CN104330385A - Device and method for detecting cut tobacco blending uniformity online - Google Patents
Device and method for detecting cut tobacco blending uniformity online Download PDFInfo
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- CN104330385A CN104330385A CN201410649927.8A CN201410649927A CN104330385A CN 104330385 A CN104330385 A CN 104330385A CN 201410649927 A CN201410649927 A CN 201410649927A CN 104330385 A CN104330385 A CN 104330385A
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Abstract
The invention discloses a device and a method for detecting cut tobacco blending uniformity online. The device comprises an infrared spectrometer, a signal processor, a support, a conveyor belt and an alarm device, wherein the conveyor belt is arranged at an outlet of a blending device of a cut tobacco production line; the support is fixed at the upper part, close to the outlet of the blending device of the cut tobacco production line, of the conveyor belt; the infrared spectrometer is fixed at the upper end of the support; diffuse reflection type detection is performed on a cut tobacco sample flowing through the conveyor belt in a non-contact manner and detected data is transmitted to the signal processor; the signal processor is connected with the alarm device and a cut tobacco production line controller; when the signal processor indicates that the uniformity is out of limits, the alarm device gives an alarm and the signal processor sends an instruction to the cut tobacco production line controller to stop the cut tobacco production line from running. The device and the method for detecting the cut tobacco blending uniformity online are capable of effectively eliminating the shortcomings of the prior art and providing support for real-time and effective control of the cut tobacco blending uniformity.
Description
Technical field
The present invention relates to a kind of online tobacco shred blending uniformity detection and method thereof.
Background technology
The main flume of cigarette product and aesthetic quality depend primarily on raw material of cigarette formula, and namely various tobacco leaf, offal are by the Homogeneous phase mixing of design proportion.
Can the degree of uniformity that various tobacco leaf, offal mix after chopping on tinuous production obtain detecting, controlling in time, is the key of raw material of cigarette formula implementation quality.At present, control to mix combo shunt volume before the many employing mixing of tobacco business, roll the degree of uniformity that cigarette near infrared spectrum method of inspection, pipe tobacco chemical index conventional detection method, manual tobacco shred physicochemical characteristic detection method etc. evaluate various tobacco leaf, offal mixes after chopping on tinuous production afterwards, also existing and evaluate the drawbacks such as delayed, evaluation is not comprehensive, can not be in real time, effectively control pipe tobacco mixture homogeneity to provide support.
Summary of the invention
The present invention is in order to solve the problem, propose a kind of online tobacco shred blending uniformity detection and method thereof, this device effectively can detect tobacco leaf offal nicotine, total reducing sugar, content of reducing sugar according near infrared spectrometer, various tobacco leaf in formula, offal nicotine, total reducing sugar, content of reducing sugar are basicly stable, the principle design that the nicotine of the pipe tobacco mixed when each component chopping, total reducing sugar, content of reducing sugar are also basicly stable, effectively can eliminate the drawback of prior art, for real-time, effective control pipe tobacco mixture homogeneity provides technical support.
To achieve these goals, the present invention adopts following technical scheme:
A kind of online tobacco shred blending uniformity detection, comprise infrared spectrometer, signal processor, support, travelling belt and warning device, wherein, support is fixed on driving-belt mixes arranging standby outlet top near scrap prodn. line, pedestal upper end is fixed with infrared spectrometer, in a non contact fashion diffuse reflection type detection is carried out to the tobacco sample flowing through travelling belt, the data detected send to signal processor every official hour, signal processor connects alarm and scrap prodn. line controller, when signal processor find pipe tobacco homogeneity have exceed standard trend time, alarm equipment alarm, alert, when signal processor finds that pipe tobacco homogeneity exceeds standard, alarm equipment alarm, alert, synchronous signal processor sends instruction to scrap prodn. line controller, stop scrap prodn. line running.
Described travelling belt is that scrap prodn. line exports with tobacco shred blending equipment the travelling belt be connected.
Described infrared spectrometer is the online AOTF near infrared spectrometer of Luminar 4030.
The middle sample position of the position alignment travelling belt of the light-emitting window of described infrared spectrometer is [6.0,7.0] cm with pipe tobacco level scope.
Based on the method for work of above-mentioned detection device, comprise the following steps:
(1) setting device parameter, collected specimens also records sample number into spectrum and detects data with corresponding, rejects sample detection data outliers, sets up near infrared detection list trade mark model;
(2) the near infrared detection list trade mark in situ method model established is utilized to carry out inside, external certificate to the sample participating in modeling sample and external certificate collection, calculation sample nicotine, total reducing sugar, the routine test value of reducing sugar index and the mean value of model predication value deviation respectively, if the verification passes, then enter step (3), if checking is not passed through, return step (1);
(3) near infrared spectrometer is loaded into single trade mark in situ method model, setting near infrared spectrometer list trade mark model is consistent with the tobacco shred cigarette trade mark to be checked, collection nicotine, total reducing sugar, reducing sugar data are transferred to signal processor, carry out the detection of pipe tobacco homogeneity, if homogeneity does not exceed standard, continue normal production, if homogeneity exceeds standard, then stop producing and reporting to the police.
In described step (1), concrete grammar comprises:
(1-1), after device installation, the optimum configurations of near infrared spectrometer is carried out;
(1-2) under the condition of pipe tobacco administration measure, the collection of spectroscopic data diffuse reflectance, numbering, the simultaneously collected specimens of an online pipe tobacco of near infrared spectrometer is carried out at interval of setting-up time;
(1-3) spectroscopic data randomly drawing 10% sample conventional sense data and correspondence, as model external certificate collection, as model external certificate, does not participate in modeling, and spectroscopic data and the corresponding chemical composition of all the other samples are associated founding mathematical models;
(1-4) carrying out Pretreated spectra to scanning the absorption spectrum that obtains, abating the noise and the impact of baseline;
(1-5) spectroscopic data and sample conventional sense data outliers is rejected;
(1-6) near infrared detection list trade mark model is set up.
In described step (1-2), its concrete grammar is: under the condition of pipe tobacco administration measure, carry out a near infrared spectrometer to the spectroscopic data diffuse reflectance collection of online pipe tobacco at interval of setting-up time, numbering, collected specimens simultaneously, during sampling, capture tobacco sample fifty-fifty flowing through the sample surfaces below instrument detection window, then pipe tobacco is loaded valve bag good seal, serial number corresponding to spectroscopic data, carry out pipe tobacco chemical index nicotine, total reducing sugar, reducing sugar conventional sense, record sample number into spectrum detects data with corresponding, sample size is more than or equal to 300.
In described step (1-4), the method for Pretreated spectra comprises first differential 9 smoothing methods (savitzy-golay).
In described step (1-5), adopt these two statistics of spectrum influence value Leverage and conventional sense value error Residual to check respectively and reject spectroscopic data and sample conventional sense data outliers.
In described step (1-6), adopt partial least square method, intersection-proof method (cross-validation) sets up near infrared detection list trade mark model, with The Unscrambler quantitative analysis software Modling model, in modeling process, successive optimization is carried out in the rejecting through exceptional value.
In described step (2), its concrete grammar comprises:
(2-1) internal verification: utilize the near infrared detection list trade mark in situ method model established to verify participation modeling sample, calculation sample nicotine, total reducing sugar, the routine test value of reducing sugar index and the mean value of model predication value deviation respectively;
(2-2) external certificate: carry out external prediction, calculation sample nicotine, total reducing sugar, the laboratory values of reducing sugar index and the mean value of external prediction value deviation respectively with the sample of the model to external checking collection established;
(2-3) the result: when the mean value of inside and outside identifying deviation has one or two to be more than or equal to 5%, should modeling again; When the mean value of inside and outside identifying deviation is all less than 5%, modeling is by checking.
In described step (3), near infrared detection list trade mark model is set up at product cigarette by step (1)-(2) by all, when cigarette composition or pipe tobacco processing technology change, again set up near infrared detection list trade mark model by step (1)-(2).
In described step (3), concrete grammar is: near infrared detection list trade mark model is loaded near infrared spectrometer, setting near infrared spectrometer list trade mark model is consistent with the tobacco shred cigarette trade mark to be checked, carry out continuous detecting, at interval of setting-up time the nicotine detected, total reducing sugar, reducing sugar data are respectively got one and are passed in the database of signal processor, signal processor calculates acquired nicotine respectively, total reducing sugar, the mobile coefficient of variation of reducing sugar n continuous detecting data, when to calculate the nicotine of acquisition simultaneously, total reducing sugar, when the mobile value for coefficient of variation of reducing sugar is all less than or equal to 5%, judge that pipe tobacco is even, continue normal production, when have in the mobile value for coefficient of variation simultaneously calculating the nicotine of acquisition, total reducing sugar, reducing sugar be more than or equal to 3% and be less than the numerical value of 5% time, judge that pipe tobacco homogeneity has the trend of exceeding standard, send sound and light alarm signal, prompting operator note, when there being the numerical value being more than or equal to 5% in the mobile value for coefficient of variation calculating the nicotine of acquisition, total reducing sugar, reducing sugar simultaneously, judge that pipe tobacco homogeneity exceeds standard, send sound and light alarm signal, prompting operator, send a stopping signal to production line control system simultaneously, stop producing, wait pending, n is integer.
The mobile coefficient of variation of described n continuous detecting data, refer to get the 1st ~ the n-th continuous print data as the first data group, calculate its coefficient of variation, as first coefficient of variation, when there being (n+1)th data again, remove the 1st data, 2nd forms the second data group jointly with (n+1)th data, calculate the coefficient of variation of new data group, as the 2nd coefficient of variation, so sequentially calculate.
Beneficial effect of the present invention is:
(1) effectively detect tobacco leaf offal nicotine, total reducing sugar, content of reducing sugar, calculate chopping and whether mix, effectively eliminate the drawback of prior art;
(2) tobacco shred blending homogeneity variation tendency can be grasped in real time, mix join defective before make effective control, reduce defective pipe tobacco incidence;
(3) the defective situation of tobacco shred blending homogeneity can be grasped in real time, stop defective pipe tobacco in time and produce, make effective control, stop the misuse of defective pipe tobacco.
Accompanying drawing explanation
Fig. 1 is structural representation of the present invention.
Wherein, 1, travelling belt; 2, support; 3, infrared spectrometer; 4, signal processor; 5, scrap prodn. line controller; 6, warning device.
Embodiment:
Below in conjunction with accompanying drawing and embodiment, the invention will be further described.
As shown in Figure 1, pick-up unit comprises near infrared spectrometer 3 (as the online AOTF near infrared spectrometer of Luminar 4030 that BRIMROSE company of the U.S. produces), signal processor 4 (high-performance computer), near infrared spectrometer is arranged on scrap prodn. line by support 2 and mixes on arranging standby outlet travelling belt 1, in a non contact fashion diffuse reflection type detection is carried out to the tobacco sample flowing through travelling belt, detect data input signal processor, signal processor automatic decision pipe tobacco homogeneity, homogeneity has when exceeding standard trend and sends sound and light signal, prompting production operator is noted, when homogeneity exceeds standard, indicating device 6 sends sound and light signal, remind production operator, and provide a stopping signal for scrap prodn. line controller 5, stop scrap prodn. line running, etc. pending.
When near infrared spectrometer is installed, more smooth position, the middle sample surface of travelling belt should be aimed in the position of light hole, highly for the mean distance apart from pipe tobacco plane is 6.5cm.During scanning optical spectrum, scan pattern is set to can the pattern (as " Ratio mode ") of background correction variable effect effectively.
Implementation step:
Influence factors different for the height of pipe tobacco in various change of background factor, particularly production line (can be built in model, be conducive to strengthening model to site environment adaptive faculty by 1 near infrared detection list trade mark in situ method modeling; The model error that the modeling of single trade mark method can eliminate pipe tobacco nicotine between the different trade mark, total reducing sugar, content of reducing sugar difference cause):
After 1.1 device installations, carry out the optimum configurations of near infrared spectrometer.
As: wavelength coverage: 1100 ~ 2300nm
Wavelength increment: 2.0nm
Scanning times: 300.
1.2 under the condition of pipe tobacco administration measure at interval of certain hour (as 1 minute) carry out an online pipe tobacco of near infrared spectrometer spectroscopic data diffuse reflectance gather, numbering, collected specimens simultaneously, tobacco sample is captured fifty-fifty flowing through the sample surfaces below instrument detection window during sampling, then pipe tobacco is loaded valve bag good seal, serial number corresponding to spectroscopic data, carry out pipe tobacco chemical index nicotine, total reducing sugar, reducing sugar conventional sense (as the chemical examination of AA3 flow injection analyzer), record sample number into spectrum detects data with corresponding.Sample size is more than or equal to 300.
1.3 randomly draw the spectroscopic data of 10% sample conventional sense data and correspondence as model external certificate collection, as model external certificate, do not participate in modeling, and spectroscopic data and the corresponding chemical composition of all the other samples are associated founding mathematical models.
The absorption spectrum that the 1.4 pairs of scanning obtains carries out Pretreated spectra (as first differential 9 smoothing methods: savitzy-golay method.First differential process can well eliminate the spectrum baseline skew and drift that sample causes due to change of background such as colors), to abate the noise and the impact of baseline.
1.5 reject spectroscopic data and sample conventional sense data outliers (outlier) (as adopted these two statistics of spectrum influence value Leverage and conventional sense value error Residual respectively to check rejecting).
1.6 set up near infrared detection list trade mark model, and (as adopted partial least square method, intersection-proof method (cross-validation), with The Unscrambler quantitative analysis software Modling model.In modeling process, successive optimization is carried out in the rejecting through exceptional value, sets up near infrared detection list trade mark in situ method model).
1.7 modelling verification
1.7.1 internal verification
The near infrared detection list trade mark in situ method model established is utilized to verify participation modeling sample, calculation sample nicotine, total reducing sugar, the routine test value of reducing sugar index and the mean value of model predication value deviation respectively.
1.7.2 external certificate
External prediction is carried out, calculation sample nicotine, total reducing sugar, the laboratory values of reducing sugar index and the mean value of external prediction value deviation respectively with the sample of the model to external checking collection established.
1.7.3 the result
When the mean value of inside and outside identifying deviation has one or two to be more than or equal to 5%, should modeling again; When the mean value of inside and outside identifying deviation is all less than 5%, modeling is by checking.
1.8 set up near infrared detection list trade mark model at product cigarette by the step of 1.1-1.7 by all.
1.9, when cigarette composition or pipe tobacco processing technology change, set up near infrared detection list trade mark model by the step of 1.1-1.9 again.
2 pipe tobacco homogeneity methods of testing and evaluatings
Near infrared detection list trade mark model is loaded near infrared spectrometer by 2.1.
2.2 setting near infrared spectrometer list trade mark models are consistent with the tobacco shred cigarette trade mark to be checked.
2.3 near infrared spectrometer continuous detecting.
2.4 every 10 seconds or certain hour interval are got the nicotine detected, total reducing sugar, reducing sugar data and are passed in the database of signal processor, signal processor calculates the mobile value for coefficient of variation of acquired nicotine, total reducing sugar, reducing sugar 30 continuous detecting data respectively, when signal processor calculate respectively acquired nicotine, total reducing sugar, reducing sugar 30 continuous detecting data mobile value for coefficient of variation be all less than or equal to 5% time, judge that pipe tobacco is even, continue normally to produce; When signal processor calculate respectively in the mobile value for coefficient of variation of acquired nicotine, total reducing sugar, reducing sugar 30 continuous detecting data have be more than or equal to 3% and be less than the numerical value of 5% time, judge that pipe tobacco homogeneity has the trend of exceeding standard, send sound and light alarm signal, prompting operator note; When signal processor calculate respectively in the mobile value for coefficient of variation of acquired nicotine, total reducing sugar, reducing sugar 30 continuous detecting data have the numerical value being more than or equal to 5% time; judge that pipe tobacco homogeneity exceeds standard; send sound and light alarm signal; prompting operator; send a stopping signal to production line control system simultaneously; stop producing, wait pending.
The present invention define nicotine, total reducing sugar, reducing sugar 30 continuous detecting data the mobile coefficient of variation get the 1st ~ 30th continuous print data exactly, as the first data group, calculate its coefficient of variation, as first coefficient of variation, when there being the 31st data again, remove the 1st data, 2nd forms the second data group jointly with the 31st data, calculate the coefficient of variation of new data group, as the 2nd coefficient of variation, so sequentially calculate.
Described in literary composition, 30 to move the coefficient of variation be exactly the detection data of getting the 1st ~ 30th continuous print nicotine or total reducing sugar or reducing sugar, calculate its coefficient of variation, when there being the detection data of the 31st nicotine or total reducing sugar or reducing sugar again, remove the 1st and detect data, 2nd is detected data and jointly forms new 30 with the 31st and detect data, calculate the coefficient of variation of 30 new data, so sequentially calculate.
Utilize such scheme, carry out the modeling of 5 products, detection, evaluation, respond well, just carry out the popularization of many trades mark product.
By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.
Claims (10)
1. an online tobacco shred blending uniformity detection, it is characterized in that: comprise infrared spectrometer, signal processor, support, travelling belt and warning device, wherein, travelling belt is positioned over scrap prodn. line and mixes the standby outlet of arranging, support is fixed on travelling belt mixes arranging standby outlet top near scrap prodn. line, pedestal upper end is fixed with infrared spectrometer, in a non contact fashion diffuse reflection type detection is carried out to the tobacco sample flowing through travelling belt, the data detected send to signal processor, signal processor connects alarm and scrap prodn. line controller, when the homogeneity of signal processor exceeds standard, alarm equipment alarm, signal processor sends instruction to scrap prodn. line controller, stop scrap prodn. line running.
2. a kind of online tobacco shred blending uniformity detection as claimed in claim 1, it is characterized in that: the middle sample position of the position alignment travelling belt of the light-emitting window of described infrared spectrometer, is [6.0,7.0] cm with pipe tobacco level scope.
3. based on the method for work of the pick-up unit such as according to any one of claim 1-2, it is characterized in that: comprise the following steps:
(1) setting device parameter, collected specimens also records sample number into spectrum and detects data with corresponding, proposes sample detection data outliers, sets up near infrared detection list trade mark model;
(2) the near infrared detection list trade mark in situ method model established is utilized to carry out inside, external certificate to the sample participating in modeling sample and external certificate collection, calculation sample nicotine, total reducing sugar, the routine test value of reducing sugar index and the mean value of model predication value deviation respectively, if the verification passes, then enter step (3), if checking is not passed through, return step (1);
(3) near infrared spectrometer is loaded into single trade mark in situ method model, setting near infrared spectrometer list trade mark model is consistent with the tobacco shred cigarette trade mark to be checked, collection nicotine, total reducing sugar, reducing sugar data are transferred to signal processor, carry out pipe tobacco homogeneity, if homogeneity does not exceed standard, continue normal production, if homogeneity exceeds standard, then stop producing and reporting to the police.
4. method of work as claimed in claim 3, is characterized in that: in described step (1), concrete grammar comprises:
(1-7), after device installation, the optimum configurations of near infrared spectrometer is carried out;
(1-8) under the condition of pipe tobacco administration measure, the collection of spectroscopic data diffuse reflectance, numbering, the simultaneously collected specimens of an online pipe tobacco of near infrared spectrometer is carried out at interval of setting-up time;
(1-9) spectroscopic data randomly drawing 10% sample conventional sense data and correspondence, as model external certificate collection, as model external certificate, does not participate in modeling, and spectroscopic data and the corresponding chemical composition of all the other samples are associated founding mathematical models;
(1-10) carrying out Pretreated spectra to scanning the absorption spectrum that obtains, abating the noise and the impact of baseline;
(1-11) spectroscopic data and sample conventional sense data outliers is rejected;
(1-12) near infrared detection list trade mark model is set up.
5. method of work as claimed in claim 3, it is characterized in that: in described step (1-2), its concrete grammar is: under the condition of pipe tobacco administration measure, carry out a near infrared spectrometer to the spectroscopic data diffuse reflectance collection of online pipe tobacco at interval of setting-up time, numbering, collected specimens simultaneously, during sampling, capture tobacco sample fifty-fifty flowing through the sample surfaces below instrument detection window, then pipe tobacco is loaded valve bag good seal, serial number corresponding to spectroscopic data, carry out pipe tobacco chemical index nicotine, total reducing sugar, reducing sugar conventional sense, record sample number into spectrum detects data with corresponding, sample size is more than or equal to 300, in described step (1-4), the method for Pretreated spectra comprises first differential 9 smoothing methods.
6. method of work as claimed in claim 3, it is characterized in that: in described step (1-5), adopt these two statistics of spectrum influence value Leverage and conventional sense value error Residual to check respectively and reject spectroscopic data and sample conventional sense data outliers.
7. method of work as claimed in claim 3, it is characterized in that: in described step (1-6), adopt partial least square method, intersection-proof method sets up near infrared detection list trade mark model, with The Unscrambler quantitative analysis software Modling model, in modeling process, successive optimization is carried out in the rejecting through exceptional value.
8. method of work as claimed in claim 3, is characterized in that: in described step (2), its concrete grammar comprises:
(2-1) internal verification: utilize the near infrared detection list trade mark in situ method model established to verify participation modeling sample, calculation sample nicotine, total reducing sugar, the routine test value of reducing sugar index and the mean value of model predication value deviation respectively;
(2-2) external certificate: carry out external prediction, calculation sample nicotine, total reducing sugar, the laboratory values of reducing sugar index and the mean value of external prediction value deviation respectively with the sample of the model to external checking collection established;
(2-3) the result: when the mean value of inside and outside identifying deviation has one or two to be more than or equal to 5%, should modeling again; When the mean value of inside and outside identifying deviation is all less than 5%, modeling is by checking.
9. method of work as claimed in claim 3, it is characterized in that: in described step (3), near infrared detection list trade mark model is set up at product cigarette by step (1)-(2) by all, when cigarette composition or pipe tobacco processing technology change, again set up near infrared detection list trade mark model by step (1)-(2).
10. method of work as claimed in claim 3, it is characterized in that: in described step (3), concrete grammar is: near infrared detection list trade mark model is loaded near infrared spectrometer, setting near infrared spectrometer list trade mark model is consistent with the tobacco shred cigarette trade mark to be checked, carry out continuous detecting, at interval of setting-up time the nicotine detected, total reducing sugar, reducing sugar data are respectively got one and are passed in the database of signal processor, signal processor calculates acquired nicotine respectively, total reducing sugar, the mobile coefficient of variation of reducing sugar n continuous detecting data, when to calculate the nicotine of acquisition simultaneously, total reducing sugar, when the mobile value for coefficient of variation of reducing sugar is all less than or equal to 5%, judge that pipe tobacco is even, continue normal production, when have in the mobile value for coefficient of variation simultaneously calculating the nicotine of acquisition, total reducing sugar, reducing sugar be more than or equal to 3% and be less than the numerical value of 5% time, judge that pipe tobacco homogeneity has the trend of exceeding standard, send sound and light alarm signal, prompting operator note, when there being the numerical value being more than or equal to 5% in the mobile value for coefficient of variation calculating the nicotine of acquisition, total reducing sugar, reducing sugar simultaneously, judging that pipe tobacco homogeneity exceeds standard, send sound and light alarm signal, prompting operator, send a stopping signal to production line control system simultaneously, stop producing, wait pending,
The mobile coefficient of variation of described n continuous detecting data, refer to get the 1st ~ the n-th continuous print data as the first data group, calculate its coefficient of variation, as first coefficient of variation, when there being (n+1)th data again, remove the 1st data, 2nd forms the second data group jointly with (n+1)th data, calculate the coefficient of variation of new data group, as the 2nd coefficient of variation, so sequentially calculate.
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| CN106053383A (en) * | 2016-06-27 | 2016-10-26 | 四川威斯派克科技有限公司 | Near-infrared online detection method for tobacco processing process |
| CN109946265A (en) * | 2017-12-21 | 2019-06-28 | 西派特（北京）科技有限公司 | Pipe tobacco pollutes On-line near infrared analyzer alarm system |
| CN108732127A (en) * | 2018-05-08 | 2018-11-02 | 河南中烟工业有限责任公司 | A kind of method of each component mixture proportion in detection pipe tobacco |
| CN108802284A (en) * | 2018-06-08 | 2018-11-13 | 湖北中烟工业有限责任公司 | A kind of detection method of reconstituted tobacco cigarette mixture proportion and blending uniformity |
| CN111543668A (en) * | 2020-05-28 | 2020-08-18 | 浙江中烟工业有限责任公司 | Design method of threshing and redrying formula module |
| CN111543668B (en) * | 2020-05-28 | 2022-03-25 | 浙江中烟工业有限责任公司 | Design method of threshing and redrying formula module |
| CN111721715A (en) * | 2020-06-05 | 2020-09-29 | 红云红河烟草(集团)有限责任公司 | Method for measuring tobacco shred blending uniformity based on combination of colorimetric value and entropy weight method |
| CN112304893A (en) * | 2020-09-17 | 2021-02-02 | 云南烟叶复烤有限责任公司 | Method for rapidly judging mixing uniformity of multi-grade tobacco leaves and storage medium |
| CN112378880A (en) * | 2020-10-29 | 2021-02-19 | 河南中烟工业有限责任公司 | System for detecting distribution uniformity of formula cut tobacco in thin cigarette |
| CN112378880B (en) * | 2020-10-29 | 2023-08-18 | 河南中烟工业有限责任公司 | Detecting system for formula tobacco shred distribution uniformity in fine cigarette |
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