Heavy load rope broken wire detection method and device, terminal equipment and storage medium

Abstract

Embodiments of the present application are suitable for the technical field of mineral transportation, and provide a broken wire detection method and device for heavy load rope, terminal equipment and storage medium. The method comprises: acquiring a monitoring image of the traction rope in motion; segmenting the monitoring image by using an edge detection algorithm to obtain a plurality of segmented images; the pixel difference at the junction edge of the adjacent two segmented images is greater than a preset pixel difference; detecting the traction rope in each segmented image respectively to obtain a detection result of the traction rope in each segmented image; and determining a target detection result of the traction rope according to the plurality of detection results. The above method can timely find out whether the traction rope has a safety hazard.

Description

Technical Field

[0001] The present application belongs to the field of mineral transportation technology, and in particular relates to a method, device, terminal equipment and storage medium for detecting broken wires in a heavy-load rope. Background Art

[0002] Cableways are crucial equipment in mines, carrying the vital role of ore transportation. Heavy-load ropes, a critical and highly dangerous component of cableway equipment, are the lifeline of cableway transportation. The safe operation, inspection, and maintenance of heavy-load ropes on cableways are fundamental to ensuring safe production in mines.

[0003] At present, in order to ensure construction safety, the method of regularly replacing heavy-load ropes is generally adopted. The replacement cycle is determined according to the rated service life of the heavy-load rope. The actual situation of the heavy-load rope cannot be taken into consideration, which will lead to waste and may also cause safety hazards due to untimely replacement of the wire rope. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, terminal device, and storage medium for detecting broken wires in a heavy-load rope, which can solve the problem of being unable to detect safety hazards in heavy-load ropes.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting broken wires in a heavy-duty rope, the method comprising:

[0006] Acquire monitoring images of the traction rope during movement;

[0007] The monitoring image is segmented using an edge detection algorithm to obtain multiple segmented images; the pixel difference at the boundary edge of two adjacent segmented images is greater than a preset pixel difference;

[0008] Detecting the traction rope in each segmented image respectively to obtain a detection result of the traction rope in each segmented image;

[0009] A target detection result of the traction rope is determined based on the multiple detection results.

[0010] In a second aspect, an embodiment of the present application provides a broken wire detection device for a heavy-duty rope, the device comprising:

[0011] An acquisition module is used to acquire monitoring images of the traction rope during movement;

[0012] A segmentation module is used to segment the monitoring image using an edge detection algorithm to obtain multiple segmented images; the pixel difference at the boundary edge of two adjacent segmented images is greater than a preset pixel difference;

[0013] A detection module is used to detect the traction rope in each segmented image respectively to obtain a detection result of the traction rope in each segmented image;

[0014] The first determination module is used to determine the target detection result of the traction rope according to multiple detection results.

[0015] In a third aspect, an embodiment of the present application provides a terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of the first aspect described above when executing the computer program.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method of the first aspect described above.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute the method of the first aspect described above.

[0018] Compared with the prior art, the beneficial effect of the embodiment of the present application is that the terminal device can first obtain a monitoring image of the traction rope when it is in motion. Because the traction rope is broken, the pixels at the broken part in the monitoring image are usually significantly different from the pixels in other normal areas of the traction rope. Therefore, the terminal device can use an edge detection algorithm to segment the monitoring image to obtain multiple segmented images. Afterwards, the traction rope in each segmented image is detected separately to obtain the detection result of the traction rope in each segmented image, so as to determine the target detection result of the traction rope based on the multiple detection results, thereby improving the accuracy of the detection of the traction rope. In addition, the monitoring image of the traction rope in motion is processed to obtain the target detection result, and the traction rope fault can be discovered in time to ensure construction safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 This is a flowchart of a method for detecting broken wires in a heavy-duty rope provided in one embodiment of the present application;

[0021] Figure 2 This is a schematic structural diagram of a broken wire detection device for a heavy-duty rope provided in one embodiment of the present application;

[0022] Figure 3 This is a structural diagram of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0024] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0025] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0026] Inspection of heavy-load ropes at mines still relies solely on traditional visual inspection methods. This approach poses safety risks to inspectors and is prone to inaccuracies such as omissions and misjudgments due to human error. The safe operation of heavy-load ropes is fundamental to safe mine production. Any minor problem could lead to a major accident, placing a significant burden on safety.

[0027] Based on this, the present application proposes a method for detecting broken wires in heavy-load ropes, which is used to automatically monitor heavy-load ropes and promptly identify safety hazards associated with heavy-load ropes. The above-mentioned method for detecting broken wires in heavy-load ropes can be applied to terminal devices such as tablet computers, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, and cameras. The embodiments of the present application do not impose any restrictions on the specific type of terminal device. A heavy-load rope broken wire detection device can be deployed in the terminal device, and the heavy-load rope broken wire detection device can be used to implement the heavy-load rope broken wire detection method of this embodiment.

[0028] See also Figure 1 , Figure 1 The following is a flowchart illustrating a method for detecting broken wires in a heavy-duty rope according to an embodiment of the present application. The method includes the following steps:

[0029] S101: Acquire a monitoring image of the traction rope during movement.

[0030] In one possible implementation, a camera device can be installed in the working environment of the traction rope to capture images of the traction rope in motion or at rest. The terminal device can obtain monitoring images from the surveillance video of the camera device at preset intervals or in real time. In this embodiment, the traction rope often breaks during movement. Based on this, the method in this embodiment of the application is mainly used to detect traction ropes in motion.

[0031] In addition, there are some locations in the traction rope where it is difficult to install a camera device. For these locations, corresponding monitoring videos can be collected by drones at preset time intervals.

[0032] It should be noted that when a traction rope is moving, the traction force it experiences during acceleration is generally greater than during constant speed or deceleration. This means that the traction rope is more likely to break during acceleration. Based on this, the terminal device can also capture the traction rope's speed and, if it determines that the speed is accelerating, capture the monitoring image at that moment.

[0033] Among them, the terminal device can specifically collect the movement speed of the traction rope according to the preset speed sensor, which will not be described in detail.

[0034] In another embodiment, when the traction rope is moving at a constant speed, the clarity of the monitoring image of the traction rope captured by the camera device is generally higher. Based on this, in order to improve the accuracy of traction rope detection, the terminal device can also obtain the monitoring image when the movement speed is determined to be constant.

[0035] The traction rope includes, but is not limited to, nylon rope and steel wire rope. In this embodiment, the lifting containers at both ends of the traction rope are used to lift ore and need to withstand significant gravity. Therefore, the traction rope in this embodiment is typically a higher-strength traction rope.

[0036] It should be noted that, in the monitoring images captured, the traction rope often shakes during actual operation, resulting in low clarity of the traction rope in the captured monitoring images. Consequently, the terminal device cannot accurately identify the traction rope.

[0037] Based on this, the terminal device can also determine the shaking amplitude and shaking direction of the traction rope during movement at the current moment; then, the monitoring image is corrected according to the shaking amplitude and shaking direction to obtain a corrected monitoring image.

[0038] Specifically, the terminal device can obtain the historical monitoring image taken in the previous frame, and compare the historical position of the traction rope in the historical monitoring image with the current position of the traction rope in the monitoring image of the current frame to determine the shaking amplitude and shaking direction generated when the traction rope moves.

[0039] Specifically, the terminal device can determine the current starting position, current middle position, and current end position of the traction rope in the monitoring image. It then calculates the starting distance between the current starting position and the historical starting position, the middle distance between the current middle position and the historical middle position, and the end distance between the current end position and the historical end position. The sum of the starting distance, middle distance, and end distance is then determined as the jitter amplitude.

[0040] Regarding the shaking direction, the terminal device can determine a first deviation direction of the upper portion of the traction rope based on the current starting position, historical starting position, current middle position, and historical middle position; and determine a second deviation direction of the lower portion based on the current middle position, historical middle position, current end position, and historical end position. The first deviation direction and the second deviation direction are then determined as the shaking direction.

[0041] In one embodiment, the correction may be: correcting the monitoring image by translation, rotation, or scaling according to the shaking amplitude and shaking direction to obtain a stable monitoring image.

[0042] It is understandable that for the first frame of monitoring image, since the speed of the traction rope is relatively low at the beginning of its movement, even if the traction rope shakes during its movement, the amplitude of the shaking is usually small. Therefore, for the first frame of monitoring image, no correction is required.

[0043] In another embodiment, when the camera captures the monitoring image, the image may be unclear due to factors such as light or dust. Therefore, during the actual processing, the clarity of the monitoring image of the current frame can be determined first, and when the clarity exceeds a preset clarity, the heavy-load rope broken wire detection method is executed.

[0044] Among them, the method for determining whether the clarity of the monitoring image of the current frame is higher than the preset clarity can be: determining the edge diffusion value of the jitter of the monitoring image of the current frame, wherein the edge diffusion value is used to describe the clarity of the monitoring image of the current frame; and then when the edge diffusion value is higher than the preset threshold, determining that the clarity of the monitoring image of the current frame is higher than the preset clarity.

[0045] S102 , segmenting the monitoring image using an edge detection algorithm to obtain a plurality of segmented images; a pixel difference at a boundary edge between two adjacent segmented images is greater than a preset pixel difference.

[0046] In one embodiment, the edge detection algorithm is used to enhance image edges using different edge operators to determine image edges. Specifically, the edge detection algorithm can be used to enhance contour edges, details, and grayscale transitions in an image to form complete object boundaries, thereby separating objects from an image or detecting regions representing the surface of the same object.

[0047] The edge detection algorithm includes, but is not limited to, a differential edge detection method, a Sobel edge detection algorithm, and a Laplace edge detection algorithm, which are not limited thereto. In this embodiment, on the basis of satisfying image segmentation, in order to reduce the amount of computation required by the terminal device, in this embodiment of the application, the segmented image is determined primarily based on the pixel values ​​of each pixel in the monitored image. That is, if there is a spike-like change or a step-like change in the pixel values ​​of the pixels within adjacent regions, then there must be an edge between the adjacent regions.

[0048] It is understandable that if there is a spike-like change or a step-like change in the pixel values ​​of the pixels in the adjacent areas, it indicates that there may be an abnormality in the traction rope corresponding to the area.

[0049] Specifically, the terminal device can first use a preset object detection network to determine a detection frame containing the traction rope from the monitoring image. It then determines the pixel value of each pixel in the detection frame and segments the traction rope within the detection frame based on the pixel value of each pixel, generating multiple segmented images.

[0050] The object detection network may be a YOLO network or a convolutional network, and there is no limitation on this. The purpose of determining the detection frame containing the traction rope is to reduce the number of pixels that the terminal device needs to process.

[0051] It is understandable that the traction rope usually moves vertically up and down, so in the monitoring image, the traction rope may only be present in a portion of the rectangular area. Therefore, the terminal device can first perform the step of determining the detection frame to reduce the number of pixels required to be processed in subsequent steps.

[0052] In one embodiment, the pixels of the aforementioned pixel point are generally composed of red pixels, blue pixels, and green pixels. Therefore, its pixel value generally includes a red pixel value, a green pixel value, and a blue pixel value. Based on this, for any pixel point, the terminal device can first determine the red pixel value, the green pixel value, and the blue pixel value of the pixel point. Thereafter, the pixel value of the pixel point is determined based on the red pixel value, the green pixel value, and the blue pixel value.

[0053] Exemplarily, the terminal device may determine the average value of the red pixel value, the green pixel value, and the blue pixel value as the pixel value of the pixel point.

[0054] In another embodiment, the terminal device can further weight the red pixel value, the green pixel value and the blue pixel value according to a preset weight to obtain the pixel value of the pixel point. In this embodiment, the way of determining the pixel value of the pixel point according to the red pixel value, the green pixel value and the blue pixel value is not limited.

[0055] In an embodiment, according to the above explanation, if the traction rope has an anomaly (for example, wire breakage or corrosion), the pixel value of the pixel point has a sharp change or a step change. Based on this, for any pixel point in the detection frame, if the pixel difference between the first pixel value of the pixel point and the second pixel value of the adjacent pixel point is less than or equal to the preset pixel difference, the terminal device can determine that the pixel point and the adjacent pixel point belong to the same segmented image; if the pixel difference between the first pixel of the pixel point and the second pixel of the adjacent pixel point is greater than the preset pixel difference, the terminal device will segment the pixel point and the adjacent pixel point in different segmented images.

[0056] It can be understood that, if the pixel difference between the first pixel value of the pixel point and the second pixel value of the adjacent pixel point is less than or equal to the preset pixel difference, it indicates that the pixel values corresponding to the pixel point and the adjacent pixel point are close. That is, there is no sharp change or step change. Therefore, the terminal device can determine the pixel point and the adjacent pixel point as pixel points in the same segmented image.

[0057] Similarly, when the pixel difference between the first pixel value of the pixel point and the second pixel value of the adjacent pixel point is greater than the preset pixel difference, it indicates that the pixel values corresponding to the pixel point and the adjacent pixel point have a sharp change or a step change. Therefore, the terminal device can segment the pixel point and the adjacent pixel point in different segmented images.

[0058] For example, for a monitoring image of a traction rope with wire breakage, the terminal device can segment the monitoring image into a segmented image A without wire breakage, a segmented image B containing wire breakage, and a segmented image C without wire breakage when performing the above S102 step. That is, the pixel difference at the boundary edge between the segmented image A and the segmented image B, and the pixel difference at the boundary edge between the segmented image B and the segmented image C, will both be greater than the preset pixel difference.

[0059] It should be particularly noted that, according to the above explanation of obtaining the segmented image, the calculation amount of obtaining the segmented image according to the pixel difference between the two adjacent pixel points is much less than the calculation amount required by the existing differential edge detection method, the Sobel edge detection algorithm and the Laplace edge detection algorithm.

[0060] S103 , detecting the traction rope in each segmented image respectively to obtain a detection result of the traction rope in each segmented image.

[0061] S104: Determine a target detection result of the traction rope according to the multiple detection results.

[0062] In one embodiment, the terminal device may use a preset detection network to detect the traction rope in each segmented image to obtain a detection result for each traction rope. The preset detection network may be pre-trained based on images of traction ropes corresponding to multiple different detection results. In this embodiment, the training method of the detection network is not described in detail.

[0063] Among them, the test results include but are not limited to: the traction rope is normal, the wire ends are broken, the broken wires are locally gathered (the broken wires are concentrated together to form a local gathering, at this time the wire rope should be scrapped immediately), the number of broken wires gradually increases (the traction rope has broken wires after a period of use, and the number of broken wires gradually increases, and the time interval between broken wires will become shorter), the traction rope is broken, the rope diameter of the traction rope is reduced (the fiber core of the traction rope is damaged, resulting in a significant reduction in the rope diameter), external corrosion of the traction rope (deep pits appear on the surface) and deformation and other one or more test results, which are not limited to this.

[0064] It should be noted that, when training the detection network, the terminal device may train a classifier for each detection result to obtain the detection result of the traction rope in each segmented image, so as to improve the accuracy of identifying the traction rope.

[0065] In one embodiment, after obtaining the detection results of the traction rope in each segmented image, if all detection results indicate that the traction rope is normal, the target detection result is determined to be normal. However, if one or more detection results are preset abnormal results, the terminal device may determine the detection results that are preset abnormal results as the target detection result. Furthermore, the terminal device may also display the target detection results and the segmented image corresponding to each target detection result to assist personnel in further observation.

[0066] In this embodiment, the terminal device can first capture a monitoring image of the traction rope in motion. If the traction rope is broken, the pixels at the broken portion of the monitoring image will typically be significantly different from those in other normal areas of the traction rope. Therefore, the terminal device can segment the monitoring image using an edge detection algorithm to obtain multiple segmented images. The traction rope in each segmented image is then detected, and a detection result for the traction rope in each segmented image is obtained. The target detection result for the traction rope is determined based on the multiple detection results, thereby improving the accuracy of traction rope detection. Furthermore, by processing the monitoring image of the traction rope in motion to obtain the target detection result, traction rope faults can be detected promptly, ensuring construction safety.

[0067] It should be noted that after the monitoring image is segmented to obtain multiple segmented images, each segmented image is detected to obtain the target detection result of the traction rope. Compared with the method of directly identifying the entire monitoring image, the accuracy of the final detection result is relatively high. In addition, because the detection network only needs to process the segmented image containing part of the traction rope each time, it obtains the classification feature used to classify the segmented image to input into the classifier for identification. At this time, the detailed feature information contained in the classification feature is usually used to characterize whether the traction rope in the segmented image is normal or to characterize whether the traction rope in the segmented image is abnormal. Compared with the overall classification feature obtained after processing the entire monitoring image, because the traction rope in the entire monitoring image is not segmented, the overall classification feature will be mixed with a lot of feature information belonging to the normal traction rope, which is not conducive to the recognition of the classifier.

[0068] See also Figure 2 , Figure 2 This is a structural block diagram of a heavy-duty rope broken wire detection device provided in an embodiment of the present application. The heavy-duty rope broken wire detection device in this embodiment includes modules for executing Figure 1 Each step in the corresponding embodiment. Please refer to Figure 1 as well as Figure 1 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 2 The heavy-load rope broken wire detection device 200 may include: an acquisition module 210, a segmentation module 220, a detection module 230 and a first determination module 240, wherein:

[0069] The acquisition module 210 is used to acquire monitoring images of the traction rope when it is in motion.

[0070] The segmentation module 220 is used to segment the monitoring image using an edge detection algorithm to obtain multiple segmented images; the pixel difference at the boundary edge of two adjacent segmented images is greater than a preset pixel difference.

[0071] The detection module 230 is configured to detect the traction rope in each segmented image and obtain a detection result of the traction rope in each segmented image.

[0072] The first determination module 240 is configured to determine a target detection result of the traction rope according to multiple detection results.

[0073] In one embodiment, the acquisition module 210 is further configured to:

[0074] The movement speed of the traction rope is collected; if the movement speed is determined to be uniform or accelerated, a monitoring image is obtained.

[0075] In one embodiment, the heavy-load rope broken wire detection device 200 further includes: after acquiring the monitoring image of the traction rope during movement, further including:

[0076] The second determination module is used to determine the shaking amplitude and shaking direction of the traction rope during movement.

[0077] The correction module is used to correct the monitoring image according to the amplitude and direction to obtain a corrected monitoring image.

[0078] In one embodiment, the segmentation module 220 is further configured to:

[0079] A preset target detection network is used to determine a detection frame containing the traction rope from the monitoring image; the pixel value of each pixel in the detection frame is determined; and the traction rope in the detection frame is segmented according to the pixel value of each pixel to obtain multiple segmented images.

[0080] In one embodiment, the segmentation module 220 is further configured to:

[0081] For any pixel point in the detection frame, determine the red pixel value, green pixel value, and blue pixel value of the pixel point; and determine the pixel value of the pixel point based on the red pixel value, green pixel value, and blue pixel value.

[0082] In one embodiment, the segmentation module 220 is further configured to:

[0083] For any pixel point in the detection frame, if the pixel difference between the first pixel value of the pixel point and the second pixel value of the adjacent pixel point is less than or equal to the preset pixel difference, it is determined that the pixel point and the adjacent pixel point belong to the same segmented image; if the pixel difference between the first pixel of the pixel point and the second pixel of the adjacent pixel point is greater than the preset pixel difference, the pixel point and the adjacent pixel point are segmented into different segmented images respectively.

[0084] In one embodiment, the first determining module 240 is further configured to:

[0085] The detection results that belong to the preset abnormal results are determined as the target detection results.

[0086] When it is understood that Figure 2 In the structural block diagram of the heavy-duty rope broken wire detection device shown, each module is used to perform Figure 1 The steps in the corresponding embodiments, and Figure 1 Each step in the corresponding embodiment has been explained in detail in the above embodiment. Figure 1 as well as Figure 1 The relevant descriptions in the corresponding embodiments will not be repeated here.

[0087] Figure 3 This is a block diagram of a terminal device provided by an embodiment of the present application. Figure 3 As shown, the terminal device 300 of this embodiment includes: a processor 310, a memory 320, and a computer program 330 stored in the memory 320 and executable by the processor 310, such as a program for a method for detecting a broken wire in a heavy-duty rope. When the processor 310 executes the computer program 330, the steps in each embodiment of the method for detecting a broken wire in a heavy-duty rope are implemented, such as Figure 1 Alternatively, the processor 310 executes the computer program 330 to implement the above Figure 2 The functions of each module in the corresponding embodiment are, for example, Figure 2 For details on the functions of modules 210 to 240, please refer to Figure 2 Related description in the corresponding embodiment.

[0088] Exemplarily, computer program 330 may be divided into one or more modules, one or more of which are stored in memory 320 and executed by processor 310 to implement the heavy-load rope broken wire detection method provided in the embodiments of the present application. One or more modules may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of computer program 330 in terminal device 300. For example, computer program 330 may implement the heavy-load rope broken wire detection method provided in the embodiments of the present application.

[0089] The terminal device 300 may include, but is not limited to, a processor 310 and a memory 320. Those skilled in the art will appreciate that Figure 3 It is only an example of the terminal device 300 and does not constitute a limitation of the terminal device 300. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0090] The processor 310 may be a central processing unit, or other general-purpose processor, a digital signal processor, an application-specific integrated circuit, an off-the-shelf programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0091] The memory 320 may be an internal storage unit of the terminal device 300, such as a hard disk or memory of the terminal device 300. The memory 320 may also be an external storage device of the terminal device 300, such as a plug-in hard disk, smart memory card, flash memory card, etc. equipped on the terminal device 300. Furthermore, the memory 320 may include both an internal storage unit of the terminal device 300 and an external storage device.

[0092] An embodiment of the present application provides a computer-readable storage medium, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for detecting broken wires in a heavy-load rope as described in the above-mentioned embodiments is implemented.

[0093] An embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device executes the method for detecting broken wires in a heavy-load rope in each of the above embodiments.

[0094] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for detecting broken wires in a heavy-duty rope, characterized in that: The method comprises: Acquire monitoring images of the traction rope during movement; The monitoring image is segmented using an edge detection algorithm to obtain a plurality of segmented images; the pixel difference at the boundary edge of two adjacent segmented images is greater than a preset pixel difference; Detecting the traction rope in each of the segmented images respectively to obtain a detection result of the traction rope in each of the segmented images; Determining a target detection result of the traction rope according to the plurality of detection results; After acquiring the monitoring image of the traction rope during movement, the method further includes: Obtain a historical monitoring image captured in a previous frame, and compare the historical position of the traction rope in the historical monitoring image with the current position of the traction rope in the monitoring image of the current frame to determine the jitter amplitude and jitter direction of the traction rope during movement; the current position includes the current head end position, the current middle position, and the current end position, and the historical position includes the historical head end position, the historical middle position, and the historical end position; Correcting the monitoring image according to the shaking amplitude and shaking direction to obtain the corrected monitoring image; The comparing the historical position of the traction rope in the historical monitoring image with the current position of the traction rope in the monitoring image of the current frame to determine the shaking amplitude and shaking direction of the traction rope during movement includes: Calculating the head end distance between the current head end position and the historical head end position, the middle distance between the current middle position and the historical middle position, and the end distance between the current end position and the historical end position; Determine the sum of the head end distance, the middle distance, and the end distance as the jitter amplitude; The first deviation direction of the upper half of the traction rope is determined based on the current head end position, the historical head end position, the current middle position and the historical middle position; and the second deviation direction of the lower half of the traction rope is determined based on the current middle position, the historical middle position, the current end position and the historical end position; the first deviation direction and the second deviation direction are determined as the shaking direction.

2. The method according to claim 1, characterized in that The obtaining of the monitoring image of the traction rope during movement includes: collecting the movement speed of the traction rope; If it is determined that the movement speed is uniform or accelerated, the monitoring image is acquired.

3. The method according to claim 1, characterized in that The monitoring image is segmented using an edge detection algorithm to obtain a plurality of segmented images, including: Using a preset target detection network, determining a detection frame containing the traction rope from the monitoring image; Determine the pixel value of each pixel in the detection frame; The traction rope in the detection frame is segmented according to the pixel value of each pixel point to obtain a plurality of segmented images.

4. The method according to claim 3, characterized in that Determining the pixel value of each pixel in the detection frame includes: For any pixel point in the detection frame, determine the red pixel value, green pixel value, and blue pixel value of the pixel point; The pixel value of the pixel point is determined according to the red pixel value, the green pixel value and the blue pixel value.

5. The method according to claim 3, characterized in that The traction rope in the detection frame is segmented according to the pixel value of each pixel point to obtain a plurality of segmented images, including: For any pixel point in the detection frame, if the pixel difference between the first pixel value of the pixel point and the second pixel value of the adjacent pixel point is less than or equal to the preset pixel difference, then it is determined that the pixel point and the adjacent pixel point belong to the same segmented image; If the pixel difference between the first pixel of the pixel point and the second pixel of the adjacent pixel point is greater than the preset pixel difference, the pixel point and the adjacent pixel point are respectively segmented into different segmented images.

6. The method according to any one of claims 1 to 5, characterized in that The step of determining the target detection result of the traction rope according to the plurality of detection results includes: The detection result belonging to the preset abnormal result is determined as the target detection result.

7. A broken wire detection device for heavy-duty ropes, characterized in that: The device comprises: An acquisition module is used to acquire monitoring images of the traction rope during movement; A segmentation module is used to segment the monitoring image using an edge detection algorithm to obtain a plurality of segmented images; the pixel difference at the boundary edge of two adjacent segmented images is greater than a preset pixel difference; a detection module, configured to detect the traction rope in each of the segmented images respectively, and obtain a detection result of the traction rope in each of the segmented images; a first determining module, configured to determine a target detection result of the traction rope according to the plurality of detection results; After acquiring the monitoring image of the traction rope during movement, the method further includes: A second determination module is configured to obtain a historical monitoring image captured in a previous frame, and compare the historical position of the traction rope in the historical monitoring image with the current position of the traction rope in the monitoring image of the current frame to determine the jitter amplitude and jitter direction of the traction rope during movement; the current position includes the current head end position, the current middle position, and the current end position, and the historical position includes the historical head end position, the historical middle position, and the historical end position; a correction module, configured to correct the monitoring image according to the shaking amplitude and shaking direction to obtain the corrected monitoring image; The second determining module is further configured to: Calculating the head end distance between the current head end position and the historical head end position, the middle distance between the current middle position and the historical middle position, and the end distance between the current end position and the historical end position; Determine the sum of the head end distance, the middle distance, and the end distance as the jitter amplitude; The first deviation direction of the upper half of the traction rope is determined based on the current head end position, the historical head end position, the current middle position and the historical middle position; and the second deviation direction of the lower half of the traction rope is determined based on the current middle position, the historical middle position, the current end position and the historical end position; the first deviation direction and the second deviation direction are determined as the shaking direction.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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