CN104198325A - Method for measuring ratio of cut stem to cut tobacco based on computer vision - Google Patents
Method for measuring ratio of cut stem to cut tobacco based on computer vision Download PDFInfo
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Abstract
The invention discloses a method for measuring a ratio of cut stem to cut tobacco based on computer vision. The method comprises the following steps: A, acquiring each cut stem image by an image acquisition system; B, preprocessing the multiple acquired cut stem images; C, respectively acquiring image features of the cut stem and calculating the feature data quantity of the cut stem; D, establishing a feature database; E, acquiring images of components of to-be-measured cut stem; F, preprocessing the images of the components of the to-be-measured cut stem; G, calculating feature data quantity of the to-be-measured cut tobacco in the images of the components of the to-be-measured cut stem, performing relevancy calculation, and analyzing and identifying the components of the cut stem according to a relevancy calculation result; H, sorting the cut stem out from the to-be-measured cut tobacco by a sorting system; and I, respectively weighing the cut stem sorted out and residual components and calculating the ratio of the cut stem components to the to-be-measured cut tobacco. According to the method disclosed by the invention, the cut stem components in the cut tobacco can be rapidly, accurately and automatically measured, the measurement efficiency and accuracy are improved, and the labor intensity of operators is reduced.
Description
Technical Field
The invention relates to a method for measuring the ratio of cut stems in cut tobacco, in particular to a method for measuring the ratio of cut stems in cut tobacco based on computer vision.
Background
The cigarette formula design is the basis and the core of the product design of cigarette enterprises, and the accurate blending of components such as cut tobacco, expanded cut tobacco, cut stems, reconstituted tobacco and the like in cigarettes has different degrees of influence on the physical indexes, smoke characteristics and sensory quality of cigarettes. Therefore, the method for rapidly and accurately measuring the proportion of the components such as cut tobacco leaves, expanded cut tobacco leaves, cut stems, reconstituted tobacco leaves and the like in the tobacco shreds has important significance for examining the accuracy of a formula design target, stabilizing the quality of a tobacco shred mixing process and homogenizing production.
Because the characteristics of the detection object are complex and relate to the bottleneck of the related technology, the determination of the tobacco shred composition still depends on manual sorting and artificial interpretation. At present, the commonly used method for measuring the ratio of cut stems is to separate the cut stems from other components in the cigarettes through water, dry the cut stems and weigh the dried cut stems, and calculate the ratio of the cut stems. The existing detection method has complex operation steps and low detection efficiency, larger errors can be generated along with the increase of workload, the method is not suitable for mass detection, the measurement efficiency and the measurement precision are difficult to adapt to the requirements of modern detection and high-quality cigarette production, and larger errors also exist among detection results of different personnel.
Due to the difference of the processing method and the characteristics of the raw materials, the difference of textures, colors, forms and edge smoothness degrees exists among different components of the cut tobacco, and the existence of the difference provides characteristic parameters for identifying each component by a computer vision means. Compared with other components, the texture and edge characteristics of the cut stems are very obvious and can be distinguished from other components by computer vision technology.
Disclosure of Invention
The invention aims to provide a method for measuring the proportion of cut stems in cut tobacco based on computer vision, which can acquire and process images of single-component cut stems through a computer, acquire characteristic data quantity of the cut stems, establish a characteristic database, analyze and identify the cut stems in multi-component cut tobacco through the characteristic database, finally realize the quick, accurate and automatic measurement of the cut stem components in the cut tobacco, improve the measurement efficiency and accuracy and reduce the labor intensity of workers.
The invention adopts the following technical scheme:
a method for measuring the ratio of cut stems in cut tobacco based on computer vision comprises the following steps:
a: placing a plurality of cut stems flatly without overlapping, and then respectively collecting images of the cut stems by using an image collection system;
b: preprocessing the acquired multiple cut stem images by using an image processing and analyzing system to remove interference and noise in each cut stem image;
c: respectively acquiring image characteristics of the cut stems in the multiple cut stem images by using an image processing and analyzing system, and then calculating characteristic data quantity of the cut stems according to the image characteristics of the cut stems;
d: establishing a characteristic database according to the characteristic data quantity of the cut stems in the multiple cut stem images by using an image processing and analyzing system;
e: the tobacco shreds to be detected are placed flatly and non-overlapped through a spreading and separating system, and an image acquisition system is used for acquiring images of all components of the tobacco shreds to be detected;
f: preprocessing the acquired images of the components of the tobacco shreds to be detected by using an image processing and analyzing system, and removing interference and noise in the images of the components of the tobacco shreds to be detected;
g: d, the image processing and analyzing system calculates the characteristic data quantity of the tobacco shreds to be detected in the component images of the tobacco shreds to be detected, calculates the correlation degree of the characteristic data quantity of the cut stems in the characteristic database established in the step D, and analyzes and identifies the cut stem components mixed in the tobacco shreds according to the calculation result of the correlation degree;
h: the image processing and analyzing system sends the analysis and identification results to a sorting system, and the sorting system sorts out cut stems in the cut tobacco to be detected;
i: and respectively weighing the mass of the cut stems sorted by the sorting system and the mass of the residual components, and calculating the proportion of the cut stem components in the cut tobacco to be detected.
In the step B, the image processing and analyzing system scans the acquired cut stem image with a 5 × 5 pixel scanning window in the order from top to bottom and from left to right, calculates a mean value and a variance Var of the cut stem image in the scanning window, and if the variance Var is greater than a set threshold TDAnd smoothing the point by adopting a rapid median filtering method to remove interference and noise in the cut stem image.
In the step C, the image processing and analyzing system converts the acquired cut stem image into an HSV color space; carrying out edge detection on the R, G, B, H, S, V component images by combining Canny and Log edge detection operators, and respectively recording the pixel variance value V of the tobacco shred area in R, G, B, H, S, V component imagesR、VG、VB、VH、VS、VV(ii) a Then, calculating four texture characteristic values of contrast, entropy, angle second moment and correlation of the tobacco shred area in the cut stem image by using the gray level co-occurrence matrix; wherein the R component image is represented in RGB colorIn the color space, the R value of each pixel is unchanged, and the G value and the B value are both zero; the G component image is represented in an RGB color space, the G value of each pixel point is unchanged, and the R value and the B value are both zero; b component images are represented in RGB color space, the B value of each pixel point is unchanged, and the R value and the G value are both zero; the H component image is represented in an HSV color space, the H value of each pixel point is unchanged, and the S value and the V value are zero; the S component image is represented in an HSV color space, the S value of each pixel point is unchanged, and the H value and the V value are zero; the V component image is represented in an HSV color space, the V value of each pixel point is unchanged, and the H value and the S value are zero; the characteristic data quantity in the step C comprises ten characteristic values which are respectively V of the tobacco shred area in the cut stem imageR、VG、VB、VH、VS、VVPixel variance values on the six components, and four texture characteristic values of contrast, entropy, angle second moment and correlation of the tobacco shred area in the cut stem image.
In the step D, the image processing and analyzing system respectively calculates the characteristic data quantity of the cut stems in each cut stem image and counts the distribution range C of each characteristic valuei(i 1, 2, …,10) and then multiplying the values of each range by the corresponding scaling factor ei(i ═ 1, 2, …,10), finally creating a feature database Ti＝Ciei(i-1, 2, …,10), wherein,the inverse of the degree of dispersion.
In the step F, the image processing and analyzing system scans the acquired component images of the tobacco shreds to be detected in the sequence from top to bottom and from left to right by adopting a scanning window with 5 multiplied by 5 pixels, calculates the mean value and the variance Var of the component images of the tobacco shreds to be detected in the scanning window, and if the variance Var is larger than a set threshold T, calculates the mean value and the variance Var of the component images of the tobacco shreds to be detected in the scanning windowDAnd smoothing the point by adopting a rapid median filtering method to remove interference and noise in each component image of the tobacco shreds to be detected.
In step G, the image processing and analyzing system calculates the data to be processed respectivelyMeasuring ten characteristic values in the tobacco shred characteristic data quantity, respectively importing the ten characteristic values into a characteristic database, then calculating the correlation degree of the tobacco shred to be measured and the cut stems by an image processing analysis system, wherein the calculation formula of the correlation degree R of the tobacco shred to be measured and the cut stems is <math>
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</math> The quantity of the ten characteristic values of the tobacco shreds to be detected within the standard range of the characteristic database is determined; x is the number ofiIn order to correspond to the characteristic value(s),the mean value of the characteristic value in the characteristic database; if the correlation degree R is greater than or equal to the correlation degree threshold T, judging the current tobacco shred to be detected to be the cut stem; if the correlation degree R is smaller than a correlation degree threshold value T, judging that the current tobacco shred to be detected is not the cut stem, wherein the correlation degree threshold value T is the discrete degree of the corresponding characteristic databaseT∈[0.25,0.75]，
Based on a computer vision technology, the method acquires and processes the cut stem image with a single component, acquires the characteristic data quantity of the cut stems, establishes a characteristic database, analyzes and identifies the cut stems in the multi-component cut tobacco through the characteristic database, sorts the cut stems, can avoid the influence of manual measurement on a test result in the prior art, and eliminates human errors; according to the method, the cut stem characteristics are collected and input into the database, finally, the cut tobacco to be tested is compared and calculated with the cut stem characteristics in the database one by one, and finally, cut stem components in the cut tobacco are sorted out, so that the testing speed is high, and other testing data such as area proportion, shape and size parameters and the like can be provided; the measurement process is fully automatically processed, so that the measurement efficiency, accuracy and measurement precision can be improved, and the labor amount is obviously reduced.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
As shown in figure 1, the method for measuring the ratio of cut stems in cut tobacco based on computer vision is characterized by comprising the following steps of:
a: placing a plurality of cut stems flatly without overlapping, and then respectively collecting images of the cut stems by using an image collection system;
b: preprocessing the acquired multiple cut stem images by using an image processing and analyzing system to remove interference and noise in each cut stem image;
when preprocessing and interference and noise removal are carried out, the image processing and analyzing system scans the acquired cut stem image by adopting a scanning window with 5 multiplied by 5 pixels from top to bottom and from left to right, then calculates the mean value and variance Var of the cut stem image in the scanning window, and if the variance Var is larger than a set threshold T, the variance Var is larger than the set threshold TDIf so, indicating that the pixel value at the position has large change, smoothing the point by adopting a rapid median filtering method to remove interference and noise in the cut stem image. The calculation of the mean and variance Var of the image belongs to the prior art in the field, and the smoothing by the fast median filtering method to remove the interference and noise also belongs to the prior art in the field, and is not described herein again.
C: respectively acquiring image characteristics of the cut stems in the multiple cut stem images by using an image processing and analyzing system, and then calculating characteristic data quantity of the cut stems according to the image characteristics of the cut stems;
when the step C is carried out, the image processing and analyzing system firstly converts the acquired cut stem image into an HSV color space; combining Canny and Log edge detection operators respectivelyR, G, B, H, S, V, edge detection is carried out on the images of the six components, and pixel variance values V of tobacco shred areas in the images of the components are recorded respectivelyR、VG、VB、VH、VS、VV(ii) a Then, calculating four texture characteristic values of contrast, entropy, angle second moment and correlation of the tobacco shred area in the cut stem image by using the gray level co-occurrence matrix; the R component image is represented in an RGB color space, the R value of each pixel point is unchanged, and the G value and the B value are both zero; the H component image is represented in an HSV color space, the H value of each pixel point is unchanged, and the S value and the V value are zero; the other component images and so on.
The characteristic data quantity in the step C includes ten characteristic values, which are respectively the pixel variance value V of the cut stem region in the R, G, B, H, S, V component imageR、VG、VB、VH、VS、VVAnd four texture characteristic values of contrast, entropy, angle second moment and correlation of the tobacco shred area in the cut stem image. Converting the image into an HSV color space, performing edge detection on the image by using Canny and Log edge detection operators, and calculating contrast, entropy, angular second moment and correlation by using a gray level co-occurrence matrix are the prior art in the field, and are not described herein again.
D: establishing a characteristic database according to the characteristic data quantity of the cut stems in the multiple cut stem images by using an image processing and analyzing system;
the image processing and analyzing system respectively calculates the characteristic data quantity of the cut stems in each cut stem image and counts the distribution range C of each characteristic valuei(i 1, 2, …,10) and then multiplying the values of each range by the corresponding scaling factor ei(i ═ 1, 2, …,10), finally creating a feature database Ti＝Ciei(i ═ 1, 2, …, 10). Wherein,the inverse of the degree of dispersion.
E: the tobacco shreds to be detected are placed flatly and non-overlapped through a spreading and separating system, and an image acquisition system is used for acquiring images of all components of the tobacco shreds to be detected;
f: b, preprocessing the acquired image by using the method in the B, and removing interference and noise in each component image of the tobacco shreds to be detected, wherein the specific process is not repeated;
g: and D, calculating the characteristic data quantity of the tobacco shreds to be detected in each component image of the tobacco shreds to be detected by the image processing and analyzing system, calculating the correlation degree of the characteristic data quantity of the cut stems in the characteristic database established in the step D, and analyzing and identifying the cut stem components mixed in the tobacco shreds according to the calculation result of the correlation degree.
In step G, the image processing and analyzing system respectively calculates ten characteristic values of the tobacco shreds to be detected, namely pixel variance values V of tobacco shred areas in R, G, B, H, S, V component imagesR、VG、VB、VH、VS、VVAnd the contrast, entropy, angular second moment and correlation of tobacco shred areas in the tobacco shred image. And respectively importing the ten characteristic values into a characteristic database, and then calculating the correlation degree of the tobacco shreds to be detected and the cut stems through an image processing and analyzing system, wherein the calculation formula of the correlation degree R of the tobacco shreds to be detected and the cut stems is as follows:
wherein <math>
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</math> Wherein n ∈ [1,10 ]]And the quantity (V in the characteristic database) in the standard range of the characteristic database in the 10 characteristic values of the tobacco shreds to be detected is representedR、VG、VB、VH、VS、VVThe distribution range corresponding to each of the ten values of the contrast, the entropy and the angular second moment is the standard range of the value); x is the number ofiThe corresponding characteristic value is represented by a value,the mean value of the characteristic value in the characteristic database;
if the correlation degree R is greater than or equal to the correlation degree threshold T, judging the current tobacco shred to be detected to be the cut stem; and if the correlation degree R is less than T, judging that the current tobacco shred to be detected is not the cut stem. Wherein, in the actual detection and identification process, the correlation threshold T corresponds to the discrete degree of the characteristic databaseT∈[0.25,0.75]，eiThe calculation method of (c) has been given in step D. The larger the discrete degree is, the larger the standard range of the characteristic database is, and the smaller the corresponding T is; conversely, the larger T.
H: the image processing and analyzing system sends the analysis and identification results to a sorting system, and the sorting system sorts out cut stems in the cut tobacco to be detected;
i: and respectively weighing the mass of the cut stems sorted by the sorting system and the mass of the residual components, and calculating the proportion of the cut stem components in the cut tobacco to be detected.
The image acquisition system comprises an illumination device, an imaging device and image acquisition software, wherein the illumination device is used for providing proper illumination for the cut stems and the cut tobacco to be detected so as to obtain clear and real images; the illuminating device can adopt light source systems such as a plane light source, an annular light source, a light-emitting LED array, a backlight source and the like which can provide uniform high-intensity illumination; the imaging device mainly comprises a lens and a camera, and is used for acquiring images of cut stems and cut tobacco to be detected by matching with image acquisition software; the image acquisition software can adopt various software in the market, such as Motic2.0 image acquisition software; the image analysis processing system can adopt an upper computer to realize related functions by matching with software programmed according to the existing image processing analysis technology, such as MATLAB image processing analysis software; the spreading and separating system comprises a mechanical device or a device combination which can smoothly and non-overlapped separate and spread the to-be-detected cut tobacco, such as a conveying belt, a vibrating screen and a vibrating platform, and the sorting system comprises a mechanical sorting machine, a mechanical arm, a positive pressure or negative pressure suction pipe and the like, and can sort out the identified cut tobacco and other cut tobacco components. All the above devices and corresponding software belong to existing products, and are not described herein again.
The invention is further illustrated by the following examples:
example 1
1) Placing 2 cut stems in a highlight LED lighting array in a flat and non-overlapping manner, and acquiring 2 cut stem images by matching a CCD camera and an automatic focusing lens with Motic2.0 image acquisition software at a computer end;
2) preprocessing the obtained 2 cut stem images by using MATLAB image processing and analyzing software to remove interference and noise in each cut stem image;
3) respectively acquiring image characteristics of the cut stems in the 2 cut stem images by the computer, and then calculating characteristic data quantity of the cut stems according to the image characteristics of the cut stems;
4) the computer establishes a characteristic database according to the characteristic data quantity of the cut stems in the 2 cut stem images;
5) the tobacco shreds to be detected are placed under a strong light LED illumination array in a flat and non-overlapping mode through a spreading separation system, and images of all components of the tobacco shreds to be detected are collected through a CCD camera and an automatic focusing lens in cooperation with Motic2.0 image collection software at a computer end;
6) preprocessing the acquired images of the components of the tobacco shreds to be detected by using MATLAB image processing analysis software to remove interference and noise in the images of the components of the tobacco shreds to be detected;
7) d, calculating the characteristic data quantity of the tobacco shreds to be detected in each component image of the tobacco shreds to be detected by the computer, carrying out correlation calculation on the characteristic data quantity of the cut stems in the characteristic database established in the step D, and analyzing and identifying the cut stem components mixed in the tobacco shreds according to the correlation calculation result;
8) the computer sends the analysis and identification results to a sorting system, and the sorting system sorts out cut stems in the cut tobacco to be detected;
9) the mass of the cut stems sorted by using the balance weight is 0.6g, and the mass of the rest components is 1.4g, so that the proportion of the cut stem components in the cut tobacco is 30%.
Example 2
1) Placing 20 cut stems under a light plane source in a flat and non-overlapping manner, and acquiring 20 cut stem images by matching a CCD camera and a micro-distance prime lens with Motic2.0 image acquisition software at a computer end;
2) preprocessing the obtained 20 cut stem images by using MATLAB image processing and analyzing software to remove interference and noise in each cut stem image;
3) respectively acquiring image characteristics of the cut stems in 20 cut stem images by a computer, and calculating characteristic data quantity of the cut stems according to the image characteristics of the cut stems;
4) the computer establishes a characteristic database according to the characteristic data quantity of the cut stems in the 20 cut stem images;
5) the method comprises the following steps of flatly and non-overlapping placing tobacco shreds to be detected under a light plane light source through a spreading separation system, and collecting component images of the tobacco shreds to be detected through a CCD camera and a microspur prime lens in cooperation with Motic2.0 image collection software at a computer end;
6) preprocessing the acquired images of the components of the tobacco shreds to be detected by using MATLAB image processing analysis software to remove interference and noise in the images of the components of the tobacco shreds to be detected;
7) d, calculating the characteristic data quantity of the tobacco shreds to be detected in each component image of the tobacco shreds to be detected by the computer, carrying out correlation calculation on the characteristic data quantity of the cut stems in the characteristic database established in the step D, and analyzing and identifying the cut stem components mixed in the tobacco shreds according to the correlation calculation result;
8) the computer sends the analysis and identification results to a sorting system, and the sorting system sorts out cut stems in the cut tobacco to be detected;
9) the mass of the cut stems sorted by using the balance weight is 1.0g, and the mass of the residual components is 4.5g, so that the proportion of the cut stem components in the cut tobacco is 18%.
Claims (6)
1. A method for measuring the ratio of cut stems in cut tobacco based on computer vision is characterized by comprising the following steps:
a: placing a plurality of cut stems flatly without overlapping, and then respectively collecting images of the cut stems by using an image collection system;
b: preprocessing the acquired multiple cut stem images by using an image processing and analyzing system to remove interference and noise in each cut stem image;
c: respectively acquiring image characteristics of the cut stems in the multiple cut stem images by using an image processing and analyzing system, and then calculating characteristic data quantity of the cut stems according to the image characteristics of the cut stems;
d: establishing a characteristic database according to the characteristic data quantity of the cut stems in the multiple cut stem images by using an image processing and analyzing system;
e: the tobacco shreds to be detected are placed flatly and non-overlapped through a spreading and separating system, and an image acquisition system is used for acquiring images of all components of the tobacco shreds to be detected;
f: preprocessing the acquired images of the components of the tobacco shreds to be detected by using an image processing and analyzing system, and removing interference and noise in the images of the components of the tobacco shreds to be detected;
g: d, the image processing and analyzing system calculates the characteristic data quantity of the tobacco shreds to be detected in the component images of the tobacco shreds to be detected, calculates the correlation degree of the characteristic data quantity of the cut stems in the characteristic database established in the step D, and analyzes and identifies the cut stem components mixed in the tobacco shreds according to the calculation result of the correlation degree;
h: the image processing and analyzing system sends the analysis and identification results to a sorting system, and the sorting system sorts out cut stems in the cut tobacco to be detected;
i: and respectively weighing the mass of the cut stems sorted by the sorting system and the mass of the residual components, and calculating the proportion of the cut stem components in the cut tobacco to be detected.
2. The method for measuring the ratio of cut stems in cut tobacco based on computer vision according to claim 1, is characterized in that: in the step B, the image processing and analyzing system scans the acquired cut stem image with a 5 × 5 pixel scanning window in the order from top to bottom and from left to right, calculates a mean value and a variance Var of the cut stem image in the scanning window, and if the variance Var is greater than a set threshold TDAnd smoothing the point by adopting a rapid median filtering method to remove interference and noise in the cut stem image.
3. The method for measuring the cut stem proportion in the cut tobacco based on computer vision according to claim 2, is characterized in that: in the step C, the image processing and analyzing system converts the acquired cut stem image into HSV color space(ii) a Carrying out edge detection on the R, G, B, H, S, V component images by combining Canny and Log edge detection operators, and respectively recording the pixel variance value V of the tobacco shred area in R, G, B, H, S, V component imagesR、VG、VB、VH、VS、VV(ii) a Then, calculating four texture characteristic values of contrast, entropy, angle second moment and correlation of the tobacco shred area in the cut stem image by using the gray level co-occurrence matrix; the R component image is represented in an RGB color space, the R value of each pixel point is unchanged, and the G value and the B value are both zero; the G component image is represented in an RGB color space, the G value of each pixel point is unchanged, and the R value and the B value are both zero; b component images are represented in RGB color space, the B value of each pixel point is unchanged, and the R value and the G value are both zero; the H component image is represented in an HSV color space, the H value of each pixel point is unchanged, and the S value and the V value are zero; the S component image is represented in an HSV color space, the S value of each pixel point is unchanged, and the H value and the V value are zero; the V component image is represented in an HSV color space, the V value of each pixel point is unchanged, and the H value and the S value are zero; the characteristic data quantity in the step C comprises ten characteristic values which are respectively V of the tobacco shred area in the cut stem imageR、VG、VB、VH、VS、VVPixel variance values on the six components, and four texture characteristic values of contrast, entropy, angle second moment and correlation of the tobacco shred area in the cut stem image.
4. The method for measuring the ratio of cut stems in cut tobacco based on computer vision according to claim 3, is characterized in that: in the step D, the image processing and analyzing system respectively calculates the characteristic data quantity of the cut stems in each cut stem image and counts the distribution range C of each characteristic valuei(i 1, 2, …,10) and then multiplying the values of each range by the corresponding scaling factor ei(i ═ 1, 2, …,10), finally creating a feature database Ti＝Ciei(i-1, 2, …,10), wherein,the inverse of the degree of dispersion.
5. The method for measuring the ratio of cut stems in cut tobacco based on computer vision according to claim 4, is characterized in that: in the step F, the image processing and analyzing system scans the acquired component images of the tobacco shreds to be detected in the sequence from top to bottom and from left to right by adopting a scanning window with 5 multiplied by 5 pixels, calculates the mean value and the variance Var of the component images of the tobacco shreds to be detected in the scanning window, and if the variance Var is larger than a set threshold T, calculates the mean value and the variance Var of the component images of the tobacco shreds to be detected in the scanning windowDAnd smoothing the point by adopting a rapid median filtering method to remove interference and noise in each component image of the tobacco shreds to be detected.
6. The method for measuring the ratio of cut stems in cut tobacco based on computer vision according to claim 5, is characterized in that: in the step G, the image processing and analyzing system respectively calculates ten characteristic values in the characteristic data quantity of the tobacco shred to be detected and respectively guides the ten characteristic values into the characteristic database, then the image processing and analyzing system calculates the correlation degree between the tobacco shred to be detected and the cut stem, and the calculation formula of the correlation degree R between the tobacco shred to be detected and the cut stem is <math>
<mrow>
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<msub>
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</msub>
</mrow>
<mn>10</mn>
</mfrac>
<mo>,</mo>
</mrow>
</math> Wherein <math>
<mrow>
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</msub>
<mo>=</mo>
<mfenced open='{' close=''>
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</math> The quantity of the ten characteristic values of the tobacco shreds to be detected within the standard range of the characteristic database is determined; x is the number ofiIn order to correspond to the characteristic value(s),the mean value of the characteristic value in the characteristic database; if the correlation degree R is greater than or equal to the correlation degree threshold T, judging the current tobacco shred to be detected to be the cut stem; if the correlation degree R is smaller than a correlation degree threshold value T, judging that the current tobacco shred to be detected is not the cut stem, wherein the correlation degree threshold value T is the discrete degree of the corresponding characteristic databaseT∈[0.25,0.75]，
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| CN116977658A (en) * | 2023-08-07 | 2023-10-31 | 江苏秦郡机械科技有限公司 | Multidimensional vibration screening method and system |
| CN116977658B (en) * | 2023-08-07 | 2024-01-26 | 江苏秦郡机械科技有限公司 | Multidimensional vibration screening method and system |
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