A method and device for recognizing the angle of twist of a tubular belt conveyor belt

Abstract

The application provides a kind of tubular belt conveyor belt torsion angle identification method and device, method includes the following steps: collection conveyor belt lap position picture information;According to the outer diameter of conveyor belt, construct data scale, and specify the scale and range of data scale;Extract the characteristic pixel points of the lap position of the picture of the conveyor belt;Keep the characteristic pixel points located in the middle position as the determination pixel points;Take the average of the minimum row number and the maximum row number in all determination pixel points as the horizontal coordinate of the lap position of the conveyor belt;Project the horizontal coordinate of the lap position of the conveyor belt to the data scale, and judge the torsion angle of the conveyor belt according to the intersection of the projection and the scale on the data scale.The device includes a camera and a picture information processing unit.According to the input information of the lap position of the conveyor belt, an identification algorithm model is established, and the rotation angle of the lap position is calculated in real time, so that the maintenance and management personnel can master the accurate torsion angle of the lap point of the conveyor belt, and provide data reference for decision-making.

Description

Technical Field

[0001] This invention relates to the field of tubular belt conveyor technology, and more specifically, to a method and apparatus for identifying the torsion angle of a tubular belt conveyor. Background Technology

[0002] Currently, belt conveyors are widely used in power, steel, coal, water conservancy, chemical, metallurgy, and building materials industries. Tubular belt conveyors, in particular, have gained even wider application across various industries due to their advantages such as enclosed conveying, flexible layout, and environmental protection.

[0003] A tubular belt conveyor is a type of belt conveyor that uses rollers to roll the conveyor belt into a cylindrical shape with overlapping edges for transporting materials. Normally, the overlap of the conveyor belt is located directly above the upper tube and directly below the lower tube. However, when improper installation occurs, the mechanical structure deforms, or the conveyor belt itself has uneven rigidity, the overlap will deviate from its normal position. The further it deviates from the normal position, the more likely a twisting accident will occur. Twisting of the tubular belt conveyor will cause wear on the conveyor belt edges or jamming into the rollers, resulting in belt tearing, material spillage, or damage to the mechanical structure.

[0004] Chinese patent application number CN201320810636.3 discloses a torsion monitoring device for a circular tube conveyor belt. The device uses the center line of the overlapping part of the circular conveyor belt as the 0° reference line and sets a sensor at each of the four positions of ±40° and ±135°. Each sensor is fixed at the lower end of the sensor bracket, and the upper end of the sensor bracket is fixed on the structural frame of the circular tube conveyor. Chinese patent application CN201410019386.0 discloses a conveyor belt torsion monitoring device and method. This method utilizes a micro-motor to drive a slider on a guide rail, with a laser displacement sensor mounted on the slider. The sensor collects the distance from the conveyor belt surface to the guide rail in real time, achieving complete acquisition of the upper semicircular portion of the conveyor belt. The distance value is then sent to a high-speed AD acquisition circuit. Proximity sensors are evenly arranged on the guide rail, collecting the position of the laser displacement sensor in real time and sending the position signal to the high-speed AD acquisition circuit, ensuring a one-to-one correspondence between the current position of the laser displacement sensor and the collected distance values. The high-speed AD acquisition circuit analyzes the state of the conveyor belt and determines whether its torsion exceeds a preset range. If it does, an alarm signal is sent to an alarm device. It is evident that traditional detection technologies require sensors installed close to the conveyor belt to determine the torsion angle based on the sensor's information. Sensor malfunctions can affect the reliable and stable operation of the conveyor, and the sensor's placement can pose safety hazards to the conveyor belt's operation. The testing method is not intelligent enough, and the signal transmission process is susceptible to interference, resulting in inaccurate test results and poor reliability. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems in the prior art: the lack of an effective method for monitoring the torsion angle of tubular belt conveyors, the poor reliability of traditional solutions, susceptibility to signal interference, and difficulty in issuing timely warnings when the torsion angle of the conveyor belt is too large.

[0006] Therefore, the first aspect of the present invention provides a method for identifying the torsion angle of a tubular belt conveyor.

[0007] A second aspect of the present invention provides a device for identifying the torsion angle of a tubular belt conveyor.

[0008] This invention provides a method for identifying the torsion angle of a tubular belt conveyor, comprising the following steps:

[0009] S1. Collect image information of the conveyor belt overlap position;

[0010] S2. Construct a data scale based on the outer diameter of the conveyor belt, and specify the scale and range of the data scale;

[0011] S3. Extract the feature pixels at the overlap position of the conveyor belt in the image;

[0012] S4. Retain the feature pixel located in the middle position as the decision pixel;

[0013] S5. Take the average of the minimum and maximum row counts among all the determined pixels as the x-coordinate of the conveyor belt overlap position;

[0014] S6. Project the horizontal coordinate of the conveyor belt overlap position onto the data scale, and determine the conveyor belt torsion angle based on the intersection of the projection and the scale on the data scale.

[0015] The method for identifying the torsion angle of a tubular belt conveyor according to the above-described technical solution of the present invention may further have the following additional technical features:

[0016] In the above technical solution, in S1, after acquiring images of the overlapping positions of the conveyor belts of the tubular belt conveyor, the acquired images are subjected to grayscale processing and / or noise reduction processing and / or enhancement processing.

[0017] In the above technical solution, in S2, the data scale uses the outer diameter of the conveyor belt as the range of the data scale, and divides the data scale evenly into N segments. The scale marked on each segment corresponds to the angle of the conveyor belt twist.

[0018] In the above technical solution, in S3, when extracting the feature pixels at the overlap position of the conveyor belt in the image, consecutive pixels are selected as feature pixels.

[0019] In the above technical solution, the method for selecting consecutive pixels as feature pixels includes:

[0020] S31. Obtain all pixels at the overlap position of the conveyor belt;

[0021] S32. Determine whether the pixels are continuous. If they are continuous, scan column by column along the first direction, record the number of pixels, and define the recorded pixels as feature pixels. If they are not continuous, proceed to the next step.

[0022] S33. Along the first direction, taking each pixel as the center, determine whether there are at least three adjacent pixels around the pixel. If there are, retain the pixel and return to S32; if not, delete the pixel and return to S32.

[0023] In the above technical solution, before determining whether the pixels are continuous, noise reduction and smoothing processing is also performed on the pixels at the overlapping positions of the conveyor belt.

[0024] In the above technical solution, retaining the feature pixel located in the middle position as the determination pixel in S4 includes the following steps:

[0025] S41. Determine whether the number of feature pixels in each column is odd or even.

[0026] S42. When the number of feature pixels in the column is odd, only the feature pixels in the middle row are retained as the judgment pixels.

[0027] S43. When the number of feature pixels in the column is even, retain the middle two rows of pixels and delete the rest; determine the distance between the two retained rows of pixels and the nearest judgment pixel, retain the pixel with the smallest distance to the judgment pixel, and delete the rest; use the retained pixel as the judgment pixel.

[0028] In the above technical solution, S1 uses a camera to capture images of the overlapping positions of the conveyor belts of the tubular belt conveyor.

[0029] In the above technical solution, before performing S3 to extract the feature pixels at the overlap position of the conveyor belt in the image, it also includes: correcting and transforming the image according to the shooting angle.

[0030] The present invention provides a device for identifying the torsion angle of a tubular belt conveyor, comprising:

[0031] A camera is used to take pictures of the overlap position of the conveyor belt of a tubular belt conveyor. The camera's shooting angle is horizontal towards the overlap position of the conveyor belt or tilted towards the overlap position of the conveyor belt.

[0032] The image information processing unit is connected to the camera and uses a method for identifying the torsion angle of a tubular belt conveyor as described in any of the above technical solutions to determine the torsion angle of the conveyor belt.

[0033] In summary, due to the adoption of the above-mentioned technical features, the beneficial effects of the present invention are:

[0034] This invention discloses a method for identifying the torsion angle of a tubular belt conveyor. Based on input information about the conveyor belt overlap position, an identification algorithm model is established to calculate the rotation angle at the overlap position in real time. This allows maintenance and management personnel to accurately determine the torsion angle of the conveyor belt overlap point, providing data reference for decision-making. This invention completely replaces manual inspection, avoiding the drawbacks of manual operations. Furthermore, it eliminates the need for sensor components used in traditional detection technologies, preventing sensor malfunctions from affecting the reliable and stable operation of the conveyor. It also overcomes the problem of signal transmission being easily interfered with, leading to inaccurate test results. The invention offers high reliability, improves system operating efficiency and identification accuracy, and reduces enterprise operating and maintenance costs.

[0035] Additional aspects and advantages of the invention will become apparent in the following description or may be learned by practice of the invention. Attached Figure Description

[0036] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0037] Figure 1 This is a system block diagram of a method for identifying the torsion angle of a tubular belt conveyor according to an embodiment of the present invention;

[0038] Figure 2 This is a flowchart of a method for identifying the torsion angle of a tubular belt conveyor according to an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of an image acquisition method in a method for identifying the torsion angle of a tubular belt conveyor according to an embodiment of the present invention;

[0040] Figure 4 This is a schematic diagram of a data scale in a method for identifying the torsion angle of a tubular belt conveyor according to an embodiment of the present invention;

[0041] Figure 5 This is a schematic diagram illustrating the principle of determining the torsion angle in a method for identifying the torsion angle of a tubular belt conveyor according to an embodiment of the present invention.

[0042] Figure 6This is a schematic diagram of another image acquisition method in a method for identifying the torsion angle of a tubular belt conveyor according to an embodiment of the present invention. Detailed Implementation

[0043] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0045] The following reference Figures 1 to 6 This invention describes a method and apparatus for identifying the torsion angle of a tubular belt conveyor, provided by some embodiments of the present invention.

[0046] Some embodiments of this application provide a method for identifying the torsion angle of a tubular belt conveyor.

[0047] The first embodiment of this invention proposes a method for identifying the torsion angle of a tubular belt conveyor. Figure 1 A system block diagram of a method for identifying the torsion angle of a tubular belt conveyor is shown. Figure 2 A flowchart of a method for identifying the torsion angle of a tubular belt conveyor is shown, as follows: Figure 1 and Figure 2 As shown, the method includes the following steps:

[0048] The first step is to collect image information of the conveyor belt overlap locations; specifically, such as... Figure 3 As shown, two cameras with horizontal shooting angles facing the overlap position of the conveyor belt are used to take pictures of the overlap position of the conveyor belt, and an image of the overlap position of the conveyor belt is acquired by either camera.

[0049] The second step is to perform grayscale, noise reduction, and enhancement processing on the images collected in the first step.

[0050] The third step is to construct a data scale based on the outer diameter of the tubular conveyor belt, and to specify the scale's graduations and range; specifically, such as... Figure 4 As shown, the data scale uses the outer diameter of the tubular conveyor belt as its range. The data scale is evenly divided into 18 segments, and the scale markings on each segment correspond to the angle of the conveyor belt's twist. That is, one scale mark on the data scale corresponds to a 10° twist of the conveyor belt. One side of the data scale has a scale mark of 0 to 180°, and the other side has a scale mark of -10° to 180°.

[0051] The fourth step is to extract the feature pixels at the conveyor belt overlap positions in the image processed in the second step. Specifically, first, all pixels at the conveyor belt overlap positions are obtained; noise reduction and smoothing are performed on the pixels; it is then determined whether the pixels are continuous. If continuous, the pixels are scanned column by column along the first direction, and the number of pixels is recorded. The recorded pixels are defined as feature pixels. If discontinuous, each pixel is taken as the center along the first direction, and it is determined whether there are at least three adjacent pixels around it. If so, the pixel is retained, and the next pixel is determined; if not, the pixel is deleted, and the next pixel is determined. The first direction can be from right to left, from left to right, etc. When determining whether there are at least three adjacent pixels around a pixel, the surrounding area refers to the eight directions: top, bottom, left, right, top left, bottom left, top right, and bottom right. If there are at least three adjacent pixels in any of these eight directions, the pixel is retained as a feature pixel.

[0052] Fifth, retain the feature pixels located in the middle position as the decision pixels. Specifically, determine whether the number of feature pixels in each column is odd or even. If the number of feature pixels in a column is odd, only the middle row of feature pixels is retained as the decision pixels. If the number of feature pixels in a column is even, the middle two rows of pixels are retained, and the remaining pixels are deleted. The distances between the two retained rows of pixels and the nearest decision pixels are determined, and the pixel with the smallest distance to the decision pixel is retained, while the remaining pixels are deleted. The retained pixels are then used as the decision pixels.

[0053] Step 6: Take the average of the minimum and maximum number of rows among all the judgment pixels as the horizontal coordinate of the conveyor belt overlap position; where the maximum and minimum number of rows refer to the relative position of the judgment pixels. In this embodiment, the judgment pixel at the top is defined as the maximum number of rows, and the judgment pixel at the bottom is defined as the minimum number of rows.

[0054] Step 7, as Figure 5 As shown, the horizontal coordinate of the conveyor belt overlap position is projected onto the data scale, and the twist angle of the conveyor belt is determined based on the intersection of the projection and the scale. That is, the data in the data scale corresponding to the intersection point after projection is taken as the final identified twist angle.

[0055] The second embodiment of the present invention proposes a method for identifying the torsion angle of a tubular belt conveyor, comprising the following steps:

[0056] The first step is to collect image information of the conveyor belt overlap locations; specifically, such as... Figure 6As shown, four cameras are positioned at the upper left, lower left, upper right, and lower right of the conveyor belt, respectively, with a 45-degree angle towards the overlap of the conveyor belt, to capture images of the overlap. An image of the overlap is acquired using any one of the cameras. In the first embodiment, only two cameras are used for horizontal acquisition, resulting in blind spots. The acquisition angles are 0°–140° and -40°–-180°; at other angles, the outer belt of the conveyor belt will obscure the overlap, making it unrecognizable. This embodiment achieves 360° full-range recognition.

[0057] The second step is to perform grayscale, noise reduction, and enhancement processing on the images collected in the first step.

[0058] The third step is to construct a data scale based on the outer diameter of the tubular conveyor belt, and to specify the scale's graduations and range; specifically, such as... Figure 4 As shown, the data scale uses the outer diameter of the tubular conveyor belt as its range. The data scale is evenly divided into 18 segments, and the scale markings on each segment correspond to the angle of the conveyor belt's twist. That is, one scale mark on the data scale corresponds to a 10° twist of the conveyor belt. One side of the data scale has a scale mark of 0 to 180°, and the other side has a scale mark of -10° to 180°.

[0059] The fourth step is to correct and transform the images processed in the second step according to the shooting angle. In this embodiment, the image captured by the upper left camera is rotated 45° clockwise, the image captured by the upper right camera is rotated 45° counterclockwise, the image captured by the lower left camera is rotated 45° clockwise, and the image captured by the lower right camera is rotated 45° counterclockwise.

[0060] The fifth step is to extract the feature pixels at the conveyor belt overlap positions in the image processed in the fourth step. Specifically, first, all pixels at the conveyor belt overlap positions are obtained; noise reduction and smoothing are performed on the pixels; it is then determined whether the pixels are continuous. If they are continuous, the first direction is scanned column by column, and the number of pixels is recorded. The recorded pixels are defined as feature pixels. If they are not continuous, each pixel is taken as the center along the first direction, and it is determined whether there are at least three adjacent pixels around it. If there are, the pixel is retained, and the next pixel is determined; if not, the pixel is deleted, and the next pixel is determined. The first direction can be from right to left, from left to right, etc. When determining whether there are at least three adjacent pixels around a pixel, the surrounding area refers to the eight directions: top, bottom, left, right, top left, bottom left, top right, and bottom right. If there are at least three adjacent pixels in any of these eight directions, the pixel is retained as a feature pixel.

[0061] Step 6: Retain the feature pixel located in the middle position as the decision pixel. Specifically, determine whether the number of feature pixels in each column is odd or even. If the number of feature pixels in the column is odd, retain only the middle row of feature pixels as the decision pixel. If the number of feature pixels in the column is even, retain the middle two rows of pixels and delete the remaining pixels. Determine the distance between the two retained rows of pixels and the nearest decision pixel, retain the pixel with the smallest distance to the decision pixel, and delete the remaining pixels. Use the retained pixel as the decision pixel.

[0062] Step 7: Take the average of the minimum and maximum number of rows among all the judgment pixels as the horizontal coordinate of the conveyor belt overlap position; where the maximum and minimum number of rows refer to the relative positions of the judgment pixels. In this embodiment, the judgment pixel at the top is defined as the maximum number of rows, and the judgment pixel at the bottom is defined as the minimum number of rows.

[0063] Step 8: Project the horizontal coordinate of the conveyor belt overlap position onto the data scale, and determine the conveyor belt torsion angle based on the intersection of the projection and the scale. That is, take the data in the data scale corresponding to the intersection point after projection as the final identified torsion angle.

[0064] Some embodiments of this application provide a device for recognizing the torsion angle of a tubular belt conveyor.

[0065] The third embodiment of the present invention proposes a device for identifying the torsion angle of a tubular belt conveyor, and based on any of the above embodiments, includes: a camera and an image information processing unit.

[0066] The camera is used to capture images of the overlap position of the conveyor belt of the tubular belt conveyor. The camera's shooting angle is horizontal or tilted towards the overlap position of the conveyor belt. The image information processing unit is connected to the camera and uses a method for identifying the twist angle of the conveyor belt of a tubular belt conveyor as described in any of the above embodiments to determine the twist angle of the conveyor belt.

[0067] In this specification, the illustrative expressions of the terms used do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0068] Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this invention shall be included within the scope of protection of this invention.

Claims

1. A method for identifying the torsion angle of a tubular belt conveyor, characterized in that, Includes the following steps: S1. Collect image information of the conveyor belt overlap position; S2. Construct a data scale based on the outer diameter of the conveyor belt, and specify the scale and range of the data scale; S3. Extract the feature pixels at the overlap position of the conveyor belt in the image; S4. Retain the feature pixel located in the middle position as the decision pixel; S5. Take the average of the minimum and maximum row counts among all the determined pixels as the x-coordinate of the conveyor belt overlap position; S6. Project the horizontal coordinate of the conveyor belt overlap position onto the data scale, and determine the conveyor belt torsion angle based on the intersection of the projection and the scale on the data scale. In S3, when extracting feature pixels at the overlap position of the conveyor belt in the image, consecutive pixels are selected as feature pixels. Methods for selecting consecutive pixels as feature pixels include: S31. Obtain all pixels at the overlap position of the conveyor belt; S32. Determine whether the pixels are continuous. If they are continuous, scan column by column along the first direction, record the number of pixels, and define the recorded pixels as feature pixels. If they are not continuous, proceed to the next step. S33. Along the first direction, taking each pixel as the center, determine whether there are at least three adjacent pixels around the pixel. If there are, retain the pixel and return to S32; if not, delete the pixel and return to S32.

2. The method for identifying the torsion angle of a tubular belt conveyor according to claim 1, characterized in that, In S1, after acquiring images of the overlap positions of the conveyor belts of the tubular belt conveyor, the acquired images are subjected to grayscale processing and / or noise reduction processing and / or enhancement processing.

3. The method for identifying the torsion angle of a tubular belt conveyor according to claim 1, characterized in that, In S2, the data scale uses the outer diameter of the conveyor belt as its range and divides the data scale into N segments. The graduations on each segment correspond to the angle of the conveyor belt's twist.

4. The method for identifying the torsion angle of a tubular belt conveyor according to claim 1, characterized in that, Before determining whether pixels are continuous, noise reduction and smoothing processing is also performed on pixels at the overlap positions of the conveyor belt.

5. The method for identifying the torsion angle of a tubular belt conveyor according to claim 1, characterized in that, The steps involved in retaining the feature pixel located in the middle position as the determination pixel in S4 are as follows: S41. Determine whether the number of feature pixels in each column is odd or even. S42. When the number of feature pixels in the column is odd, only the feature pixels in the middle row are retained as the judgment pixels. S43. When the number of feature pixels in the column is even, retain the middle two rows of pixels and delete the rest; determine the distance between the two retained rows of pixels and the nearest judgment pixel, retain the pixel with the smallest distance to the judgment pixel, and delete the rest; use the retained pixel as the judgment pixel.

6. A method for identifying the torsion angle of a tubular belt conveyor according to any one of claims 1 to 5, characterized in that, In S1, a camera is used to capture images of the overlapping positions of the conveyor belts of the tubular belt conveyor.

7. The method for identifying the torsion angle of a tubular belt conveyor according to claim 6, characterized in that, Before performing S3 to extract the feature pixels at the conveyor belt overlap position in the image, the process also includes: correcting and transforming the image according to the shooting angle.

8. A device for identifying the torsion angle of a tubular belt conveyor, characterized in that, include: A camera is used to take pictures of the overlap position of the conveyor belt of a tubular belt conveyor. The camera's shooting angle is horizontal towards the overlap position of the conveyor belt or tilted towards the overlap position of the conveyor belt. The image information processing unit is connected to the camera and uses the method for identifying the torsion angle of a tubular belt conveyor as described in any one of claims 1 to 7 to determine the torsion angle of the conveyor belt.

Images