CN117745718B - Information interaction method based on cloud manufacturing - Google Patents
Information interaction method based on cloud manufacturing Download PDFInfo
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- CN117745718B CN117745718B CN202410181842.5A CN202410181842A CN117745718B CN 117745718 B CN117745718 B CN 117745718B CN 202410181842 A CN202410181842 A CN 202410181842A CN 117745718 B CN117745718 B CN 117745718B
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Abstract
The invention discloses an information interaction method based on cloud manufacturing, which belongs to the technical field of information processing and comprises the following steps: s1, acquiring a welding image of a welding workpiece by using a CCD camera of a welding robot; s2, determining the position of a welding seam of a welding image; s3, determining a welding result of the welding robot; and S4, transmitting the welding result and the welding image of the welding robot to the cloud for storage, and completing information interaction. According to the invention, the welding robot work state in the cloud manufacturing industry is accurately estimated, and the estimated result is uploaded to the cloud for a user to check, so that theoretical support is provided for the user to know the welding result, and information interaction is realized.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to an information interaction method based on cloud manufacturing.
Background
Along with the continuous promotion of the automation and the intelligent degree of manufacturing industry, industrial robots are gradually applied to industrial production and play a quite important role in the industrial production, and in the field of processing and manufacturing, the application of the industrial robots is also more and more mature, and the human beings can carry out accurate control on the industrial robots in a high-efficiency and intelligent manner by inputting a computer algorithm of a control instruction into a robot control system so as to achieve the high-efficiency production target.
Taking a welding robot as an example, the welding robot can weld products according to set steps as long as the welding robot is programmed. However, in the prior art, the welding robot cannot accurately evaluate the processing condition of the welding seam and upload the processing condition to the cloud for a user to check, so that the information interaction effect is poor, and the user cannot know the cloud manufacturing condition in time.
Disclosure of Invention
The invention provides an information interaction method based on cloud manufacturing for solving the problems.
The technical scheme of the invention is as follows: an information interaction method based on cloud manufacturing comprises the following steps:
S1, acquiring a welding image of a welding workpiece by using a CCD camera of a welding robot;
s2, determining the position of a welding seam of a welding image;
s3, acquiring a standard weld joint diagram of the welding workpiece, and determining a welding result of the welding robot according to the weld joint position of the welding image and the standard weld joint diagram of the welding workpiece;
And S4, transmitting the welding result and the welding image of the welding robot to the cloud for storage, and completing information interaction.
Further, S2 comprises the following sub-steps:
S21, taking pixel values of all pixel points in the welding image as training samples;
S22, determining a significant position function according to the training sample;
s23, randomly dividing the training sample into a first training sub-sample and a second training sub-sample;
S24, determining a significant pixel point interval corresponding to a significant position function according to the first training subsamples and the second training subsamples;
s25, determining the welding seam position according to the significant pixel point interval.
The beneficial effects of the above-mentioned further scheme are: according to the method, a function reflecting the position of the remarkable pixel point is constructed according to the maximum/small pixel values and the standard deviation of the pixel values of all the pixel points in the welding image, then the training sample is split randomly, the remarkable interval corresponding to the function is determined, the pixel values of the pixel points belonging to the remarkable interval are special, and the area where the welding seam is located can be accurately determined.
Further, in S22, the expression of the salient position function Y is:
; where exp (·) represents the exponential function, x max represents the maximum value of the training samples, x min represents the minimum value of the training samples, and β represents the standard deviation of the training samples.
Further, in S24, the calculation formula of the left endpoint y l of the salient pixel interval is:
; wherein α 1 represents the standard deviation of the first training sub-sample, α 2 represents the standard deviation of the second training sub-sample, b 1 represents the median of the first training sub-sample, b 2 represents the median of the second training sub-sample, and min (·) represents the minimum function;
In S24, the calculation formula of the right endpoint y r of the salient pixel interval is:
; where max (·) represents the maximum function.
Further, in S25, the specific method for determining the weld position is as follows: and sequentially connecting the pixel points with the pixel values belonging to the significant pixel point intervals to serve as welding seam positions.
Further, S3 comprises the following sub-steps:
S31, acquiring a standard weld joint diagram of a welded workpiece, and determining a curvature change threshold value of the standard weld joint;
s32, determining the weld joint offset rate of the welding image according to the weld joint position of the welding image;
s33, constructing welding qualification constraint conditions;
s34, judging whether the curvature change threshold of the standard weld joint and the weld joint deviation rate of the welding image meet welding qualification constraint conditions, if so, judging that the welding result of the welding robot is qualified, and if not, judging that the welding result of the welding robot is unqualified.
The beneficial effects of the above-mentioned further scheme are: in the invention, the width of the welding line is very narrow and is a straight line or a curve, so the invention inputs the deviation rate condition of the welding line of the welding image and the curvature change threshold value of the standard welding line into the welding qualification constraint condition for prejudgment, and fully considers the curve change condition of the welding line to the final welding result.
Further, in S31, the calculation formula of the curvature change threshold q of the standard weld is:
; wherein θ 1 represents an included angle of a standard weld start point, θ 2 represents an included angle of a standard weld end point, u 1 represents a start point position of a standard weld in a standard weld diagram, u 2 represents an end point position of a standard weld in a standard weld diagram, and dis (·) represents a euclidean distance function.
And constructing a rectangular coordinate system by taking the starting point of the welding seam in the standard welding seam diagram as an origin, making a tangent line of the starting point of the welding seam, and determining the positive included angle between the tangent line of the starting point of the welding seam and the X axis in the rectangular coordinate system as theta 1,θ2 in the same way.
Further, in S32, the calculation formula of the weld joint offset rate p of the welding image is:
; wherein T i represents the gradient amplitude of the ith pixel point in the weld position of the welding image, I represents the number of pixel points of the weld position of the welding image, v 1 represents the starting point position of the weld position in the welding image, v 2 represents the end point position of the weld position in the welding image, dis (·) represents the Euclidean distance function, c represents a constant, Representing a round-up function, ln (·) represents a logarithmic function.
Further, in S33, the expression of the welding qualification constraint is:
; where q represents the curvature change threshold value of the standard weld, and p represents the weld displacement rate of the welding image.
The beneficial effects of the invention are as follows: according to the invention, the welding result of the welding workpiece of the welding robot in the cloud manufacturing industry is prejudged, the welding position of the welding workpiece is analyzed and compared with the standard welding line diagram by accurately grabbing the welding position in the welding image, whether the welding result of the welding robot is qualified or not is judged, the accurate evaluation of the manual work state of the welding robot in the cloud manufacturing industry is completed, and the evaluation result is uploaded to the cloud for a user to check, so that theoretical support is provided for knowing the welding result by the user, and information interaction is realized.
Drawings
Fig. 1 is a flow chart of a method of information interaction based on cloud manufacturing.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides an information interaction method based on cloud manufacturing, which comprises the following steps:
S1, acquiring a welding image of a welding workpiece by using a CCD camera of a welding robot;
s2, determining the position of a welding seam of a welding image;
s3, acquiring a standard weld joint diagram of the welding workpiece, and determining a welding result of the welding robot according to the weld joint position of the welding image and the standard weld joint diagram of the welding workpiece;
And S4, transmitting the welding result and the welding image of the welding robot to the cloud for storage, and completing information interaction.
In an embodiment of the present invention, S2 comprises the following sub-steps:
S21, taking pixel values of all pixel points in the welding image as training samples;
S22, determining a significant position function according to the training sample;
s23, randomly dividing the training sample into a first training sub-sample and a second training sub-sample;
S24, determining a significant pixel point interval corresponding to a significant position function according to the first training subsamples and the second training subsamples;
s25, determining the welding seam position according to the significant pixel point interval.
According to the method, a function reflecting the position of the remarkable pixel point is constructed according to the maximum/small pixel values and the standard deviation of the pixel values of all the pixel points in the welding image, then the training sample is split randomly, the remarkable interval corresponding to the function is determined, the pixel values of the pixel points belonging to the remarkable interval are special, and the area where the welding seam is located can be accurately determined.
In the embodiment of the present invention, in S22, the expression of the significant position function Y is:
; where exp (·) represents the exponential function, x max represents the maximum value of the training samples, x min represents the minimum value of the training samples, and β represents the standard deviation of the training samples.
In the embodiment of the present invention, in S24, the calculation formula of the left endpoint y l of the significant pixel interval is:
; wherein α 1 represents the standard deviation of the first training sub-sample, α 2 represents the standard deviation of the second training sub-sample, b 1 represents the median of the first training sub-sample, b 2 represents the median of the second training sub-sample, and min (·) represents the minimum function;
In S24, the calculation formula of the right endpoint y r of the salient pixel interval is:
; where max (·) represents the maximum function.
In the embodiment of the present invention, in S25, the specific method for determining the weld position is as follows: and sequentially connecting the pixel points with the pixel values belonging to the significant pixel point intervals to serve as welding seam positions.
In an embodiment of the present invention, S3 comprises the following sub-steps:
S31, acquiring a standard weld joint diagram of a welded workpiece, and determining a curvature change threshold value of the standard weld joint;
s32, determining the weld joint offset rate of the welding image according to the weld joint position of the welding image;
s33, constructing welding qualification constraint conditions;
s34, judging whether the curvature change threshold of the standard weld joint and the weld joint deviation rate of the welding image meet welding qualification constraint conditions, if so, judging that the welding result of the welding robot is qualified, and if not, judging that the welding result of the welding robot is unqualified.
In the invention, the width of the welding line is very narrow and is a straight line or a curve, so the invention inputs the deviation rate condition of the welding line of the welding image and the curvature change threshold value of the standard welding line into the welding qualification constraint condition for prejudgment, and fully considers the curve change condition of the welding line to the final welding result.
In the embodiment of the present invention, in S31, the calculation formula of the curvature change threshold q of the standard weld is:
; wherein θ 1 represents an included angle of a standard weld start point, θ 2 represents an included angle of a standard weld end point, u 1 represents a start point position of a standard weld in a standard weld diagram, u 2 represents an end point position of a standard weld in a standard weld diagram, and dis (·) represents a euclidean distance function.
And constructing a rectangular coordinate system by taking the starting point of the welding seam in the standard welding seam diagram as an origin, making a tangent line of the starting point of the welding seam, and determining the positive included angle between the tangent line of the starting point of the welding seam and the X axis in the rectangular coordinate system as theta 1,θ2 in the same way.
In the embodiment of the present invention, in S32, the calculation formula of the weld joint offset rate p of the welding image is:
; wherein T i represents the gradient amplitude of the ith pixel point in the weld position of the welding image, I represents the number of pixel points of the weld position of the welding image, v 1 represents the starting point position of the weld position in the welding image, v 2 represents the end point position of the weld position in the welding image, dis (·) represents the Euclidean distance function, c represents a constant, Representing a round-up function, ln (·) represents a logarithmic function.
In the embodiment of the present invention, in S33, the expression of the welding qualification constraint condition is:
; where q represents the curvature change threshold value of the standard weld, and p represents the weld displacement rate of the welding image.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (5)
1. An information interaction method based on cloud manufacturing is characterized by comprising the following steps:
S1, acquiring a welding image of a welding workpiece by using a CCD camera of a welding robot;
s2, determining the position of a welding seam of a welding image;
s3, acquiring a standard weld joint diagram of the welding workpiece, and determining a welding result of the welding robot according to the weld joint position of the welding image and the standard weld joint diagram of the welding workpiece;
s4, transmitting a welding result and a welding image of the welding robot to a cloud end for storage, and completing information interaction;
the step S3 comprises the following substeps:
S31, acquiring a standard weld joint diagram of a welded workpiece, and determining a curvature change threshold value of the standard weld joint;
s32, determining the weld joint offset rate of the welding image according to the weld joint position of the welding image;
s33, constructing welding qualification constraint conditions;
S34, judging whether the curvature change threshold value of the standard weld joint and the weld joint deviation rate of the welding image meet welding qualification constraint conditions, if so, judging that the welding result of the welding robot is qualified, otherwise, judging that the welding result of the welding robot is unqualified;
in S31, the calculation formula of the curvature change threshold q of the standard weld is: ; wherein, theta 1 represents an included angle of a standard weld joint start point, theta 2 represents an included angle of a standard weld joint end point, u 1 represents a start point position of a standard weld joint in a standard weld joint diagram, u 2 represents an end point position of the standard weld joint in the standard weld joint diagram, and dis (·) represents a Euclidean distance function;
In S32, the calculation formula of the weld joint offset rate p of the welding image is as follows: ; wherein T i represents the gradient amplitude of the ith pixel point in the weld position of the welding image, I represents the number of pixel points of the weld position of the welding image, v 1 represents the starting point position of the weld position in the welding image, v 2 represents the end point position of the weld position in the welding image, dis (·) represents the Euclidean distance function, c represents a constant, Representing an upward rounding function, ln (·) representing a logarithmic function;
in S33, the expression of the welding qualification constraint condition is: ; where q represents the curvature change threshold value of the standard weld, and p represents the weld displacement rate of the welding image.
2. The cloud manufacturing-based information interaction method according to claim 1, wherein the S2 includes the sub-steps of:
S21, taking pixel values of all pixel points in the welding image as training samples;
S22, determining a significant position function according to the training sample;
s23, randomly dividing the training sample into a first training sub-sample and a second training sub-sample;
S24, determining a significant pixel point interval corresponding to a significant position function according to the first training subsamples and the second training subsamples;
s25, determining the welding seam position according to the significant pixel point interval.
3. The cloud manufacturing-based information interaction method according to claim 2, wherein in S22, the expression of the significant location function Y is: ; where exp (·) represents the exponential function, x max represents the maximum value of the training samples, x min represents the minimum value of the training samples, and β represents the standard deviation of the training samples.
4. The information interaction method based on cloud manufacturing according to claim 3, wherein in S24, a calculation formula of the left endpoint y l of the salient pixel point interval is: ; wherein α 1 represents the standard deviation of the first training sub-sample, α 2 represents the standard deviation of the second training sub-sample, b 1 represents the median of the first training sub-sample, b 2 represents the median of the second training sub-sample, and min (·) represents the minimum function;
in S24, the calculation formula of the right endpoint y r of the salient pixel interval is: ; where max (·) represents the maximum function.
5. The cloud manufacturing-based information interaction method according to claim 2, wherein in S25, the specific method for determining the position of the weld is as follows: and sequentially connecting the pixel points with the pixel values belonging to the significant pixel point intervals to serve as welding seam positions.
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| CN112207482A (en) * | 2020-09-23 | 2021-01-12 | 上海交通大学 | Multi-information monitoring and control system and method for welding quality control |
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