CN106599902B - A kind of sugar crystal Classification and Identification and crystalline quality control method based on image - Google Patents
A kind of sugar crystal Classification and Identification and crystalline quality control method based on image Download PDFInfo
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Abstract
The invention discloses a kind of sugar crystal Classification and Identification and crystalline quality control method based on image, belong to image recognition sorting technique and industrial control system field.Step: 1) under the collaboration to electro-optical device, the original sample image of sugar crystal is obtained from micro-photographing apparatus, original sample image is pre-processed using Preprocessing Technique, and pretreated image is split, obtains the region of interest of sugar crystal sample image；2) it is directed to region of interest, the characteristic parameter of every width sample image is therefrom extracted according to given identification feature and multi-categorizer in parallel is trained using it；3) the setting control period obtains image to be classified as unit of a control period within the period, is classified after pretreatment using trained multi-categorizer in parallel, the crystalline state information of sugar crystal is calculated according to classification results；4) executive device movement is controlled according to crystalline state information.This method can realize the automation of cane sugar boiling and crystal process on the whole.
Description
Technical field
The present invention relates to image recognition sorting technique and industrial control system, in particular to sugar crystal image multi-categorizer,
The method and apparatus classified using multi-categorizer to sugar crystal image, and sugared crystalline is carried out using identification classification results
Measure the control method of control.
Background technique
Industrially, sugar crystallization process needs operator constantly to observe crystal grain situation in tank, corresponding to take
Operation.Majority sugar refinery is all that sampling probe taking-up massecuite sample is observed under lamp or hand is twisted with feeling and judged, Bu Nengzhi at present
It connects and observes crystal grain situation of change in tank, bring certain difficulty to the accuracy of operation.To solve this problem, Ren Menkai
It sends out, have developed and sugared microexamination mirror is boiled based on video technique, by the microscope on tank skin, can clearly observe tank
The situation of change of interior crystal finds abnormal grain growth in time, to take corresponding operation.In fact, this is still relied on
It works in human eye, only labour is transferred to eye-observation and boils sugared show from the crystal quality situation for directly distinguishing that probe samples
On the image of micro mirror acquisition, and have not been changed artificial conventional operation mode.It is aobvious to sugar is boiled in order to realize the automation of sugar boiling process
The sugar crystal image automatic identification classification tool that micro- peephole acquires out has very important significance.
However, whole clearing, when acquiring image, light is from crystal back on the one hand since sugar crystal surface is more smooth
It injects, passes through crystal, just very high in the brightness that the smoother plane transmission of crystal goes out, other parts are then darker, acquire out
Image is one, and there are the images of critical noisy；On the other hand, various crystal are mixed in the crystal to conform to quality requirements, it is such as poly-
Brilliant, viscous brilliant, pseudo- crystalline substance etc., so that the type of sample greatly increases.Therefore, both at home and abroad at present to the identification of sugar crystal classification still without
Effective method.
The automatic recognition classification of sugar crystal is an important ring for the automation of sugar boiling process, it is obtained the result is that after
The decision-making foundation of continuous control system, cannot accurately identify classification results, just cannot achieve the automation of sugar boiling process.
Summary of the invention
It is an object of the invention to solve problems of the prior art, and provide a kind of sugar crystal based on image point
Class identification and crystalline quality control method.
Sugar crystal Classification and Identification and crystalline quality control method based on image, comprise the steps of:
1) under the collaboration to electro-optical device, the original sample image of sugar crystal is obtained from micro-photographing apparatus, utilizes figure
As preconditioning technique pre-processes original sample image, and pretreated image is split, obtains sugar crystal sample
The region of interest of this image；
2) it is directed to region of interest, characteristic parameter and the utilization of every width sample image are therefrom extracted according to given identification feature
It is trained multi-categorizer in parallel；
3) the setting control period obtains image to be classified as unit of a control period within the period, sharp after pretreatment
Classified with trained multi-categorizer in parallel, the crystalline state information of sugar crystal is calculated according to classification results；
4) executive device movement is controlled according to crystalline state information, carry out adding water or adds honey.
In above-mentioned technical proposal, following implementation is can be used in each step:
The training of parallel connection multi-categorizer comprises the steps of in step 2):
2.1) correction, identification are accepted or rejected for the characteristic selection sugar crystal image recognition feature of sugar crystal and in fit procedure
Feature includes: single crystal grain size, crystal grain number, crystalline areas and image area ratio, transparency and shape；
2.2) result of regulation identification classification only has two classes, and one kind is the sugar crystal image to conform to quality requirements, is defined as
Clear, another kind of is the sugar crystal image for not meeting quality requirement, is defined as unclear；
2.3) it is directed to region of interest, according to the sugar crystal identification feature of setting, feature extraction goes out each of each sample image
Kind quantitative description parameter, the sample data set 1 used as training decision tree classifier；By stochastical sampling to sample data set
1 progress double sampling generates different data sample set, the sample data set 2 used as training SVM classifier；Two kinds points
Class device carries out stand-alone training and test based on the different views of same data, and two groups of different sample datas are respectively divided into training
Data and test data, training data are used to carry out classifier to the model trained for training classifier, test data
Assessment is service check；
2.4) simple subprogram traverses entire training dataset, classifies to the sugar crystal data set of known class: fighting to the finish
For plan Tree Classifier, before carrying out the identification classification of the last one feature, according to identification feature, it is unsatisfactory for currently identifying special
Sugar crystal data when sign are labeled with the label of unclear, exit the training process of epicycle；Otherwise it is instructed into the classification of next round
Practice, is performed until the last one feature；By the training that last is taken turns, can obtain finally as a result, all sugar crystal image
Data are all assigned to two classifications, and one kind is to may determine that up-to-standard clear classification, and one kind is to may determine that quality not
Qualified unclear classification；
2.5) above two single classifier identification classification results obtained are based on, integrate two kinds points by combined method
Class is as a result, obtain a final classification device；The combined method uses following algorithm:
It is as follows to define error rate:
Wherein, xjFor j-th of sample that training data is concentrated, Ci(xj) it is i-th of classifier to sample xjClassification knot
Fruit, i=1 or 2；I is indicator, if parameter A is true, otherwise I (A)=1 is 0；wjThe jth concentrated for training data
The weight of a sample；yjFor target classification result；
Calculate decision tree classifier importance beThe importance of SVM classifier is
By parameter alpha1,α2For updating training sample weight, weight update mechanism are as follows:
Wherein ZjFor standardizing factor, for ensuring w1 (j+1)+w2 (j+1)=1；
By w1 (j+1)And w2 (j+1)The weight of decision tree classifier and SVM classifier, is used for when respectively as next subseries
Calculate the final classification result of current sample.
The crystalline state information that sugar crystal is calculated in step 3) comprises the steps of:
3.1) the setting control period, as unit of a control period, within the control period under the collaboration to electro-optical device,
The original image to be classified of specified quantity is obtained from micro-photographing apparatus；
3.2) to every original image to be classified, pretreatment and image segmentation are carried out, according to given identification feature from
The middle characteristic parameter for extracting each image is input to trained multi-categorizer in parallel and classifies, up-to-standard figure
As stamping clear label, image off quality stamps unclear label；
3.3) to tagged identification classification results, it is automatically converted into sugar crystal image file using IDL program,
This document is identified image classification file；
3.4) batch processing is carried out to image classification file, obtains the sugar crystal figure for stamping clear/unclear label respectively
The quantity of picture, according to quantity obtain current sugar crystal locating for crystalline state information.
Above-mentioned batch processing process are as follows:
3.4.1) by the image file category having been classified distribute distinguished with classification naming entitled clear,
In the file of unclear；
3.4.2 the quantity of image file in each file) is counted；
3.4.3 all image files in the file of clear, unclear in this control period) are deleted, so as under
The use in one control period；
3.4.4 the autoexec) is periodically executed, is i.e. after the timing of control cycle length arrives, executes primary batch
File is handled, using statistical result as the information source of this control decision.
It is as follows according to sugar crystal crystalline state information control executive device action step in step 4):
4.1) the data block DATA BLOCK in PLC host computer reads in each classification quantity in this control period manually, makees
For the foundation of subsequent control movement, quantity of classifying is as analog input data；
4.2) PLC timer is reset；
4.3) analog input data are compared in PLC host computer with given threshold, obtain corresponding switch letter
Number: if the quantity of unclear classification is greater than a certain threshold value N1, PLC charging motor obtains enabling signal, and motor drives feed pump
It opens, material enters tank, boils tank charging with the control of Ratio control method, while opened loop control boils level in tank；If clear classification
Sugar crystal amount of images be greater than a certain threshold value N2, PLC discharging machine enabling signal, motor drives discharging pump to open, and material goes out
Tank, while opened loop control boils level in tank and makes to boil tank stopping crystallization；
4.4) PLC wait next time timing arrive, repeat above-mentioned 4.1) -4.3) step.
The present invention has the advantages that as follows:
1, it to decision tree classifier is trained by sample set and SVM support vector machine classifier is effectively combined, obtains
To classifying quality preferably multi-categorizer in parallel is identified, carried out automatically to by boiling the collected sugar crystal image of sugared microexamination mirror
Analysis and classification.
2, the control system based on PLC controller and device are using classification results as the work of crystalline quality control system
Foundation realizes the automation of cane sugar boiling and crystal process on the whole.
Detailed description of the invention
Fig. 1 is sugar crystal image recognition categorizing system and crystalline quality control method work flow diagram；
Fig. 2 is the training flow chart of decision tree and SVM classifier；
Fig. 3 is classification results figure of the decision tree classifier under sugar crystal single features data；
Fig. 4 is decision tree and SVM parallel connection Combination of Multiple Classifiers schematic diagram；
Fig. 5 is to carry out classification work flow diagram to sugar crystal image to be identified using multi-categorizer in parallel；
Fig. 6 is the control system work flow diagram based on PLC controller；
Specific embodiment
The present invention is further elaborated in the following with reference to the drawings and specific embodiments, so that those skilled in the art can be more
Essence of the invention is understood well.
Due to the interference of the critical noisy and various mixed crystals of image, discovery utilizes single point based on decision-tree model
Class device, it is difficult to carry out accurately identification classification.The purpose of the present invention is study a kind of more accurate sugar crystal image recognition point
Class system and crystalline quality control method.Using decision tree and SVM support vector machines parallel connection multi-categorizer, in this control period
The sugar crystal image of acquisition is automatically analyzed and is identified classification, as unit of a control period, quantitatively calculates our institutes
The sugar crystal quality information needed, and the work foundation of later crystallization quality control system is utilized this information as, it is then real
The automation of existing cane sugar boiling and crystal process.In addition, this sugared image analysis and automatic recognition system improve sugared crystalline quality judgement
Objectivity, while the work load for boiling sugared operator is also mitigated, improve the accuracy of analysis.
, it is specified that the identification classification results of every 20min are converged with 20min for a control period in the present embodiment
Always, as the information source of each control decision.
As shown in Figure 1, sugar crystal image recognition classification proposed by the present invention and crystalline quality control method, by microimaging
Device 1, to electro-optical device 2, image recognition multi-categorizer 3, PLC upper computer software 4, the control system based on PLC controller and dress
It sets 5, PLC timer 6 and computer 7 forms.Wherein, image recognition multi-categorizer 3, PLC upper computer software 4, based on PLC control
The control system of device and the communication and information processing of the parts such as device 5 and PLC timer 6 are relied on computer 7 and are completed.
A variety of sugar crystal image recognition features proposed by the present invention, they include: single crystal grain size, crystal
Numbers of particles, crystalline areas and image area ratio, transparency and shape.
As shown in Fig. 2, being located in advance using Preprocessing Technique to sample image from sugar crystal original image 11
Reason 12, including gray scale adjustment, median filtering, image enhancement etc., and 13 are split to pretreated image, obtain sugar crystal
The region of interest of image.
For region of interest, according to the sugar crystal identification feature set in step before, feature extraction 14 goes out each sample
The various quantitative description parameters of image, the sample data set 1 15 used as training decision tree classifier.
Data obtained in the previous step are generated by 16 double sampling of random sampling technique by random sampling technique different
Data sample set, as the sample data set 2 17 that uses of training SVM classifier.
Two kinds of classifiers carry out stand-alone training and test based on the different views of same data.By two groups of different sample numbers
According to respectively training data 19 and test data 18 is divided into, the former is used for for training classifier 21, the latter to the mould trained
It is service check that type, which carries out classifier evaluation 20,.
The result of regulation identification classification only has two classes, and one kind is the image to conform to quality requirements, is defined as clear, another
Class is the image for not meeting quality requirement, is defined as unclear.
Classifier 1 is based on decision-tree model, can be used for handling continuous characteristic as under predictive variable and target variable
Classification, obtain regression tree.It can be with using the classification that the machine learning software with tree visualization tool carries out under single features
Obtain structure as shown in Figure 3.
As shown in figure 3, the node 31 on the left side indicates data, that is, unclear classification of non-mass qualification, it is purely only a kind of
Classification, but the node 32 on the right is still mixed with clear and unclear two classification, needs further progress training.At this time after
Continuous addition feature is to increase the tree node in upper figure, until obtaining satisfied result, that is to say, that until leaf node is that only have
Until a kind of pure node of classification, this classification or it is that may determine that up-to-standard clear classification or is that may determine that matter
Measure underproof unclear classification.
Simple subprogram traverses entire training dataset 1, classifies to the sugar crystal data set 1 of known class.To decision
For Tree Classifier, before carrying out the identification classification of the last one feature, according to identification feature, each sugar crystal data
(image) or the label for being labeled with unclear exits the training process of epicycle or enters the classification based training of next round, and one
Straight row is to a last feature.By last take turns training, can obtain finally as a result, all sugar crystal image datas all
Two classifications are assigned to, one kind is to may determine that up-to-standard clear classification, and one kind may determine that off quality
Unclear classification.
Training process when decision is exactly the process for increasing node and forming tree, is obtained after training similar to binary tree
Structural model, this is also the purpose of decision tree classifier training.To the decision tree classifier trained test set testing classification
Performance, it is ensured that classifying quality.
Classifier 2 is based on SVM support vector machines.The SVM has the learning method of supervision for one on statistical concepts,
For carrying out classification and regression analysis.SVM is a kind of basis for having solid theory, novel small-sample learning method simultaneously.
The theoretical basis of SVM is the VC dimension theory of structural risk minimization principle and basic statistical study theory.
The principle of SVM is that the point in lower dimensional space is mapped in higher dimensional space, them is made to become linear separability.Make again
Classification boundaries are judged with the principle of linear partition, and this principle can be realized by kernel function.The kernel function has 4
Kind:
(1) linear kernel function
(2) Polynomial kernel function
(3) Radial basis kernel function
(4) Sigmoid kernel function
The selection of kernel function has apparent influence for nicety of grading, and different kernel functions has different adaptability, such as
What suitable kernel function of selection is one important problem when carrying out svm classifier.Firstly the need of kernel function is determined, then selection is closed
Practical problem is transformed into higher dimensional space using kernel function by suitable parameter.For example, when selecting Radial basis kernel function, C and γ this
Two parameters, value are related to the height of nicety of grading.Under normal circumstances, C and γ value common method is cross validation
Grid data service.Herein using Radial basis kernel function as SVM kernel function.
Simple subprogram traverses entire training dataset 2, carries out identification classification to the data set 2 of known class.To svm classifier
For device, trained process is exactly to obtain rule according to the sugar crystal training data of these known class, establishes disaggregated model
Process.It final all sugar crystal training datas or is categorized into up-to-standard clear classification or is categorized into off quality
Unclear classification.
To the SVM classifier trained test set testing classification performance, it is ensured that classifying quality.
A kind of decision-making foundation that representative result combined method is final as decision-making device 43 is selected, to classifier 1
Decision tree classifier 41 and classifier 2SVM classifier 42 identify that classification results merge and recombination obtains a kind of more points of parallel connection
Class device 44, as shown in Figure 4.
The comprehensive performance of multi-categorizer is built upon on the basis of the difference between multiple classifiers, and classifier otherness can
To be generated by the way that different parameters is arranged.Herein, different node-classification feature selectings can be used for decision tree；
And SVM then can choose the parameter etc. of different kernel function or kernel function.
Based on above two single classifier identification classification results obtained, because more than image interference factor and mixing each
Kind improper crystal, therefore obtained classification accuracy rate does not reach requirement, therefore use a kind of combined method to integrate two kinds of classification
There is the final classification device of more preferable classification capacity to get more accurate to classifying for clear and unclear as a result, obtaining one
Classifier.This decision-making technique direct intervention classification results.
The combined method uses following algorithm.In the algorithm, the importance of two classifier classification results relies on
In its classification error rate.The definition of error rate is
Wherein, xjFor j-th of sample that training data is concentrated, Ci(xj) it is i-th of classifier to sample xjClassification knot
Fruit, i=1 or 2；I is indicator, if parameter A is true, otherwise I (A)=1 is 0；wjThe jth concentrated for training data
The weight of a sample；yjFor target classification result.Concept based on error rate, then the importance of decision tree classifier beThe importance of SVM classifier is
It will be apparent that the more high then importance of error rate is lower, then this classifier gets over " right to speak " of final classification result
It is low.
Parameter alpha1,α2For updating training sample weight, the weight update mechanism of the algorithm is determined by following formula
Wherein ZjFor standardizing factor, for ensuring w1 (j+1)+w2 (j+1)=1.
By w1 (j+1)And w2 (j+1)The weight of decision tree classifier and SVM classifier, is used for when respectively as next subseries
Calculate the final classification result of current sample.That is, the training result of single classifier is to the important of final decision result
Degree is by weights influence, to a certain sample data, if it identifies that classification results are consistent with known class, in final decision
In weight it is just high, i.e., influence assembled classifier model result a possibility that with regard to big；Conversely, will pay for, this point is reduced
Class device directly affects " right to speak " to next sample data in weight, the measure for being promoted and being punished by this weight,
Can effectively integrated decision-making Tree Classifier and SVM classifier classification advantage, promoted sorter model precision so that obtain group
Merge connection classifier to significantly improve the classification accuracy of unfiled image data.
As shown in figure 5, the image 51 of every classification to be identified will also be pre-processed by being similar to model training process
And segmentation, the characteristic parameter of each image is therefrom extracted according to given identification feature, be input to this system decision tree and
SVM parallel connection multi-categorizer 52 is classified, the result is that or stamping the up-to-standard clear label of symbol or stamping symbol
Unclear label off quality.
To tagged identification classification results 53, sugar crystal image file 54 is automatically converted into using IDL program.
Image file at this time is different from original image file, from the data file transition for having played label, therefore it is
It is identified image classification file 55.
The autoexec .bat 56 write under Windows operating system, function is as follows:
By the image file category having been classified distribution 57 with i.e. difference entitled clear, the unclear's of classification naming
In file.
Count the quantity of image file in 58 each files.
All image files in the file of clear, unclear in 59 control periods are deleted, so as to next
Control the use in period.
As shown in fig. 6, every PLC 20min timing executes an autoexec to after 61, using statistical result as
The information source of this control decision.PLC circulation step:
Data block DATA BLOCK in upper computer software reads in each classification quantity 62 in this control period manually, as
The foundation of subsequent control movement, it is notable that classification quantity is analog data.
PLC timer restarts timing.
Analog input data are compared with given threshold in PLC host computer, obtain corresponding switching signal.
If the quantity of unclear classification is greater than a certain threshold value N1 63, PLC charging motor obtains enabling signal 64, motor
Feed pump 65 is driven to open, material enters tank.To guarantee crystalline quality, it is necessary to assure between each charging (green molasses, methylene and water)
Proportionate relationship can be controlled with Ratio control method and boil tank charging 66, while opened loop control boils level 67 in tank.Otherwise compare
The sugar crystal amount of images of clear classification.
If the sugar crystal amount of images of clear classification be greater than a certain threshold value N2 69, PLC discharging machine enabling signal 70,
Motor drives discharging pump to open 71, and material goes out tank, while opened loop control boils level 72 in tank.It must make to boil tank stopping crystallization simultaneously
73。
It feeds in the process flow of cane sugar boiling and crystal or only or only discharges, the two operations will not deposit simultaneously
, in this way it is necessary that charging and discharging can not carry out with being without end, level height arrival upper limit value 68 and lower limit value 74
When can motor automatic stop be operated, to ensure equipment safety.As for the assignment problem of upper limit or lower limit, according to
Specifically boil tank situation and industrial requirements, limit value data and current level data, DB block be written, convenient for PLC ipc monitor,
Assignment.
Timing arrives next time for PLC waiting, repeats PLC circulation step.
Above-mentioned embodiment is only a preferred solution of the present invention, so it is not intended to limiting the invention.Have
The those of ordinary skill for closing technical field can also make various changes without departing from the spirit and scope of the present invention
Change and modification.Therefore all mode technical solutions obtained for taking equivalent substitution or equivalent transformation, all fall within guarantor of the invention
It protects in range.
Claims (4)
1. a kind of sugar crystal Classification and Identification and crystalline quality control method based on image, which is characterized in that by following steps group
At:
1) under the collaboration to electro-optical device, the original sample image of sugar crystal is obtained from micro-photographing apparatus, it is pre- using image
Processing technique pre-processes original sample image, and is split to pretreated image, obtains sugar crystal sample graph
The region of interest of picture；
2) it is directed to region of interest, the characteristic parameter of every width sample image is therefrom extracted according to given identification feature and utilizes its right
Multi-categorizer in parallel is trained；
3) the setting control period obtains image to be classified as unit of a control period within the period, and instruction is utilized after pretreatment
The multi-categorizer in parallel perfected is classified, and the crystalline state information of sugar crystal is calculated according to classification results；
4) executive device movement is controlled according to crystalline state information, carry out adding water or adds honey；
The training of parallel connection multi-categorizer comprises the steps of in step 2):
2.1) correction, identification feature are accepted or rejected for the characteristic selection sugar crystal image recognition feature of sugar crystal and in fit procedure
It include: single crystal grain size, crystal grain number, crystalline areas and image area ratio, transparency and shape；
2.2) result of regulation identification classification only has two classes, and one kind is the sugar crystal image to conform to quality requirements, is defined as
Clear, another kind of is the sugar crystal image for not meeting quality requirement, is defined as unclear；
2.3) it is directed to region of interest, according to the sugar crystal identification feature of setting, feature extraction goes out the various fixed of each sample image
Characterising parameter is measured, the sample data set 1 used as training decision tree classifier；By stochastical sampling to sample data set 1 into
Row double sampling generates different data sample set, the sample data set 2 used as training SVM classifier；Two kinds of classification
Device carries out stand-alone training and test based on the different views of same data, and two groups of different sample datas are respectively divided into trained number
According to and test data, for training classifier, test data is used to carry out classifier to the model trained commenting training data
Estimate i.e. service check；
2.4) simple subprogram traverses entire training dataset, classifies to the sugar crystal data set of known class: to decision tree
For classifier, before carrying out the identification classification of the last one feature, according to identification feature, when being unsatisfactory for current identification feature
Sugar crystal data be labeled with the label of unclear, exit the training process of epicycle；Otherwise enter the classification based training of next round,
It is performed until the last one feature；By the training that last is taken turns, can obtain finally as a result, all sugar crystal image data
All be assigned to two classifications, one kind is to may determine that up-to-standard clear classification, one kind be may determine that it is off quality
Unclear classification；
2.5) above two single classifier identification classification results obtained are based on, two kinds of classification knots are integrated by combined method
Fruit obtains a final classification device；The combined method uses following algorithm:
It is as follows to define error rate:
Wherein, xjFor j-th of sample that training data is concentrated, Ci(xj) it is i-th of classifier to sample xjClassification results, i=1
Or 2；I is indicator, if parameter A is true, otherwise I (A)=1 is 0；wjJ-th sample concentrated for training data
Weight；yjFor target classification result；
Calculate decision tree classifier importance beThe importance of SVM classifier isIts
Middle ε1And ε2The respectively classification error rate of decision tree classifier and SVM classifier；
By parameter alpha1,α2For updating training sample weight, weight update mechanism are as follows:
Wherein ZjFor standardizing factor, for ensuring w1 (j+1)+w2 (j+1)=1；
By w1 (j+1)And w2 (j+1)The weight of decision tree classifier and SVM classifier when respectively as next subseries, for calculating
The final classification result of current sample.
2. the sugar crystal Classification and Identification and crystalline quality control method, feature according to claim 1 based on image exists
In the crystalline state information for calculating sugar crystal in step 3) comprises the steps of:
3.1) setting control the period, as unit of a control period, control the period in electro-optical device collaboration under, from show
The original image to be classified of specified quantity is obtained in micro- photographic device；
3.2) to every original image to be classified, pretreatment and image segmentation is carried out, is therefrom mentioned according to given identification feature
The characteristic parameter for taking each image is input to trained multi-categorizer in parallel and classifies, and up-to-standard image is beaten
Upper clear label, image off quality stamp unclear label；
3.3) to tagged identification classification results, sugar crystal image file, this article are automatically converted into using IDL program
Part is identified image classification file；
3.4) batch processing is carried out to image classification file, obtains the sugar crystal image for stamping clear/unclear label respectively
Quantity, according to quantity obtain current sugar crystal locating for crystalline state information.
3. the sugar crystal Classification and Identification and crystalline quality control method, feature according to claim 2 based on image exists
In the batch processing process are as follows:
3.4.1) the image file category having been classified is distributed with i.e. difference entitled clear, the unclear's of classification naming
In file；
3.4.2 the quantity of image file in each file) is counted；
3.4.3 all image files in the file of clear, unclear in this control period are deleted) so as to next
Control the use in period；
3.4.4 it) periodically executes the autoexec and executes a batch processing after the timing of i.e. control cycle length arrives
File, using statistical result as the information source of this control decision.
4. the sugar crystal Classification and Identification and crystalline quality control method, feature according to claim 1 based on image exists
In as follows according to sugar crystal crystalline state information control executive device action step in step 4):
4.1) the data block DATA BLOCK in PLC host computer reads in each classification quantity in this control period manually, as rear
The foundation of continuous control action, quantity of classifying is as analog input data；
4.2) PLC timer is reset；
4.3) analog input data are compared in PLC host computer with given threshold, obtain corresponding switching signal: such as
The quantity of fruit unclear classification is greater than a certain threshold value N1, and PLC charging motor obtains enabling signal, and motor drives feed pump to open,
Material enters tank, boils tank charging with the control of Ratio control method, while opened loop control boils level in tank；If the sugar of clear classification
Crystal amount of images is greater than a certain threshold value N2, PLC discharging machine enabling signal, and motor drives discharging pump to open, and material goes out tank, together
When opened loop control boil in tank level and make to boil tank and stop crystallization；
4.4) PLC wait next time timing arrive, repeat above-mentioned 4.1) -4.3) step.
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| CN112949695A (en) * | 2021-02-05 | 2021-06-11 | 华南师范大学 | Liquid crystal phase state identification method and system, electronic equipment and storage medium |
| CN113307435B (en) * | 2021-05-25 | 2022-12-13 | 中煤能源研究院有限责任公司 | Dynamic PCA (principal component analysis) -based evaporative crystallization solid-liquid separation system and method |
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