Method and system for detecting width and uniformity of cigarette ash packing carbon thread

Abstract

The invention provides a method and a system for detecting the width and uniformity of a cigarette ash packing carbon thread, and belongs to the technical field of cigarette detection. The detection method comprises the following steps: acquiring a multi-angle initial image of a to-be-detected cigarette before combustion and a multi-angle sample image in a combustion stage; pixel difference is carried out on the initial image and the sample image, and a residual image is generated; performing binarization processing on the residual image through an adaptive threshold algorithm, and generating a binary image presenting a carbon wire region; processing the binary image by using a transverse projection method, determining the starting position and the ending position of the carbon thread, and automatically positioning and identifying a carbon thread area by using projection peak analysis; and segmenting the carbon wire region, and calculating the width and uniformity of the carbon wire in the segmented carbon wire region. Accurate and efficient detection of the width and uniformity of the cigarette ash packing carbon thread under the complex background and different illumination conditions is realized.

Description

Technical Field

[0001] The invention relates to the technical field of cigarette detection, and in particular to a detection method and a detection system for the width and regularity of ash and char lines of cigarette packages. Background Art

[0002] When a cigarette is burning, the width and uniformity of the char line directly reflect the uniformity of the cigarette's burning and the stability of the burning state. It is one of the key factors affecting the quality of cigarette burning and smoking, and is of great significance to the appearance quality of cigarettes, the smoking experience of consumers, and the consistency evaluation of products. Cigarettes with narrower char line widths and higher uniformity usually burn more evenly and stably, and the ash column is not easy to collapse. However, char lines with different widths and poor uniformity may indicate that the cigarette burns unstably, and are prone to problems such as ash inclusion and ash column breakage, thus affecting the consumer's experience.

[0003] Traditional methods for detecting the apparent burning quality of cigarettes usually rely on manual detection or simple image processing technology. Manual detection methods are inefficient and easily affected by subjective factors, and cannot meet the needs of fast and accurate quality control. Some detection methods based on image processing mainly rely on single-angle image acquisition and simple threshold segmentation methods. Due to the complex burning state and background interference, the recognition and segmentation accuracy of the char line area is limited. In addition, when dealing with the width and uniformity of the char line, traditional methods lack the extraction of multi-scale features and the effective suppression of image noise, resulting in insufficient stability and accuracy of the results in practical applications.

[0004] With the development of deep learning and image processing technology, char line detection methods based on convolutional neural networks (CNN) and multi-scale feature analysis have gradually become a research hotspot. Convolutional neural networks have powerful feature extraction capabilities and can identify and extract significant features of the char line area during cigarette burning. However, the complexity of cigarette ash images, especially the background interference in the ash column area and the irregular char line morphology after burning, poses challenges to automatic detection.

[0005] In view of this, the inventor, based on many years of experience in production design in this field and related fields, has designed a detection method and system for the width and uniformity of the ash charcoal line of a cigarette package after repeated experiments, in order to solve the problems existing in the prior art. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for detecting the width and regularity of the ash and char lines of cigarette packages. The method can accurately and efficiently detect the width and regularity of the ash and char lines of cigarette packages, overcoming the limitations of low efficiency of traditional manual detection, great influence of subjective factors, and limited detection accuracy based on simple image processing technology.

[0007] To achieve the above-mentioned purpose, the present invention provides a method and system for detecting the width and uniformity of the ash and char line of a cigarette package, wherein the initial images of the cigarette to be tested at multiple angles before combustion and the sample images at multiple angles during the combustion stage are obtained;

[0008] Perform pixel difference between the initial image and the sample image and generate a residual image;

[0009] The residual image is binarized by an adaptive threshold algorithm, and a binary image showing the carbon line area is generated;

[0010] The binary image is processed by lateral projection method, and the starting and ending positions of the carbon line are determined. The projection peak analysis is used to automatically locate and identify the carbon line area.

[0011] The carbon line area is divided, and the carbon line width and carbon line uniformity are calculated in the divided carbon line area.

[0012] The present invention also provides a cigarette package ash char line width and uniformity detection system, characterized in that the system implements the above method and includes the following modules:

[0013] An image acquisition module is used to obtain gray images at multiple angles and under different lighting conditions;

[0014] A residual image generation module generates a residual image through a multi-threshold difference algorithm;

[0015] Image binarization module, which uses an adaptive local dynamic threshold algorithm to binarize the residual image;

[0016] The horizontal projection module automatically locates the starting and ending positions of the carbon line through the projection peak extraction technology;

[0017] Adaptive processing module, which optimizes the characteristics of carbon line area based on multi-scale filtering method of convolutional neural network;

[0018] Carbon line width and uniformity calculation module, which calculates carbon line width and uniformity through multi-channel convolution kernel feature extraction and dynamic projection analysis method;

[0019] The data output module generates a trend chart and test report of the carbon line width and uniformity to evaluate the apparent burning quality of cigarettes.

[0020] The present invention also provides a computer-readable storage medium on which a computer program is stored, characterized in that the computer program implements the steps of the above-mentioned calculation method when executed by a processor.

[0021] The present invention also proposes a computer device, comprising a memory and a processor, wherein a computer program that can be run on the processor is stored in the memory, and the device is characterized in that the steps of the above-mentioned calculation method are implemented when the processor executes the computer program.

[0022] Compared with the prior art, the present invention has the following characteristics and advantages:

[0023] The detection method and detection system for the width and uniformity of the charcoal line of cigarette ash proposed in the present invention obtains images of the cigarette burning process through a multi-angle imaging system, generates residual images using a differential method to reduce background noise, performs binarization processing using an adaptive threshold algorithm, determines the position of the charcoal line using a lateral projection method, optimizes the image using an adaptive processing module, and accurately locates the charcoal line boundary based on contour tracking and multi-scale analysis, and finally calculates the charcoal line width and uniformity. The technical effect of this solution is to improve the efficiency and stability of cigarette burning quality detection, to be able to perform accurate and robust detection under complex backgrounds and different lighting conditions, and to effectively support the dynamic monitoring and evaluation of the apparent burning quality of cigarettes, thereby improving the appearance quality of cigarettes, the smoking experience of consumers, and the consistency evaluation of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings described herein are only for explanation purposes and are not intended to limit the scope of the present invention in any way. In addition, the shapes and proportional dimensions of the various components in the figures are only schematic, used to help understand the present invention, and are not specifically limited to the shapes and proportional dimensions of the various components of the present invention. Those skilled in the art can select various possible shapes and proportional dimensions to implement the present invention according to the teachings of the present invention.

[0025] Figure 1 It is a schematic diagram of the detection process of carbon line width and uniformity of the present invention;

[0026] Figure 2 It is a regional schematic diagram of carbon line width and uniformity of the present invention;

[0027] Figure 3 It is a schematic diagram of the detection method of the width and uniformity of the cigarette pack ash charcoal line of the present invention. DETAILED DESCRIPTION

[0028] The details of the present invention can be more clearly understood with reference to the accompanying drawings and the description of the specific embodiments of the present invention. However, the specific embodiments of the present invention described herein are only used for the purpose of explaining the present invention and cannot be understood as limiting the present invention in any way. Under the guidance of the present invention, technicians can conceive of any possible variations based on the present invention, which should all be considered to belong to the scope of the present invention.

[0029] The present invention proposes a method and system for detecting the width and uniformity of the ash line of a cigarette package, wherein Figure 1 and Figure 3 As shown, the detection method includes:

[0030] Obtaining initial images of the tested cigarette at multiple angles before combustion, and sample images at multiple angles during the combustion stage;

[0031] Perform pixel difference between the initial image and the sample image and generate a residual image;

[0032] The residual image is binarized by an adaptive threshold algorithm, and a binary image showing the carbon line area is generated;

[0033] The binary image is processed by lateral projection method, and the starting and ending positions of the carbon line are determined. The projection peak analysis is used to automatically locate and identify the carbon line area.

[0034] The carbon line area is divided, and the carbon line width and carbon line uniformity are calculated in the divided carbon line area.

[0035] The present invention proposes a detection method and system for the width and uniformity of the charcoal line of cigarette pack ash. By using the image data obtained by the multi-angle imaging system, combined with the residual image generation and the adaptive threshold algorithm, the background interference is effectively reduced and the recognition accuracy of the charcoal line area is improved. The application of the lateral projection method further ensures the accurate positioning of the charcoal line area, while the calculation of the charcoal line width and uniformity provides a quantitative evaluation of the apparent combustion quality of the cigarette, and finally realizes the accurate and efficient detection of the width and uniformity of the charcoal line of cigarette pack ash under complex backgrounds and different lighting conditions.

[0036] In an optional embodiment of the present invention, the initial image and the sample image are pixel-differentiated by a multi-threshold difference algorithm, which can adapt to different lighting conditions and background grayscale levels, and improve the accuracy and robustness of char line area recognition. Its precise pixel difference algorithm can effectively separate the char line area from the background, reduce the interference of background noise, and provide reliable image data for subsequent binarization processing and accurate segmentation of the char line area. The application of the difference method can significantly improve the accuracy of the measurement of char line width and uniformity, thereby providing an efficient and reliable detection method for cigarette quality control.

[0037] Specifically, after preprocessing and alignment, the pixel difference between the initial image before combustion and the sample image during combustion is performed to generate a residual image. The specific method is as follows:

[0038] Pixel difference: Perform pixel-by-pixel difference calculation on the pre-processed image before combustion and the image during combustion. Assume that the initial image is I0(x, y) and the sample image during combustion is It(x, y). Then, the value of each pixel of the residual image R(x, y) is calculated as follows:

[0039] R(x, y) = |I t (x, y)-I0(x, y)|

[0040] The difference calculation emphasizes the changing area of ​​the char line during the combustion process, thereby suppressing the interference of the static background.

[0041] An adaptive threshold segmentation algorithm is applied to the residual image after difference. The threshold is dynamically adjusted according to the brightness of each local area in the image, so that areas with significant changes in grayscale values ​​(such as char lines and burnt areas) are retained, while background noise is suppressed. Adaptive thresholding can be achieved through thresholding after Otsu algorithm or Gaussian blur.

[0042] In an optional embodiment of the present invention, the residual image is binarized by an adaptive threshold algorithm, and the method further includes optimizing the binary image by a multi-scale filtering method based on a convolutional neural network, filtering out subtle noise at different scales and enhancing the continuity of the charcoal line edge. The method of binarizing the residual image by an adaptive threshold algorithm and optimizing the binary image by a multi-scale filtering method based on a convolutional neural network not only improves the accuracy of charcoal line area recognition, but also effectively removes noise in the image and enhances the continuity of the charcoal line edge through multi-scale filtering technology, thereby improving the accuracy of charcoal line width and uniformity measurement. In addition, the application of convolutional neural networks makes the detection method more adaptable and robust to different lighting conditions and background interference, which is of great significance for improving the efficiency and reliability of cigarette product quality control.

[0043] Specifically, the residual image is divided into multiple local area blocks (such as 15×15 or 25×25 pixels) so that different binarization thresholds can be set for each block. Gaussian weighted filtering is used to assign different weights to pixels in the area block according to their distance from the center, and the local threshold T is calculated by weighted averaging of neighboring pixel values. The global offset parameter CC is set, and T′=TC is fine-tuned based on the local threshold to adapt the threshold of each block to the illumination changes in the carbon line area. Generate a binary image: According to the threshold T′, binarize each pixel of the residual image:

[0044]

[0045] Use dilation and erosion operations to eliminate noise points and fill small holes in the charcoal lines to obtain a binary image with clear charcoal lines.

[0046] In an optional embodiment of the present invention, the detection method further includes: removing background noise in the binary image by adaptive filtering, and optimizing the edge features of the charcoal line area in combination with edge enhancement technology to ensure the integrity and continuity of the charcoal line contour. By adding the steps of removing background noise by adaptive filtering and optimizing the edge features of the charcoal line area by edge enhancement technology to the detection method, the adaptive filtering effectively removes the background noise in the binary image, while the edge enhancement technology ensures the integrity and continuity of the charcoal line contour, which not only improves the accuracy of charcoal line area recognition, but also enhances the recognition ability of charcoal line edges, thereby making the measurement results of charcoal line width and uniformity more accurate.

[0047] Specifically, for areas with more background noise, an adaptive median filter is used to better preserve the edge features of the charcoal line. The median filter window size is adaptively adjusted to avoid smoothing the charcoal line edges.

[0048] Use the Sobel operator or Laplacian operator to perform edge detection and highlight the edges of the charcoal line area. In the image after edge detection, the edge is superimposed with the original binary image to enhance the contrast of the edge contour and make the contour of the charcoal line area clearer. Combine the binary image after denoising and edge enhancement operations, and output the optimized image to ensure the integrity and continuity of the charcoal line area.

[0049] In an optional embodiment of the present invention, the detection method further includes: based on contour tracking and multi-scale analysis methods, the boundaries of the charcoal line area are accurately located, and the starting and ending boundaries of the charcoal line are identified using multi-scale convolution feature extraction and dynamic edge tracking technology. By using contour tracking and multi-scale analysis methods to accurately locate the boundaries of the charcoal line area, not only the accuracy of charcoal line area identification is improved, but also the boundaries are accurately located through multi-scale convolution feature extraction and dynamic edge tracking technology, and a complete outline of the charcoal line area is generated. The feature maps extracted at different scales are fused, and the convolution operation is used to enhance the image details, which can ensure the accurate segmentation of the charcoal line contour, thereby identifying the starting and ending boundaries of the charcoal line.

[0050] Specifically, to ensure the precise positioning of the charcoal line boundary, this method uses a convolutional neural network model that combines ResNeXT50 and Swin-Transformer for multi-scale feature extraction, and cooperates with an adaptive gating mechanism and dynamic edge tracking to achieve high-precision segmentation of the charcoal line boundary.

[0051] In an optional example of this implementation, the contour enhancement technology based on edge detection is combined in the contour tracking process to ensure the accuracy and continuity of the carbon line boundary by adaptively adjusting the weight of the edge feature. It is able to ensure that even when the carbon line shape is irregular or the background noise interference is large, the carbon line boundary can be accurately tracked, thereby improving the accuracy of the carbon line width and uniformity measurement.

[0052] Specifically, the ResNeXT50 network performs convolutional feature extraction on carbon line regions of different scales to ensure the resolution of the carbon line regions. The Swin-Transformer network uses the self-attention mechanism to model long-distance dependencies between different regions to ensure that the details of the carbon line boundaries are preserved.

[0053] The extracted feature map is channel-enhanced using the Squeeze-and-Excitation module (SE module) to highlight the salient features of the charcoal line area and ensure accurate separation of the gray column area from the background area. Dynamic edge tracking is performed on the convolution feature map to track the changes in the charcoal line boundary pixel by pixel to ensure accurate boundary positioning and generate a complete outline of the charcoal line area. Feature maps extracted at different scales are fused, and convolution operations are used to enhance image details to ensure accurate segmentation of the charcoal line outline.

[0054] In an optional embodiment of the present invention, segmenting the carbon line region includes: extracting features through a multi-channel convolution kernel, which can capture the details and contours of the carbon line from multiple angles and scales, thereby enhancing the recognition and segmentation accuracy of the carbon line region. The design of the multi-channel convolution kernel enables the network to simultaneously focus on the thickness changes and overall morphology of the carbon line, thereby providing richer and more accurate data in the subsequent calculation of the carbon line width and neatness.

[0055] Specifically, feature extraction through multi-channel convolution kernels involves convolution operations on binary images using convolution kernels of different scales to extract features of the char line area at different scales. The process utilizes the powerful feature extraction capabilities of convolutional neural networks, which can identify and extract significant features of the char line area during cigarette burning.

[0056] In an optional embodiment of the present invention, segmenting the carbon line region includes: using a dynamic projection analysis method to evaluate the change trend of the carbon line along the axial direction of the sample. By dynamically analyzing the projection change of the carbon line region, the sensitivity and adaptability to the change of the carbon line morphology are improved, so that the measurement of the carbon line width and uniformity is more accurate and reliable.

[0057] In an optional embodiment of the present invention, the calculation formula of the carbon line width is:

[0058]

[0059] Where W is the carbon line width, expressed as the average width of the carbon line area; Figure 2 As shown, a is the starting coordinate of the carbon line in the x-axis direction, in mm; b is the ending coordinate of the carbon line in the x-axis direction, in mm; Hx is the coordinate of the highest point of the carbon line in the y-axis direction corresponding to the horizontal coordinate x (x∈[a,b]), in mm; Lx is the coordinate of the lowest point of the carbon line in the y-axis direction corresponding to the horizontal coordinate x (x∈[a,b]), in mm; L is the length between the starting point a and the ending point b of the carbon line in the x-axis direction, in mm.

[0060] In an optional embodiment of the present invention, the calculation formula of carbon line uniformity is:

[0061] U=H max -H min

[0062] Where U is the uniformity of the carbon line, which is expressed by the distance between the highest point and the lowest point of the carbon line area along the axial direction of the sample; H _max H is the maximum value of the projection on the y axis between the starting point a and the ending point b of the carbon line, in mm; _min It is the minimum value of the projection on the y-axis between the starting point a and the ending point b of the carbon line, in mm.

[0063] The invention provides a method and system for detecting the width and uniformity of charcoal lines on cigarette packs. The method uses a lateral projection method to process binary images and determine charcoal line areas.

[0064] Specifically, the binary image is accumulated row by row, and the number of non-zero pixels in each column is calculated. Columns with higher accumulated values ​​usually correspond to the main area of ​​charcoal lines. The peak position is analyzed based on the lateral projection curve. The peak interval usually represents the range of charcoal lines. The projection threshold is set to remove the peak noise below the threshold to accurately locate the starting and ending coordinates of the charcoal line. Based on the starting and ending points of the peak position, the charcoal line area is delineated and a labeling box is generated to ensure that the subsequent steps are further processed in the charcoal line area.

[0065] The present invention also provides a cigarette package ash char line width and uniformity detection system, wherein the system implements the above method and includes the following modules:

[0066] An image acquisition module is used to obtain gray images at multiple angles and under different lighting conditions;

[0067] A residual image generation module generates a residual image through a multi-threshold difference algorithm;

[0068] Image binarization module, which uses an adaptive local dynamic threshold algorithm to binarize the residual image;

[0069] The horizontal projection module automatically locates the starting and ending positions of the carbon line through the projection peak extraction technology;

[0070] Adaptive processing module, which optimizes the characteristics of carbon line area based on multi-scale filtering method of convolutional neural network;

[0071] Carbon line width and uniformity calculation module, which calculates carbon line width and uniformity through multi-channel convolution kernel feature extraction and dynamic projection analysis method;

[0072] The data output module generates a trend chart and test report of the carbon line width and uniformity to evaluate the apparent burning quality of cigarettes.

[0073] Specifically, the image acquisition module includes:

[0074] A high-resolution still imaging camera used to obtain static images of the char lines during cigarette burning.

[0075] A dynamic imaging camera is used to capture real-time images of the changing char lines as a cigarette burns.

[0076] Through dual-camera collaborative imaging, the charcoal line characteristics of cigarette burning can be captured at different time points and viewing angles, ensuring clear images even under complex background conditions.

[0077] The image preprocessing process is to process the collected raw image data through the preprocessing module, including:

[0078] Color calibration: Use a standard color chart to calibrate the image to eliminate color deviations under different lighting conditions.

[0079] Light uniformity adjustment: According to the changes in ambient light, adjust the light uniformity of the image to ensure the clarity and consistency of the cigarette charcoal line image during the burning process.

[0080] Image denoising: Use noise filtering algorithms to remove interference factors such as smoke and ash generated during cigarette burning, thereby enhancing the quality and effectiveness of the image.

[0081] The present invention also provides a computer-readable storage medium on which a computer program is stored, wherein the computer program implements the steps of the above-mentioned calculation method when executed by a processor.

[0082] The present invention further provides a computer device, comprising a memory and a processor, wherein a computer program that can be run on the processor is stored in the memory, wherein the processor implements the steps of the above-mentioned calculation method when executing the computer program.

[0083] The detailed explanation of the above-mentioned embodiments is only intended to explain the present invention so as to facilitate a better understanding of the present invention. However, these descriptions cannot be interpreted as limitations on the present invention for any reason. In particular, the various features described in different embodiments may also be arbitrarily combined with each other to form other embodiments. Unless there is a clear description to the contrary, these features should be understood to be applicable to any embodiment and are not limited to the described embodiments.

Claims

1. A method for detecting the width and uniformity of the ash line of a cigarette package, characterized in that: The detection method comprises: Obtaining initial images of the tested cigarette at multiple angles before combustion, and sample images at multiple angles during the combustion stage; Perform pixel difference between the initial image and the sample image and generate a residual image; The residual image is binarized by an adaptive threshold algorithm, and a binary image showing the carbon line area is generated; The binary image is processed by lateral projection method, and the starting and ending positions of the carbon line are determined. The projection peak analysis is used to automatically locate and identify the carbon line area. The carbon line area is divided, and the carbon line width and carbon line uniformity are calculated in the divided carbon line area.

2. The method for detecting the width and uniformity of the ash line of a cigarette package according to claim 1, characterized in that: The pixel difference between the initial image and the sample image is performed using a multi-threshold difference algorithm.

3. The method for detecting the width and uniformity of the ash line of a cigarette package according to claim 1, characterized in that: The residual image is binarized by an adaptive threshold algorithm, which also includes: The binary image is optimized by a multi-scale filtering method based on convolutional neural network to filter out subtle noise at different scales and enhance the coherence of the carbon line edges.

4. The method for detecting the width and uniformity of the ash line of a cigarette package according to claim 1, characterized in that: The detection method further comprises: The background noise in the binary image is removed by adaptive filtering, and the edge features of the charcoal line area are optimized in combination with edge enhancement technology to ensure the integrity and continuity of the charcoal line contour.

5. The method for detecting the width and uniformity of the ash line of a cigarette package according to claim 1, characterized in that: The detection method further comprises: Based on contour tracking and multi-scale analysis methods, the boundaries of the carbon line area are accurately located, and multi-scale convolution feature extraction and dynamic edge tracking technology are used to identify the start and end boundaries of the carbon line.

6. The method for detecting the width and uniformity of the ash line of a cigarette package according to claim 5, characterized in that: The contour tracking process is combined with the contour enhancement technology based on edge detection, and the weight of the edge feature is adjusted adaptively to ensure the accuracy and continuity of the carbon line boundary.

7. The method for detecting the width and uniformity of the ash line of a cigarette package according to claim 1, characterized in that: Segmenting the carbon line region, including: extracting features through multi-channel convolution kernels.

8. The method for detecting the width and uniformity of the ash line of a cigarette package according to claim 1, characterized in that: Segmenting the carbon line area includes: using a dynamic projection analysis method to evaluate the change trend of the carbon line along the axial direction of the sample.

9. The method for detecting the width and uniformity of the ash line of a cigarette package according to claim 1, characterized in that: The calculation formula of the carbon line width is: Wherein, W is the width of the carbon line, expressed as the average width of the carbon line area; a is the starting coordinate of the carbon line in the x-axis direction, in mm; b is the ending coordinate of the carbon line in the x-axis direction, in mm; Hx is the coordinate of the highest point of the carbon line in the y-axis direction corresponding to the horizontal coordinate x (x∈[a,b]), in mm; Lx is the coordinate of the lowest point of the carbon line in the y-axis direction corresponding to the horizontal coordinate x (x∈[a,b]), in mm; L is the length of the carbon line from the starting point a to the ending point b in the x-axis direction, in mm.

10. The method for detecting the width and uniformity of the ash line of a cigarette package according to claim 1, characterized in that: The calculation formula of the carbon line uniformity is: U=H max -H min Where U is the uniformity of the carbon line, which is expressed by the distance between the highest point and the lowest point of the carbon line area along the axial direction of the sample; H _max H is the maximum value of the projection on the y axis between the starting point a and the ending point b of the carbon line, in mm; _min It is the minimum value of the projection on the y-axis between the starting point a and the ending point b of the carbon line, in mm.

11. A cigarette pack ash line width and uniformity detection system, characterized in that: The system implements the method described in any one of claims 1 to 10, and comprises the following modules: An image acquisition module is used to obtain gray images at multiple angles and under different lighting conditions; A residual image generation module generates a residual image through a multi-threshold difference algorithm; Image binarization module, which uses an adaptive local dynamic threshold algorithm to binarize the residual image; The horizontal projection module automatically locates the starting and ending positions of the carbon line through the projection peak extraction technology; Adaptive processing module, which optimizes the characteristics of carbon line area based on multi-scale filtering method of convolutional neural network; Carbon line width and uniformity calculation module, which calculates carbon line width and uniformity through multi-channel convolution kernel feature extraction and dynamic projection analysis method; The data output module generates a trend chart and test report of the carbon line width and uniformity to evaluate the apparent burning quality of cigarettes.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the computing method according to any one of claims 1 to 10 are implemented.

13. A computer device comprising a memory and a processor, wherein a computer program capable of running on the processor is stored in the memory, characterized in that: When the processor executes the computer program, the steps of the computing method according to any one of claims 1 to 10 are implemented.

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