CN101634624A - Calibration method for infrared moisture meter system - Google Patents
Calibration method for infrared moisture meter system Download PDFInfo
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Abstract
The invention relates to a calibration method for an infrared moisture meter system, including a calibration method for an static infrared moisture meter system and the application of the calibration method for the static infrared moisture meter system in a modeling method of calibration of an online infrared moisture meter, namely a calibration method for an online infrared moisture meter. The calibration method for the on-line infrared moisture meter sequentially includes the following steps: (1) a wide-domain model is established on a static infrared moisture meter; (2) a dynamic wide-domain model is established on the static infrared moisture meter; (3) a narrow-domain model is established; (4) relation functions for detection signals of the dynamic and the static infrared moisture meters are established; (5) transition models for dynamic and static relation functions are established. The calibration method for the infrared moisture meter, which is used for calibrating the online infrared moisture meter, has the advantages of fastness, accuracy, convenience and simplicity and can save a great amount of labor power and material resources.
Description
Technical field
The present invention relates to a kind of measurement instrument modeling method, be specifically related to a kind of calibration method for infrared moisture meter system.
Background technology
At present, the manufacturer of infrared moisture meter was solidificated in original signal in the system before dispatching from the factory, calibration of Infrared Moisture Meter is generally taked single-point revised law and multiple spot secondary modeling, the single-point revised law is at 3～5 samples of same point sampling, does infrared moisture meter earlier and detects and do Oven Method again and detect the difference of averaging and carry out correction as a result.Multiple spot secondary modeling is to get can cover more than 5 points of moisture sample that tested material may occur, and infrared moisture meter testing result and baking oven testing result are returned, and sets up monobasic linear function model.For existing two kinds of scaling methods, single-point revised law data processing is simple, but detects for the material that is greater than or less than the sample water percentage, and error is bigger, and the multiple spot modeling is comparatively accurate for the single-point revised law.But in actual production process, the multiple spot modeling mainly has the following disadvantages: (1) is for online Dynamic IR Moisture Meter, no matter infrared moisture meter is installed in the unapproachable place of eminence or lower or personnel, all will be placed under the infrared moisture meter with sample and detect, eminence is inconvenient or dangerous so can't enter people's place such as the cold-room of redrying machine just can not be demarcated.(2) no matter static Moisture Meter in laboratory or online Dynamic IR Moisture Meter, the infrared instrument of each moisture all to be taken to few 5 samples detect demarcation so a production line have 8 online infrared moisture meters to need 40 samples at least, used chronic then one day slowly half a day then soon, short run tobacco leaf processing infrared moisture meter is not all demarcated the end raw material just not to have been had, very impracticable.(3) general every batch of tobacco leaf of site operation personnel is only demarcated once or once a day, because infrared moisture meter is very responsive to the tested material color, and raw material is agricultural byproducts, tobacco leaf grading all is that sense organ is judged, have and mix level, mix the position phenomenon, so same grade tobacco leaf color consistance is relatively poor, the stability of tobacco formulation also influences the color of tested material in addition, this brings a lot of chances for the tested material change color, therefore above two kinds of scaling methods are because of birth defects, can not accomplish every two hours full-time the demarcation once, cause the testing result poor reliability to be inevitable.
The laboratory infrared moisture meter can not replace the water percentage of Oven Method (fast) detection material for above-mentioned reasons, and online infrared moisture teller generally only is used for reference on the leaf tobacco production line.Have only the problems referred to above of having solved, we could pass through the accurately automatically control of online infrared moisture meter realization to tobacco leaf moisture content, reduce each process procedure moisture content fluctuation, and ability further improves the quality of product, reduces loss.
Summary of the invention
The objective of the invention is to overcome exist in the prior art and detect hysteresis quality and the low deficiency of infrared moisture detection accuracy, a kind of calibration method for infrared moisture meter system is provided.
The objective of the invention is to be achieved through the following technical solutions:
Calibration method for infrared moisture meter system of the present invention is static calibration method for infrared moisture meter system, and static calibration method for infrared moisture meter system comprises in regular turn:
(1) setting up wide domain model on the static infrared moisture meter: in material real work moisture scope, choosing the difference that covers actual material possible range water percentage on production line takes a sample respectively, institute's sample thief is at least five samples, each sample detects with the indoor static infrared moisture meter respectively and the record detected signal value, use the water percentage and the recording detection data of baking oven test sample then respectively, baking oven is detected data and the infrared moisture meter detected signal value returns, the wide territory=f1 of opening relationships function y (x static state);
(2) set up dynamic wide domain model on static infrared moisture meter: difference obtains one group of sample on production line, each sample is detected with the indoor static infrared moisture meter respectively and the record detected signal value, distinguish the water percentage and the recording detection data of test sample then with baking oven, according to current data or historical data weight, determine to duplicate the current data number of times, with the historical data of copy data and employing together as modeling data, again correct recurrence, function is corrected in foundation, and the like, set up the wide territory=f2 of kinematic function y (x static state) that constantly corrects;
(3) set up narrow domain model: in fixing station material real work moisture scope, get dynamic wide domain model data, to organize detected signal value and bring the predicted value that the wide territory=f2 of dynamic wide domain model function y (x static state) that step (2) obtains calculates the sample moisture into, then baking oven detection data and predicted value are returned, narrow territory=the f3 of opening relationships function y (y corrects in wide territory), with this kind method, can systematically set up the narrow domain model of same material different station, promptly static infrared moisture meter application model with available data.
In step (1), when on production line, choosing the difference that covers actual material possible range water percentage and taking a sample respectively, get three groups of samples first at least.
Calibration method for infrared moisture meter system of the present invention is online infrared moisture meter scaling method, may further comprise the steps in regular turn:
(1) setting up wide domain model on the static infrared moisture meter: in material real work moisture scope, choosing the difference that covers actual material possible range water percentage on production line takes a sample respectively, institute's sample thief is at least five samples, at least get three groups of samples first, each sample detects with the indoor static infrared moisture meter respectively and the record detected signal value, use the water percentage and the recording detection data of baking oven test sample then respectively, baking oven is detected data and the infrared moisture meter detected signal value returns, the wide territory=f1 of opening relationships function y (x static state);
(2) set up dynamic wide domain model on static infrared moisture meter: difference obtains one group of sample on production line, each sample is detected with the indoor static infrared moisture meter respectively and the record detected signal value, distinguish the water percentage and the recording detection data of test sample then with baking oven, according to current data or historical data weight, determine to duplicate the current data number of times, with the historical data of copy data and employing together as modeling data, again correct recurrence, function is corrected in foundation, and the like, set up the wide territory=f2 of kinematic function y (x static state) that constantly corrects;
(3) set up narrow domain model: in fixing station material real work moisture scope, get dynamic wide domain model data, to organize detected signal value and bring the predicted value that the wide territory=f2 of dynamic wide domain model function y (x static state) that step (2) obtains calculates the sample moisture into, then baking oven detection data and predicted value are returned, narrow territory=the f3 of opening relationships function y (y corrects in wide territory), with this kind method, can systematically set up the narrow domain model of same material different station, promptly static infrared moisture meter application model with available data.
(4) set up dynamic and static infrared moisture meter detection signal relation function: in fixing station material real work moisture scope, get one group of sample, write down the detected signal value of Dynamic IR Moisture Meter on this station during sampling, should organize sample then and detect and write down detected signal value with the indoor static infrared moisture meter respectively, the detected signal value of Dynamic IR Moisture Meter and the detected signal value of static infrared moisture detector are returned, set up regression equation, x static state=f4 (x is dynamic);
(5) set up dynamic and static relation function metastasis model: for same sample, make the predicted value of the Dynamic IR Moisture Meter on the station equate with the predicted value of indoor static infrared moisture meter, be y dynamically=the narrow territory=f3 of y (the wide territory of y)=f3 (f2 (x static state))=f3 (f2 (f4 (x is dynamic))), obtain metastasis model y dynamically=f5 (x is dynamic), by the network platform, metastasis model is copied on the online infrared moisture meter by system.
In step (1), the wide territory=f1 of regression equation y (x static state) of foundation can be line shape function or binomial function or exponential function or logarithmic function or power function, finally chooses maximum coefficient of determination R
2Corresponding function is as relation function.
In step (2), the wide territory=f2 of dynamic wide territory function y (x static state) of foundation can be line shape function or binomial function or exponential function or logarithmic function or power function, finally chooses maximum coefficient of determination R
2Corresponding function is as relation function.
In step (3), the narrow territory=f3 of relation function y (the wide territory of y) can be line shape function or binomial function or exponential function or logarithmic function or power function, finally chooses maximum coefficient of determination R
2Corresponding function is as relation function.
In step (4), the relation function x static state=f4 (x is dynamic) of foundation can be line shape function or binomial function or exponential function or logarithmic function or power function, finally chooses maximum coefficient of determination R
2Corresponding function is as relation function.
The present invention is by the network platform, by dynamic modeling technology (at the static infrared moisture meter in laboratory) and model transfer techniques (at online Dynamic IR Moisture Meter), be implemented in sensing chamber on-the-spot dynamic water instrument is controlled, reach the purpose that solves detection hysteresis quality and online water content detection accuracy." dynamic modeling " solves the variation issue of raw material, changes according to raw material, determines suitable modeling frequency, guarantees breadboard static Moisture Meter and Oven Method testing result consistance, thereby realizes dynamic water instrument testing result reliability." model transfer " application network technology solves dynamic water instrument and static Moisture Meter deviation and multimachine and concentrates modeling problem, realizes and guarantee on-the-spot dynamic water instrument testing result reliability.
Demarcate online infrared moisture meter with infrared moisture meter scaling method of the present invention, fast, accurately, conveniently, simply, can save a large amount of human and material resources.
Embodiment
The following examples will help those skilled in the art more fully to understand the present invention, but not limit the present invention in any way.
Whole modeling of the present invention can be divided into three levels:
(1) hardware layer
Comprise: be installed in on-the-spot dynamic moisture teller, be installed in breadboard static moisture teller, baking oven, computer, network system that equipment is linked together etc. fast.
Technical characterstic in this aspect is: the equipment on the network of being connected need provide digital interface, system by interface can carry out instruction manipulation to equipment, network is connected on the hardware should have interference protection measure.
(2) software platform
This software platform decapacitation also needs to provide for the user expansion interface of program for the equipment on the network provides outside communication support, instruction manipulation, the basic application interface, to make things convenient for special user's software function customization and application extension.As extra mathematical model or data base administration.
(3) user uses
Except that the basic controlling interface that software platform provided, the user also can be used for demarcating or measurement data being imported database automatically carrying out production quality control according to the actual conditions customization operations interface of oneself, the new mathematical model of Interface design that more can utilize software platform to provide.
Embodiment 1
(1) static calibration method for infrared moisture meter system, this method comprises in regular turn:
(1) setting up wide domain model on the static infrared moisture meter: in material real work moisture scope, choosing the difference that covers actual material possible range water percentage on production line takes a sample respectively, institute's sample thief is at least five samples, each sample detects with the indoor static infrared moisture meter respectively and the record detected signal value, use the water percentage and the recording detection data of baking oven test sample then respectively, baking oven is detected data and the infrared moisture meter detected signal value returns, the wide territory=f1 of opening relationships function y (x static state);
In real work moisture scope, according to data such as the table 1 that above-mentioned steps records, data are divided into three groups, and seven every group, alternative regression equation is:
y
Wide territory=-34.009x
Static 2+ 115.82x
Static-77.376, R
2=0.9833;
y
Wide territory=31.113x
Static-25.321, R
2=0.9662;
y
Wide territory=6.1376x
Static 3.3285, R
2=0.9086;
y
Wide territory=0.4549e
2.6586x it is static, R
2=0.8832;
y
Wide territory=38.612ln (x
Static)+5.2072, R
2=0.9767.
According to choosing maximum coefficient of determination R
2Principle is so select y for use
Wide territory=-34.009x
Static 2+ 115.82x
Static-77.376 as relation function, as Fig. 1.
The wide domain model data of table 1
(2) set up dynamic wide domain model on static infrared moisture meter: difference obtains one group of sample on production line, each sample is detected with the indoor static infrared moisture meter respectively and the record detected signal value, distinguish the water percentage and the recording detection data of test sample then with baking oven, according to current data or historical data weight, determine copy step (2) data number of times, with the historical data of copy data and employing together as modeling data, again correct recurrence, function is corrected in foundation, and the like, set up the kinematic function y that constantly corrects
Order in wide territory Just=f2 (x
Static).Alternative regression equation is:
y
Wide territory is corrected=-15.348x
Static 2+ 67.707x
Static-46.641, R
2=0.9659;
y
Wide territory is corrected=29.573x
Static-23.192, R
2=0.9623;
y
Wide territory is corrected=6.8902x
Static 2.9794, R
2=0.9001;
y
Wide territory is corrected=0.6648e
2.3906x it is static, R
2=0.8812;
y
Wide territory is corrected=36.539ln (x
Static)+5.8021, R
2=0.966.
According to choosing maximum coefficient of determination R
2Principle is so select y for use
Wide territory is corrected=-15.348x
Static 2+ 67.707x
Static-46.641 or y
Wide territory is corrected=36.539ln (x
Static)+5.8021 are as relation function, and this example is selected y for use
Wide territory is corrected=-15.348x
Static 2+ 67.707x
Static-46.641, see Fig. 2.And the like, set up the kinematic function of constantly correcting.
The wide domain model data value that table 2 is corrected
(3) set up narrow domain model: in fixing station material real work moisture scope, get dynamic wide domain model data, to organize detected signal value brings the wide territory=f2 of dynamic wide domain model function y (x static state) that step (2) obtains into and calculates the predicted value that sample moisture contains rate, then baking oven detection data and predicted value are returned,, the narrow territory=f3 of opening relationships function y (y corrects in wide territory).
In station real work moisture 17.0%～21.0% scope before tobacco leaf is roasting; get the qualified data of table 2; with the detected signal value of indoor infrared moisture meter according to step (2) obtain the predicted value of dynamic wide domain model function; as table 3; baking oven detected value and predicted value are returned the opening relationships function, and alternative regression equation is:
y
Narrow territory=0.1441y
Wide territory is corrected 2-4.6102y
Wide territory is corrected+ 54.328, R
2=0.9366;
y
Narrow territory=0.6101y
Wide territory is corrected+ 7.1732, R
2=0.8903;
y
Narrow territory=3.2244y
Wide territory is corrected 0.590, R
2=0.8794;
y
Narrow territory=9.9664e
0.0333y wide territory is corrected, R
2=0.8904;
y
Narrow territory=10.958ln (y
Wide territory is corrected)-13.501, R
2=0.8788.
According to choosing maximum coefficient of determination R
2Principle is so select y for use
Narrow territory=0.1441y
Wide territory is corrected 2-4.6102y
Wide territory is corrected+ 54.328 as relation function, as Fig. 3.
The narrow domain model function data of table 3 value
(4) set up dynamic and static infrared moisture meter detection signal relation function: in fixing station material real work moisture scope, get one group of sample, write down the detected signal value of Dynamic IR Moisture Meter on this station during sampling, should organize sample then and detect and write down detected signal value with the indoor static infrared moisture meter respectively, the detected signal value of Dynamic IR Moisture Meter and the detected signal value of static infrared moisture detector are returned, set up regression equation, x static state=f4 (x is dynamic).
The signal value such as the table 4 of Dynamic IR Moisture Meter are set up regression equation on roasting preceding station sample indoor static infrared moisture meter and the station, obtain dynamic and static infrared moisture meter detection signal relation function x
Static=f4 (x
Dynamically), alternative regression equation is:
x
Static=0.0903x
Dynamically 2+ 0.7464x
Dynamically+ 0.1918, R
2=0.9922;
x
Static=0.9637x
Dynamically+ 0.0618, R
2=0.9921;
x
Static=1.0252x
Dynamically 0.9471, R
2=0.9923;
x
Static=0.47e
0.7913x dynamically, R
2=0.9914;
x
Static=1.151ln (x
Dynamically)+1.0122, R
2=0.9887.
According to choosing maximum coefficient of determination R
2Principle is so select x for use
Static=0.9637x
Dynamically+ 0.0618 or x
Static=1.0252x
Dynamically 0.9471Or x
Static=0.0903x
Dynamically 2+ 0.7464x
Dynamically+ 0.1918, R
2=0.9922 as relation function, and this example is selected x for use
Static=0.9637x
Dynamically+ 0.0618, see Fig. 4.
The dynamic and static infrared moisture meter detection signal of table 4 relation function data
(6) set up dynamic and static relation function metastasis model:, make the predicted value of the Dynamic IR Moisture Meter on the station equate, i.e. y with the predicted value of indoor static infrared moisture meter for same sample
Dynamically=y
Narrow territory=f3 (y
Wide territory)=f3 (f2 (x
Static))=f3 (f2 (f4 (x
Dynamically))), obtain metastasis model y dynamically=f5 (x
Dynamically).
Y in the co-relation function
Narrow territory=0.1441y
Wide territory is corrected 2-4.6102y
Wide territory is corrected+ 54.328,
y
Wide territory=-34.009x
Static 2+ 115.82x
Static-77.376
x
Static=0.9637x
Dynamically+ 0.0618, the substitution following formula obtains:
y
Dynamically=y
Narrow territory=0.1441y
Wide territory 2-4.6102y
Wide territory+ 54.328
=0.1441 (34.009x
Static 2+ 115.82x
Static-77.376)
2-4.6102 (34.009x
Static 2+ 115.82x
Quiet Attitude-77.376)+54.328
=0.1441 (34.009 (0.9637x
Dynamically+ 0.0618)
2+ 115.82 (0.9637x
Dynamically+ 0.0618)-77.376)
2-4.6102 (34.009 (0.9637x
Dynamically+ 0.0618)
2+ 115.82 (0.9637x
Dynamically+ 0.0618)-77.376)+54.328
Same method system can construct the function model on each station automatically.
In the leaf tobacco production process, can demarcate online infrared moisture meter on each station by demarcating the indoor static infrared moisture meter.
Claims (7)
1. a calibration method for infrared moisture meter system is characterized in that calibration method for infrared moisture meter system is static calibration method for infrared moisture meter system, and static calibration method for infrared moisture meter system comprises in regular turn:
(1) setting up wide domain model on the static infrared moisture meter: in material real work moisture scope, choosing the difference that covers actual material possible range water percentage on production line takes a sample respectively, institute's sample thief is at least five samples, each sample detects with the indoor static infrared moisture meter respectively and the record detected signal value, use the water percentage and the recording detection data of baking oven test sample then respectively, baking oven is detected data and the infrared moisture meter detected signal value returns, the wide territory=f1 of opening relationships function y (x static state);
(2) set up dynamic wide domain model on static infrared moisture meter: difference obtains one group of sample on production line, each sample is detected with the indoor static infrared moisture meter respectively and the record detected signal value, distinguish the water percentage and the recording detection data of test sample then with baking oven, according to current data or historical data weight, determine to duplicate the current data number of times, with the historical data of copy data and employing together as modeling data, again correct recurrence, function is corrected in foundation, and the like, set up the wide territory=f2 of kinematic function y (x static state) that constantly corrects;
(3) set up narrow domain model: in fixing station material real work moisture scope, get dynamic wide domain model data, to organize detected signal value and bring the predicted value that the wide territory=f2 of dynamic wide domain model function y (x static state) that step (2) obtains calculates the sample moisture into, then baking oven detection data and predicted value are returned, narrow territory=the f3 of opening relationships function y (y corrects in wide territory), with this kind method, can systematically set up the narrow domain model of same material different station, promptly static infrared moisture meter application model with available data.
2. static calibration method for infrared moisture meter system according to claim 1 is characterized in that: in step (1), when choosing the difference that covers actual material possible range water percentage taking a sample respectively on production line, get three groups of samples first at least.
3. calibration method for infrared moisture meter system according to claim 2 is characterized in that: calibration method for infrared moisture meter system is online infrared moisture meter scaling method, may further comprise the steps in regular turn:
(1) setting up wide domain model on the static infrared moisture meter: in material real work moisture scope, choosing the difference that covers actual material possible range water percentage on production line takes a sample respectively, institute's sample thief is at least five samples, at least get three groups of samples first, each sample detects with the indoor static infrared moisture meter respectively and the record detected signal value, use the water percentage and the recording detection data of baking oven test sample then respectively, baking oven is detected data and the infrared moisture meter detected signal value returns, the wide territory=f1 of opening relationships function y (x static state);
(2) set up dynamic wide domain model on static infrared moisture meter: difference obtains one group of sample on production line, each sample is detected with the indoor static infrared moisture meter respectively and the record detected signal value, distinguish the water percentage and the recording detection data of test sample then with baking oven, according to current data or historical data weight, determine to duplicate the current data number of times, with the historical data of copy data and employing together as modeling data, again correct recurrence, function is corrected in foundation, and the like, set up the wide territory=f2 of kinematic function y (x static state) that constantly corrects;
(3) set up narrow domain model: in fixing station material real work moisture scope, get dynamic wide domain model data, to organize detected signal value and bring the predicted value that the wide territory=f2 of dynamic wide domain model function y (x static state) that step (2) obtains calculates the sample moisture into, then baking oven detection data and predicted value are returned, narrow territory=the f3 of opening relationships function y (y corrects in wide territory), with this kind method, can systematically set up the narrow domain model of same material different station, promptly static infrared moisture meter application model with available data;
(4) set up dynamic and static infrared moisture meter detection signal relation function: in fixing station material real work moisture scope, get one group of sample, write down the detected signal value of Dynamic IR Moisture Meter on this station during sampling, should organize sample then and detect and write down detected signal value with the indoor static infrared moisture meter respectively, the detected signal value of Dynamic IR Moisture Meter and the detected signal value of static infrared moisture detector are returned, set up regression equation, x static state=f4 (x is dynamic);
(5) set up dynamic and static relation function metastasis model: for same sample, make the predicted value of the Dynamic IR Moisture Meter on the station equate with the predicted value of indoor static infrared moisture meter, be y dynamically=the narrow territory=f3 of y (the wide territory of y)=f3 (f2 (x static state))=f3 (f2 (f4 (x is dynamic))), obtain metastasis model y dynamically=f5 (x is dynamic), by the network platform, metastasis model is copied on the online infrared moisture meter by system.
4. calibration method for infrared moisture meter system according to claim 3 is characterized in that: in step (1), the wide territory=f1 of regression equation y (x static state) of foundation can be line shape function or binomial function or exponential function or logarithmic function or power function.
5. calibration method for infrared moisture meter system according to claim 3, it is characterized in that: in step (2), the wide territory=f2 of dynamic wide territory function y (x static state) of foundation can be line shape function or binomial function or exponential function or logarithmic function or power function.
6. calibration method for infrared moisture meter system according to claim 3 is characterized in that: in step (3), the narrow territory=f3 of relation function y (the wide territory of y) can be line shape function or binomial function or exponential function or logarithmic function or power function.
7. calibration method for infrared moisture meter system according to claim 3 is characterized in that: in step (4), the relation function x static state=f4 (x is dynamic) of foundation can be line shape function or binomial function or exponential function or logarithmic function or power function.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102095831A (en) * | 2010-06-24 | 2011-06-15 | 龙岩烟草工业有限责任公司 | Moisture meter management method and system |
| CN103175799A (en) * | 2011-12-22 | 2013-06-26 | 株式会社堀场制作所 | Method of calibrating and calibration apparatus for a moisture concentration measurement apparatus |
| CN104777268A (en) * | 2014-01-13 | 2015-07-15 | 苏州朗博校准检测有限公司 | Chemical method trace moisture meter calibration detection method |
| CN106932546A (en) * | 2017-03-02 | 2017-07-07 | 安徽广深机电设备有限公司 | The paddy MOISTURE MEASUREMENT SYSTEM and detection method of a kind of crop dryer |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5436456A (en) * | 1993-10-07 | 1995-07-25 | Brown & Williamson Tobacco Corporation | Moisture meter curve calibration system |
| CN101055248B (en) * | 2007-04-28 | 2010-12-15 | 吉林燃料乙醇有限责任公司 | Method for analyzing high moisture corn and freezing corn moisture using near infrared spectrum technology |
-
2008
- 2008-07-25 CN CN200810140797XA patent/CN101634624B/en not_active Expired - Fee Related
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102095831A (en) * | 2010-06-24 | 2011-06-15 | 龙岩烟草工业有限责任公司 | Moisture meter management method and system |
| CN103175799A (en) * | 2011-12-22 | 2013-06-26 | 株式会社堀场制作所 | Method of calibrating and calibration apparatus for a moisture concentration measurement apparatus |
| CN103175799B (en) * | 2011-12-22 | 2017-04-12 | 株式会社堀场制作所 | Method of calibrating and calibration apparatus for a moisture concentration measurement apparatus |
| CN104777268A (en) * | 2014-01-13 | 2015-07-15 | 苏州朗博校准检测有限公司 | Chemical method trace moisture meter calibration detection method |
| CN106932546A (en) * | 2017-03-02 | 2017-07-07 | 安徽广深机电设备有限公司 | The paddy MOISTURE MEASUREMENT SYSTEM and detection method of a kind of crop dryer |
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|---|---|
| CN101634624B (en) | 2011-01-12 |
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