusting the local threshold according to the local area characteristics combined with fuzzy logic rules; S24, fusing the initial threshold and the local threshold using a region growing weight model to generate a fused final threshold; S25, iteratively optimizing the threshold value through the expectation maximization algorithm to obtain the final optimized threshold value; S26. Binarize the grayscale image according to the final optimized threshold.

3. The porous material pore feature recognition and calculation method according to claim 1, characterized in that: The method of using the multi-stage morphological connectivity refinement segmentation method to perform fine identification and segmentation on the binary image and calculate pore characteristic parameters includes: S31, preprocessing the binary image for pore extraction; S32, marking the connected areas formed by all foreground pixels according to the processed binary image; S33, performing morphological reconstruction based on the marked connected regions; S34. Refine and distinguish the pores and the background of the image after morphological reconstruction.

4. The porous material pore feature recognition and calculation method according to claim 3, characterized in that: The step S34 includes: performing distance transformation on the morphologically reconstructed image, and performing watershed segmentation through the extreme points on the distance transformation image, so as to refine the boundary between the connected pores and the background and accurately segment the connected area; wherein the calculation expression of the distance transformation is as follows: In the expression Represents pixels The distance transformation value of Represents the Euclidean distance between two pixels.

5. The porous material pore feature recognition and calculation method according to claim 1, characterized in that: The method of using the morphological-based Morse-Smale complex topological feature segmentation method to perform fine identification and segmentation on the binary image and calculate pore feature parameters specifically includes: S41, generating a gradient field by gradient calculation of the grayscale image for scalar field construction; S42. Construct a scalar field based on the preprocessed gradient field , define key points; S43, constructing a Morse-Smale complex segmentation unit according to the key points through streamline tracing to obtain a pore area.

6. The porous material pore feature recognition and calculation method according to claim 5, characterized in that: The key points in step S42 include maximum points, minimum points and saddle points; the classification of the key points is determined by the eigenvalues ​​of the Hessian matrix 𝐻(𝑓): when When taking the extreme point, Take the saddle point.

7. The porous material pore feature recognition and calculation method according to claim 6, characterized in that: The Morse-Smale complex segmentation unit in step S43 is formed by the intersection of an upper-manifold and a lower-manifold; wherein the upper-manifold is a set of pixels whose gradient rises to a maximum point; and the lower-manifold is a set of pixels whose gradient falls to a minimum point.

8. The porous material pore feature recognition and calculation method according to claim 1, characterized in that: The preset value is no greater than 1.0%.

9. A porous material pore feature recognition computing device, characterized in that: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the porous material pore feature identification calculation method as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a porous material pore feature recognition calculation program, which, when executed by a processor, implements the steps of the porous material pore feature recognition calculation method according to any one of claims 1 to 8.

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