A method and system for evaluating the performance of thorny felt filter cloth based on permeability detection

Abstract

This invention relates to the field of new material-related service technology, and discloses a method and system for evaluating the performance of thorn-punched felt filter cloth based on permeability detection. The method includes: acquiring a cross-sectional microscopic image of the thorn-punched felt filter cloth; acquiring pore structure data of the pore network structure in the cross-sectional microscopic image; tracing the permeation path of the cross-sectional microscopic image based on the pore structure data, and simultaneously acquiring the structural tortuosity of the permeation path; measuring the gas flow rate through the thorn-punched felt filter cloth, and analyzing the gas permeability of the thorn-punched felt filter cloth using the gas flow rate; calibrating the permeability characteristics of the thorn-punched felt filter cloth based on the structural tortuosity and the gas permeability to obtain the equivalent flow permeability of the thorn-punched felt filter cloth; and determining the performance level of the thorn-punched felt filter cloth based on a preset performance standard and the equivalent flow permeability. This invention can improve the comprehensiveness of the performance evaluation of thorn-punched felt filter cloth.

Description

Technical Field

[0001] This invention relates to the field of new material-related service technology, and in particular to a method and system for evaluating the performance of thorny felt filter cloth based on permeability detection. Background Technology

[0002] Punched felt filter cloth, as a key filtration material, is widely used in industrial dust removal, flue gas purification, and material separation. Its performance directly affects filtration efficiency, operating energy consumption, and service life; therefore, accurate and reliable evaluation of the performance of punched felt filter cloth is crucial. Currently, the industry commonly uses the method of indirectly assessing the filtration performance of punched felt filter cloth by measuring its air permeability. This testing method calculates its gas permeability by measuring the gas flow rate through the filter cloth under a specific pressure difference, using this as the primary basis for performance judgment.

[0003] However, existing technologies rely on gas permeability measurements, which ignore the complex microscopic pore network structure within the filter cloth. This method fails to reveal the true flow path, tortuosity, and pore size distribution of the gas within the filter cloth. The permeability values ​​obtained through this method cannot fully represent the actual flow resistance and filtration performance of the filter cloth under real-world conditions. Therefore, existing evaluation methods, failing to delve into the microscopic structure of the material, result in significant discrepancies between the evaluation results and actual performance, failing to provide reliable data support for filter cloth performance optimization and accurate selection. A new method that comprehensively evaluates both microstructure and macroscopic characteristics is urgently needed. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method and system for evaluating the performance of thorny felt filter cloth based on permeability detection to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for evaluating the effectiveness of punched felt filter cloth based on permeability detection, comprising: S1: Obtain a cross-sectional microscopic image of the barbed felt filter cloth; S2: Obtain the pore structure data of the pore network structure in the cross-sectional micro-image; S3: Based on the pore structure data, track the permeation path of the cross-sectional micro-image and simultaneously obtain the structural tortuosity of the permeation path; S4: Measure the gas flow rate through the felt filter cloth and analyze the gas permeability of the felt filter cloth using the gas flow rate; S5: Based on the structural tortuosity and the gas permeability, the permeability characteristics of the thorned felt filter cloth are calibrated to obtain the equivalent flow permeability of the thorned felt filter cloth; S6: Determine the performance level of the thorned felt filter cloth based on the preset performance standard and the equivalent flow permeability.

[0006] Optionally, obtaining a cross-sectional microscopic image of the thorny felt filter cloth includes: The thorned felt filter cloth is sliced ​​to obtain a cross-sectional sample of the thorned felt filter cloth; Scanning the cross-sectional sample yields a cross-sectional microscopic image of the thorny felt filter cloth.

[0007] Optionally, obtaining the pore structure data of the pore network structure in the cross-sectional micro-image includes: The cross-sectional microscopic image is binarized to obtain a black and white image of the cross-sectional microscopic image; Remove image noise from the black and white image to obtain the pore network structure of the cross-sectional micro-image; Extract the geometric features of the pore network structure; Based on the geometric features, the pore network structure is parametrically calculated to obtain the pore structure data of the pore network structure in the cross-sectional micro-image.

[0008] Optionally, the step of tracking the permeation path of the cross-sectional microscopic image based on the pore structure data and simultaneously acquiring the structural tortuosity of the permeation path includes: Based on the pore structure data, spatial topology analysis is performed on the thorned felt filter cloth to obtain the pore network topology of the thorned felt filter cloth. The permeation path of the cross-sectional microscopic image is marked based on the pore network topology; The ratio of the total projected length of the permeation path to the thickness of the felt filter cloth is taken as the structural tortuosity of the permeation path.

[0009] Optionally, measuring the gas flow rate through the felt filter cloth and analyzing the gas permeability of the felt filter cloth using the gas flow rate includes: Measure the gas volumetric flow rate of the thorned felt filter cloth; Based on the gas volumetric flow rate and the physical characteristics of the thorned felt filter cloth, a permeability analysis was performed on the thorned felt filter cloth to obtain its gas permeability. The formula for calculating the gas permeability is as follows: in, It is the gas permeability, It is the gas volumetric flow rate. It is the dynamic viscosity of the gas. It is the thickness of the thorny felt filter cloth. It is the cross-sectional area of ​​the thorny felt filter cloth. It's a pressure difference.

[0010] Optionally, measuring the gas volumetric flow rate of the punched felt filter cloth includes: A sealing permeability test was performed on the thorned felt filter cloth to obtain the real-time flow rate sequence of the thorned felt filter cloth; By filtering out outliers from the real-time flow rate sequence, the gas volume flow rate of the thorny felt filter cloth is obtained.

[0011] Optionally, the step of calibrating the permeability characteristics of the thorned felt filter cloth based on the structural tortuosity and the gas permeability to obtain the equivalent flow permeability of the thorned felt filter cloth includes: The flow efficiency of the thorned felt filter cloth is obtained by performing a reverse analysis based on the structural tortuosity. Based on the flow efficiency and the gas permeability, the flow resistance of the thorned felt filter cloth is compensated to obtain the equivalent flow permeability of the thorned felt filter cloth.

[0012] Optionally, the formula for calculating the equivalent flow permeability includes: in, It is the equivalent flow permeability, It is the gas permeability, It is the porosity in the pore structure data. This refers to the circulation efficiency. It is the average pore diameter in the pore structure data. It is the standard deviation of the aperture distribution.

[0013] Optionally, determining the performance level of the punched felt filter cloth based on a preset performance standard and the equivalent flow permeability includes: The equivalent flow permeability is quantified as the performance index of the thorny felt filter cloth; The efficiency index is matched based on a preset efficiency standard to obtain the efficiency level of the thorny felt filter cloth.

[0014] To address the aforementioned problems, the present invention also provides a system for evaluating the effectiveness of needle-punched felt filter cloth based on permeability detection, the system comprising: Cross-sectional microscopic image acquisition module: used to acquire cross-sectional microscopic images of the barbed felt filter cloth; Pore ​​structure data extraction module: used to obtain pore structure data of the pore network structure in the cross-sectional micro image; Permeation path analysis module: used to track the permeation path of the cross-sectional microscopic image based on the pore structure data, and simultaneously obtain the structural tortuosity of the permeation path; Gas permeability measurement module: used to measure the gas flow rate through the felt filter cloth and analyze the gas permeability of the felt filter cloth using the gas flow rate; Permeability calibration module: used to calibrate the permeability of the thorned felt filter cloth based on the structural tortuosity and the gas permeability, and obtain the equivalent flow permeability of the thorned felt filter cloth; Performance level determination module: used to determine the performance level of the thorny felt filter cloth based on the preset performance standard and the equivalent flow permeability.

[0015] Beneficial effects 1. By acquiring cross-sectional microscopic images and extracting pore structure data, key parameters such as porosity, average pore size, and standard deviation of pore size distribution within the filter cloth can be accurately captured. Combined with analysis of the structural tortuosity of the permeation path, the true flow patterns of gas within the filter cloth can be revealed, such as the impact of path complexity on flow efficiency. This microscopic analysis allows evaluation to move beyond surface-level gas flow data, uncovering the intrinsic relationship between filter cloth structure and permeation performance, providing a more fundamental basis for performance assessment.

[0016] 2. By calibrating permeability characteristics through structural tortuosity and gas permeability, the obtained equivalent flow permeability more accurately reflects the comprehensive performance of the filter cloth under actual working conditions. Compared to the gas permeability calculated solely by gas flow rate using traditional methods, the equivalent flow permeability integrates multiple factors such as porosity, flow efficiency, and pore size distribution, effectively compensating for the limitations of a single indicator and significantly improving the consistency between evaluation results and actual performance. This not only provides precise guidance for filter cloth performance optimization, such as addressing issues like uneven pore size distribution or excessive path tortuosity, but also provides reliable data support for filter cloth selection in industrial dust removal, flue gas purification, and other fields, significantly enhancing the scientific rigor and practicality of the evaluation. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for evaluating the effectiveness of thorny felt filter cloth based on permeability detection, according to an embodiment of the present invention. Figure 2 This is a functional block diagram of a permeability detection-based performance evaluation system for punctured felt filter cloth provided in an embodiment of the present invention. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for evaluating the performance of needle-punched felt filter cloth based on permeability detection. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for evaluating the performance of needle-punched felt filter cloth based on permeability detection can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a method for evaluating the performance of punched felt filter cloth based on permeability detection, according to an embodiment of the present invention. In this embodiment, the method for evaluating the performance of punched felt filter cloth based on permeability detection includes: S1: Obtain a cross-sectional microscopic image of the barbed felt filter cloth.

[0021] In this embodiment of the invention, obtaining a cross-sectional microscopic image of the quilled felt filter cloth includes: The thorned felt filter cloth is sliced ​​to obtain a cross-sectional sample of the thorned felt filter cloth; Scanning the cross-sectional sample yields a cross-sectional microscopic image of the thorny felt filter cloth.

[0022] Specifically, samples taken from the thorny felt filter cloth undergo a series of meticulous slicing and scanning processes to obtain cross-sectional microscopic images that accurately reflect its internal fiber arrangement and pore structure. The first step is slicing, which typically involves resin curing to preserve the original structure of the sample, followed by ultrathin slicing using precision instruments.

[0023] In detail, the resin curing process locks the loose and complex fiber network structure of the filter cloth in place, effectively preventing pore deformation, fiber displacement, or structural collapse caused by physical compression during subsequent slicing. This ensures that the final image accurately reflects the undisturbed original internal structure of the filter material.

[0024] Furthermore, using precision instruments to perform ultrathin sectioning yields an extremely smooth and flat observation cross-section, providing an ideal sample surface for subsequent scanning electron microscopy imaging. Combined with the high magnification and high resolution characteristics of the scanning electron microscope, it is possible to clearly capture minute pores and fiber details at the micrometer or even submicrometer level.

[0025] Furthermore, the prepared cross-sectional samples are photographed at high magnification using equipment such as scanning electron microscopes. The purpose of this step is to provide high-quality, distortion-free raw image data for subsequent calculations of key performance parameters such as porosity and tortuosity.

[0026] For example, in the development of needle-punched felt filter cloth with waterproof and oil-resistant functions, to evaluate the microstructure of a novel needle-punched felt filter cloth, a 10mm × 10mm sample was first cut from the filter bag. To prevent the fragile needle-punched felt from breaking or shifting during cutting, the sample was first vacuum impregnated and cured in epoxy resin. After curing, it was cut into 5mm thick pieces using an ultrathin slicer. Cross-sectional sample.

[0027] In detail, the sample was placed under a scanning electron microscope and scanned at 500x magnification, ultimately obtaining a high-resolution cross-sectional microscopic image of 2048×2048 pixels. This clear image lays a solid foundation for subsequent analysis of the pore size distribution and fiber arrangement of the needle-punched felt filter cloth.

[0028] S2: Obtain the pore structure data of the pore network structure in the cross-sectional micro-image.

[0029] In this embodiment of the invention, obtaining the pore structure data of the pore network structure in the cross-sectional micro-image includes: The cross-sectional microscopic image is binarized to obtain a black and white image of the cross-sectional microscopic image; Remove image noise from the black and white image to obtain the pore network structure of the cross-sectional micro-image; Extract the geometric features of the pore network structure; Based on the geometric features, the pore network structure is parametrically calculated to obtain the pore structure data of the pore network structure in the cross-sectional micro-image.

[0030] Specifically, the original image containing complex grayscale information is converted into an image containing only two pixel values, typically black and white, to clearly distinguish between the fiber entity and the channel space. The OTSU algorithm is used to classify all pixels in the image into foreground (e.g., fibers) and background (e.g., channels) based on the relationship between their grayscale values ​​and a threshold.

[0031] Furthermore, by employing the OTSU algorithm to automate the thresholding method, the uncertainty caused by visual differences or subjective judgment in manual interpretation is eliminated, ensuring that the segmentation criteria for each analysis are consistent and repeatable. More importantly, it simplifies complex grayscale image information into purely binary data, laying the foundation for subsequent efficient and accurate morphological calculations and geometric quantization analysis by computers.

[0032] Furthermore, the input grayscale image is analyzed, the number of pixels at each grayscale level is counted, and a grayscale histogram of the image is generated. Each grayscale value from 0 to 255 is then tested as a candidate threshold. For each candidate threshold... The image's pixels are divided into two categories: the first category consists of pixels with gray values ​​in the range [0, 1]. Pixels within the range of ], the second category is pixels with grayscale values ​​in [ The algorithm then calculates the inter-class variance between these two classes of pixels, within the range of +1,255. This variance measures the difference in grayscale distribution between the two classes of pixels; the larger the variance, the clearer the distinction between the two classes.

[0033] Specifically, after iterating through all possible thresholds, the threshold that maximizes the inter-class variance is found. This threshold is determined as the optimal segmentation threshold for the image. Binarization is then performed using this threshold. Finally, this optimal threshold is applied to the entire image. All pixels with gray values ​​less than or equal to the threshold are assigned a value of 0, and all pixels with gray values ​​greater than the threshold are assigned a value of 255, thus completing the binarization process.

[0034] In detail, during binarization, isolated pixels or tiny clumps often appear in the image due to sensor noise or minute impurities. To obtain a clean pore network structure, this noise must be cleaned up. Morphological filtering methods are commonly used, which can effectively remove tiny noise points and avoid these invalid information interfering with subsequent calculations of porosity and pore size distribution. The key is that this method can remove noise while preserving the outline and size of the real pore structure to the greatest extent possible, thus ensuring the validity of the data without sacrificing structural accuracy.

[0035] Furthermore, after obtaining a clear image of the channel network, the goal of this step is to identify each individual channel and measure its basic geometric dimensions. Using a connected component labeling image segmentation algorithm, each unconnected channel region in the image can be assigned a unique label. Subsequently, each labeled region is traversed and calculated to extract a series of geometric features such as area, perimeter, equivalent diameter, and roundness.

[0036] Furthermore, based on the geometric features of the massive number of individual pores extracted in the previous step, the entire pore network structure is parametrically calculated, ultimately obtaining pore structure data that can macroscopically characterize the overall microstructure of the filter cloth.

[0037] In detail, the pore structure data includes: total porosity, average pore size, and standard deviation of pore size distribution.

[0038] Specifically, the total porosity of pore structure data reflects the looseness or dust-holding capacity of a material. It is calculated by summing the areas of all individual pore regions identified after image segmentation to obtain the total pore area. Dividing this sum by the total area of ​​the analyzed microscopic image yields the total porosity. The calculation formula is: ,in, It is the total channel area. It is the total area of ​​the analyzed microscopic image.

[0039] Furthermore, the average pore size reflects the overall average size of the channels and is directly related to filtration accuracy. Since most channels are not perfectly circular, the calculation first requires converting the area of ​​each individual channel into its equivalent diameter, that is, the diameter of a circle with the same area as the channel. The formula for calculating the diameter is: After obtaining the equivalent diameters of all channels, the arithmetic mean of these equivalent diameters is calculated, which is the average aperture. The calculation formula is: .

[0040] Furthermore, the standard deviation of the aperture distribution reflects the uniformity of all aperture sizes. It is calculated using a standard statistical formula: the square of the difference between the equivalent diameter of each aperture and its mean, the sum of all these squared differences, divided by the total number of apertures, and then the square root is taken. A smaller standard deviation indicates a more uniform aperture size. The formula is: .

[0041] For example, based on the OTSU algorithm, the optimal grayscale threshold of 112 is automatically determined. The image is then binarized to generate a binary image containing only pure black pixels (0 pixels, representing channels) and pure white pixels (255 pixels, representing fibers). Next, a morphological opening operation is performed on this binary image using a 3×3 structuring element. This successfully removes approximately 1500 isolated noise points with an area less than 10 square pixels, resulting in a clean and clear channel network structure. Subsequently, a connected component labeling image segmentation algorithm is used to process the denoised image, successfully identifying and labeling 2850 independent channel regions. Taking one channel as an example, its area is calculated to be 850. The perimeter is 120. Finally, statistical calculations were performed based on the geometric data of all 2850 channels.

[0042] In detail, the formula for calculating the total porosity using pore structure data is as follows: The average porosity of the filter cloth sample was found to be 0.82, calculated using the formula for average pore size: An average aperture of 65 was obtained. The formula for calculating the standard deviation of pore size distribution is as follows: The standard deviation of the pore size distribution was 8. This set of precise quantitative data provides crucial microscopic-level information for evaluating the dust holding capacity, initial resistance, and abrasion resistance of filter cloth when used in conjunction with high-efficiency filter cages.

[0043] S3: Based on the pore structure data, track the permeation path of the cross-sectional micro-image and simultaneously obtain the structural tortuosity of the permeation path.

[0044] In this embodiment of the invention, the step of tracking the permeation path of the cross-sectional microscopic image based on the pore structure data and simultaneously acquiring the structural tortuosity of the permeation path includes: Based on the pore structure data, spatial topology analysis is performed on the thorned felt filter cloth to obtain the pore network topology of the thorned felt filter cloth. The permeation path of the cross-sectional microscopic image is marked based on the pore network topology; The ratio of the total projected length of the permeation path to the thickness of the felt filter cloth is taken as the structural tortuosity of the permeation path.

[0045] Specifically, the denoised binarized aperture image is skeletonized to extract the centerlines of all apertures, forming a connected skeleton network. Then, key nodes in the network, such as path intersections and endpoints, are identified, thus constructing an aperture network topology containing nodes and connections.

[0046] Furthermore, a complex two-dimensional pore region containing a large amount of pixel information is cleverly transformed into a one-dimensional topological network composed of nodes and connections. This approach greatly reduces the computational complexity of subsequent path search, making efficient pathfinding possible in complex pore structures. More importantly, by stripping away secondary information such as pore width and focusing on the flow essence of connectivity, an ideal digital map is provided for simulating how gas passes through the filter material.

[0047] In detail, after obtaining the topological network, the main paths most likely to be followed by gas molecules as they pass through the filter media are simulated and identified. Based on this pore network topology, Dijkstra's algorithm is used to calculate the connection path with the least resistance from the upstream to the downstream side of the filter media. This calculated path is marked as the main permeation path, representing the mainstream channel for macroscopic gas flow.

[0048] Furthermore, before running the graph theory algorithm, each path in the topological network—that is, the edge connecting two nodes—is assigned a weight. This weight needs to represent the resistance encountered by the gas flow through that path. Typically, the weight of a path is directly proportional to its geometric length and inversely proportional to its average width. That is, the longer and narrower the path, the higher its weight, representing greater resistance.

[0049] Furthermore, the inlets and outlets for gas flow need to be explicitly defined in the topology network. Typically, all nodes upstream of the microscopic image are defined as the start-point set, and all nodes downstream are defined as the end-point set.

[0050] In detail, based on the weighted topology network described above, classic shortest path algorithms such as Dijkstra's algorithm are used to calculate the connected path with the minimum cumulative weight from any node in the starting set to any node in the ending set. Dijkstra's algorithm can explore all possible paths and guarantees that the path found has the lowest total weight among all reachable paths.

[0051] Specifically, it precisely identifies the flow path of least resistance within a complex topological network. This avoids the significant errors of human observation and subjective judgment, transforming the question of how gas is most likely to flow from a vague guess into a computational problem with a definite solution. The path it marks is based on the algorithm's globally optimal solution, realistically representing the mainstream channels of macroscopic gas flow.

[0052] Furthermore, the total projected length of the permeation path marked in the previous step is... That is, the actual path length measured on the two-dimensional image, and the physical thickness of the filter material sample itself. Compare them. The formula for calculating the tortuosity of a structure is: .

[0053] For example, in evaluating a needle-punched felt filter cloth with waterproof and oil-repellent properties, the clear pore network image of the sample was skeletonized to generate a pore network topology consisting of 1560 nodes and 2180 paths, fully depicting its internal connectivity. Based on this topology, Dijkstra's algorithm was used to calculate the minimum resistance permeation path for gas molecules from the upstream to the downstream side of the filter cloth. The algorithm results showed that this path meanders through 45 different pore nodes, which were highlighted on the image.

[0054] In detail, the total length of the projected path of the marked infiltration path onto the two-dimensional image was measured using image measurement tools. The thickness is 3.2 mm. The physical thickness L of the filter bag sample used for testing is known to be 1.8 mm. According to the formula... The calculated tortuosity of the filter bag is as follows: =3.2mm / 1.8mm≈1.8. This visually quantifies the complexity of gas flow within the high-temperature filter bag, providing crucial data support for optimizing the balance between dust collection efficiency and operating resistance.

[0055] S4: Measure the gas flow rate through the felt filter cloth and analyze the gas permeability of the felt filter cloth using the gas flow rate.

[0056] In this embodiment of the invention, measuring the gas flow rate through the felt filter cloth and analyzing the gas permeability of the felt filter cloth using the gas flow rate includes: Measure the gas volumetric flow rate of the thorned felt filter cloth; Based on the gas volumetric flow rate and the physical characteristics of the thorned felt filter cloth, a permeability analysis was performed on the thorned felt filter cloth to obtain its gas permeability. The formula for calculating the gas permeability is as follows: in, It is the gas permeability, It is the gas volumetric flow rate. It is the dynamic viscosity of the gas. It is the thickness of the thorny felt filter cloth. It is the cross-sectional area of ​​the thorny felt filter cloth. It's a pressure difference.

[0057] Specifically, the needle-punched felt filter cloth sample to be tested is securely clamped onto a fixture with a specified test area, ensuring a leak-free seal around the perimeter. Then, a constant, preset pressure difference is applied across the sample, and the equipment automatically adjusts the fan suction to maintain this pressure difference. Under this stable condition, the volume of gas passing vertically through the sample test area per unit time is accurately measured, thus obtaining the gas volumetric flow rate. .

[0058] Furthermore, by fixing the sample onto a fixture with a specified test area and applying a constant, pre-set pressure difference, this method creates a strictly controlled experimental environment for gas flow measurement. This standardized operating procedure ensures that the test conditions are consistent, whether for quality inspection of different batches of samples or for comparing the performance of different formulations during the research and development process. This results in highly comparable and repeatable measurement results, providing reliable raw data for subsequent analysis.

[0059] In detail, based on the measured gas volumetric flow rate The permeability of needle-punched felt filter cloth was analyzed using a series of known physical characteristics. These physical characteristics include: the cross-sectional area of ​​the test area. Thickness of filter cloth sample The applied pressure difference and the dynamic viscosity of the gas under test conditions. Substituting these parameters into the calculation formula, the calculated gas permeability is obtained. .

[0060] Furthermore, the cross-sectional area of ​​the test region is determined. This parameter does not refer to the entire size of the sample, but rather to the area through which the gas actually passes vertically on the testing instrument. It is determined by the dimensions of the sample clamp of the permeability tester used.

[0061] Specifically, the thickness of the filter cloth sample is measured directly using a fabric thickness gauge or micrometer designed specifically for compressible materials such as textiles. To eliminate errors that may be caused by uneven thickness of the sample itself, measurements are usually taken at multiple different locations on the sample, and then the arithmetic mean of all readings is taken as the final thickness.

[0062] In detail, the applied pressure difference is set, which is a control variable pre-set by the operator according to the test standard during the experiment. After the specified pressure difference value is entered on the air permeability tester, the instrument will automatically adjust the fan or air pump to ensure that the pressure difference on both sides of the sample remains at this constant set value throughout the entire test.

[0063] Furthermore, the dynamic viscosity of the gas is determined. This parameter is a physical property of the test medium itself, not directly measured in the experiment. Its value mainly depends on the temperature of the test environment to obtain the dynamic viscosity of air at that temperature.

[0064] Furthermore, using the widely accepted Darcy's Law, an experimental reading dependent on specific test conditions—gas flow rate—is successfully converted into the material's inherent physical property, gas permeability. The formula incorporates all relevant physical characteristics such as test area, sample thickness, pressure difference, and gas dynamic viscosity. Its purpose is to isolate the influence of these external factors and obtain standardized performance indicators that are only related to the microstructure of the filter material itself.

[0065] For example, in the development of needle-punched felt filter cloth with waterproof and oil-repellent functions, to test the effectiveness of a new type of waterproof and oil-repellent needle-punched felt, a standard air permeability tester was used to test a piece of filter cloth with a thickness of [missing information]. A filter cloth sample with a diameter of 0.0018 m is clamped at the test port, and the cross-sectional area A of the test area is 0.00385. Set the test pressure difference. The pressure was 125 Pa. After the gas flow stabilized, the instrument measured and displayed the gas volumetric flow rate Q through the sample as 0.0417. .

[0066] Furthermore, the tests were conducted in a standard laboratory environment, where the dynamic viscosity of air was... Approximately Substitute all known data into the penetration rate calculation formula: The calculation process is as follows: ≈ Finally, the basic gas permeability of the waterproof and oil-repellent filter cloth sample was calculated. This value is a direct indicator of the sample's macroscopic permeability and is also the initial data necessary for subsequent permeability calibration.

[0067] In this embodiment of the invention, measuring the gas volume flow rate of the felt filter cloth includes: A sealing permeability test was performed on the thorned felt filter cloth to obtain the real-time flow rate sequence of the thorned felt filter cloth; By filtering out outliers from the real-time flow rate sequence, the gas volume flow rate of the thorny felt filter cloth is obtained.

[0068] Specifically, on a standard air permeability tester, needle-punched felt samples are tested continuously for a period of time under a preset constant pressure difference, and the flow rate readings during this period are continuously recorded. This differs from a single reading; rather, it aims to obtain a real-time flow rate sequence that reflects the fluctuation of flow rate over time.

[0069] In detail, this sequence can capture the process of reaching a steady state in the early stage of the test as well as the small fluctuations during the test. It provides an intuitive basis for judging whether the entire test process is stable and whether the data is reliable. Furthermore, it creates the necessary data foundation for subsequent rigorous statistical analysis and outlier removal.

[0070] Furthermore, by continuously recording flow readings over a period of time, this method not only obtains an average value, but more importantly, it can fully capture the dynamic process of the test from initiation to stabilization, as well as the minute fluctuations in the stable state. This provides an intuitive basis for judging whether the test process is smooth and whether the data is reliable, and also creates the necessary data foundation for subsequent statistical analysis and outlier removal.

[0071] In detail, due to environmental disturbances or momentary sensor instability, the acquired real-time flow rate sequence may contain outliers that deviate from the normal fluctuation range. To ensure the accuracy of the results, statistical methods must be used to identify and remove these outliers. Commonly used methods include the 3σ criterion. After removing outlier data points, the average value of the remaining valid data points in the sequence is calculated, and this average value is ultimately determined as the gas volumetric flow rate of the sample.

[0072] Specifically, due to environmental disturbances or momentary sensor instability, the collected data may contain outliers that deviate from the normal fluctuation range. Statistical methods such as the 3σ criterion can be used to scientifically identify and remove these problematic data points that may interfere with the results in an objective and repeatable manner. After data cleaning, the average value of the remaining valid data points is calculated. This process effectively eliminates the influence of random errors, greatly ensuring the accuracy and reliability of the final gas volumetric flow rate.

[0073] For example, in the development of needle-punched felt filter cloth with waterproof and oil-resistant functions, in order to obtain its gas volumetric flow rate Q, the following precision operation is performed at 125 Under a constant pressure differential, a 60-second seal permeability test was conducted on the sample. The tester acquired data at a frequency of once per second, ultimately obtaining the real-time flow rate sequence: [151.2,149.8,150.1,149.5,165.3,149.9,...]( h).

[0074] Furthermore, the 3σ criterion was used for analysis. The calculated mean of the original sequence was 150.5. h, with a standard deviation of 2.1. h. According to the 3σ criterion, the range of valid data should be [150.5-3×2.1, 150.5+3×2.1], i.e., [144.2, 156.8]. 165.3 was found in the sequence. The reading h exceeded the upper limit of the range, therefore it was identified as an outlier and removed. After removing the outlier, the average of the remaining 59 valid data points was recalculated, resulting in a final gas volumetric flow rate of 150.0. h. This precise value, obtained after data cleaning, is used as input for penetration rate calculation, ensuring data quality throughout the entire evaluation process.

[0075] S5: Based on the structural tortuosity and the gas permeability, the permeability characteristics of the thorned felt filter cloth are calibrated to obtain the equivalent flow permeability of the thorned felt filter cloth.

[0076] In this embodiment of the invention, the step of calibrating the permeability characteristics of the thorned felt filter cloth based on the structural tortuosity and the gas permeability to obtain the equivalent flow permeability of the thorned felt filter cloth includes: The flow efficiency of the thorned felt filter cloth is obtained by performing a reverse analysis based on the structural tortuosity. Based on the flow efficiency and the gas permeability, the flow resistance of the thorned felt filter cloth is compensated to obtain the equivalent flow permeability of the thorned felt filter cloth.

[0077] Specifically, the flow efficiency of gas within the channel is quantified based on the previously calculated structural tortuosity. Structural tortuosity itself is an inverse representation of flow efficiency: the larger the tortuosity value, the longer and more tortuous the path the gas must travel, and the lower its flow efficiency. The obtained structural tortuosity is used as the core parameter characterizing flow efficiency and directly applied to subsequent compensation calculations.

[0078] In detail, structural tortuosity itself is an inverse representation of flow efficiency. This approach avoids introducing more complex flow efficiency parameters, instead directly using the tortuosity value, which has been precisely calculated in the preceding steps, as the core parameter in the final compensation calculation. This not only makes the logical chain of the entire evaluation method clearer and more direct, but also ensures the robustness of the model input.

[0079] Furthermore, based on the flow efficiency and gas permeability quantified in the first step, and combined with other microscopic parameters such as porosity and pore size distribution, flow resistance compensation is applied to the filter cloth. Specifically, all parameters are substituted into a calculation formula to obtain the final equivalent flow permeability.

[0080] Furthermore, macroscopic experimental data is organically combined with multiple key microstructural parameters. This flow resistance compensation mechanism essentially uses precise microstructural information to calibrate macroscopic measurement results. Its advantage is that the resulting equivalent flow permeability surpasses the limitations of traditional single-indicator methods, more realistically and comprehensively reflecting the material's overall performance under actual working conditions, thereby greatly improving the accuracy and predictive value of performance evaluation.

[0081] In this embodiment of the invention, the formula for calculating the equivalent flow permeability includes: in, It is the equivalent flow permeability, It is the gas permeability, It is the porosity in the pore structure data. This refers to the circulation efficiency. It is the average pore diameter in the pore structure data. It is the standard deviation of the aperture distribution.

[0082] Specifically, The gas permeability is directly measured through physical experiments. It represents the air permeability of the filter material at an ideal, macroscopic level and is the starting point for all subsequent correction calculations.

[0083] Furthermore, It represents the volume ratio of pores in filter media and is an indicator of its openness. It appears in cubic form in the molecule, meaning that a small increase in porosity can significantly increase the overall filter media's openness. Because a larger open space provides more channels for gas flow.

[0084] Furthermore, It is the flow efficiency, quantifying the tortuousness and complexity of the actual path gas molecules take through the filter media. It appears as a square in the denominator; a larger τ value indicates a more tortuous path, greater resistance encountered by the fluid, and a significant reduction in flow rate. . This represents the proportion of fibrous solids in the filter media. As part of the denominator, it indicates that the more solids there are, the less space is available for gas, thus reducing... .

[0085] In detail, It primarily focuses on correcting for the internal space of the material, while also considering the effects of porosity and flow efficiency. Higher porosity and lower tortuosity result in a larger value for this correction term, thereby increasing the final equivalent permeability. This step allows for a more profound and accurate quantification of the overall geometric structure of the material on fluid permeability, making it far more comprehensive and precise than the analysis of any single geometric parameter.

[0086] Furthermore, It is the average value of all channel dimensions. Measure the dispersion of all channel dimensions. The larger the aperture, the more uneven the aperture size, indicating the presence of many apertures of varying sizes.

[0087] In detail, This primarily corrects for the uniformity of pore size. The smaller the standard deviation of pore size distribution, the more uniform the pore size; the closer this value is to 1, the smaller its reduction effect on the result. Accurately simulating real-world conditions, even a material with a highly non-uniform pore size distribution and a large average pore size will have numerous tiny bottlenecks that become the main flow resistance points, leading to a decrease in overall permeability.

[0088] For example, in the development of needle-punched felt filter cloth with waterproof and oil-resistant properties, all data is integrated and detailed calculations are performed step by step. The gas permeability is... The porosity is 0.82, the flow efficiency is 1.8, and the average pore size is 65. The standard deviation of the pore size distribution is 8. The final equivalent flow permeability is calculated using the formula for equivalent flow permeability. .

[0089] S6: Determine the performance level of the thorned felt filter cloth based on the preset performance standard and the equivalent flow permeability.

[0090] In this embodiment of the invention, determining the performance level of the punched felt filter cloth based on a preset performance standard and the equivalent flow permeability includes: The equivalent flow permeability is quantified as the performance index of the thorny felt filter cloth; The efficiency index is matched based on a preset efficiency standard to obtain the efficiency level of the thorny felt filter cloth.

[0091] Specifically, this step is a confirmation and assignment process. It formally establishes the equivalent flow permeability, calculated after dual calibration of macroscopic testing and microstructure in the previous steps, as the sole and core quantitative indicator for measuring the overall performance of the filter cloth. Subsequent ratings will be based entirely on this more scientific and comprehensive value, rather than the traditional, uncalibrated air permeability value.

[0092] In detail, by formally establishing the equivalent flow permeability, calibrated for microstructure, as the sole and core performance indicator, it abandons the traditional, uncalibrated single air permeability value, thereby fundamentally enhancing the scientific content of the evaluation system. This approach ensures that the final rating uses a more comprehensive value that better reflects the true overall performance of the material, making the entire evaluation result more reliable and convincing.

[0093] Furthermore, it is necessary to pre-establish a clear set of performance standards for different types of products, such as those requiring high temperature resistance, water and oil resistance, etc. This standard is typically a grading table that divides performance indicators into several consecutive numerical intervals and assigns a corresponding performance level to each interval, such as: Excellent, Good, Average, and Poor. After obtaining the performance indicators of a sample, simply match them with this standard to find its corresponding interval, and thus determine its performance level.

[0094] Furthermore, by pre-setting a clear grading table for different types of products, complex numerical judgments are transformed into simple interval matching, thereby eliminating subjectivity and arbitrariness in the rating process and ensuring that all samples are objectively and consistently evaluated under the same benchmark. More importantly, it successfully transforms precise but not intuitive scientific data for non-professionals into a qualitative grade that is easy to understand, communicate, and make quick decisions, thus improving the efficiency of the evaluation results.

[0095] For example, in the final evaluation stage of the research and development project of needle-punched felt filter cloth with waterproof and oil-repellent functions, the calculated equivalent flow permeability will be used. = This is formally quantified as the core indicator for evaluating the overall performance of the sample.

[0096] Furthermore, for this type of waterproof and oil-resistant filter cloth, the following performance standards are pre-set: when When the efficiency is excellent, it indicates that the thorned felt filter cloth has an ideal structure and excellent air permeability, maintaining extremely low operating resistance while effectively waterproofing and oil-proofing. when When the efficiency is good, it indicates that the quilled felt filter cloth has a balanced structure, good air permeability, reliable performance, and wide applicability. when When the efficiency is medium, it indicates that the thorned felt filter cloth has a relatively dense structure and average air permeability, which meets the basic filtration requirements. when If the efficiency is poor, it indicates that the structure of the felt filter cloth is too dense or the coating treatment has too much impact, resulting in high operating resistance, and process optimization is required.

[0097] The performance level of the waterproof and oil-resistant needle-punched felt sample was determined to be excellent. This clear rating provides quantifiable evidence of the success of the research and development project and can be directly used to guide subsequent production quality inspection standards.

[0098] like Figure 2 The diagram shown is a functional block diagram of a permeability detection-based thorned felt filter cloth performance evaluation system provided in an embodiment of the present invention.

[0099] The permeability detection-based thorned felt filter cloth performance evaluation system 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the permeability detection-based thorned felt filter cloth performance evaluation system 100 may include a cross-sectional microscopic image acquisition module 101, a pore structure data extraction module 102, a permeation path analysis module 103, a gas permeability measurement module 104, a permeation characteristic calibration module 105, and a performance level determination module 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0100] In this embodiment, the functions of each module / unit are as follows: The cross-sectional microscopic image acquisition module 101 is used to acquire cross-sectional microscopic images of the barbed felt filter cloth; The pore structure data extraction module 102 is used to obtain pore structure data of the pore network structure in the cross-sectional micro image; The permeation path analysis module 103 is used to track the permeation path of the cross-sectional micro-image based on the pore structure data, and simultaneously acquire the structural tortuosity of the permeation path; The gas permeability measurement module 104 is used to measure the gas flow rate through the felt filter cloth and to analyze the gas permeability of the felt filter cloth using the gas flow rate. The permeability calibration module 105 is used to calibrate the permeability of the thorned felt filter cloth based on the structural tortuosity and the gas permeability, so as to obtain the equivalent flow permeability of the thorned felt filter cloth. The efficiency level determination module 106 is used to determine the efficiency level of the thorny felt filter cloth based on the preset efficiency standard and the equivalent flow permeability.

[0101] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0102] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0103] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0104] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0105] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for evaluating the efficiency of a needled felt filter cloth based on permeability detection, characterized in that, The method comprises: S1: obtaining a cross-section microscopic image of the needled felt filter cloth; S2: obtaining channel structure data of the channel network structure in the cross-section microscopic image; S3: tracking a permeation path of the cross-section microscopic image based on the channel structure data, and synchronously obtaining a structural tortuosity of the permeation path; S4: measuring a gas flow rate through the needled felt filter cloth, and analyzing a gas permeability of the needled felt filter cloth by using the gas flow rate; S5: calibrating a permeation characteristic of the needled felt filter cloth based on the structural tortuosity and the gas permeability, and obtaining an equivalent flow permeability of the needled felt filter cloth; S6: judging an efficiency grade of the needled felt filter cloth based on a preset efficiency standard and the equivalent flow permeability.

2. The method according to claim 1, wherein the method is characterized by, The obtaining of the cross-section microscopic image of the needled felt filter cloth comprises: performing slicing processing on the needled felt filter cloth to obtain a cross-section sample of the needled felt filter cloth; scanning the cross-section sample to obtain the cross-section microscopic image of the needled felt filter cloth.

3. The method according to claim 1, wherein the method is characterized by, The obtaining of the channel structure data of the channel network structure in the cross-section microscopic image comprises: performing binaryzation processing on the cross-section microscopic image to obtain a black-and-white image of the cross-section microscopic image; removing image noise of the black-and-white image to obtain a channel network structure of the cross-section microscopic image; extracting geometric features of the channel network structure; performing parameterized calculation on the channel network structure based on the geometric features to obtain the channel structure data of the channel network structure in the cross-section microscopic image.

4. The method according to claim 1, wherein the method is characterized by, The tracking of the permeation path of the cross-section microscopic image based on the channel structure data and the synchronous obtaining of the structural tortuosity of the permeation path comprise: performing spatial topology analysis on the needled felt filter cloth based on the channel structure data to obtain a channel network topology structure of the needled felt filter cloth; labeling a permeation path of the cross-section microscopic image based on the channel network topology structure; taking a ratio of a total projection length of the permeation path to a thickness of the needled felt filter cloth as the structural tortuosity of the permeation path.

5. The method according to claim 1, wherein the method is characterized by, The measuring of the gas flow rate through the needled felt filter cloth and the analyzing of the gas permeability of the needled felt filter cloth by using the gas flow rate comprise: measuring a gas volume flow rate of the needled felt filter cloth; performing permeability analysis on the needled felt filter cloth based on the gas volume flow rate and physical features of the needled felt filter cloth to obtain the gas permeability of the needled felt filter cloth, wherein a calculation formula of the gas permeability is: wherein, is the gas permeability, is the gas volumetric flow rate, is the dynamic viscosity of the gas, is the thickness of the needled felt filter cloth, is the cross-sectional area of the needled felt filter cloth, is the pressure difference.

6. The method according to claim 5, wherein the method is characterized by, The measuring of the gas volume flow rate of the needled felt filter cloth comprises: performing a sealed permeability test on the needled felt filter cloth to obtain a real-time flow rate sequence of the needled felt filter cloth; screening out an abnormal value of the real-time flow rate sequence to obtain the gas volume flow rate of the needled felt filter cloth.

7. The method according to claim 1, wherein the method is characterized by, The calibrating of the permeation characteristic of the needled felt filter cloth based on the structural tortuosity and the permeability to obtain the equivalent flow permeability of the needled felt filter cloth comprises: performing reverse analysis on the needled felt filter cloth based on the structural tortuosity to obtain a flow-through efficiency of the needled felt filter cloth; performing flow resistance compensation on the needled felt filter cloth based on the flow-through efficiency and the gas permeability to obtain the equivalent flow permeability of the needled felt filter cloth.

8. The method according to claim 7, wherein the method is characterized by, The calculation formula of the equivalent flow permeability comprises: wherein, is the equivalent flow permeability, is the gas permeability, is the porosity in the pore structure data, is the flow efficiency, is the average pore size in the pore structure data, is the pore size distribution standard deviation.

9. The method according to claim 1, wherein the method is characterized by, The performance level of the needled felt filter cloth is determined based on the preset performance standard and the equivalent flow permeability, and the method comprises the following steps: The equivalent flow permeability is quantified as a performance index of the needled felt filter cloth; The performance index is matched based on the preset performance standard to obtain the performance level of the needled felt filter cloth.

10. A system for evaluating the efficiency of a needled felt filter cloth based on permeability detection, characterized by, The system comprises: A cross-section microscopic image acquisition module for acquiring a cross-section microscopic image of the needled felt filter cloth; A pore structure data extraction module for acquiring pore structure data of a pore network structure in the cross-section microscopic image; A permeation path analysis module for tracking a permeation path of the cross-section microscopic image based on the pore structure data and synchronously acquiring a structure tortuosity of the permeation path; A gas permeability measurement module for measuring a gas flow through the needled felt filter cloth and analyzing a gas permeability of the needled felt filter cloth based on the gas flow; A permeation characteristic calibration module for calibrating permeation characteristics of the needled felt filter cloth based on the structure tortuosity and the gas permeability to obtain an equivalent flow permeability of the needled felt filter cloth; A performance level determination module for determining the performance level of the needled felt filter cloth based on the preset performance standard and the equivalent flow permeability.

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