Method and system for measuring porosity of fiber filtration medium structure

Abstract

The application provides a kind of method and system for measuring and calculating the porosity of fiber filter medium structure, relating to medium porosity measurement and calculation technical field, specific steps include: continuous tomography of fiber filter medium and binary processing, forming three-dimensional binary volume;Obtain the minimum size of the medium, calculate the sampling adaptation coefficient by combining the single-layer scanning thickness to determine the sampling density, identify the initial outer contour and select the initial contour point;Set the bending weight coefficient by offset distance, differentially adjust the initial contour point and iteratively optimize, surface reconstruction obtains the actual outer contour and actual volume of the medium;Screen effective flow-through pores and accumulate to obtain the total volume of pore portion;Calculate the ratio of the two to obtain the structural porosity.The application realizes the accurate matching of medium size, sampling density and initial contour point, accurately captures the real outer contour of the medium, improves the accuracy of medium volume and porosity calculation, and solves the defects of extensive fitting and insufficient accuracy in the prior art.

Description

Technical Field

[0001] This invention relates to the field of media porosity measurement technology, specifically to a method and system for measuring the porosity of fiber filter media structures. Background Technology

[0002] Fiber filter media are widely used in chemical, environmental protection, and pharmaceutical fields. Their pore structure (including pore size, distribution, and connectivity) directly determines core performance characteristics such as filtration efficiency and flow performance. Therefore, accurately measuring the porosity of fiber filter media is of great significance for optimizing filtration effects and improving product performance.

[0003] In the prior art, CN109406364A discloses a method for calculating the porosity of a fiber filter medium, comprising the following steps: Step 10) Scanning the fiber filter medium under a scanning electron microscope to obtain a single-layer scanning electron microscope image of the fiber filter medium; performing binarization processing on the scanning electron microscope image to extract the binary image of the fiber filter medium and pores; Step 20) Creating a simulated reconstruction image of the fiber filter medium and converting the simulated reconstruction image of the fiber filter medium into a script file; Step 30) Generating a simulated three-dimensional physical model of the fiber filter medium; Step 40) Calculating the porosity of the simulated three-dimensional physical model of the fiber filter medium, wherein the porosity is the porosity of the fiber filter medium under test. This calculation method can quickly, accurately, efficiently, and at low cost calculate the porosity of the fiber filter medium.

[0004] The existing technology still has the following core shortcomings: it does not achieve accurate matching between the size of the fiber filter medium, the sampling density and the initial contour points. At the same time, although the existing technology uses edge detection and other means to identify the initial outer contour and select the initial contour points, it does not perform targeted optimization and adjustment of the initial contour points, nor does it fully consider the actual characteristics of the fiber filter medium contour, such as bending and irregularity. It only uses a simplified fitting method to replace the accurate contour optimization process, without correcting and improving the initial outer contour, resulting in a large deviation in the actual shape fitting of the fiber filter medium and insufficient accuracy in volume measurement.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for calculating the porosity of fiber filter media structure, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for calculating the porosity of a fiber filter medium, comprising the following steps: S1. Based on the preset single-layer scanning thickness, the fiber filter medium is continuously tomographically scanned along the thickness direction to obtain multi-layer tomographic images. After binarizing each tomographic image, they are stacked and reconstructed in the order of tomographic scanning to form a three-dimensional binary body that marks the spatial distribution of fibers and pores. S2. Obtain the minimum size of the fiber filter medium based on the three-dimensional binary volume, calculate the sampling adaptation coefficient in combination with the single-layer scanning thickness to determine the sampling density, use the edge detection algorithm to identify the initial outer contour of the fiber filter medium, and select a number of initial contour points on the initial outer contour according to the sampling density. S3. Select three consecutive initial contour points in sequence, connect the first and last initial contour points to construct a spatial straight line, calculate the offset distance from the middle initial contour point to the spatial straight line, set the bending weight coefficient according to the offset distance, and adjust the initial contour points differently. Iterate until the preset conditions are met, and then traverse to obtain the optimized contour points. Use the optimized contour points to reconstruct the surface and fit to obtain the actual outer contour and actual volume of the fiber filter medium. S4. Based on the actual outer contour and three-dimensional binary body, fluid porosity test is performed on the fiber filter medium. The fluid velocity and pressure change within a preset time period are recorded. Based on the pore space distribution, fluid velocity and pressure change, effective flow pores are screened and their volumes are accumulated to obtain the total volume of the pore part. S5. Calculate the ratio of the total volume of the pore portion to the actual volume, which is used as the structural porosity of the fiber filter medium.

[0008] Furthermore, the tomographic image is binarized to transform it into a binary image of fibers and pores. The specific logic is as follows: Each tomographic image was processed by grayscale conversion and threshold segmentation to mark the fibrous region and the pore region; The marked fault images are binarized by assigning a value of 1 to the fiber region and a value of 0 to the pore region to obtain the corresponding binarized image of each fault image. Repeat the above steps to iterate through all tomographic images and obtain the binarized images corresponding to all tomographic images.

[0009] Furthermore, the minimum size of the fiber filter media is obtained based on the three-dimensional binary volume, and the specific logic is as follows: In the three-dimensional binary volume, all voxels belonging to the fiber region are traversed, and the maximum and minimum coordinate values ​​of the fiber region in the X, Y, and Z coordinate axes are determined respectively. The difference between the maximum and minimum coordinate values ​​in each coordinate axis direction is used to obtain the external dimensions of the fiber filter media in the three coordinate axis directions; The external dimensions in the three coordinate axes are compared, and the external dimension with the smallest value is selected as the minimum dimension of the fiber filter medium.

[0010] Furthermore, the sampling adaptation coefficient is calculated by combining the minimum size and single-layer scan thickness to determine the sampling density. The specific logic is as follows: The minimum size of the fiber filter medium is calculated as a ratio to the single-layer scanning thickness, and the quotient is used as the sampling adaptation coefficient. A preset sampling density baseline value is used, and the sampling adaptation coefficient is multiplied by the sampling density baseline value to obtain the sampling density.

[0011] Furthermore, several initial contour points are selected on the initial outer contour according to the sampling density. The specific logic is as follows: Based on a determined sampling density, the distance between two adjacent initial contour points is calculated, where the distance is the reciprocal of the sampling density; Randomly select an outer contour point as the initial contour point. Starting from this initial contour point, select subsequent initial contour points in a clockwise or counterclockwise direction, according to the above-mentioned spacing, until the distance between the last selected initial contour point and the starting endpoint along the initial outer contour is less than or equal to the above-mentioned spacing, and finally obtain several initial contour points.

[0012] Furthermore, the offset distance from the initial intermediate contour point to the straight line in space is obtained, and the specific logic is as follows: Based on the three consecutive initial contour points selected, determine the spatial straight line constructed by the first and last two initial contour points; Extract the three-dimensional coordinates of the intermediate initial contour point, and the three-dimensional coordinates of the projection point of the intermediate initial contour point on the above-mentioned spatial straight line; The distance between two points in space is calculated using the formula for the distance between two points in space. The straight-line distance between the intermediate initial contour point and the corresponding projection point is the offset distance from the intermediate initial contour point to the straight line in space.

[0013] Furthermore, the bending weight coefficient is set based on the offset distance, and the specific logic is as follows: Two offset distance thresholds are preset, namely the first offset distance threshold and the second offset distance threshold, wherein the first offset distance threshold is less than the second offset distance threshold; Compare the offset distance with two offset distance thresholds: If the offset distance is less than the first offset distance threshold, the corresponding contour segment is considered to have a slight bend, and the bend weight coefficient is set to [value missing]. ; If the offset distance is greater than or equal to the first offset distance threshold and less than or equal to the second offset distance threshold, the corresponding contour segment is considered to have moderate bending, and the bending weight coefficient is set to [value missing]. ; If the offset distance is greater than the second offset distance threshold, the corresponding contour segment is considered heavily bent, and the bending weight coefficient is set to [value missing]. ; in, ; The initial contour points are adjusted based on the differences in the bending weight coefficients, and iterated until the preset conditions are met, as follows: If the bending weight coefficient of a contour segment is In this case, the original sampling density remains unchanged, and no new contour points are added; If the bending weight coefficient of a contour segment is If the initial contour point is in the middle between the first initial contour point and the middle initial contour point, and in the middle between the middle initial contour point and the last initial contour point, then add one new initial contour point each. If the bending weight coefficient of a contour segment is If the initial contour point in the middle of the contour segment is removed, the contour curve is refitted based on the initial contour points at the beginning and end, and the initial contour point in the middle is reset. Iterate through all three consecutive initial contour points in groups and perform differential adjustments to the initial contour points segment by segment. The initial outer contour is reconstructed based on the adjusted initial contour points, and the initial contour points are reselected along the initial outer contour according to the sampling density. The process of offset distance calculation, bending weight assignment and initial contour point differentiation adjustment is repeated until the offset distance from all intermediate initial contour points to the corresponding spatial lines is less than the first offset distance threshold.

[0014] Furthermore, based on the pore space distribution, fluid velocity, and pressure changes, effective flow pores are selected, with the following specific logic: Based on the spatial distribution of pores in a three-dimensional binary volume, the spatial phase of all pores is located, and the spatial coordinates, size and distribution range of each pore are extracted to clarify the spatial positional relationship of each pore. The extracted pores are classified according to the fluid flow rate and pressure change, as follows: Wherein, the fluid velocity is the volumetric flow rate of the fluid passing through the pores within a preset time period; the pressure change is the pressure difference between the inlet and outlet of the fiber filter medium when the fluid passes through the pores within a preset time period. If the fluid velocity in a pore is greater than a preset velocity threshold and the pressure change is less than a preset pressure change threshold, then the pore is defined as an effective flow pore. If the fluid velocity in a pore is less than or equal to a preset velocity threshold, or the pressure change is greater than or equal to a preset pressure change threshold, then the pore is defined as an invalid pore.

[0015] To achieve the above objectives, the present invention also provides the following technical solution: A system for calculating the structural porosity of a fiber filter medium, the system being used to execute any of the above-described methods for calculating the structural porosity of a fiber filter medium, comprising: The image acquisition module is used to continuously perform tomographic scanning of the fiber filter medium along the thickness direction based on a preset single-layer scanning thickness to obtain multi-layer tomographic images. After binarizing each tomographic image, the images are stacked and reconstructed in the order of tomographic scanning to form a three-dimensional binary body that marks the spatial distribution of fibers and pores. The edge detection module is used to obtain the minimum size of the fiber filter medium based on the three-dimensional binary volume, calculate the sampling adaptation coefficient in combination with the single-layer scanning thickness to determine the sampling density, identify the initial outer contour of the fiber filter medium using the edge detection algorithm, and select a number of initial contour points on the initial outer contour according to the sampling density. The contour reconstruction module is used to select three consecutive initial contour points in sequence, connect the first and last two initial contour points to construct a spatial straight line, calculate the offset distance from the middle initial contour point to the spatial straight line, set the bending weight coefficient according to the offset distance, and adjust the initial contour points differently. After iterating until the preset conditions are met, the optimized contour points are obtained by traversing. The optimized contour points are used to reconstruct the surface and fit the actual outer contour and actual volume of the fiber filter medium. The pore testing module is used to perform fluid pore testing on fiber filter media based on the actual outer contour and three-dimensional binary body. It records the fluid velocity and pressure changes within a preset time period, and selects effective flow pores based on the pore spatial distribution, fluid velocity and pressure changes, and accumulates their volumes to obtain the total pore volume. The ratio calculation module is used to calculate the ratio of the total volume of the pore portion to the actual volume, which serves as the structural porosity of the fiber filter medium.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention uses a preset single-layer scanning thickness to perform continuous tomographic scanning of the fiber filter medium along the thickness direction to construct a three-dimensional binary volume that marks the spatial distribution of fibers and pores. Based on this three-dimensional binary volume, the minimum size of the fiber filter medium is accurately obtained. The sampling adaptation coefficient is calculated by combining the single-layer scanning thickness and the minimum size to determine the sampling density. An edge detection algorithm is used to identify the initial outer contour of the fiber filter medium. According to the sampling density, several initial contour points are selected on the initial outer contour, thus achieving accurate matching between the size of the fiber filter medium, the sampling density, and the initial contour points.

[0017] Based on this, the present invention sequentially selects three consecutive initial contour points, connects the first and last initial contour points to construct a spatial straight line, calculates the offset distance from the middle initial contour point to the spatial straight line, sets the bending weight coefficient according to the offset distance, and adjusts the initial contour points differently. After iterating until the preset conditions are met, the optimized contour points are obtained by traversing. The optimized contour points are used to carry out surface reconstruction and fit the actual outer contour and actual volume of the fiber filter medium. This can accurately capture the real shape characteristics of the fiber filter medium, which are bent and irregular. Compared with the defects of the prior art, which uses a simplified model for coarse fitting, has insufficient accuracy in edge detection and recognition of the initial outer contour, and cannot adapt to the irregular shape of the medium, the present invention effectively avoids the problem of excessive contour fitting deviation, greatly improves the accuracy of the actual volume measurement of the fiber filter medium, and thus ensures the accuracy of the porosity measurement results, improving the practicality and accuracy of the measurement method. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a block diagram of the module composition of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Example: Please see Figure 1 The present invention provides a technical solution: A method for calculating the porosity of a fiber filter medium, comprising the following steps: S1. Based on the preset single-layer scanning thickness, the fiber filter medium is continuously tomographically scanned along the thickness direction to obtain multi-layer tomographic images. After binarizing each tomographic image, they are stacked and reconstructed in the order of tomographic scanning to form a three-dimensional binary body that marks the spatial distribution of fibers and pores. In this embodiment, the single-layer scanning thickness is a constant tomographic scanning layer thickness preset based on the fiber particle size characteristics of the fiber filter medium and the CT resolution.

[0022] In this embodiment, continuous tomographic scanning is performed on the fiber filter medium along the thickness direction to obtain multi-layer tomographic images. The specific logic is as follows: Using a preset single-layer scanning thickness as a fixed interlayer spacing, continuous tomographic scanning with equal step lengths is performed from top to bottom along the overall thickness direction of the fiber filter medium. The CT scanning trajectory is controlled to keep the horizontal cross-section parallel and the scanning layers without overlap or gaps, and multiple two-dimensional tomographic images with equal spacing and equal layer thickness are acquired sequentially. All tomographic images are stored sequentially according to the actual scanning order to ensure that the spatial position of each tomographic image corresponds one-to-one with the thickness direction of the medium.

[0023] Based on the above embodiments, the tomographic images are binarized to convert them into binary images of fibers and pores. The specific logic is as follows: Each tomographic image was processed by grayscale conversion and threshold segmentation to mark the fibrous region and the pore region; The marked fault images are binarized by assigning a value of 1 to the fiber region and a value of 0 to the pore region to obtain the corresponding binarized image of each fault image. Repeat the above steps to iterate through all tomographic images and obtain the binarized images corresponding to all tomographic images.

[0024] The grayscale and threshold segmentation processes for each tomographic image were performed using conventional existing techniques, which will not be elaborated further here.

[0025] Based on the above embodiments, after binarizing each tomographic image, they are stacked and reconstructed in the order of tomographic scanning to form a three-dimensional binary volume of the spatial distribution of marked fibers and pores. The specific logic is as follows: Following the principle of "from top to bottom, in the order of scanning", all binarized tomographic images are stacked sequentially to ensure that the spatial position of each image layer is consistent with the actual interlayer position of the fiber filter medium, thus avoiding interlayer misalignment. By integrating information from all binarized tomographic images, a complete three-dimensional spatial model is constructed, clearly presenting the spatial distribution of fiber and pore regions, and finally forming a three-dimensional binarized volume that marks the spatial distribution of fibers and pores.

[0026] S2. Obtain the minimum size of the fiber filter medium based on the three-dimensional binary volume, calculate the sampling adaptation coefficient in combination with the single-layer scanning thickness to determine the sampling density, use the edge detection algorithm to identify the initial outer contour of the fiber filter medium, and select a number of initial contour points on the initial outer contour according to the sampling density. Based on the above embodiments, the minimum size of the fiber filter media is obtained based on a three-dimensional binary volume, and the specific logic is as follows: In the three-dimensional binary volume, all voxels belonging to the fiber region are traversed, and the maximum and minimum coordinate values ​​of the fiber region in the X, Y, and Z coordinate axes are determined respectively. The difference between the maximum and minimum coordinate values ​​in each coordinate axis direction is used to obtain the external dimensions of the fiber filter media in the three coordinate axis directions; The external dimensions in the three coordinate axes are compared, and the external dimension with the smallest value is selected as the minimum dimension of the fiber filter medium.

[0027] The minimum size of the fiber filter media is determined for the following reasons: The sampling density needs to be precisely matched with the minimum size of the medium. If the sampling interval is greater than the minimum size, key details such as fine fibers and tiny pores will be missed, leading to deviations in contour recognition and porosity calculation. If the sampling interval is too small, redundant data will be generated, reducing the calculation efficiency. Therefore, the optimal sampling density needs to be determined by the minimum size to avoid unreasonable sampling.

[0028] The minimum size defines the "minimum standard" for sampling, ensuring that the selected initial contour points can completely cover the entire range of the fiber filter media, avoiding deviations in the outer contour fitting and pore distribution calculation due to sampling omissions, and providing reliable support for offset distance calculation and contour reconstruction.

[0029] For fiber filter media of different batches and sizes, using the smallest size as a unified reference can avoid the problem of mismatched sampling density caused by differences in media size, ensure that the porosity calculation standard of all samples is consistent, and improve the accuracy and comparability of the calculation results.

[0030] Based on the above embodiments, the sampling adaptation coefficient is calculated by combining the minimum size and single-layer scanning thickness to determine the sampling density. The specific logic is as follows: The minimum size of the fiber filter medium is calculated as a ratio to the single-layer scanning thickness, and the quotient is used as the sampling adaptation coefficient. A preset sampling density baseline value is used, and the sampling adaptation coefficient is multiplied by the sampling density baseline value to obtain the sampling density.

[0031] Based on the above, it should be noted that: The thickness of a single layer is the accuracy benchmark for tomographic scanning, directly determining the accuracy level of contour sampling. The minimum size of the fiber filter medium is the core criterion for its shape and structure. The ratio of the two can accurately quantify "the number of scanning layers that can be accommodated within the minimum size range of the medium," thereby clarifying the required level of sampling precision. This ratio can intuitively reflect the compatibility between the medium size and the scanning accuracy. As a sampling adaptation coefficient, it can ensure that the subsequently set sampling density matches both the actual size of the medium and the scanning accuracy requirements, effectively avoiding the omission of details due to overly sparse sampling and the waste of efficiency due to overly dense sampling.

[0032] The sampling density benchmark value is preset based on the imaging accuracy of CT, the conventional particle size range of fiber filter media, and the porosity calculation requirements. It is a conventional experience value in the industry and can be preset to 1-5 particles / mm.

[0033] Based on the above embodiments, an edge detection algorithm is used to identify the initial outer contour of the fiber filter medium, and several initial contour points are selected on the initial outer contour according to the sampling density. The specific logic is as follows: The Canny edge detection algorithm is selected to extract the edges of the preprocessed 3D binary volume. It traverses all voxels in the 3D space and identifies the boundary voxels between the fiber region and the pore region. All boundary voxels constitute the initial 3D outer contour of the fiber filter medium. Based on the sampling density determined above, the selection interval between two adjacent initial contour points is calculated, and the interval is the reciprocal of the sampling density; Randomly select an outer contour point on the initial outer contour as the starting endpoint. In a clockwise or counterclockwise direction, using the starting endpoint as a reference, select subsequent initial contour points in sequence according to the selection interval calculated above. Simultaneously record the three-dimensional coordinates of each initial contour point until the distance along the initial outer contour between the last selected initial contour point and the starting endpoint is less than or equal to the above selection interval, thus completing the initial selection of the initial contour points. After the initial selection of the initial contour points is completed, the total number of initial contour points is counted. Combined with the minimum size of the fiber filter medium and the sampling density, it is verified whether the number of initial contour points meets the coverage requirements. If there are missing contour points in some areas, contour points are added to the corresponding areas to finally obtain several evenly distributed and fully covered initial contour points.

[0034] The method of identifying the initial outer contour of fiber filter media using edge detection algorithms is an existing technology and will not be elaborated further here.

[0035] S3. Select three consecutive initial contour points in sequence, connect the first and last initial contour points to construct a spatial straight line, calculate the offset distance from the middle initial contour point to the spatial straight line, set the bending weight coefficient according to the offset distance, and adjust the initial contour points differently. Iterate until the preset conditions are met, and then traverse to obtain the optimized contour points. Use the optimized contour points to reconstruct the surface and fit to obtain the actual outer contour and actual volume of the fiber filter medium. Based on the above embodiments, three consecutive initial contour points are selected in sequence, and the first and last two initial contour points are connected to construct a spatial straight line. The offset distance from the middle initial contour point to the spatial straight line is calculated. Among the initial contour points obtained above, three consecutive initial contour points are selected in sequence. Based on the three selected consecutive initial contour points, the first and last two initial contour points are connected to form a spatial straight line. Extract the three-dimensional coordinates of the intermediate initial contour point, and the three-dimensional coordinates of the projection point of the intermediate initial contour point onto the spatial straight line constructed above; The distance between two points in space is calculated using the formula for the distance between two points in space. The straight-line distance between the intermediate initial contour point and the corresponding projection point is the offset distance from the intermediate initial contour point to the straight line in space.

[0036] Based on the above embodiments, a bending weight coefficient is set according to the offset distance, and the specific logic is as follows: Two offset distance thresholds are preset, namely the first offset distance threshold and the second offset distance threshold, wherein the first offset distance threshold is less than the second offset distance threshold; Compare the offset distance with two offset distance thresholds: If the offset distance is less than the first offset distance threshold, the corresponding contour segment is considered to have a slight bend, and the bend weight coefficient is set to [value missing]. ; If the offset distance is greater than or equal to the first offset distance threshold and less than or equal to the second offset distance threshold, the corresponding contour segment is considered to have moderate bending, and the bending weight coefficient is set to [value missing]. ; If the offset distance is greater than the second offset distance threshold, the corresponding contour segment is considered heavily bent, and the bending weight coefficient is set to [value missing]. ; Among them, the bending weight coefficient is positively correlated with the degree of contour bending. That is, the smaller the offset distance, the milder the degree of contour bending, and the smaller the corresponding bending weight coefficient value; the larger the offset distance, the more severe the degree of contour bending, and the larger the corresponding bending weight coefficient value. Therefore, a slight bend corresponds to a bend weighting coefficient. Moderate bending corresponds to bending weight coefficient Severe bending corresponds to bending weighting coefficient ,satisfy ; The initial contour points are adjusted based on the differences in the bending weight coefficients, and iterated until the preset conditions are met, as follows: If the bending weight coefficient of a contour segment is At that time, the distribution of contour points conforms to the actual shape, maintaining the original sampling density without adding new contour points; If the bending weight coefficient of a contour segment is When the contour has reasonable curvature, the original three points are insufficient to accurately represent the contour curvature. Therefore, one new initial contour point is added between the first initial contour point and the middle initial contour point, and between the middle initial contour point and the last initial contour point. If the bending weight coefficient of a contour segment is If the original intermediate initial contour points have abnormal positions and deviate from the real contour, simply adding points cannot correct the distortion. Therefore, the intermediate initial contour points of the contour segment are removed, the contour curve is refitted based on the first and last initial contour points, and the intermediate initial contour points are reset. Iterate through all three consecutive initial contour points in groups and perform differential adjustments to the initial contour points segment by segment. Adjusting only three sets of consecutive initial contour points in a single iteration can only correct local contour distortion and cannot achieve a smooth and regular overall contour in one go. Furthermore, after adding and resetting contour points, the newly generated contour points still exhibit slight bending and offset errors, requiring multiple iterations for gradual optimization. Therefore, it is necessary to reconstruct the initial outer contour based on the adjusted initial contour points and reselect initial contour points along the initial outer contour according to the sampling density. This process involves iteratively executing offset distance calculation, bending weight assignment, and initial contour point differentiation adjustment until the offset distance from all intermediate initial contour points to their corresponding spatial lines is less than the first offset distance threshold. Using this threshold as the termination condition ensures that the overall outer contour of the fiber filter medium has no obvious protrusions, depressions, or deformed inflection points, and that each contour segment is in a smooth state with slight bending. Further surface reconstruction and outer contour fitting on this basis can accurately restore the true shape of the medium, effectively reducing errors in volume and porosity calculations caused by contour distortion, and improving the accuracy and reliability of the calculation results.

[0037] The first offset distance threshold and the second offset distance threshold are determined using the quartile method based on the offset distance statistics of multiple sets of historical samples. The lower quartile of the dataset is used as the first offset distance threshold, and the upper quartile is used as the second offset distance threshold, so as to achieve an objective classification and determination of the degree of contour bending.

[0038] Based on the above embodiments, surface reconstruction is performed using optimized contour points to obtain the actual outer contour and actual volume of the fiber filter medium. The specific logic is as follows: Based on all optimized contour points that meet the iteration termination condition after iterative optimization, a three-dimensional surface interpolation fitting algorithm is used to reconstruct a continuous surface for the discretely distributed optimized contour points. By fitting and interpolating to fill the spatial gaps between contour points, a closed, smooth, and realistic three-dimensional surface model is constructed, forming the complete actual outer contour of the fiber filter medium. Based on the reconstructed three-dimensional closed surface model, a spatial voxel integral or geometric volume subdivision algorithm is used to statistically calculate the overall effective spatial volume within the three-dimensional binary body, and the actual volume of the fiber filter medium is accurately obtained.

[0039] The three-dimensional surface interpolation fitting algorithm, spatial voxel integration algorithm and geometric volume subdivision algorithm mentioned above are all conventional existing technologies in the field for characterizing three-dimensional shapes and calculating model volumes. They can be implemented using conventional existing technologies, and the specific principles and implementation steps of these algorithms will not be elaborated in this invention.

[0040] S4. Based on the actual outer contour and three-dimensional binary body, fluid porosity test is performed on the fiber filter medium. The fluid velocity and pressure change within a preset time period are recorded. Based on the pore space distribution, fluid velocity and pressure change, effective flow pores are screened and their volumes are accumulated to obtain the total volume of the pore part. Based on the above embodiments, fluid porosity tests are performed on the fiber filter media based on the actual outer contour and three-dimensional binary volume, and the changes in fluid velocity and pressure are recorded within a preset time period. The specific logic is as follows: The actual outer contour of the fiber filter medium obtained by fitting is used as the spatial boundary constraint to limit the effective range of fluid testing and avoid data distortion caused by the test area exceeding the actual shape of the fiber filter medium. Based on the constructed three-dimensional binary volume, the spatial range of all regions marked as pores (with a value of 0) inside the medium is accurately located, and the initial distribution position of the pore space is determined. A fluid porosity testing system was set up, and the fiber filter media sample was fixed in the test fixture. The test fixture was ensured to fit and seal with the actual outer contour of the medium to prevent fluid leakage from affecting the test accuracy. Set stable fluid input parameters, including fluid type, initial input pressure, and initial flow rate, and keep the input parameters constant throughout the test to avoid fluctuations in operating conditions from interfering with the test data. A fixed test duration is preset as the preset time period. This time period must be sufficient for the fluid to fully flow through all the flowable pores inside the medium, and for the flow rate and pressure to reach a stable fluctuation state. The specific duration can be preset based on the thickness, fiber particle size and pore distribution characteristics of the fiber filter medium. Within a preset time period, the instantaneous flow velocity data of the fluid flowing through the medium pore channel and the instantaneous pressure data of the medium inlet and outlet are continuously collected in real time by the flow velocity sensor and pressure sensor of the test system, and the flow velocity value and pressure value corresponding to each time node are recorded synchronously. After the test, the collected instantaneous flow velocity data and instantaneous pressure data were organized, with a focus on recording the fluid flow velocity and pressure changes within a preset time period. The specific organization and recording logic is as follows: calculate the volumetric flow rate (i.e., fluid velocity) of the fluid passing through each pore within the preset time period; calculate the pressure difference (i.e., pressure change) between the inlet and outlet of the fiber filter medium when the fluid passes through the pores within the preset time period.

[0041] Based on the above embodiments, and based on the spatial distribution of pores in a three-dimensional binary volume, the spatial positioning, size extraction, and distribution analysis of all pores are performed. The specific process is as follows: Based on the constructed 3D binary volume, all voxels in the 3D space are traversed, all voxels with a value of 0 are selected (i.e., voxels in the pore region), voxels with a value of 1 in the fiber region are excluded, the overall spatial range of the pore region is determined, and a voxel set of the pore region is formed. A three-dimensional connected domain analysis algorithm is used to perform connectivity detection on the selected set of voxels in the pore region. Pore voxels that are adjacent to each other (including face adjacent, edge adjacent, and corner adjacent) are divided into the same connected region. Each connected region corresponds to an independent pore, thus achieving accurate differentiation between a single pore and other pores. For each independent pore (connected region), traverse all voxels contained therein, extract the three-dimensional coordinates of each voxel, and determine the coordinate range of the pore in three-dimensional space (i.e. the maximum and minimum coordinate values ​​in each coordinate axis direction), thereby clarifying the spatial location of a single pore. Based on the spatial coordinate range of a single pore, the size of the pore in the X, Y, and Z coordinate axes (the difference between the maximum and minimum coordinate values ​​of each axis) is calculated. At the same time, by counting the total number of voxels contained in the pore and combining the volume of a single voxel (determined by the single-layer scanning thickness and voxel resolution), the actual volume size of each pore is calculated, thus completing the accurate extraction of pore size. By integrating the spatial coordinates and dimensional data of all independent pores, the spatial distribution patterns of each pore in the three-dimensional binary volume are sorted out, the spatial positional relationships between different pores are clarified, and a complete list of pore spatial distributions is formed.

[0042] The three-dimensional connected component analysis algorithm is a conventional existing technology in the field of three-dimensional voxel analysis and pore localization. It can be implemented using conventional existing technology, and its specific principles and implementation steps will not be elaborated here.

[0043] Based on the above embodiments, effective flow pores are selected according to pore space distribution, fluid velocity, and pressure change. The specific logic is as follows: Based on the pore space distribution list obtained above, and combined with the fluid velocity and pressure changes recorded in the fluid pore test, all independent pores are classified and screened as follows: Wherein, the fluid velocity is the volumetric flow rate of the fluid passing through the pores within a preset time period; the pressure change is the pressure difference between the inlet and outlet of the fiber filter medium when the fluid passes through the pores within a preset time period. If the fluid velocity in a pore is greater than a preset velocity threshold and the pressure change is less than a preset pressure change threshold, then the pore is defined as an effective flow pore.

[0044] The reason is that the judgment condition indicates that the fluid can pass through the pore smoothly and the flow efficiency reaches the effective flow standard; at the same time, the fluid is less blocked by fibers and has less pore resistance when flowing through the pore, the pore flow resistance is low and the flow is smooth, which can achieve stable and effective fluid flow, and meets the core characteristics of an effective flow pore.

[0045] If the fluid velocity in a pore is less than or equal to a preset velocity threshold, or the pressure change is greater than or equal to a preset pressure change threshold, then the pore is defined as an invalid pore.

[0046] The reason is that such pores are either closed pores (no fluid passes through, and the flow rate is 0) or semi-blocked pores (the fluid flow is very small, and the flow rate does not reach the effective standard); and the flow resistance of the pores is extremely high. When the fluid passes through, it has to overcome a large resistance. The reason may be that the pores are severely blocked, the size is too small, or the pore channels are tortuous and deformed. In the end, normal and stable fluid flow cannot be achieved, which does not meet the judgment requirements for effective flow pores.

[0047] The flow rate threshold and pressure change threshold are determined based on multiple sets of historical sample test data to obtain a baseline value, and then the baseline value is increased by 20% to obtain the final preset threshold.

[0048] Based on the above embodiments, the total volume of the pore portion is obtained by accumulating the effective flow pore volumes, and the specific logic is as follows: Based on all the effective flow pores obtained from the previous screening, the actual volume size of each effective flow pore recorded during the pore space positioning and size extraction process is retrieved. This volume size is calculated by counting the total number of voxels contained in a single effective flow pore and combining it with the volume of a single voxel. The actual volume of each effective flow pore is counted one by one, and the volumes of all effective flow pores are summed to obtain the total volume of the pore portion.

[0049] S5. Calculate the ratio of the total pore volume to the actual volume, which is used as the structural porosity of the fiber filter medium. The formula used is as follows: in, The structural porosity of the fiber filter medium. This refers to the total volume of the pore portion of the fiber filter media. This represents the actual volume of the fiber filter media.

[0050] Please see Figure 2 The present invention also provides a technical solution: A system for calculating the structural porosity of a fiber filter medium, the system being used to execute any of the above-described methods for calculating the structural porosity of a fiber filter medium, comprising: The image acquisition module is used to continuously perform tomographic scanning of the fiber filter medium along the thickness direction based on a preset single-layer scanning thickness to obtain multi-layer tomographic images. After binarizing each tomographic image, the images are stacked and reconstructed in the order of tomographic scanning to form a three-dimensional binary body that marks the spatial distribution of fibers and pores. The edge detection module is used to obtain the minimum size of the fiber filter medium based on the three-dimensional binary volume, calculate the sampling adaptation coefficient in combination with the single-layer scanning thickness to determine the sampling density, identify the initial outer contour of the fiber filter medium using the edge detection algorithm, and select a number of initial contour points on the initial outer contour according to the sampling density. The contour reconstruction module is used to select three consecutive initial contour points in sequence, connect the first and last two initial contour points to construct a spatial straight line, calculate the offset distance from the middle initial contour point to the spatial straight line, set the bending weight coefficient according to the offset distance, and adjust the initial contour points differently. After iterating until the preset conditions are met, the optimized contour points are obtained by traversing. The optimized contour points are used to reconstruct the surface and fit the actual outer contour and actual volume of the fiber filter medium. The pore testing module is used to perform fluid pore testing on fiber filter media based on the actual outer contour and three-dimensional binary body. It records the fluid velocity and pressure changes within a preset time period, and selects effective flow pores based on the pore spatial distribution, fluid velocity and pressure changes, and accumulates their volumes to obtain the total pore volume. The ratio calculation module is used to calculate the ratio of the total volume of the pore portion to the actual volume, which serves as the structural porosity of the fiber filter medium.

[0051] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0052] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0053] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0054] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for calculating the porosity of a fiber filter medium, characterized in that, The specific steps include: S1. Based on the preset single-layer scanning thickness, the fiber filter medium is continuously tomographically scanned along the thickness direction to obtain multi-layer tomographic images. After binarizing each tomographic image, they are stacked and reconstructed in the order of tomographic scanning to form a three-dimensional binary body that marks the spatial distribution of fibers and pores. S2. Obtain the minimum size of the fiber filter medium based on the three-dimensional binary volume, calculate the sampling adaptation coefficient in combination with the single-layer scanning thickness to determine the sampling density, use the edge detection algorithm to identify the initial outer contour of the fiber filter medium, and select a number of initial contour points on the initial outer contour according to the sampling density. S3. Select three consecutive initial contour points in sequence, connect the first and last initial contour points to construct a spatial straight line, calculate the offset distance from the middle initial contour point to the spatial straight line, set the bending weight coefficient according to the offset distance, and adjust the initial contour points differently. Iterate until the preset conditions are met, and then traverse to obtain the optimized contour points. Use the optimized contour points to reconstruct the surface and fit to obtain the actual outer contour and actual volume of the fiber filter medium. S4. Based on the actual outer contour and three-dimensional binary body, fluid porosity test is performed on the fiber filter medium. The fluid velocity and pressure change within a preset time period are recorded. Based on the pore space distribution, fluid velocity and pressure change, effective flow pores are screened and their volumes are accumulated to obtain the total volume of the pore part. S5. Calculate the ratio of the total volume of the pore portion to the actual volume, which is used as the structural porosity of the fiber filter medium; Two offset distance thresholds are preset, namely the first offset distance threshold and the second offset distance threshold, wherein the first offset distance threshold is less than the second offset distance threshold; Compare the offset distance with two offset distance thresholds: If the offset distance is less than the first offset distance threshold, the corresponding contour segment is considered to have a slight bend, and the bend weight coefficient is set to [value missing]. ; If the offset distance is greater than or equal to the first offset distance threshold and less than or equal to the second offset distance threshold, the corresponding contour segment is considered to have moderate bending, and the bending weight coefficient is set to [value missing]. ; If the offset distance is greater than the second offset distance threshold, the corresponding contour segment is considered heavily bent, and the bending weight coefficient is set to [value missing]. ; in, ; The initial contour points are adjusted based on the differences in the bending weight coefficients, and iterated until the preset conditions are met, as follows: If the bending weight coefficient of a contour segment is In this case, the original sampling density remains unchanged, and no new contour points are added; If the bending weight coefficient of a contour segment is If the initial contour point is in the middle between the first initial contour point and the middle initial contour point, and in the middle between the middle initial contour point and the last initial contour point, then add one new initial contour point each. If the bending weight coefficient of a contour segment is If the initial contour point in the middle of the contour segment is removed, the contour curve is refitted based on the initial contour points at the beginning and end, and the initial contour point in the middle is reset. Iterate through all three consecutive initial contour points in groups and perform differential adjustments to the initial contour points segment by segment. The initial outer contour is reconstructed based on the adjusted initial contour points, and the initial contour points are reselected along the initial outer contour according to the sampling density. The process of offset distance calculation, bending weight assignment and initial contour point differentiation adjustment is repeated until the offset distance from all intermediate initial contour points to the corresponding spatial lines is less than the first offset distance threshold.

2. The method for calculating the porosity of the fiber filter media structure according to claim 1, characterized in that, The tomographic image is binarized to transform it into a binary image of fibers and pores. The specific logic is as follows: Each tomographic image was processed by grayscale conversion and threshold segmentation to mark the fibrous region and the pore region; The marked fault images are binarized by assigning a value of 1 to the fiber region and a value of 0 to the pore region to obtain the corresponding binarized image of each fault image. Repeat the above steps to iterate through all tomographic images and obtain the binarized images corresponding to all tomographic images.

3. The method for calculating the porosity of the fiber filter media structure according to claim 2, characterized in that, The minimum size of the fiber filter media is obtained based on a three-dimensional binary model. The specific logic is as follows: In the three-dimensional binary volume, all voxels belonging to the fiber region are traversed, and the maximum and minimum coordinate values ​​of the fiber region in the X, Y, and Z coordinate axes are determined respectively. The difference between the maximum and minimum coordinate values ​​in each coordinate axis direction is used to obtain the external dimensions of the fiber filter media in the three coordinate axis directions; The external dimensions in the three coordinate axes are compared, and the external dimension with the smallest value is selected as the minimum dimension of the fiber filter medium.

4. The method for calculating the porosity of the fiber filter media structure according to claim 1, characterized in that, The sampling density is determined by calculating the sampling adaptation coefficient based on the minimum size and single-layer scan thickness, as follows: The minimum size of the fiber filter medium is calculated as a ratio to the single-layer scanning thickness, and the quotient is used as the sampling adaptation coefficient. A preset sampling density baseline value is used, and the sampling adaptation coefficient is multiplied by the sampling density baseline value to obtain the sampling density.

5. The method for calculating the porosity of the fiber filter media structure according to claim 4, characterized in that, Based on the sampling density, several initial contour points are selected on the initial outer contour. The specific logic is as follows: Based on a determined sampling density, the distance between two adjacent initial contour points is calculated, where the distance is the reciprocal of the sampling density; Randomly select an outer contour point as the initial contour point. Starting from this initial contour point, select subsequent initial contour points in a clockwise or counterclockwise direction, according to the above-mentioned spacing, until the distance between the last selected initial contour point and the starting endpoint along the initial outer contour is less than or equal to the above-mentioned spacing, and finally obtain several initial contour points.

6. The method for calculating the porosity of the fiber filter media structure according to claim 1, characterized in that, The offset distance from the initial intermediate contour point to the straight line in space is obtained, and the specific logic is as follows: Based on the three consecutive initial contour points selected, determine the spatial straight line constructed by the first and last two initial contour points; Extract the three-dimensional coordinates of the intermediate initial contour point, and the three-dimensional coordinates of the projection point of the intermediate initial contour point on the above-mentioned spatial straight line; The distance between two points in space is calculated using the formula for the distance between two points in space. The straight-line distance between the intermediate initial contour point and the corresponding projection point is the offset distance from the intermediate initial contour point to the straight line in space.

7. The method for calculating the porosity of the fiber filter media structure according to claim 1, characterized in that, Based on the pore space distribution, fluid velocity, and pressure changes, effective flow pores are selected. The specific logic is as follows: Based on the spatial distribution of pores in a three-dimensional binary volume, the spatial phase of all pores is located, and the spatial coordinates, size and distribution range of each pore are extracted to clarify the spatial positional relationship of each pore. The extracted pores are classified according to the fluid flow rate and pressure change, as follows: Wherein, the fluid velocity is the volumetric flow rate of the fluid passing through the pores within a preset time period; the pressure change is the pressure difference between the inlet and outlet of the fiber filter medium when the fluid passes through the pores within a preset time period. If the fluid velocity in a pore is greater than a preset velocity threshold and the pressure change is less than a preset pressure change threshold, then the pore is defined as an effective flow pore. If the fluid velocity in a pore is less than or equal to a preset velocity threshold, or the pressure change is greater than or equal to a preset pressure change threshold, then the pore is defined as an invalid pore.

8. A system for calculating the structural porosity of a fiber filter medium, the system being used to execute the method for calculating the structural porosity of a fiber filter medium according to any one of claims 1-7, characterized in that, include: The image acquisition module is used to continuously perform tomographic scanning of the fiber filter medium along the thickness direction based on a preset single-layer scanning thickness to obtain multi-layer tomographic images. After binarizing each tomographic image, the images are stacked and reconstructed in the order of tomographic scanning to form a three-dimensional binary body that marks the spatial distribution of fibers and pores. The edge detection module is used to obtain the minimum size of the fiber filter medium based on the three-dimensional binary volume, calculate the sampling adaptation coefficient in combination with the single-layer scanning thickness to determine the sampling density, identify the initial outer contour of the fiber filter medium using the edge detection algorithm, and select a number of initial contour points on the initial outer contour according to the sampling density. The contour reconstruction module is used to select three consecutive initial contour points in sequence, connect the first and last two initial contour points to construct a spatial straight line, calculate the offset distance from the middle initial contour point to the spatial straight line, set the bending weight coefficient according to the offset distance, and adjust the initial contour points differently. After iterating until the preset conditions are met, the optimized contour points are obtained by traversing. The optimized contour points are used to reconstruct the surface and fit the actual outer contour and actual volume of the fiber filter medium. The pore testing module is used to perform fluid pore testing on fiber filter media based on the actual outer contour and three-dimensional binary body. It records the fluid velocity and pressure changes within a preset time period, and selects effective flow pores based on the pore spatial distribution, fluid velocity and pressure changes, and accumulates their volumes to obtain the total pore volume. The ratio calculation module is used to calculate the ratio of the total volume of the pore portion to the actual volume, which serves as the structural porosity of the fiber filter medium.

Images