CN113281115A - Control method for intelligent shearing of tobacco leaves - Google Patents
Control method for intelligent shearing of tobacco leaves Download PDFInfo
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- CN113281115A CN113281115A CN202110384694.3A CN202110384694A CN113281115A CN 113281115 A CN113281115 A CN 113281115A CN 202110384694 A CN202110384694 A CN 202110384694A CN 113281115 A CN113281115 A CN 113281115A
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- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
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- G01N1/286—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q involving mechanical work, e.g. chopping, disintegrating, compacting, homogenising
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Abstract
The invention provides a control method for intelligent tobacco leaf cutting, which comprises the following steps: acquiring images of tobacco leaves to be cut by an industrial camera, and performing image recognition according to the tobacco leaf images to extract the shape, the size and the position of the tobacco leaves; acquiring a cutting graph, and carrying out cutting typesetting design on the tobacco leaf image according to the shape and the size of the tobacco leaf; and the shearing machine tool shears the tobacco leaves to be sheared according to the shearing typesetting design and the tobacco leaf positions, and automatically conveys the tobacco leaves to a sorting area after shearing. The invention can solve the problems of low shearing precision and low working efficiency of the existing manual operation for shearing the tobacco leaves, can improve the intelligence and the working efficiency of the tobacco leaf shearing, and reduces the production cost.
Description
Technical Field
The invention relates to the technical field of cigarette production, in particular to a control method for intelligent tobacco leaf cutting.
Background
The physical characteristics of the tobacco leaves are closely related to the internal quality of the tobacco leaves, and meanwhile, the processing performance of the tobacco leaves can be reflected, and the cigarette manufacturing process, the product style, the cost and other economic factors can be directly influenced. In recent years, with the continuous improvement of the processing technology level of cigarettes, the industry develops a great deal of research work on the aspect of physical characteristics of tobacco leaves. In order to research the influence of different shapes of tobacco leaves on the characteristics of the tobacco leaves, the tobacco leaves are often cut in different shapes, and if the tobacco leaves are manually cut, the cutting precision and the working efficiency of the tobacco leaves are low due to the influence of artificial subjective factors, so that the problem that the research result is not ideal can be caused.
Disclosure of Invention
The invention provides a control method for intelligent tobacco leaf shearing, which solves the problems of low shearing precision and low working efficiency in the manual operation of the conventional tobacco leaf shearing, can improve the intelligence and the working efficiency of the tobacco leaf shearing, and reduces the production cost.
In order to achieve the above purpose, the invention provides the following technical scheme:
a control method for intelligent tobacco leaf cutting comprises the following steps:
acquiring images of tobacco leaves to be cut by an industrial camera, and performing image recognition according to the tobacco leaf images to extract the shape, the size and the position of the tobacco leaves;
acquiring a cutting graph, and carrying out cutting typesetting design on the tobacco leaf image according to the shape and the size of the tobacco leaf;
and the shearing machine tool shears the tobacco leaves to be sheared according to the shearing typesetting design and the tobacco leaf positions, and automatically conveys the tobacco leaves to a sorting area after shearing.
Preferably, the method further comprises the following steps:
photographing the tobacco leaves in the sorting area, and carrying out tobacco leaf sorting identification according to the photographed tobacco leaf photo so as to determine the visual coordinate of the tobacco stems or the excess materials;
and sending the visual coordinates of the tobacco stems or the residual materials to a sorting robot, so that the sorting robot sorts and clamps the tobacco stems.
Preferably, the image recognition according to the tobacco leaf image includes:
carrying out image preprocessing on the tobacco leaf image to improve the image quality, wherein the image preprocessing comprises the following steps: shading correction, gray scale correction, noise filtering and image enhancement;
acquiring gray values corresponding to all pixel points of the tobacco leaf image to form a gray image of the tobacco leaf, and identifying the shape and the material of the tobacco leaf according to the gray image.
Preferably, the image recognition according to the tobacco leaf image further includes:
and positioning the tobacco leaves to be cut according to the tobacco leaf image, and determining the three-dimensional coordinates of the tobacco leaves according to the positioning coordinates.
Preferably, the cutting and layout design of the tobacco leaf images according to the tobacco leaf shapes and the tobacco leaf sizes includes:
determining a cuttable target area of the tobacco according to the shape and the size of the tobacco, and typesetting the pattern position in the target area according to the cutting pattern;
and determining a cutting path diagram according to the graphic position typesetting, so that the tobacco leaf is cut by a cutting machine according to the cutting path diagram.
Preferably, the shearing machine shears the tobacco leaves to be sheared according to the shearing typesetting design and the tobacco leaf positions, and the shearing machine comprises:
the shearing machine tool shears and positions the tobacco leaves to be sheared according to the tobacco leaf positions, and opens an air suction device of the shearing machine tool to adsorb the tobacco leaves in a cutting work area;
and the shearing machine tool obtains the shearing path diagram according to the shearing typesetting design and controls a cutter to perform graphic cutting on tobacco leaves according to the shearing path diagram.
Preferably, the tobacco leaf sorting and identifying according to the photographed tobacco leaf photo includes:
performing speckle detection on the tobacco leaf photo to find out a speckle area with different characteristics from the surrounding area, wherein the area characteristics comprise: lightness, color, and grayscale;
and identifying the shape and the chromaticity of the spot area to determine whether the spot area is the tobacco stem or the residual material.
Preferably, the tobacco leaf sorting and identifying according to the photographed tobacco leaf photo further comprises:
and positioning the tobacco leaves in the sorting area, obtaining the centroid coordinate of the spot area according to the tobacco leaf picture, and determining the visual coordinate of the tobacco stems or the excess materials according to the centroid coordinate.
Preferably, the method further comprises the following steps:
the completeness of the tobacco leaves is checked according to the tobacco leaf image, if the tobacco leaves are incomplete or missing, the lack of the completeness of the tobacco leaves is alarmed, the tobacco leaves are transmitted to a sorting area, and then the sorting robot is controlled to sort and clamp out the tobacco leaves;
and carrying out surface inspection on the tobacco leaves according to the tobacco leaf images, marking and positioning the damaged areas of the tobacco leaves if the surfaces of the tobacco leaves have damage defects, and avoiding the damaged areas when the cutting patterns are typeset.
Preferably, the method further comprises the following steps:
and (4) carrying out shape inspection on tobacco leaves according to the tobacco leaf image, if the size of the cut image is larger than that of the tobacco leaves, carrying out undersize alarm on the tobacco leaves, conveying the tobacco leaves to a sorting area, and then controlling the sorting robot to sort and clamp the tobacco leaves.
The invention provides a control method for intelligent tobacco leaf cutting, which is characterized in that an industrial camera is used for collecting images of tobacco leaves, and cutting patterns are automatically typeset according to the images of the tobacco leaves, so that a cutting machine tool cuts the tobacco leaves according to the cutting typesetting design and the tobacco leaf positions. The problem of current tobacco leaf shearing adopt manual operation to have shearing precision and work efficiency low is solved, the intelligence and the work efficiency of tobacco leaf shearing can be improved, reduction in production cost.
Drawings
In order to more clearly describe the specific embodiments of the present invention, the drawings to be used in the embodiments will be briefly described below.
Fig. 1 is a schematic diagram of a control method for intelligent tobacco leaf cutting provided by the invention.
Fig. 2 is a schematic structural diagram of an intelligent tobacco leaf shearing device provided by the invention.
Detailed Description
In order to make the technical field of the invention better understand the scheme of the embodiment of the invention, the embodiment of the invention is further described in detail with reference to the drawings and the implementation mode.
The method aims at the problems that the tobacco leaves are cut in different shapes at present, the cutting precision and the working efficiency of the tobacco leaves are low due to the influence of artificial subjective factors, and the research result is not ideal. The invention provides a control method for intelligent tobacco leaf cutting, which is characterized in that an industrial camera is used for collecting images of tobacco leaves, and cutting patterns are automatically typeset according to the images of the tobacco leaves, so that a cutting machine tool cuts the tobacco leaves according to the cutting typesetting design and the tobacco leaf positions. The problem of current tobacco leaf cut adopt manual operation to have shearing precision and work efficiency low is solved, can improve the work efficiency of tobacco leaf cut and letter sorting, reduction in production cost.
As shown in fig. 1, a control method for intelligent tobacco leaf cutting includes:
s1: and acquiring images of the tobacco leaves to be cut by an industrial camera, and identifying the images according to the tobacco leaf images to extract the shape, the size and the position of the tobacco leaves.
S2: and acquiring a cutting graph, and cutting, typesetting and designing the tobacco leaf image according to the shape and the size of the tobacco leaf.
S3: and the shearing machine tool shears the tobacco leaves to be sheared according to the shearing typesetting design and the tobacco leaf positions, and automatically conveys the tobacco leaves to a sorting area after shearing.
Specifically, as shown in fig. 2, the shearing apparatus includes: shearing machine 1, industrial camera 2, industrial computer 3, letter sorting camera 4 and letter sorting robot 5. During shearing, the material is placed in a to-be-sheared area of a shearing machine tool, an industrial camera is opened through an industrial personal computer to shoot and position the tobacco material to be sheared, and a tobacco image is obtained. And carrying out image recognition on the tobacco leaf image to obtain the shape, the size and the position of the tobacco leaf. After shooting is finished, the required cutting graphics can be drawn through the graphic typesetting software of the industrial personal computer, the required cutting quantity is set, typesetting design of the cutting graphics is carried out on the tobacco leaf images, and finally, a cutting typesetting design file is exported to a cutting machine tool, so that the cutting machine tool cuts the tobacco leaves according to the cutting typesetting design. The method can improve the intelligence and the working efficiency of tobacco leaf shearing and reduce the production cost.
The method further comprises the following steps:
s4: and photographing the tobacco leaves in the sorting area, and carrying out tobacco leaf sorting identification according to the photographed tobacco leaf photo so as to determine the visual coordinate of the tobacco stems or the excess materials.
S5: and sending the visual coordinates of the tobacco stems or the residual materials to a sorting robot, so that the sorting robot sorts and clamps the tobacco stems.
Specifically, after cutting, the machine tool automatically conveys cut tobacco leaves to a sorting area, the tobacco leaves are photographed and positioned through a sorting camera to determine visual coordinates of tobacco stems or remainders, the visual coordinates are sent to a sorting robot to sort the waste materials or the tobacco stems into waste bins, and then cut required leaves are automatically conveyed to a finished product bin.
Further, the image recognition according to the tobacco leaf image comprises:
carrying out image preprocessing on the tobacco leaf image to improve the image quality, wherein the image preprocessing comprises the following steps: shading correction, gamma correction, noise filtering, and image enhancement.
Acquiring gray values corresponding to all pixel points of the tobacco leaf image to form a gray image of the tobacco leaf, and identifying the shape and the material of the tobacco leaf according to the gray image.
In practical applications, the image preprocessing functions to improve image quality for image recognition. The method can comprise the following steps: shadow correction, i.e. the smooth compensation of uneven illumination across the scene. And 2, gray correction, namely, linear or nonlinear transformation is carried out on the input gray value so as to improve the image quality. And thirdly, noise filtering, which generally adopts a low (frequency) pass (pass) arithmetic unit to suppress noise. Image enhancement, namely image contour enhancement, adopts a high (frequency) pass (pass) arithmetic unit.
On the other hand, the image preprocessing also comprises the steps of carrying out data compression on the image, converting the tobacco leaf image into a gray level image, further obtaining a threshold value of the gray level of the image, converting the tobacco leaf image into a binary image, further compressing the binary image, and further identifying the shape and the size of the tobacco leaf according to the binary image.
Further, the image recognition according to the tobacco leaf image further comprises: and positioning the tobacco leaves to be cut according to the tobacco leaf image, and determining the three-dimensional coordinates of the tobacco leaves according to the positioning coordinates.
In practical application, the position of the tobacco leaves is positioned, firstly, the set point of the cutting area is selected as a positioning reference point, and then, the visual coordinate of the tobacco leaves is determined according to the relative position of the tobacco leaves and the positioning reference point, wherein the visual coordinate is a three-dimensional coordinate. And the shearing machine tool determines the position of the tobacco leaves according to the three-dimensional coordinates of the tobacco leaves, and then shears according to the position of the tobacco leaves.
The cutting and typesetting design of the tobacco leaf images according to the tobacco leaf shapes and the tobacco leaf sizes comprises the following steps:
and determining a cuttable target area of the tobacco according to the shape and the size of the tobacco, and performing graphic position typesetting according to the cutting graph in the target area. And determining a cutting path diagram according to the graphic position typesetting, so that the tobacco leaf is cut by a cutting machine according to the cutting path diagram.
Further, the shearing machine tool shears the tobacco leaves to be sheared according to the shearing typesetting design and the tobacco leaf positions, and the shearing machine tool comprises:
and the shearing machine tool shears and positions the tobacco leaves to be sheared according to the tobacco leaf positions, opens an air suction device of the shearing machine tool and adsorbs the tobacco leaves in a cutting work area.
And the shearing machine tool obtains the shearing path diagram according to the shearing typesetting design and controls a cutter to perform graphic cutting on tobacco leaves according to the shearing path diagram.
In practical application, the air suction fan is arranged on the shearing machine tool, and when the air suction fan is operated, the shearing platform of the shearing machine tool forms negative pressure adsorption force to tightly adsorb tobacco leaves in a shearing working area so as to carry out cutting operation. And the cutting machine controls a cutter to perform graphical cutting on the tobacco leaves according to the received cutting path diagram, wherein the cutter can adopt a vibration cutter or a circular cutter.
The tobacco leaf sorting and identifying method based on the photographed tobacco leaf photo comprises the following steps:
performing speckle detection on the tobacco leaf photo to find out a speckle area with different characteristics from the surrounding area, wherein the area characteristics comprise: lightness, color, and grayscale.
And identifying the shape and the chromaticity of the spot area to determine whether the spot area is the tobacco stem or the residual material.
In particular, speckle detection refers to finding areas in a digital image that differ from surrounding areas in characteristics, including lighting or color, etc. The spot detection can completely depict the area information of the pixels, and the obtained local area characteristics are used for accurately obtaining the position and shape information of the tobacco stems to be sorted, and then the position information is sent to a sorting robot for grabbing and sorting. The spot detection can adopt a local extremum method, which mainly finds out a local extremum of a function to realize the spot detection.
Further, the tobacco leaf sorting and identifying according to the photographed tobacco leaf photo further comprises: and positioning the tobacco leaves in the sorting area, obtaining the centroid coordinate of the spot area according to the tobacco leaf picture, and determining the visual coordinate of the tobacco stems or the excess materials according to the centroid coordinate.
Specifically, by obtaining the centroid coordinate of each spot area, when the spot area is judged to be the tobacco stem or the residual material, the visual three-dimensional coordinate of the tobacco stem or the residual material is calculated according to the centroid coordinate, so that the sorting robot can perform accurate sorting.
The method further comprises the following steps:
s6: and (4) carrying out completeness inspection on the tobacco leaves according to the tobacco leaf image, if the tobacco leaves are incomplete or missing, carrying out insufficient tobacco leaf completeness alarm, transmitting the tobacco leaves to a sorting area, and controlling the sorting robot to sort and clamp the tobacco leaves.
S7: and carrying out surface inspection on the tobacco leaves according to the tobacco leaf images, marking and positioning the damaged areas of the tobacco leaves if the surfaces of the tobacco leaves have damage defects, and avoiding the damaged areas when the cutting patterns are typeset.
The method further comprises the following steps:
s8: and (4) carrying out shape inspection on tobacco leaves according to the tobacco leaf image, if the size of the cut image is larger than that of the tobacco leaves, carrying out undersize alarm on the tobacco leaves, conveying the tobacco leaves to a sorting area, and then controlling the sorting robot to sort and clamp the tobacco leaves.
In practical application, the tobacco leaves are inspected through the tobacco leaf images, and the inspection tasks mainly comprise completeness inspection, shape inspection and surface inspection. The completeness test is to test whether the tobacco leaves are complete or missing. The shape inspection is to inspect the size, shape and position of the tobacco leaves. The surface inspection is to inspect the surfaces of the tobacco leaves in different shapes and can distinguish defect damages. After cutting, the cut tobacco leaves are conveyed to a sorting area, visual sorting is carried out through a sorting camera, the position information of the tobacco stems or the residual materials is sent to a sorting robot, and then the cut required leaves are automatically conveyed to a finished product box.
Therefore, the invention provides a control method for intelligent tobacco leaf cutting, which is characterized in that an industrial camera is used for acquiring images of tobacco leaves, and the cut images are automatically typeset according to the images of the tobacco leaves, so that a cutting machine tool cuts the tobacco leaves according to the cut typeset design and the tobacco leaf positions. The problem of current tobacco leaf shearing adopt manual operation to have shearing precision and work efficiency low is solved, the intelligence and the work efficiency of tobacco leaf shearing can be improved, reduction in production cost.
The construction, features and functions of the present invention have been described in detail with reference to the embodiments shown in the drawings, but the present invention is not limited to the embodiments shown in the drawings, and all equivalent embodiments modified or modified by the spirit and scope of the present invention should be protected without departing from the spirit of the present invention.
Claims (10)
1. A control method for intelligent tobacco leaf cutting is characterized by comprising the following steps:
acquiring images of tobacco leaves to be cut by an industrial camera, and performing image recognition according to the tobacco leaf images to extract the shape, the size and the position of the tobacco leaves;
acquiring a cutting graph, and carrying out cutting typesetting design on the tobacco leaf image according to the shape and the size of the tobacco leaf;
and the shearing machine tool shears the tobacco leaves to be sheared according to the shearing typesetting design and the tobacco leaf positions, and automatically conveys the tobacco leaves to a sorting area after shearing.
2. The control method for intelligent tobacco leaf cutting according to claim 1, further comprising:
photographing the tobacco leaves in the sorting area, and carrying out tobacco leaf sorting identification according to the photographed tobacco leaf photo so as to determine the visual coordinate of the tobacco stems or the excess materials;
and sending the visual coordinates of the tobacco stems or the residual materials to a sorting robot, so that the sorting robot sorts and clamps the tobacco stems.
3. The control method for intelligent tobacco leaf cutting according to claim 2, wherein the image recognition according to the tobacco leaf image comprises:
carrying out image preprocessing on the tobacco leaf image to improve the image quality, wherein the image preprocessing comprises the following steps: shading correction, gray scale correction, noise filtering and image enhancement;
acquiring gray values corresponding to all pixel points of the tobacco leaf image to form a gray image of the tobacco leaf, and identifying the shape and the material of the tobacco leaf according to the gray image.
4. The control method for intelligent tobacco leaf cutting according to claim 3, wherein the image recognition according to the tobacco leaf image further comprises:
and positioning the tobacco leaves to be cut according to the tobacco leaf image, and determining the three-dimensional coordinates of the tobacco leaves according to the positioning coordinates.
5. The method for controlling intelligent tobacco leaf cutting according to claim 4, wherein the cutting and typesetting design of the tobacco leaf images according to the tobacco leaf shapes and the tobacco leaf sizes comprises the following steps:
determining a cuttable target area of the tobacco according to the shape and the size of the tobacco, and typesetting the pattern position in the target area according to the cutting pattern;
and determining a cutting path diagram according to the graphic position typesetting, so that the tobacco leaf is cut by a cutting machine according to the cutting path diagram.
6. The control method for intelligently shearing tobacco leaves according to claim 5, wherein the shearing machine tool shears the tobacco leaves to be sheared according to the shearing typesetting design and the tobacco leaf positions, and comprises the following steps of:
the shearing machine tool shears and positions the tobacco leaves to be sheared according to the tobacco leaf positions, and opens an air suction device of the shearing machine tool to adsorb the tobacco leaves in a cutting work area;
and the shearing machine tool obtains the shearing path diagram according to the shearing typesetting design and controls a cutter to perform graphic cutting on tobacco leaves according to the shearing path diagram.
7. The control method for intelligent tobacco leaf cutting according to claim 6, wherein the tobacco leaf sorting and identifying according to the photographed tobacco leaf photo comprises:
performing speckle detection on the tobacco leaf photo to find out a speckle area with different characteristics from the surrounding area, wherein the area characteristics comprise: lightness, color, and grayscale;
and identifying the shape and the chromaticity of the spot area to determine whether the spot area is the tobacco stem or the residual material.
8. The control method for intelligent tobacco leaf cutting according to claim 7, wherein the tobacco leaf sorting and identification is performed according to the photographed tobacco leaf photo, and further comprising:
and positioning the tobacco leaves in the sorting area, obtaining the centroid coordinate of the spot area according to the tobacco leaf picture, and determining the visual coordinate of the tobacco stems or the excess materials according to the centroid coordinate.
9. The control method for intelligent tobacco leaf cutting according to claim 2, further comprising:
the completeness of the tobacco leaves is checked according to the tobacco leaf image, if the tobacco leaves are incomplete or missing, the lack of the completeness of the tobacco leaves is alarmed, the tobacco leaves are transmitted to a sorting area, and then the sorting robot is controlled to sort and clamp out the tobacco leaves;
and carrying out surface inspection on the tobacco leaves according to the tobacco leaf images, marking and positioning the damaged areas of the tobacco leaves if the surfaces of the tobacco leaves have damage defects, and avoiding the damaged areas when the cutting patterns are typeset.
10. The method for controlling intelligent tobacco leaf cutting according to claim 9, further comprising:
and (4) carrying out shape inspection on tobacco leaves according to the tobacco leaf image, if the size of the cut image is larger than that of the tobacco leaves, carrying out undersize alarm on the tobacco leaves, conveying the tobacco leaves to a sorting area, and then controlling the sorting robot to sort and clamp the tobacco leaves.
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| CN115157108A (en) * | 2022-06-22 | 2022-10-11 | 北京烁科精微电子装备有限公司 | Slip sheet monitoring system and monitoring method |
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