CN111968086A - Chemical industry sight glass phase-splitting detection method based on machine vision - Google Patents
Chemical industry sight glass phase-splitting detection method based on machine vision Download PDFInfo
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Abstract
The invention relates to a chemical sight glass split-phase detection method based on machine vision, which comprises the steps of firstly, obtaining a sight glass liquid-phase image by using a vision sensor, and transmitting the image to a vision data processing module; then, processing the visual data, including preprocessing, data space conversion and data feature extraction; and finally, mapping the visual data characteristic value and the liquid phase state, including calibration and characteristic value area division. By the method, the liquid phase of the chemical sight glass can be accurately and quickly identified, the phase splitting accuracy of the sight glass is improved, the stability of a chemical production process is ensured, and the unmanned chemical production site can be realized.
Description
Technical Field
The invention particularly relates to a chemical sight glass phase splitting detection method based on machine vision.
Background
In the liquid phase separation process of chemical production, chemical materials are constantly changed in production equipment. Each material state corresponds to a liquid phase, and the liquid phase of the material needs to be observed in real time through a sight glass to control the production operation steps. At present, chemical plants mainly obtain the state of liquid phase by manual observation and take corresponding operations. The manual observation mode brings many uncertain factors to the production process, such as wrong judgment of personnel on the liquid phase, failure to respond to the change of the liquid phase in time and the like. Some chemical plants have high production environment risk and are not suitable for personnel to observe on site.
At present, the phase splitting of the chemical sight glass generally adopts a manual observation operation method, and field personnel adjust a control valve to carry out material distribution by observing the liquid phase state in the sight glass. The personnel observe the liquid color in the sight glass, the information such as the liquid flow state, etc., judge the liquid phase in the sight glass according to the information, and then operate the size of the control valve to divide the material.
The way of manual observation operation has several disadvantages as follows:
the manual observation and judgment has subjectivity, and the judgment error of the liquid phase state of the sight glass easily occurs, so that the waste of production materials is caused.
The response time of personnel to the liquid phase change has uncertainty, so the stability of the production process cannot be ensured. Many chemical processes are not suitable for on-site observation by personnel.
As the technology of vision sensors matures and the cost decreases, and the computing power of edge devices is continuously enhanced, the use of machine vision technology in industrial production scenarios is increasing. The machine vision technology simulates the visual function of human through a computer, extracts information from the image, processes and understands the information, and finally is used for finishing the actual detection function. A liquid phase detection technology for a chemical viewing mirror based on machine vision is the first application of the machine vision technology in the field of liquid phase splitting of the chemical viewing mirror, a viewing mirror liquid phase picture is collected through a vision sensor, visual feature extraction calculation is carried out at an edge end, and a liquid phase visual signal is converted into a liquid phase state signal and is output in real time. The system has the characteristics of non-contact, unattended operation, high accuracy, low power consumption, low delay and the like.
Disclosure of Invention
The invention aims to provide a chemical sight glass phase splitting detection method based on machine vision, which aims to solve the prior technical problem.
In order to achieve the purpose, the invention provides the following technical scheme: a chemical industry sight glass phase splitting detection method based on machine vision comprises three steps of liquid phase state vision perception, vision data processing and vision characteristic and liquid phase state mapping.
Preferably, the specific operation steps are as follows:
A. visual perception of liquid phase state
Firstly, positioning data to be detected, selecting a detection area on a liquid phase image of a sight glass, wherein the area is a square area, and then performing median filtering on the image data in the area;
B. visual data processing
(1) Data pre-processing
Firstly, positioning data to be detected, selecting a detection area on a liquid phase image of a sight glass, wherein the area is a square area, and then performing median filtering on the image data in the area;
(2) and converting the value of each pixel point in the image square region from the color space to the gray value space.
Calculating a formula of S ═ k (a1 ═ R + a2 ═ G + a3 ═ B)/3;
(3) feature extraction
Performing convolution calculations within the selected region using a convolution kernel:
V＝kernel*I,
kernel＝(a11,a12,a13,a21,a22,a23,a31,a32,a33)
converting the convolved two-dimensional image into a one-dimensional feature vector, extracting numerical values in a certain proportion from the feature vector, and calculating the average value of the numerical values:
C. visual feature to liquid phase state mapping
(1) Calibration
Respectively inputting pictures of each phase of the liquid phase of the sight glass, calculating characteristic values of the pictures according to a method in the visual data processing module, and taking the obtained characteristic values as response reference values; for n liquid phases, the reference values obtained are (F1, F2 … Fn);
(2) eigenvalue interval partitioning
Determining the characteristic value interval of each phase according to the principle that no intersection exists in the characteristic value intervals of the phases, wherein the formula is as follows:
(Fl,Fr)＝(Fi-αi,Fi+βi)；
(3) mapping the characteristic value to a liquid phase state, and calculating the characteristic value of each frame of sight glass liquid phase image detected by the vision sensor by using a calculation method in the second step;
if the characteristic value falls within the characteristic value interval corresponding to a certain state in the step 2, the frame image corresponds to the state phase, so that the liquid level phase splitting is realized.
Preferably, in step B (2), S is a luminance value, R, G, B is three components of a color space value, a1, a2, a3 weighting coefficients, and k is a scaling coefficient; in the step B (3), V is the gray image after convolution, kernel is a convolution kernel, parameters a11 and a12 … in the convolution kernel are set values, and I is the image of the detection area; n is the length of the feature vector, P is the proportion value of the selected feature value number in the total number, the value range (0.5, 1) of P, and F is the feature response value of the visual data.
Preferably, F in step C (2)iIs a reference value of the i-th phase, αiAs a reference feature left offset value, βiAs a reference feature right offset value, alphai、βiAnd shifting the parameter for the set characteristic value interval.
Compared with the prior art, the invention has the beneficial effects that:
(1) the chemical sight glass liquid phase can be accurately and quickly identified, and the state of each phase of the liquid phase of the sight glass can be accurately detected;
(2) the change of the liquid phase of the low delay response sight glass ensures the stability of the chemical production process;
(3) the production site does not need personnel to participate, and the production safety is improved.
Description of the drawings:
FIG. 1: a flow chart of steps of the invention;
FIG. 2: the equipment of the invention is schematically shown.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The patent provides a chemical industry sight glass phase-splitting detection method based on machine vision, and the complete chemical industry sight glass liquid phase-splitting function is realized on the basis of hardware such as a vision sensor, edge equipment and the like.
Example one
A chemical industry sight glass phase splitting detection method based on machine vision comprises three steps of liquid phase state vision perception, vision data processing and vision characteristic and liquid phase state mapping.
Visual perception of liquid-phase state
The vision sensor arranged on the outer side of the sight glass is used for capturing the liquid-phase image of the sight glass in real time and transmitting the image to the vision processing module of the edge device through a serial port.
Second, visual data processing
1. And (4) preprocessing data. The data to be detected is firstly positioned, and a detection area is selected on the liquid phase image of the sight glass, wherein the area is a square area. And then performing median filtering on the image data in the region.
2. The value of each pixel point in the image square region is converted from the color space to the gray value space, and the calculation formula is S ═ k × (a1 × R + a2 × G + a3 ×/B)/3.
Where S is the luminance value, R, G, B is the three components of the color space value, a1, a2, a3 weighting coefficients, and k is the scaling coefficient.
3. And (3) feature extraction, namely performing convolution calculation by using a 3-dimensional convolution kernel in a selected area:
V＝kernel*I,
kernel＝(a11,a12,a13,a21,a22,a23,a31,a32,a33)
in the formula: v is the convolved gray image, kernel is the convolution kernel, the parameters a11 and a12 … inside the convolution kernel are set values, and I is the image of the detection region.
And converting the convolved two-dimensional image into a one-dimensional feature vector. Extracting a certain proportion of numerical values from the feature vector, and calculating the average value of the numerical values:
wherein, N is the length of the feature vector, P is the proportion value of the selected feature value number in the total number, the value range (0.5, 1) of P, and F is the feature response value of the visual data.
Thirdly, mapping the visual characteristic value with the liquid phase state
1. And calibrating, namely respectively inputting pictures of each phase of the liquid phase of the sight glass, calculating characteristic values of the pictures according to a method in the visual data processing module, and taking the obtained characteristic values as response reference values. For n liquid phases, the reference values obtained are (F1, F2 … Fn).
2. And (3) dividing the characteristic value intervals, and determining the characteristic value interval of each phase according to the principle that no intersection exists among the characteristic value intervals of the phases, wherein the formula is as follows: (F)l,Fr)＝(Fi-αi,Fi+βi)。
Wherein FiIs a reference value of the i-th phase, αiAs a reference feature left offset value, βiIs taken as a referenceCharacteristic right offset value, alphai、βiAnd shifting the parameter for the set characteristic value interval.
3. And mapping the characteristic value to the liquid phase state, and calculating the characteristic value of each frame of sight glass liquid phase image detected by the vision sensor by using the calculation method in the second step. If the characteristic value falls within the characteristic value interval corresponding to a certain state in the step 2, the frame image corresponds to the state phase, so that the liquid level phase splitting is realized.
Four, liquid phase state output
And (3) transmitting the liquid phase visual data of the sight glass into a visual data processing module (II), wherein the visual data processing module describes a data characteristic value, and then transmitting the characteristic value into a characteristic value mapping part (3) of a visual characteristic value and liquid phase state mapping module (III). And outputting a liquid phase state.
Example two
A chemical industry sight glass phase splitting detection method based on machine vision comprises three steps of liquid phase state vision perception, vision data processing and vision characteristic and liquid phase state mapping.
Visual perception of liquid-phase state
The vision sensor arranged on the outer side of the sight glass is used for capturing the liquid-phase image of the sight glass in real time and transmitting the image to the vision processing module of the edge device through a serial port.
Second, visual data processing
1. And (4) preprocessing data. The data to be detected is firstly positioned, and a detection area is selected on the liquid phase image of the sight glass, wherein the area is a square area. And performing Gaussian filtering and mean filtering on the image data in the region.
2. The value of each pixel point in the image square region is converted from the color space to the gray value space, and the calculation formula is S ═ k × (a1 × R + a2 × G + a3 ×/B)/3.
Where S is the luminance value, R, G, B is the three components of the color space value, a1, a2, a3 weighting coefficients, and k is the scaling coefficient.
3. And (3) feature extraction, namely performing convolution calculation by using other multidimensional convolution kernels (such as 5-dimension and 7-dimension) in the selected area:
V＝kernel*I,
kernel＝(a11,a12,a13,a21,a22,a23,a31,a32,a33)
in the formula: v is the convolved gray image, kernel is the convolution kernel, the parameters a11 and a12 … inside the convolution kernel are set values, and I is the image of the detection region.
And converting the convolved two-dimensional image into a one-dimensional feature vector. Extracting a certain proportion of numerical values from the feature vector, and calculating the average value of the numerical values:
wherein, N is the length of the feature vector, P is the proportion value of the selected feature value number in the total number, the value range (0.5, 1) of P, and F is the feature response value of the visual data.
Thirdly, mapping the visual characteristic value with the liquid phase state
1. And calibrating, namely respectively inputting pictures of each phase of the liquid phase of the sight glass, calculating characteristic values of the pictures according to a method in the visual data processing module, and taking the obtained characteristic values as response reference values. For n liquid phases, the reference values obtained are (F1, F2 … Fn).
2. And (3) dividing the characteristic value intervals, and determining the characteristic value interval of each phase according to the principle that no intersection exists among the characteristic value intervals of the phases, wherein the formula is as follows: (F)l,Fr)＝(Fi-αi,Fi+βi)。
Wherein FiIs a reference value of the i-th phase, αiAs a reference feature left offset value, βiAs a reference feature right offset value, alphai、βiAnd shifting the parameter for the set characteristic value interval.
3. And mapping the characteristic value to the liquid phase state, and calculating the characteristic value of each frame of sight glass liquid phase image detected by the vision sensor by using the calculation method in the second step. If the characteristic value falls within the characteristic value interval corresponding to a certain state in the step 2, the frame image corresponds to the state phase, so that the liquid level phase splitting is realized.
Four, liquid phase state output
And (3) transmitting the liquid phase visual data of the sight glass into a visual data processing module (II), describing a data characteristic value by the visual data processing module, transmitting the characteristic value into a characteristic value mapping part (3) of a visual characteristic value and liquid phase state mapping module (III), and outputting a liquid phase state.
In the above embodiments, the liquid-phase image of the viewing mirror is transmitted to the edge device in real time, including the data transmission method described in the embodiments; the vision data processing module of the sight glass liquid phase comprises data preprocessing, a data characteristic extraction mode and the like in the embodiment; the mapping of the data characteristic value and the liquid phase state of the liquid phase of the sight glass comprises the calibration method, characteristic value interval division, characteristic value mapping and the like in the embodiment.
According to the embodiment, the liquid phase of the chemical sight glass can be accurately and quickly identified, the phase splitting accuracy of the sight glass is improved, the stability of a chemical production process is ensured, and the unmanned chemical production site can be realized.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A chemical industry sight glass phase splitting detection method based on machine vision is characterized in that: the method comprises three steps of liquid phase state visual perception, visual data processing and visual characteristic and liquid phase state mapping;
a phase splitting method of a chemical industry sight glass based on machine vision is used for detecting each phase of a liquid phase in the chemical industry sight glass; the method comprises the following steps: 1) installing a visual sensor at the outer side of a chemical engineering sight glass, and acquiring a liquid phase image of the chemical engineering sight glass in real time; 2) transmitting the liquid-phase image of the sight glass to edge equipment in real time, wherein the method comprises the data transmission method in the technical scheme; 3) the visual data of the liquid phase of the sight glass is processed at the edge end in real time; 4) the vision data processing module of the sight glass liquid phase comprises the data preprocessing, the data characteristic extraction mode and the like in the specific technical scheme; 5) mapping the data characteristic value of the liquid phase of the sight glass with the liquid phase state; the method comprises the steps of calibration method, characteristic value interval division, characteristic value mapping and the like.
2. The machine vision-based chemical industry view mirror split-phase detection method according to claim 1, characterized in that: the specific operation steps are as follows:
A. visual perception of liquid phase state
Firstly, positioning data to be detected, selecting a detection area on a liquid phase image of a sight glass, wherein the area is a square area, and then performing median filtering on the image data in the area;
B. visual data processing
(1) Data pre-processing
Firstly, positioning data to be detected, selecting a detection area on a liquid phase image of a sight glass, wherein the area is a square area, and then performing median filtering on the image data in the area;
(2) converting the value of each pixel point in the image square region from a color space to a gray value space;
calculating a formula of S ═ k (a1 ═ R + a2 ═ G + a3 ═ B)/3;
(3) feature extraction
Performing convolution calculations within the selected region using a convolution kernel:
V＝kernel*I,
kernel＝(a11,a12,a13,a21,a22,a23,a31,a32,a33)
after being convolvedConverting the two-dimensional image into a one-dimensional feature vector, extracting numerical values in a certain proportion from the feature vector, and calculating the average value of the numerical values:
C. visual feature to liquid phase state mapping
(1) Calibration
Respectively inputting pictures of each phase of the liquid phase of the sight glass, calculating characteristic values of the pictures according to a method in the visual data processing module, and taking the obtained characteristic values as response reference values; for n liquid phases, the reference values obtained are (F1, F2 … Fn);
(2) eigenvalue interval partitioning
Determining the characteristic value interval of each phase according to the principle that no intersection exists in the characteristic value intervals of the phases, wherein the formula is as follows:
(Fl,Fr)＝(Fi-αi,Fi+βi)；
(3) mapping the characteristic value to a liquid phase state, and calculating the characteristic value of each frame of sight glass liquid phase image detected by the vision sensor by using a calculation method in the second step;
if the characteristic value falls within the characteristic value interval corresponding to a certain state in the step 2, the frame image corresponds to the state phase, so that the liquid level phase splitting is realized.
3. The machine vision-based chemical industry view mirror split-phase detection method according to claim 1, characterized in that: in step B (2), S is a brightness value, R, G, B is three components of a color space value, a1, a2 and a3 are weighting coefficients, and k is a scaling coefficient; in the step B (3), V is the gray image after convolution, kernel is a convolution kernel, parameters a11 and a12 … in the convolution kernel are set values, and I is the image of the detection area; n is the length of the feature vector, P is the proportion value of the selected feature value number in the total number, the value range (0.5, 1) of P, and F is the feature response value of the visual data.
4. The machine vision-based chemical view mirror split-phase device according to claim 1The detection method is characterized in that: in step C (2) FiIs a reference value of the i-th phase, αiAs a reference feature left offset value, βiAs a reference feature right offset value, alphai、βiAnd shifting the parameter for the set characteristic value interval.
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