A method and system for correcting deviation of a steel wire rope of a traveling grab

Abstract

The application discloses a kind of vehicle grab bucket steel wire rope deviation correction control method and system, and the method is as follows: after grab bucket completes the action of grabbing, the image of four steel wire ropes between grab bucket and travelling crane is shot, wherein the two outer steel wire ropes are used to control the direction of grabbing of grab bucket;The image containing steel wire rope that is shot is input into steel wire rope identification model, and the steel wire rope identification model outputs the mask diagram of four steel wire ropes and the point set of the pixel point corresponding to four steel wire ropes;Based on the point set corresponding to the two outer steel wire ropes, the two outer steel wire ropes are fitted, the included angle between the two outer steel wire ropes and the horizontal direction is determined, the travelling crane is controlled to move, so that the included angle between the two outer steel wire ropes and the horizontal direction is about 90 degrees, and then the rope is controlled to rise, so as to effectively reduce the winding risk of steel wire rope in the process of winding.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of grab control, and more particularly, the present application relates to a method and system for correcting deviation of a grab wire of a traveling crane. BACKGROUND

[0002] The grab of a traveling crane is a mechanical device used for loading and unloading bulk materials such as coal, ore, sand, etc., and is widely used in ports, mines, construction sites, etc. It is usually installed on the hook of the traveling crane and is controlled by a steel wire to open and close, thereby achieving the grabbing and releasing of materials. The main function of the grab of a traveling crane is to improve the efficiency of bulk material loading and unloading, reduce labor costs, and ensure safety during operation.

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

[0004] Since the steel wire may not be tightened vertically upward, but at an angle, the arrangement of the steel wire on the winding drum becomes chaotic due to the change in the direction of force, which can easily lead to winding. Winding not only causes excessive bending and twisting of the steel wire, further exacerbating 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 also increase the operating costs of the enterprise. SUMMARY

[0005] The present application provides a method for correcting deviation of a grab wire of a traveling crane, which aims to reduce the winding phenomenon of the steel wire during tightening.

[0006] The present application is implemented as follows: a method for correcting deviation of a grab wire of a traveling crane, the method being specifically as follows:

[0007] (1) After the grab completes the grabbing action, an image of the four steel wires between the grab and the traveling crane is taken, wherein the two outer steel wires are used to control the grabbing direction of the grab;

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

[0009] (3) fitting the two outer steel wire ropes based on the point sets corresponding to the two outer steel wire ropes, determining the included angle of the two outer steel wire ropes with the horizontal direction, controlling the movement of the travelling crane so that the included angle of the two outer steel wire ropes with the horizontal direction is 90±3.5°, and then controlling the rope to control the upward movement of the grab.

[0010] Further, the YOLOv11 network model is trained through the image sample containing the steel wire rope, and the trained YOLOv11 network model is used as the steel wire rope recognition model.

[0011] Further, the determination method of the point set corresponding to the two outer steel wire ropes is as follows:

[0012] The four point sets P1, P2, P3 and P4 corresponding to the four steel wire ropes are obtained, the maximum longitudinal coordinates and the horizontal coordinates corresponding to the maximum longitudinal coordinates of the point set P1, the point set P2, the point set P3 and the point set P4 are obtained, and the point set in which the maximum value and the minimum value of the horizontal coordinates corresponding to the maximum longitudinal coordinates are located is the point set corresponding to the two outer steel wire ropes.

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

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

[0015] Further, the slope m of the straight line corresponding to the two outer steel wire ropes is subjected to arctangent to convert it into a radian value ε, and the radian value ε is converted into an angle value θ.

[0016] Further, the mask image M i is represented as follows:

[0017]

[0018] wherein, M i (x,y) represents the pixel point corresponding to the (x,y) position in the mask image M i , if M i (x,y) = 1, the pixel point corresponding to the (x,y) position in the mask image M i is marked as a steel wire rope, and if M i (x,y) = 0, the pixel point corresponding to the (x,y) position in the mask image M i is marked as a background.

[0019] Further, the system comprises:

[0020] The image acquisition unit, the PLC controller in communication connection with the image acquisition unit, and the PLC controller in communication connection with the travelling crane, wherein the PLC controller is integrated with the steel wire rope recognition model;

[0021] After the grab bucket completes the grabbing action, the image acquisition unit acquires the image of the steel wire rope, and the PLC controller controls the crane based on the above crane grab bucket steel wire rope deviation control method.

[0022] Further, the image acquisition unit is a camera fixed on the crane.

[0023] Further, the YOLOv11 network model is trained by containing the image sample of the steel wire rope, and the trained YOLOv11 network model is used as the steel wire rope identification model.

[0024] The present application shoots the steel wire rope by the camera, and then determines the angle between the two outer steel wire ropes and the horizontal direction after the grab bucket completes the grabbing action, and controls the crane based on the angle, so that the included angle between the two outer steel wire ropes and the horizontal direction is close to 90 degrees, and then the grab bucket is controlled to rise by the winding control, thereby effectively reducing the winding risk of the steel wire rope in the winding process. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 The structure schematic diagram of the crane grab bucket steel wire rope deviation control system provided for the embodiment of the present application is shown in the figure.

[0026] Figure 2 The flow chart of the crane grab bucket steel wire rope deviation control method provided for the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0027] The specific embodiments of the present application will be further described in detail below by comparing the figures and describing the embodiments, to help the skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present application.

[0028] Figure 1 The structure schematic diagram of the crane grab bucket steel wire rope deviation control system provided for the embodiment of the present application is shown in the figure, only the part related to the embodiment of the present application is shown for the convenience of description, and the system comprises:

[0029] The image acquisition unit, the PLC controller in communication connection with the image acquisition unit, the PLC controller in communication connection with the crane, and the steel wire rope identification model integrated in the PLC controller;

[0030] After the grab bucket completes the grabbing action, the image acquisition unit acquires the image of the four steel wire ropes between the grab bucket and the crane, inputs the PLC controller, the PLC controller controls the crane based on the included angle between the two outer steel wire ropes and the horizontal direction, so that the included angle between the two outer steel wire ropes and the horizontal direction is 90±3.5°, and then the grab bucket is controlled to rise by the winding control, thereby effectively reducing the winding risk of the steel wire rope in the winding process.

[0031] Figure 2A flowchart of the method for correcting the deviation of the steel wire rope of the grab bucket of the overhead traveling crane is provided in the embodiment of the present application, and the method is as follows:

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

[0033] (2) The images containing the steel wire ropes are input into the steel wire rope identification model, and the steel wire rope identification model outputs the mask image of the four steel wire ropes and the point set of the pixel points corresponding to the four steel wire ropes;

[0034] (3) The two outer steel wire ropes are fitted based on the point set corresponding to the two outer steel wire ropes, the included angle between the two outer steel wire ropes and the horizontal direction is determined, the overhead traveling crane is controlled to move, so that the included angle between the two outer steel wire ropes and the horizontal direction is 90±3.5°, and then the rope is controlled to be wound to make the grab bucket rise, thereby effectively reducing the winding risk of the steel wire rope in the winding process.

[0035] In the embodiment of the present application, the image acquisition unit uses a camera with a resolution of 400 million pixels, which is suitable for various indoor and outdoor monitoring scenes. The camera uses a 1 / 2.7 inch Progressive Scan CMOS sensor, supports 2560x1440 full HD resolution, and provides clear and smooth video images. It has infrared irradiation, so it can capture clear images even in dark environments, supports day and night conversion, and ensures good performance in low light conditions. The camera is installed on the overhead traveling crane and can move with the left and right movement of the overhead traveling crane. The camera is directly opposite the steel wire rope connecting the grab bucket and the overhead traveling crane, and the viewing angle of the camera can cover four steel wire ropes. The present application collects image data containing four steel wire ropes through the camera.

[0036] In the embodiment of the present application, the steel wire rope identification model is built by using a YOLOv11 network model. The YOLOv11 network model is trained through image samples containing steel 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 steel wire rope identification model to identify the steel wire ropes in the input image and output the point set of the pixels corresponding to each steel wire rope.

[0037] The YOLOv11 network model integrates detection and segmentation into one, and uses a simple method to realize instance segmentation. The network mainly consists of three parts, namely the backbone network, the fusion network and the prediction network. The backbone network down-samples the input image of 1280x1280 five times to obtain three feature maps of different sizes, i.e., 40x40, 80x80 and 160x160. Then, the fusion network fuses the features of the feature maps of different sizes. The fusion network adopts a network structure of two feature pyramids. 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 class, bounding box and alpha mask coefficients for each target. The segmentation branch outputs alpha mask prototypes for the current input image. Then, for each target, the alpha mask coefficients and the alpha mask prototype matrix 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 is scaled or padded to obtain an image C i of a specified size. i The image C i is input into the trained YOLOv11 network model, and the YOLOv11 network model outputs a mask image M i and a point set corresponding to each steel wire pixel. i The steel wire corresponding pixel points are identified, i.e.,

[0039]

[0040] wherein, M i (x,y) represents the pixel point corresponding to the (x,y) position in the mask image M i . The mask image M i and the image C i have the same size. If M i (x,y) = 1, the pixel point corresponding to the (x,y) position in the mask image M i is identified as a steel wire. If M i (x,y) = 0, the pixel point corresponding to the (x,y) position in the mask image M i is identified as background. Therefore, the image C i is divided into steel wires and background, and the steel wire recognition in the image C i is realized.

[0041] The four steel wire ropes correspond to four point sets, represented by sets P1, P2, P3, and P4 respectively. The maximum ordinate and the corresponding abscissa of each set P1, P2, P3, and P4 are obtained. The point sets containing the maximum and minimum values ​​of the abscissas corresponding to the maximum ordinate are the point sets corresponding to the two outer steel wire ropes. A straight line is fitted to the point sets corresponding to the two outer steel wire ropes, with each straight line corresponding to one steel wire rope, to determine the angle between the two outer steel wire ropes relative to the horizontal direction.

[0042] Sets P1, P2, P3, and P4 are represented as follows:

[0043]

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

[0045] To obtain the maximum y-coordinate of all pixels in sets P1, P2, P3, and P4, use y = ... 1m y 2m y 3m and y 4m This means, that is:

[0046]

[0047] In determining y 1m y 2m y 3m and y 4m The corresponding x-coordinates are represented by x. 1m x 2m x 3m and x 4m This means obtaining x. 1m x 2m x 3m and x 4m The maximum value x in max and minimum value x min ,Right now:

[0048]

[0049] The above maximum value x max and minimum value xmin The point set where the maximum value x max and the minimum value x min are located is linearly fitted, and the included angle of the two outer steel ropes relative to the horizontal direction is determined.

[0050] The linear fitting process of the point set corresponding to the two outer steel ropes is described below, and the set P is taken as an example for description, and the details are as follows:

[0051] The set P to be fitted is composed of a series of points (x j ,y j ), j takes a value of 1 to n, and the goal is to find a straight line y = mx + b that can best fit these data points. The least square method is to select the line parameters m and b so that the sum of the squares of the perpendicular distances of 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] Wherein, m is the slope of the fitted straight line, b is the intercept of the fitted straight line, y j represents the actual value of the vertical coordinate of the jth point in the set P, and y′ j represents the predicted value of the jth point in the set P.

[0054] The m and b corresponding to the minimum S(m, b) can be obtained through multiple iterations, that is:

[0055]

[0056] The fitted straight line equation and the required parameter m are obtained, the slope m is converted into radian value ε through arctangent, and the radian value ε is converted into angle value θ, that is:

[0057]

[0058] The present application determines the angle of the two outer steel ropes relative to the horizontal direction after the grab bucket completes the grabbing action by shooting the steel ropes with a camera, controls the travelling crane based on the angle, so that the included angle of the two outer steel ropes relative to the horizontal direction is close to 90 degrees, and then controls the grab bucket to rise, thereby effectively reducing the risk of entanglement of the steel ropes during the winding process.

[0059] The present application is described by way of example, and it is obvious that the specific implementation of the present application is not limited by the above method. Any non-essential improvement or direct application of the inventive concept and technical solution to other occasions is within the protection scope of the present application.

Claims

1. A method for controlling the deviation of a crane grab bucket wire rope, characterized in that, The method is as follows: (1) After the grab bucket completes the grabbing action, take an image of the four steel wire ropes between the grab bucket and the crane, of which the two outer steel wire ropes are used to control the grabbing direction of the grab bucket. (2) Input the captured image containing the steel rope into the steel wire rope recognition model. The steel wire rope recognition model outputs the mask image of the four steel wire ropes and the point set of the corresponding pixels of the four steel wire ropes. (3) Fit the two outer wire ropes based on the point set corresponding to the two outer wire ropes, determine the angle between the two outer wire ropes and the horizontal direction, control the movement of the crane so that the angle between the two outer wire ropes and the horizontal direction is about 90 degrees, and then retract the rope to control the grab bucket to rise. The specific method for determining the corresponding point set based on the two outer steel 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 corresponding abscissa of the maximum ordinate are obtained for point sets P1, P2, P3 and P4 respectively. The point sets containing the maximum and minimum values ​​of the abscissas corresponding to the maximum ordinate are the point sets corresponding to the two outer steel wire ropes.

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

3. The method for controlling the deviation of the wire rope in a crane grab bucket as described in claim 1, characterized in that, The specific fitting method for steel wire rope is as follows: The least squares method is used to fit the point sets corresponding to the two outer steel wire ropes to a straight line to determine the slope m and intercept b of the straight line 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 described in claim 3, characterized in that, The slope m of the two outer steel wire ropes corresponding to the straight line is arctangented and converted into radian value ε. Then, the radian value ε is converted into angle value θ.

5. The method for controlling the deviation of the wire rope of the crane grab bucket as described in claim 1, characterized in that, Mask image M i It is expressed as follows: Among them, M i (x,y) represents the mask image M i The pixel at position (x,y) in the middle, if M i If (x,y)=1, then the mask image M i The pixel at position (x, y) is labeled as a steel wire rope. If M i If (x,y)=0, then the mask image M i The pixel at position (x, y) is marked as the background.

6. The crane grab bucket wire rope correction control system as described in claim 1, characterized in that, The system includes: The image acquisition unit, the PLC controller which is connected to the image acquisition unit, and the PLC controller which is connected to the crane, wherein the PLC controller integrates a wire rope recognition model; After the grab bucket completes the grabbing action, the image acquisition unit acquires an image of the wire rope and inputs it into the PLC controller to control the crane based on the crane grab bucket wire rope correction control method described in any one of claims 1 to 5.

7. The crane grab bucket wire rope correction control system as described in claim 6, characterized in that, The image acquisition unit is a camera fixed on the vehicle.

8. The crane grab bucket wire rope correction control system as described in claim 6, characterized in that, The YOLOv11 network model was trained using image samples containing steel wire ropes, and the trained YOLOv11 network model was used as a steel wire rope recognition model.

Images