CN111948104A - Stem granularity detection and classification control method - Google Patents

Stem granularity detection and classification control method Download PDF

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Publication number
CN111948104A
CN111948104A CN202010605702.8A CN202010605702A CN111948104A CN 111948104 A CN111948104 A CN 111948104A CN 202010605702 A CN202010605702 A CN 202010605702A CN 111948104 A CN111948104 A CN 111948104A
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stem
stems
length
control method
width
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徐大勇
堵劲松
李善莲
邓国栋
张玉海
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Zhengzhou Tobacco Research Institute of CNTC
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Zhengzhou Tobacco Research Institute of CNTC
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/02Investigating particle size or size distribution
    • G01N15/0205Investigating particle size or size distribution by optical means
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/02Investigating particle size or size distribution
    • G01N15/0272Investigating particle size or size distribution with screening; with classification by filtering
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/02Investigating particle size or size distribution
    • G01N2015/0288Sorting the particles

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  • Chemical & Material Sciences (AREA)
  • Dispersion Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
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  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

A stem particle size detection and classification control method includes the steps of separating stems according to samples taken according to detection purposes, collecting the separated stems, fully dispersing the stems through a dispersing device, separating the stems from one another, collecting all stem images through an image collecting device, and processing and analyzing the collected stem images. The stem particle size detection and classification control method provided by the invention can accurately determine the particle size of the stems, and is more accurate and reliable than the traditional screening method and stable in detection result.

Description

Stem granularity detection and classification control method
Technical Field
The invention relates to the technical field of tobacco processing, in particular to a stem particle size detection and classification control method.
Background
The definition of a stem label in tobacco terminology part 2 tobacco products and tobacco processing GB/T18771.2-2015 is: the tobacco shred has a shape similar to that of toothpick, and is not expanded or the expansion effect does not reach the tobacco stem required by the rolling process. The rolled stem sticks in the cigarettes easily cause the phenomena of the prickling of the cigarettes, the opening burst of the cigarettes during the combustion and the like. Due to the fact that the number of the stems mixed in the tobacco shreds is large, complete removal is not practical, and control over the stems is always a difficult problem in the tobacco industry.
Currently, the related research on the stem in the tobacco industry mainly focuses on the following aspects:
(1) controlling the stem content in the threshed and redried leaves.
(2) Research and development of stem separation/elimination and content detection methods.
(3) And controlling the proportion of the cigarette stems in the rolling process.
(4) Analyzing the relationship between the content of the stems in the cigarettes and the quality of the cigarettes.
With the progress of technology and equipment in recent years, great progress is indeed made in the control of the content of cut stems (including the content of cut stems in shreds and the content of cut stems in cigarettes). However, the control levels of stems among different enterprises, cigarettes with different circumferential specifications and different batches are different, and the probability of quality accidents caused by stems is also different greatly.
The main reasons for this are:
1) so far, the tobacco industry has not provided a unified stem control standard aiming at cigarettes with different circumference specifications, and each cigarette enterprise has different standards for stems and does not perform differentiation definition aiming at products with different circumference specifications.
2) The representation of the stem content is only represented by weight ratio, and can only represent the quality of the stems, and the main factor influencing the quality accident probability is the number of the stems, but no method for counting because of the large number exists at present.
3) The method for accurately detecting the size and the granularity of the stems does not exist, a general screening method has the defects of serious overscreening phenomenon, unstable detection result, poor precision, incapability of accurately representing the sizes of the stems due to the size of a screen, and difficulty in carrying out classification statistics and process control on most of the stems which are not greatly distinguished.
Disclosure of Invention
In order to overcome the existing defects, the invention provides a stem granularity detection and classification control method.
A method for detecting and classifying the particle size of stems includes such steps as separating stems from a sample, collecting the separated stems, dispersing the stems sufficiently by a dispersing unit, separating them from each other, collecting the images of all stems by an image collector, analyzing the images of all stems, calculating the length and width of each stem, and classifying and counting the number ratio according to the length and width of each stem.
The sample is a cut tobacco sample in a tobacco making process, a cut tobacco sample in a cigarette and a stem rejecting sample in a processing process.
The stem separation adopts the existing stem content detection device, and ensures that all stems with the length of more than 1mm are separated
The dispersing device is realized by adopting a multistage differential belt, a vibration groove and dilute phase pneumatic transmission.
The image acquisition is realized by adopting a common camera, X-ray imaging and hyperspectral camera imaging, and the image precision is more than 0.5 mm/pixel.
The image processing analysis adopts image processing software to calculate the distance between two points with the longest distance between the edges of each cut stem as the length of the cut stem, and makes vertical lines equally divided along the vertical direction of the length by 3-5, and the average value of the line segment distance between two points where each vertical line intersects with the edges of the cut stems is used as the width of the cut stems.
Wherein, according to the sample to cigarette circumference specification, carry out the classification statistics according to the form:
Figure BDA0002558915320000031
wherein, P is the malignant tagging rate;
Figure BDA0002558915320000032
detecting the total number of the stems;
Figure BDA0002558915320000033
the number of stems with the width of more than 0.7mm and the length of more than or equal to 6mm is calculated for the conventional cigarette; and
the number of stems with the width of more than 0.7mm and the length of more than or equal to 4mm is counted for the middle cigarette; and
the number of the stems of the thin cigarettes is greater than 0.7mm in width and greater than or equal to 3mm in length.
The stem particle size detection and classification control method provided by the invention can accurately determine the particle size of the stems, and is more accurate and reliable than the traditional screening method and stable in detection result. The quantity of the stems is represented, the probability of the quality defect of the cigarettes caused by the stems can be evaluated, and the method has a strong guiding significance for controlling the quality of the cigarettes. The method unifies the standard of the stems on the basis of the statistics of a large amount of data of cigarettes with different specifications in the industry, and performs stem classification statistics according to cigarettes with different circumferences, thereby being beneficial to the quality control of the cigarettes with different types.
Detailed Description
The stem particle size detection and classification control method provided by the invention is described in detail below with reference to specific embodiments.
A method for detecting and classifying the particle size of stems includes such steps as separating stems from a sample, collecting the separated stems, dispersing the stems sufficiently by a dispersing unit, separating them from each other, collecting the images of all stems by an image collector, analyzing the images of all stems, calculating the length and width of each stem, and classifying and counting the number ratio according to the length and width of each stem.
The samples are cut tobacco samples in the tobacco making process, cut tobacco samples in cigarettes and stem rejecting samples in the processing process.
The existing stem content detection device is adopted for stem separation, and the stem with the length of more than 1mm is ensured to be completely separated
The dispersing device is realized by adopting a multistage differential belt, a vibration groove and dilute phase pneumatic transmission.
The image acquisition is realized by adopting a common camera, X-ray imaging and hyperspectral camera imaging, and the image precision is more than 0.5 mm/pixel.
And the image processing analysis adopts image processing software to calculate the distance between two points with the longest distance between the edges of the stems as the length of the stems, and makes vertical lines equally divided along the vertical direction of the length by 3-5, and the average value of the line segment distance between two points where each vertical line intersects with the edges of the stems is used as the width of the stems.
Wherein, according to the sample to cigarette circumference specification, carry out the classification statistics according to the form:
Figure BDA0002558915320000041
wherein, P is the malignant tagging rate;
Figure BDA0002558915320000042
detecting the total number of the stems;
Figure BDA0002558915320000051
the number of stems with the width of more than 0.7mm and the length of more than or equal to 6mm is calculated for the conventional cigarette; and
the number of stems with the width of more than 0.7mm and the length of more than or equal to 4mm is counted for the middle cigarette; and
the number of the stems of the thin cigarettes is greater than 0.7mm in width and greater than or equal to 3mm in length.
With reference to the embodiments, a stem particle size detection and classification characterization method is further described:
(1) sampling: in order to compare the risk effect of two different batches of on-site sorting processes of the same conventional cigarette, 200g of complete cross-section tobacco shred samples are respectively sampled at the outlet of an on-site winnowing device and are marked as a sample A and a sample B;
(2) stem and stick separation: the existing tester for the cut stem content in cut stems is utilized to perform multiple cut stem and stem separation by adjusting the separation wind speed so as to ensure the stem and stem separation to be clean;
(3) acquiring a stem image: the stem is fully dispersed by using a 3-level differential speed dispersing device consisting of a one-level vibration groove and a two-level differential speed belt, and an industrial linear array CCD camera is arranged at the tail end of the belt to collect the images of the stem in real time, wherein the image resolution is 0.2 mm/pixel.
(4) Image processing and analysis: the length and width of each cut stem are detected by photoshop software.
(5) And (3) statistical calculation: the total number of the stems of the sample A and the sample B is 1794 and 1285 respectively, the number of the stems with the width of more than 0.7mm and the length of more than or equal to 6mm is 812 and 722, and the malignant stem percentage is 45.3 percent and 56.2 percent respectively.
Finally, it should be noted that the above examples are only intended to describe the technical solutions of the present invention and not to limit the technical methods, the present invention can be extended in application to other modifications, variations, applications and embodiments, and therefore all such modifications, variations, applications, embodiments are considered to be within the spirit and teaching scope of the present invention.

Claims (7)

1. The stem granularity detection and classification control method is characterized in that a sample taken according to a detection purpose is subjected to stem separation, separated stems are collected, the stems are fully dispersed and separated from each other through a dispersing device, an image acquisition device acquires all stem images, the acquired stem images are processed and analyzed, the length and the width of each stem are calculated, and the number ratio is calculated according to the circumference specification of a cigarette corresponding to the sample and the length and the width of each stem through classification statistics.
2. The stem granularity detection and classification control method according to claim 1, wherein the samples are cut tobacco samples in a tobacco making process, cut tobacco samples in cigarettes and stem rejected samples in a processing process.
3. The stem particle size detection and classification control method according to claim 1, characterized in that the stem separation uses an existing stem content detection device and ensures that all stems with a length of more than 1mm are separated.
4. The stem particle size detection and classification control method according to claim 1, wherein the dispersing device is realized by adopting a multi-stage differential belt, a vibration groove and dilute phase pneumatic transmission.
5. The stem particle size detection and classification control method according to claim 1, wherein image acquisition is achieved by using a common camera, X-ray imaging and hyperspectral camera imaging, and the image precision is greater than 0.5 mm/pixel.
6. The method for detecting and classifying the particle size of the stems according to claim 1, wherein the image processing analysis adopts image processing software to calculate the distance between two points with the longest distance between the edges of each stem as the length of the stem, and make vertical lines equally divided along the vertical direction of the length from 3 to 5, and the average value of the distance between two line segments of the intersection of each vertical line and the edge of the stem is used as the width of the stem.
7. The stem particle size detection and classification control method according to claim 1, wherein classification statistics is performed according to the sample to cigarette circumference specification according to formats:
Figure FDA0002558915310000021
wherein, P is the malignant tagging rate;
Figure FDA0002558915310000022
detecting the total number of the stems;
Figure FDA0002558915310000023
the number of stems with the width of more than 0.7mm and the length of more than or equal to 6mm is calculated for the conventional cigarette; and
the number of stems with the width of more than 0.7mm and the length of more than or equal to 4mm is counted for the middle cigarette; and
the number of the stems of the thin cigarettes is greater than 0.7mm in width and greater than or equal to 3mm in length.
CN202010605702.8A 2020-06-29 2020-06-29 Stem granularity detection and classification control method Pending CN111948104A (en)

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WO2018040302A1 (en) * 2016-08-31 2018-03-08 上海创和亿电子科技发展有限公司 Method and system for measuring width of cut tobacco piece or cut stem, and server having said system
CN107991303A (en) * 2017-12-18 2018-05-04 云南烟叶复烤有限责任公司 A kind of leaf of beating based on double spectral techniques goes stalk quality detection device and detection method
CN108007945A (en) * 2017-12-06 2018-05-08 中国烟草总公司郑州烟草研究院 A kind of assay method that thick stalk rate and length stalk rate in offal are quantitatively detected based on X-ray transmission image
CN109239082A (en) * 2018-09-21 2019-01-18 杭州安脉盛智能技术有限公司 Tobacco structure quality online test method and system based on machine vision technique
CN110378618A (en) * 2019-07-26 2019-10-25 杭州安脉盛智能技术有限公司 Quality evaluating method and system based on online pipe tobacco surface defects detection
CN111289543A (en) * 2020-03-05 2020-06-16 云南中烟工业有限责任公司 Device and method for measuring medium and long stem rate of tobacco stems

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN201993324U (en) * 2011-01-10 2011-09-28 南京文采科技有限责任公司 Tobacco stem sliver separation effect detecting device
CN102359963A (en) * 2011-07-14 2012-02-22 红塔烟草(集团)有限责任公司 Method for measuring rate of long tobacco stalks by image analysis process
CN102359962A (en) * 2011-07-14 2012-02-22 红塔烟草(集团)有限责任公司 Equipment for determining long tobacco stalk rate by using image analysis
CN103148791A (en) * 2013-02-28 2013-06-12 云南昆船设计研究院 Method for measuring cabo structure and distribution of cabo structure
CN104596423A (en) * 2015-01-07 2015-05-06 云南昆船设计研究院 Method and device for detecting tobacco stem outline structures based on images
WO2018040302A1 (en) * 2016-08-31 2018-03-08 上海创和亿电子科技发展有限公司 Method and system for measuring width of cut tobacco piece or cut stem, and server having said system
CN106923369A (en) * 2017-04-14 2017-07-07 湖北中烟工业有限责任公司 A kind of beating and double roasting offal online classification collection method
CN108007945A (en) * 2017-12-06 2018-05-08 中国烟草总公司郑州烟草研究院 A kind of assay method that thick stalk rate and length stalk rate in offal are quantitatively detected based on X-ray transmission image
CN107991303A (en) * 2017-12-18 2018-05-04 云南烟叶复烤有限责任公司 A kind of leaf of beating based on double spectral techniques goes stalk quality detection device and detection method
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CN110378618A (en) * 2019-07-26 2019-10-25 杭州安脉盛智能技术有限公司 Quality evaluating method and system based on online pipe tobacco surface defects detection
CN111289543A (en) * 2020-03-05 2020-06-16 云南中烟工业有限责任公司 Device and method for measuring medium and long stem rate of tobacco stems

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Application publication date: 20201117