Crane grab bucket steel wire rope deviation rectification control method and system

Abstract

The invention discloses a vehicle grab bucket steel wire rope deviation rectification control method and system, and the method specifically comprises the following steps: after the grab bucket completes the grabbing action, shooting images of four steel wire ropes between the grab bucket and a travelling crane, and controlling the grabbing direction of the grab bucket through two steel wire ropes on the outer side; a shot image containing the steel wire ropes is input into a steel wire rope recognition model, and the steel wire rope recognition model outputs mask images of the four steel wire ropes and a point set of pixel points corresponding to the four steel wire ropes; the two steel wire ropes on the outer side are fitted based on the point sets corresponding to the two steel wire ropes on the outer side, the included angle between the two steel wire ropes on the outer side and the horizontal direction is determined, the crane is controlled to move so that the included angle between the two steel wire ropes on the outer side and the horizontal direction can be about 90 degrees, then the rope is collected to control the grab bucket to ascend, and therefore the winding risk of the steel wire ropes in the rope collecting process is effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of grab bucket control, and more specifically, the present invention relates to a deviation correction control method and system for a steel wire rope of a crane grab bucket. Background Art

[0002] Crane grab is a mechanical equipment used for loading and unloading bulk materials (such as coal, ore, sand and gravel, etc.), which is widely used in ports, mines, construction sites and other fields. It is usually installed on the hook of the crane, and the opening and closing are controlled by wire rope to realize the grabbing and releasing of materials. The main function of the crane grab is to improve the efficiency of bulk material loading and unloading, reduce labor costs, and ensure the safety of operations.

[0003] The wire rope in the crane grab is mainly used for connection and transmission. The wire rope firmly connects the grab and the crane hook to ensure that it can withstand the tension and impact during operation. For mechanically driven crane grabs, the wire rope is also responsible for transmitting power. Through the winding or releasing action of the crane winch, the wire rope can control the lifting and opening of the grab. When the crane tightens the wire rope, the grab closes, and when it is relaxed, the grab opens. This requires not only sufficient strength and toughness of the wire rope, but also good wear resistance and fatigue resistance to ensure long-term stable operation. The wire rope is crucial to the safety and efficiency of the crane grab system.

[0004] Since the wire rope may not be tightened vertically upwards, but tightened at a certain angle, the arrangement of the wire rope on the drum will become disorderly due to the change in the direction of the force, which can easily lead to rope winding. Rope winding not only causes excessive bending and twisting of the wire rope, further aggravating its damage, but also slows down the lifting speed and increases the operation time, thereby reducing the overall production efficiency. At the same time, frequent maintenance and replacement of damaged parts will also increase the operating costs of the enterprise. Summary of the invention

[0005] The invention provides a deviation correction control method for a grab bucket steel wire rope of a hybrid vehicle, aiming to reduce the rope winding phenomenon of the steel wire rope during the tightening process.

[0006] The present invention is implemented in this way, a method for controlling deviation of a steel wire rope of a crane grab bucket, the method is specifically as follows:

[0007] (1) After the grab bucket completes the grabbing action, take images of the four steel wire ropes between the grab bucket and the crane, of which the outer two steel wire ropes are used to control the grabbing direction of the grab bucket;

[0008] (2) The captured image containing the steel rope is input into the steel rope recognition model, and the steel rope recognition model outputs a mask image of the four steel ropes and a point set of pixel points corresponding to the four steel ropes;

[0009] (3) The two outer wire ropes are fitted based on the point sets corresponding to the two outer wire ropes, the angles between the two outer wire ropes and the horizontal direction are determined, and the movement of the crane is controlled so that the angles between the two outer wire ropes and the horizontal direction are 90±3.5°. The ropes are then retracted to control the grab bucket to rise.

[0010] Furthermore, the YOLOv11 network model is trained using image samples containing wire ropes, and the trained YOLOv11 network model is used as a wire rope recognition model.

[0011] Furthermore, the method for determining the corresponding point set based on the two outer wire ropes is as follows:

[0012] The four steel wire ropes correspond to four point sets P1, P2, P3 and P4. The maximum ordinate and the horizontal coordinate corresponding to the maximum ordinate of point set P1, point set P2, point set P3 and point set P4 are obtained respectively. The point sets with the maximum and minimum values ​​in the horizontal coordinate corresponding to the maximum ordinate are the point sets corresponding to the two outer steel wire ropes.

[0013] Furthermore, the fitting method of the wire rope is as follows:

[0014] The least square method is used to perform straight line fitting on the point set corresponding to the two outer steel wire ropes to determine the slope m and intercept b of the straight line corresponding to the two outer steel wire ropes.

[0015] Furthermore, the slope m of the straight line corresponding to the two outer steel wire ropes is inversely tangented and converted into a radian value ε, and then the radian value ε is converted into an angle value θ.

[0016] Furthermore, the mask M i It is expressed as follows:

[0017]

[0018] Among them, M i (x,y) represents the mask image M i The pixel point corresponding to the (x, y) position in M i (x,y)=1, then the mask image M i The pixel point corresponding to the (x, y) position in the image is marked as a wire rope. i (x, y) = 0, then the mask image M i The pixel corresponding to the (x, y) position is marked as the background.

[0019] Furthermore, it is characterized in that the system comprises:

[0020] An image acquisition unit, a PLC controller connected to the image acquisition unit, the PLC controller connected to the crane, wherein a wire rope recognition model is integrated in the PLC controller;

[0021] After the grab bucket completes the grabbing action, the image acquisition unit collects the image of the wire rope and inputs it into the PLC controller to control the crane based on the above-mentioned crane grab bucket wire rope deviation correction control method.

[0022] Furthermore, the image acquisition unit is a camera fixed on the vehicle.

[0023] Furthermore, the YOLOv11 network model is trained using image samples containing wire ropes, and the trained YOLOv11 network model is used as a wire rope recognition model.

[0024] The present invention uses a camera to shoot the wire rope, and then determines the angle of the two outer wire ropes with the horizontal direction after the grab bucket completes the grabbing action, and controls the driving based on the angle to make the angle between the two outer wire ropes and the horizontal direction close to 90 degrees, and then retracts the rope to control the grab bucket to rise, thereby effectively reducing the risk of the wire rope being entangled during the rope retraction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the structure of a crane grab bucket wire rope deviation correction control system provided in an embodiment of the present invention;

[0026] Figure 2 The present invention provides a flowchart of a crane grab bucket wire rope deviation correction control method. DETAILED DESCRIPTION

[0027] The specific implementation modes of the present invention are further explained in detail below by describing the embodiments with reference to the accompanying drawings, so as to help those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention.

[0028] Figure 1 The schematic diagram of the structure of the crane grab wire rope deviation correction control system provided in the embodiment of the present invention, for the convenience of explanation, only the part related to the embodiment of the present invention is shown, and the system includes:

[0029] An image acquisition unit, a PLC controller connected to the image acquisition unit, the PLC controller connected to the crane, and a wire rope recognition model integrated in the PLC controller;

[0030] After the grab completes the grabbing action, the image acquisition unit collects images of the four steel ropes between the grab and the crane and inputs them into the PLC controller. The PLC controller controls the crane based on the angle between the two outer steel ropes and the horizontal direction, so that the angle between the two outer steel ropes and the horizontal direction is 90±3.5°. The grab is then controlled to rise by retracting the ropes, effectively reducing the risk of entanglement of the wire ropes during the retraction process.

[0031] Figure 2The flowchart of the crane grab wire rope deviation correction control method provided by the embodiment of the present invention is as follows:

[0032] (1) After the grab bucket completes the grabbing action, take images of the four steel wire ropes between the grab bucket and the crane, of which the outer two steel wire ropes are used to control the grabbing direction of the grab bucket;

[0033] (2) The captured image containing the steel rope is input into the steel rope recognition model, and the steel rope recognition model outputs a mask image of the four steel ropes and a point set of pixel points corresponding to the four steel ropes;

[0034] (3) The two outer wire ropes are fitted based on the point sets corresponding to the two outer wire ropes, the angles between the two outer wire ropes and the horizontal direction are determined, and the movement of the crane is controlled so that the angles between the two outer wire ropes and the horizontal direction are 90±3.5°. The ropes are then retracted to control the rise of the grab bucket, thereby effectively reducing the risk of wire rope entanglement during the retraction process.

[0035] In an embodiment of the present invention, the image acquisition unit uses a 4-megapixel camera, which is suitable for various indoor and outdoor monitoring scenarios. The camera uses a 1 / 2.7-inch Progressive Scan CMOS sensor, supports 2560×1440 full HD resolution, and provides clear and smooth video images. It has infrared illumination, can capture clear images even in dark environments, supports day and night conversion, and ensures excellent performance under low light conditions. The camera is installed on the crane and can move with the left and right movement of the crane. The camera is facing the wire rope connecting the grab bucket and the crane, and the camera's viewing angle can cover four wire ropes. The present invention collects image data containing four wire ropes through the camera.

[0036] In an embodiment of the present invention, a wire rope recognition model is built using a YOLOv11 network model. The YOLOv11 network model is trained using image samples containing wire ropes until the recognition accuracy of the YOLOv11 network model reaches the set requirements. The trained YOLOv11 network model can be used as a wire rope recognition model to identify wire ropes in an input image and output a point set of pixels corresponding to each wire rope.

[0037] The YOLOv11 network model integrates detection and segmentation, and uses a simple method to achieve instance segmentation. The network is mainly divided into three parts, namely the backbone network, the fusion network neck and the prediction network head. The backbone network downsamples the 1280×1280 input image five times to obtain three feature maps of different sizes, 40×40, 80×80 and 160×160, and then the feature batches of feature maps of different sizes are fused through the fusion network; the fusion network adopts a network structure of two feature pyramids, and finally each fused feature map is connected to the prediction network; the prediction network has two branches, namely the detection branch and the segmentation branch. The detection branch predicts the category, border and α mask coefficients Coefficients for each target. The segmentation branch outputs α mask prototypes Prototype for the current input image. Then, for each target, the α mask coefficients and α mask prototype matrices are multiplied to obtain the instance segmentation result of the input image.

[0038] The image data of the four steel ropes collected by the image acquisition unit are scaled or filled to obtain an image C of a specified size. i , image C i Input into the trained YOLOv11 network model, the YOLOv11 network model outputs the mask map M of the wire rope i And the point set of pixels corresponding to each wire rope, the mask map M i The pixel points corresponding to the wire rope are marked, namely:

[0039]

[0040] Among them, M i (x,y) represents the mask image M i The pixel point corresponding to the (x, y) position in the mask image M i and image C i have the same size, if M i (x,y)=1, then the mask image M i The pixel point corresponding to the (x, y) position in the image is marked as a wire rope. i (x, y) = 0, then the mask image M i The pixel corresponding to the position (x, y) in image C is marked as the background. i It is divided into wire rope and background, realizing image C i Wire rope identification in.

[0041] The four point sets corresponding to the four steel wire ropes are represented by set P1, set P2, set P3 and set P4 respectively. The maximum ordinate and the abscissa corresponding to the maximum ordinate of set P1, set P2, set P3 and set P4 are obtained respectively. The point set where the maximum and minimum values ​​in the abscissa corresponding to the maximum ordinate are located is the point set corresponding to the two outer steel wire ropes. A straight line is fitted to the point set corresponding to the two outer steel wire ropes, and one straight line corresponds to one steel wire rope, so as to determine the angle between the two outer steel wire ropes and the horizontal direction.

[0042] Set P1, set P2, set P3 and set P4 are expressed as follows:

[0043]

[0044] Among them, (x 1j ,y 1j ) represents the coordinates of the jth pixel in the set P1, (x 2s ,y 2s ) represents the coordinates of the sth pixel in the set P2, (x 3k ,y 3k )The coordinates of the kth pixel in the set P3, (x 4d ,y 4d ) represents the coordinates of the dth pixel in set P4.

[0045] Get the maximum vertical coordinates of all pixels in set P1, set P2, set P3 and set P4, using y 1m ,y 2m ,y 3m and 4m Indicates that:

[0046]

[0047] In determining y 1m ,y 2m ,y 3m and 4m The corresponding horizontal coordinates are x 1m 、x 2m 、x 3m and x 4m Indicates, get x 1m 、x 2m 、x 3m and x 4m The maximum value x in max and the minimum value x min ,Right now:

[0048]

[0049] The maximum value x max and the minimum value xmin The point set corresponds to the two outer wire ropes, and then the maximum value x max and the minimum value x min A straight line is fitted to the point set where the outer wire ropes are located, and the angle between the two outer wire ropes and the horizontal direction is determined.

[0050] The following is an explanation of the straight line fitting process of the point set corresponding to the two outer wire ropes, taking the set P as an example, as follows:

[0051] The set P to be fitted consists of a series of points (x j ,y j ), j ranges from 1 to n, and the goal is to find a straight line y=mx+b that fits the data points as well as possible. The least squares method is to select the straight line parameters m and b so that the sum of the squares of the vertical distances from all data points to the straight line is minimized. That is, the goal is to minimize the following objective function S(m,b):

[0052]

[0053] Where m is the slope of the fitted line, b is the intercept of the fitted line, and y is j Represents the actual value of the ordinate of the jth point in the set P, y′ j Represents the predicted value of the j-th point in the set P.

[0054] Through multiple iterations, we can obtain the m and b corresponding to the minimum S(m,b), that is:

[0055]

[0056] Get the fitted straight line equation and the required parameter m, convert the slope m into radian value ε through inverse tangent, and then convert the radian value ε into angle value θ, that is:

[0057]

[0058] The present invention uses a camera to shoot the wire rope, and then determines the angle of the two outer wire ropes with the horizontal direction after the grab bucket completes the grabbing action, and controls the driving based on the angle to make the angle between the two outer wire ropes and the horizontal direction close to 90 degrees, and then retracts the rope to control the grab bucket to rise, thereby effectively reducing the risk of the wire rope being entangled during the rope retraction process.

[0059] The present invention has been described exemplarily. Obviously, the specific implementation of the present invention is not limited to the above-mentioned method. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A crane grab bucket wire rope deviation correction control method, characterized in that: The method is specifically as follows: (1) After the grab bucket completes the grabbing action, take images of the four steel wire ropes between the grab bucket and the crane, of which the outer two steel wire ropes are used to control the grabbing direction of the grab bucket; (2) The captured image containing the steel rope is input into the steel rope recognition model, and the steel rope recognition model outputs a mask image of the four steel ropes and a point set of pixel points corresponding to the four steel ropes; (3) The two outer wire ropes are fitted based on the point sets corresponding to the two outer wire ropes, the angles between the two outer wire ropes and the horizontal direction are determined, and the movement of the crane is controlled so that the angles between the two outer wire ropes and the horizontal direction are about 90 degrees. The ropes are then retracted to control the grab bucket to rise.

2. The method for controlling the deviation of the wire rope of the crane grab bucket according to claim 1, characterized in that: The YOLOv11 network model is trained using image samples containing wire ropes, and the trained YOLOv11 network model is used as a wire rope recognition model.

3. The method for controlling the deviation of the wire rope of the crane grab bucket according to claim 1, characterized in that: The method for determining the corresponding point set based on the two outer wire ropes is as follows: The four steel wire ropes correspond to four point sets P1, P2, P3 and P4. The maximum ordinate and the horizontal coordinate corresponding to the maximum ordinate of point set P1, point set P2, point set P3 and point set P4 are obtained respectively. The point sets with the maximum and minimum values ​​in the horizontal coordinate corresponding to the maximum ordinate are the point sets corresponding to the two outer steel wire ropes.

4. The method for controlling the deviation of the wire rope of the crane grab bucket as claimed in claim 3 is characterized in that: The fitting method of wire rope is as follows: The least square method is used to perform straight line fitting on the point set corresponding to the two outer steel wire ropes to determine the slope m and intercept b of the straight line corresponding to the two outer steel wire ropes.

5. The method for controlling deviation of the wire rope of a crane grab bucket as claimed in claim 4, characterized in that: Take the inverse tangent of the slope m of the straight line corresponding to the two outer steel wire ropes, convert it into a radian value ε, and then convert the radian value ε into an angle value θ.

6. The method for controlling the deviation of the wire rope of the crane grab bucket as claimed in claim 1, characterized in that: Mask M i It is expressed as follows: Among them, M i (x,y) represents the mask image M i The pixel point corresponding to the (x, y) position in M i (x,y)=1, then the mask image M i The pixel point corresponding to the (x, y) position in the image is marked as a wire rope. i (x,y)=0, then the mask image M i The pixel corresponding to the (x, y) position is marked as the background.

7. The crane grab bucket wire rope deviation correction control system as claimed in claim 1, characterized in that: The system comprises: An image acquisition unit, a PLC controller connected to the image acquisition unit, the PLC controller connected to the crane, wherein a wire rope recognition model is integrated in the PLC controller; After the grab bucket completes the grabbing action, the image acquisition unit acquires the image of the wire rope and inputs it into the PLC controller to control the crane based on the crane grab bucket wire rope deviation correction control method according to any one of claims 1 to 6.

8. The crane grab bucket wire rope deviation correction control system as claimed in claim 7, characterized in that: The image acquisition unit is a camera fixed on the vehicle.

9. The crane grab bucket wire rope deviation correction control system as claimed in claim 7, characterized in that: The YOLOv11 network model is trained using image samples containing wire ropes, and the trained YOLOv11 network model is used as a wire rope recognition model.

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