A method of filter rod mass analytical detection

Abstract

The application provides a filter rod quality analysis detection method, comprising: obtaining a CT tomogram of a filter rod sample and performing three-dimensional reconstruction and re-slicing to obtain a two-dimensional layer chromatogram of the filter rod sample; performing edge detection on each two-dimensional layer chromatogram to obtain the outer edge of the filter rod cross section; performing filter rod porosity calculation and filter rod adhesion identification based on the outer edge and the two-dimensional layer chromatogram; wherein the filter rod porosity calculation step is as follows: generating a mask image based on the outer edge, and performing a bitwise AND operation on the two-dimensional layer chromatogram of the filter rod and the mask image to obtain a filter rod cross section image; counting the number of all pixel points in the filter rod cross section image and the number of pixel points greater than a set gray threshold; calculating the porosity of the cross section by a set formula to obtain the porosity of the cross section; and summing and averaging the porosities of all cross sections to obtain the overall porosity of the filter rod.

Description

Technical Field

[0001] This invention relates to the field of cigarette product testing technology, and more specifically, to a method for analyzing and testing the quality of filter rods. Background Technology

[0002] The filter rod is the most important component of cigarette products, its main function being to filter harmful components such as tar and nicotine produced after the cigarette burns. Simultaneously, adding a filter tip to the inhalation section of the cigarette effectively reduces the irritation of smoke to the throat and lungs, improves the taste of the smoke, and thus affects the smoking experience. Some filters with added flavorings or other additives can directly improve the smoking experience, giving the cigarette more flavor. Therefore, the quality of the cigarette filter rod directly affects the smoking quality and the overall quality of the cigarette product. Porosity and adhesion are important evaluation criteria for filter rod quality, directly affecting filter rod pressure drop and cigarette draw resistance. Filter rod porosity refers to the proportion of voids in the cigarette filter rod, usually determined by the fiber structure of the filter paper and the filter element material. Porosity directly affects the draw resistance, filtration effect, and smoke flowability of the cigarette. When the filter rod's porosity is too high, the draw resistance is greatly reduced, but the filter rod's ability to filter harmful substances is also greatly reduced. Conversely, when the filter rod's porosity is too low, the draw resistance is greatly increased, severely affecting the draw experience. Therefore, an appropriate porosity helps maintain smooth and even smoke flow. Adhesion refers to the improper contact of materials within the cigarette filter rod due to humidity, temperature, or other external factors, causing structural parts of the filter rod to stick together, thus affecting its physical properties. Adhesion can narrow or block the filter rod's pore channels, affecting airflow and the smooth flow of smoke, increasing draw resistance. Furthermore, adhesion can also lead to uneven distribution of filter rod materials, reducing its filtration efficiency and consequently affecting the aroma, taste, and health benefits of tobacco. Therefore, to ensure the quality of cigarette filter rods, it is essential to accurately test the filter rod's porosity and effectively monitor for adhesion to avoid potential quality problems.

[0003] Currently, the detection of filter rod porosity and adhesion mainly relies on manual inspection and simple physical testing methods, such as air permeability testing and tensile testing. While these methods can reflect some of the filter rod's performance to a certain extent, their cumbersome processes, poor accuracy, and lack of real-time capability make them unsuitable for the high-efficiency, high-precision testing requirements of modern cigarette production lines. Furthermore, traditional testing methods struggle to provide a comprehensive and accurate analysis of filter rod porosity and adhesion phenomena. Therefore, developing a technology capable of simultaneously and efficiently detecting both porosity and adhesion phenomena in filter rods is of significant practical importance.

[0004] Chinese patent (CN116823759A) discloses a method for quantitatively characterizing the opening quality of filter rod filament bundles. This patent evaluates the opening quality of the filament bundles by taking online real-time photos of the opened filament bundles and preprocessing the images using image processing technology to calculate the pore distribution between the filament bundles.

[0005] However, this patent uses a camera to photograph the opened filter rod to obtain an image of the opened filament bundle. It detects the porosity of the filament bundle after the filter rod is opened, which requires damaging the filter rod. Furthermore, the above solution uses visible light imaging, which, when applied to the finished filter rod, cannot penetrate multiple layers of packaging material. It can only detect surface defects and cannot effectively detect the internal quality of the finished filter rod.

[0006] In order to solve the above problems, people have been seeking an ideal technological solution. Summary of the Invention

[0007] Therefore, it is necessary to provide a method for analyzing and detecting the quality of filter rods, addressing the aforementioned technical problems. This method utilizes microfocus CT imaging technology to perform three-dimensional scanning of cigarette samples, acquiring two-dimensional tomographic images. The overall porosity of the filter rod and the presence of adhesion within it are calculated through processing of these two-dimensional tomographic images. This method improves the efficiency and accuracy of detecting filter rod porosity and adhesion, enabling online real-time monitoring of filter rod porosity and adhesion defects.

[0008] To achieve the above objectives, the first aspect of the present invention provides a method for analyzing and detecting the quality of filter rods, comprising:

[0009] CT tomographic images of the filter rod sample were acquired and three-dimensional reconstruction and reslicing were performed to obtain two-dimensional tomographic images of the filter rod sample.

[0010] Edge detection is performed on each two-dimensional tomographic image to obtain the outer edge of the filter rod cross-section;

[0011] Filter rod porosity calculation and filter rod adhesion identification are performed based on the outer edge and the two-dimensional tomographic image.

[0012] The steps for calculating the porosity of the filter rod are as follows:

[0013] A mask image is generated based on the outer edge, and a bitwise AND operation is performed on the two-dimensional tomographic image of the filter rod and the mask image to obtain a cross-sectional image of the filter rod.

[0014] The total number of pixels N in the cross-sectional image of the filter rod and the number of pixels n greater than the set grayscale threshold are counted.

[0015] Through formula The porosity Ф of this cross-section is calculated;

[0016] Repeat the above process for the two-dimensional tomographic images of all filter rods to obtain the porosity of all cross sections of the filter rods, and sum and average all the porosities to obtain the overall porosity of the filter rods.

[0017] The steps for identifying filter rod adhesion are as follows:

[0018] Edge detection is performed on all targets within the contour based on the outer edge, and the area of ​​all target contours is calculated. All target contour areas are traversed, and if the target contour area is greater than a set area threshold, it is determined that the filter rod is stuck at the cross section; otherwise, it is determined that there is no sticking. The above steps are repeated on the two-dimensional tomographic images of all filter rods to realize filter rod sticking identification.

[0019] This technical solution employs CT scanning technology, utilizing the strong penetrating power of X-rays to non-destructively penetrate the filter rod packaging paper and obtain a two-dimensional tomographic image of the cigarette's interior. Based on an edge detection algorithm, the filter rod is segmented from the external air portion. By processing the grayscale images of each cross-section of the filter rod, the overall porosity of the filter rod and the presence of adhesion within it are determined. This fundamentally solves the problem of non-destructive testing of filter rod quality and meets the rigid requirement of production lines to perform 100% rapid and non-destructive quality screening of all finished products.

[0020] To achieve the above objectives, a second aspect of the present invention provides a filter rod quality analysis and testing device, comprising:

[0021] The acquisition module is configured to acquire CT tomographic images of the filter rod sample and perform three-dimensional reconstruction and reslicing to obtain two-dimensional tomographic images of the filter rod sample.

[0022] The edge detection module is configured to perform edge detection on each two-dimensional tomographic image to obtain the outer edge of the filter rod cross-section.

[0023] The quality analysis module is configured to calculate the filter rod porosity and identify filter rod adhesion based on the outer edge and the two-dimensional tomographic image;

[0024] The steps for calculating the porosity of the filter rod are as follows:

[0025] A mask image is generated based on the outer edge, and a bitwise AND operation is performed on the two-dimensional tomographic image of the filter rod and the mask image to obtain a cross-sectional image of the filter rod.

[0026] The total number of pixels N in the cross-sectional image of the filter rod and the number of pixels n greater than the set grayscale threshold are counted.

[0027] Through formula The porosity Ф of this cross-section is calculated;

[0028] Repeat the above process for the two-dimensional tomographic images of all filter rods to obtain the porosity of all cross sections of the filter rods, and sum and average all the porosities to obtain the overall porosity of the filter rods.

[0029] The steps for identifying filter rod adhesion are as follows:

[0030] Edge detection is performed on all targets within the contour based on the outer edge, and the area of ​​all target contours is calculated. All target contour areas are traversed, and if the target contour area is greater than a set area threshold, it is determined that the filter rod is stuck at the cross section; otherwise, it is determined that there is no sticking. The above steps are repeated on the two-dimensional tomographic images of all filter rods to realize filter rod sticking identification.

[0031] To achieve the above objectives, a third aspect of the present invention provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0032] Memory, used to store computer programs;

[0033] The processor, when executing a program stored in memory, implements the filter rod quality analysis and detection method as described in the first aspect.

[0034] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the filter rod quality analysis and detection method as described in the first aspect.

[0035] To achieve the above objectives, the fifth aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the filter rod quality analysis and detection method as described in the first aspect.

[0036] The beneficial effects of this invention are as follows:

[0037] This invention utilizes microfocus CT imaging technology to perform three-dimensional scanning of cigarette samples and acquire two-dimensional tomographic images. By processing the two-dimensional tomographic images of the filter rod, the overall porosity of the filter rod and the presence of adhesion within the filter rod are calculated. This method improves the detection efficiency and accuracy of filter rod porosity and adhesion, enabling real-time monitoring of filter rod porosity and adhesion defects. Employing a non-destructive testing method, it avoids damaging the cigarette pack and cigarettes, preserving the sample intact and preventing damage caused by destructive testing. Attached Figure Description

[0038] Figure 1 This is a schematic flowchart of the filter rod quality analysis and testing method described in this invention.

[0039] Figure 2 This is a schematic diagram of CT scan operation.

[0040] Figure 3 This is a schematic diagram of a CT scan.

[0041] Figure 4 A three-dimensional reconstruction model of a cigarette filter rod;

[0042] Figure 5 Two-dimensional tomographic image of a cigarette filter rod.

[0043] In the figure, 1. X-ray source; 2. Platform; 3. Sample base; 4. Sample holder; 5. Flat panel detector; 6. Virtual flat panel detector. Detailed Implementation

[0044] To address the aforementioned issues, this invention proposes a method for analyzing and detecting the quality of filter rods. Based on microfocus CT imaging technology, a three-dimensional scan of cigarette samples is performed to acquire two-dimensional tomographic images. The overall porosity of the filter rod and the presence of adhesion within the rod are calculated through processing of the two-dimensional tomographic images. This method improves the efficiency and accuracy of detecting filter rod porosity and adhesion defects, enabling real-time monitoring of filter rod porosity and adhesion defects.

[0045] The technical solution of the present invention will be further described in detail below through specific embodiments.

[0046] Example 1

[0047] This embodiment provides a method for analyzing and testing the quality of filter rods, such as... Figure 1 As shown, it includes the following steps:

[0048] S1. Obtain CT tomographic images of the filter rod sample and perform three-dimensional reconstruction and reslicing to obtain two-dimensional tomographic images of the filter rod sample.

[0049] Specifically, the detection structure of the CT equipment is as follows: Figure 2 As shown, the specific detection process includes:

[0050] S11, use the sample holder 4 to fix the filter rod sample, and adjust the position of the filter rod sample to ensure that the filter rod sample is perpendicular to the sample base 3.

[0051] S12, turn on the CT equipment, place the sample holder 4 into the slot of the platform 2 inside the CT equipment, and ensure that the sample holder 4 is fixed on the platform 2 to prevent the sample from falling off when the platform 2 rotates.

[0052] S13, set the X-ray source tube voltage to 150kV, X-ray source tube current to 90μA, scanning thickness to 0.004mm, scanning interval to 0.004mm, CT scanning mode to cone-beam scanning, and CT scanning mode to Normal scanning. Move the platform 2 on the stage to center the sample under test within the X-ray scanning range. Control the rotation of the platform 2 to ensure that the cigarette sample within a 360° range is within the X-ray scanning position.

[0053] S14, remove the sample holder 4, perform air calibration on the CT equipment, place the central axis calibration rod on the platform 2, and perform central axis calibration.

[0054] S15, place the sample holder 4 on the platform 2, start the CT scan to scan the cigarette inside the filter rod sample, collect two-dimensional projection data at different angles according to the rotation interval, and transmit the digital signal received by the flat panel detector 5 to the computer for storage.

[0055] Specifically, the schematic diagram of a CT scan is as follows: Figure 3 As shown.

[0056] S16, preprocess the acquired CT tomographic images.

[0057] The two-dimensional projection data acquired at each angle during the aforementioned CT scan are weighted to correct the cone-beam distortion. The formula is as follows:

[0058]

[0059] In the formula, P W (β,a,b) is the weighted two-dimensional projection data; P(β,a,b) is the collected two-dimensional projection data. β is the weighting factor; β is the angle of X-ray emission; a, b are the horizontal and vertical coordinates of the pixel to be reconstructed mapped onto the virtual detector 6.

[0060] S17. The corrected and weighted projection data is then subjected to one-dimensional filtering along the projection of the flat panel detector 5, which is perpendicular to the CT equipment. The formula is as follows:

[0061]

[0062] In the formula, is the filtered data; H(a) is the convolution kernel.

[0063] S18, Reconstruct the image by backprojecting the above data.

[0064] One-dimensional filtered projection data along the X-ray direction The back projection calculation is performed using the following formula:

[0065]

[0066] In the formula, R is the distance from the ray source to the rotation center; U is similar to the weighting factor in the two-dimensional equidistant fan beam projection reconstruction algorithm, and its calculation method is: U(x,y,β)=R+xcosβ+ysinβ.

[0067] In the aforementioned back-projection reconstructed image, due to the large spacing along the Z-axis during sampling, interpolation calculations are required along the Z-axis. Bilinear interpolation is used here, and the specific process is as follows:

[0068] Select the four pixels closest to the pixel P(x,y) to be interpolated, and denote them as Q. 11 (x1,y1),Q 12 (x1,y2),Q 21 (x2,y1),Q 22 (x2, y2). First, interpolation is performed in the x-direction, with the following formula:

[0069]

[0070] Next, interpolation is performed in the y-direction, using the following formula:

[0071]

[0072] Thus, the final pixel value of the pixel P to be interpolated is obtained.

[0073] Through the above steps, a three-dimensional reconstruction model of the filter rod sample can now be constructed, such as... Figure 4 As shown;

[0074] The 3D reconstructed model is resliced ​​to output multiple 2D tomographic images, such as... Figure 5 As shown.

[0075] S2. Perform edge detection on each two-dimensional tomographic image to obtain the outer edge of the filter rod cross-section, specifically as follows:

[0076] S21, Gaussian filtering and convolution are performed on the obtained two-dimensional tomographic image to smooth high-frequency noise and preserve low-frequency components, thereby reducing noise. The Gaussian formula function is as follows:

[0077]

[0078] In the formula, G(x,y) represents the Gaussian function value, corresponding to the filter weight; x,y represent the coordinate offset relative to the filter center; σ represents the standard deviation, which determines the distribution range of the Gaussian function and the degree of blurring of the filter. A larger σ will lead to a stronger blurring effect.

[0079]

[0080] In the formula, I(x,y) represents the original image; G(i,j) represents the filter template; I'(x,y) represents the filtered image; and k is the radius of the filter, which determines the range of the filter.

[0081] S22, use an edge detection algorithm to detect the outer edge of the filter rod in the image.

[0082] Specifically, based on edge tracking algorithms, continuous boundary lines or contour lines are extracted by analyzing the connectivity between pixels. The basic idea is to start from an initial edge point and search for other connected edge points along the edge direction until a complete edge path is formed.

[0083] S23. Based on the set of contours found in the image by the edge detection algorithm described above, by traversing all detected contours, the number of pixels in each contour is counted, and the number of pixels represents the area of ​​the contour. By comparing the area of ​​all contours, the contour with the largest area is extracted, and this contour is the outer edge of the filter rod cross-section.

[0084] S3. Calculate the porosity of the filter rod based on the outer edge and the two-dimensional tomographic image.

[0085] S31, Generate a mask image based on the outer edge extracted in S2, fill the inner part of the outer edge with white, and fill the rest with black.

[0086] A bitwise AND operation is performed on the two-dimensional tomographic image and the mask image of the filter rod to obtain a cross-sectional image of the filter rod, in which the original image inside the outer edge is retained, while the rest is black.

[0087] Therefore, the influence of external air noise on the filter rod will be eliminated, and the porosity inside the filter rod can then be calculated.

[0088] S32, based on the difference in grayscale values ​​between the pores and fibers of the filter rod, a threshold is set, and the number of pixels in the cross-sectional image of the filter rod obtained in S31 that are greater than this threshold is counted, denoted as n. At the same time, the total number of pixels in the cross-sectional image of the filter rod is counted, denoted as N.

[0089] The porosity of this cross-section is obtained through calculation, using the following formula:

[0090]

[0091] In the formula, Ф represents the porosity of the cross section; n represents the number of pixels in the cross-sectional image of the filter rod that are greater than a set threshold; and N represents the total number of pixels in the cross section of the filter rod.

[0092] S33. Repeat the above process for the two-dimensional tomographic images of all filter rods to obtain the porosity of all cross sections of the filter rods. Sum and average all the porosities to obtain the overall porosity of the filter rods. The calculation formula is as follows:

[0093]

[0094] In the formula, k represents the total number of filter rod cross-sections; Ф 总 This indicates the overall porosity of the filter rod, Ф i This represents the porosity of the i-th filter rod cross section.

[0095] S4. Filter rod adhesion identification is performed based on the outer edge and the two-dimensional tomographic image. Specifically:

[0096] S41, based on the maximum contour image of the filter rod obtained in S23, perform edge detection on all targets within the contour.

[0097] The principle of edge detection is described in S22.

[0098] S42: Calculate the area of ​​all target contours obtained from edge detection. Refer to S23 for the specific principle of area calculation.

[0099] S43, iterate through all target contour areas, set an area threshold. If there is a target contour area greater than this threshold, it is determined that the filter rod is stuck at that cross section; otherwise, it means that there is no sticking.

[0100] S44. Repeat the above steps for the two-dimensional tomographic images of all filter rods to effectively detect whether there is adhesion inside the filter rods.

[0101] This invention provides a method for analyzing the quality of filter rods. It utilizes industrial CT technology to scan the filter rods, obtaining two-dimensional tomographic images of the samples. An edge detection algorithm is used to segment the filter rod portion from the external air portion. By processing the grayscale images of each cross-section of the filter rod, the overall porosity of the filter rod and the presence of adhesion within the filter rod are determined.

[0102] Furthermore, this invention solves the problems of low efficiency and insufficient accuracy currently faced in filter rod testing processes, improves testing accuracy and efficiency, and can bring more economic benefits.

[0103] Furthermore, this invention uses mechanical equipment to replace manual labor for inspection, saving manpower and having important guiding significance in production practice.

[0104] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0105] Example 2

[0106] Based on the same inventive concept, this application also provides a filter rod quality analysis and testing device for implementing the filter rod quality analysis and testing method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the filter rod quality analysis and testing device provided below can be found in the limitations of the filter rod quality analysis and testing method described above, and will not be repeated here.

[0107] The filter rod quality analysis and testing device includes:

[0108] The acquisition module is configured to acquire CT tomographic images of the filter rod sample and perform three-dimensional reconstruction and reslicing to obtain two-dimensional tomographic images of the filter rod sample.

[0109] The edge detection module is configured to perform edge detection on each two-dimensional tomographic image to obtain the outer edge of the filter rod cross-section.

[0110] The quality analysis module is configured to calculate the filter rod porosity and identify filter rod adhesion based on the outer edge and the two-dimensional tomographic image;

[0111] The steps for calculating the porosity of the filter rod are as follows:

[0112] A mask image is generated based on the outer edge, and a bitwise AND operation is performed on the two-dimensional tomographic image of the filter rod and the mask image to obtain a cross-sectional image of the filter rod.

[0113] The total number of pixels N in the cross-sectional image of the filter rod and the number of pixels n greater than the set grayscale threshold are counted.

[0114] Through formula The porosity Ф of this cross-section is calculated;

[0115] Repeat the above process for the two-dimensional tomographic images of all filter rods to obtain the porosity of all cross sections of the filter rods, and sum and average all the porosities to obtain the overall porosity of the filter rods.

[0116] The steps for identifying filter rod adhesion are as follows:

[0117] Edge detection is performed on all targets within the contour based on the outer edge, and the area of ​​all target contours is calculated. All target contour areas are traversed, and if the target contour area is greater than a set area threshold, it is determined that the filter rod is stuck at the cross section; otherwise, it is determined that there is no sticking. The above steps are repeated on the two-dimensional tomographic images of all filter rods to realize filter rod sticking identification.

[0118] Example 3

[0119] This embodiment provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0120] Memory, used to store computer programs;

[0121] The processor, when executing the program stored in the memory, implements the filter rod quality analysis and detection method as described in Example 1.

[0122] Example 4

[0123] Based on the above embodiments, this embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the filter rod quality analysis and detection method described in Embodiment 1.

[0124] Example 5

[0125] Based on the above embodiments, this embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the filter rod quality analysis and detection method described in Embodiment 1.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A method for analyzing and testing the quality of filter rods, characterized in that, include: CT tomographic images of the filter rod sample were acquired and three-dimensional reconstruction and reslicing were performed to obtain two-dimensional tomographic images of the filter rod sample. Edge detection is performed on each two-dimensional tomographic image to obtain the outer edge of the filter rod cross-section; Filter rod porosity calculation and filter rod adhesion identification are performed based on the outer edge and the two-dimensional tomographic image. The steps for calculating the porosity of the filter rod are as follows: A mask image is generated based on the outer edge, and a bitwise AND operation is performed on the two-dimensional tomographic image of the filter rod and the mask image to obtain a cross-sectional image of the filter rod. The total number of pixels N in the cross-sectional image of the filter rod and the number of pixels n greater than the set grayscale threshold are counted. Through formula The porosity Ф of this cross-section is calculated. Repeat the above process for the two-dimensional tomographic images of all filter rods to obtain the porosity of all cross sections of the filter rods, and sum and average all the porosities to obtain the overall porosity of the filter rods. The steps for identifying filter rod adhesion are as follows: Edge detection is performed on all targets within the contour based on the outer edge, and the area of ​​all target contours is calculated. All target contour areas are traversed, and if the target contour area is greater than a set area threshold, it is determined that the filter rod is stuck at the cross section; otherwise, it is determined that there is no sticking. The above steps are repeated on the two-dimensional tomographic images of all filter rods to realize filter rod sticking identification.

2. The method for analyzing and detecting the quality of a filter rod according to claim 1, characterized in that, The step of performing edge detection on the two-dimensional tomographic image of the filter rod to obtain the outer edge of the filter rod cross-section includes: Gaussian filtering is applied to the two-dimensional tomographic image of the filter rod, and the outer edge of the filter rod in the image is detected based on an edge detection algorithm; Traverse all detected outer edges, count the number of pixels within each outer edge, and select the contour with the largest number of pixels as the outer edge of the filter rod cross-section.

3. A method for analyzing and testing the quality of a filter rod according to claim 1 or 2, characterized in that, Obtaining CT tomographic images of the filter rod sample includes: Use a sample holder to fix the filter rod sample, and adjust the position of the filter rod sample to ensure that the filter rod sample is perpendicular to the sample base; Turn on the CT equipment and place the sample holder into the mounting platform slot inside the CT equipment, ensuring that the sample holder is fixed on the mounting platform; The X-ray source tube voltage is set to 100kV, the X-ray source tube current to 70μA, the scanning thickness to 0.004mm, the scanning interval to 0.004mm, the CT scanning mode to cone-beam scanning, and the CT scanning mode to Normal scanning. The worktable is moved to position the filter rod sample in the center of the X-ray scanning range; the rotation of the worktable is controlled to ensure that the filter rod sample is in the X-ray scanning position within a 360° range. Remove the sample holder, perform air calibration on the CT equipment, and place the central axis calibration rod on the platform to perform central axis calibration. Place the sample holder on the platform and start the CT scan to scan the cigarette inside the filter rod sample. Collect two-dimensional projection data at different angles according to the rotation interval.

4. The method for analyzing and testing the quality of a filter rod according to claim 3, characterized in that, The steps for 3D reconstruction and reslicing include: The two-dimensional projection data collected at each angle are weighted. The weighted projection data is filtered in one dimension along the projection of the flat panel detector perpendicular to the CT equipment. Back projection calculations were performed on the one-dimensional filtered projection data along the X-ray direction to obtain a three-dimensional reconstruction model of the cigarette inside the filter rod sample. The 3D reconstructed model is resliced ​​to output multiple 2D tomographic images.

5. The method for analyzing and testing the quality of a filter rod according to claim 4, characterized in that, In the back projection calculation process, bilinear interpolation is used to perform interpolation calculations in the Z-axis direction.

6. A filter rod quality analysis and testing device, characterized in that, include: The acquisition module is configured to acquire CT tomographic images of the filter rod sample and perform three-dimensional reconstruction and reslicing to obtain two-dimensional tomographic images of the filter rod sample. The edge detection module is configured to perform edge detection on each two-dimensional tomographic image to obtain the outer edge of the filter rod cross-section. The quality analysis module is configured to calculate the filter rod porosity and identify filter rod adhesion based on the outer edge and the two-dimensional tomographic image; Among them, among them, The steps for calculating the porosity of filter rods are as follows: A mask image is generated based on the outer edge, and a bitwise AND operation is performed on the two-dimensional tomographic image of the filter rod and the mask image to obtain a cross-sectional image of the filter rod. The total number of pixels N in the cross-sectional image of the filter rod and the number of pixels n greater than the set grayscale threshold are counted. Through formula The porosity Ф of this cross-section is calculated. Repeat the above process for the two-dimensional tomographic images of all filter rods to obtain the porosity of all cross sections of the filter rods, and sum and average all the porosities to obtain the overall porosity of the filter rods. The steps for identifying filter rod adhesion are as follows: Edge detection is performed on all targets within the contour based on the outer edge, and the area of ​​all target contours is calculated. All target contour areas are traversed, and if the target contour area is greater than a set area threshold, it is determined that the filter rod is stuck at the cross section; otherwise, it is determined that there is no sticking. The above steps are repeated on the two-dimensional tomographic images of all filter rods to realize filter rod sticking identification.

7. A computer device, characterized in that: It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor, when executing a program stored in memory, implements the filter rod quality analysis and detection method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the filter rod quality analysis and testing method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the filter rod quality analysis and testing method according to any one of claims 1 to 5.

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