A method and system for intelligent monitoring and positioning of a conveyor belt surface damage

Abstract

The application relates to the technical field of conveyor belt detection, in particular to a conveyor belt surface damage intelligent monitoring and positioning method and system. The method comprises the following steps: acquiring a conveyor belt image containing a preset number of laser lines collected by a camera device, performing image preprocessing on the conveyor belt image to obtain a preset number of ROI images; performing image enhancement processing and connected domain number analysis on each ROI image to obtain the number of connected domains in each ROI image, and judging whether the conveyor belt is damaged according to the number of connected domains of each ROI image; if it is determined that the conveyor belt is not damaged initially, performing pixel point sampling on each ROI image to obtain a pixel value array, and judging whether the conveyor belt is damaged based on the pixel value array obtained from each ROI image; if it is determined that the conveyor belt is damaged based on the number of connected domains or the pixel value array, the damage position is located, and an alarm signal containing the damage position is sent. The application can accurately identify the damage of the conveyor belt.

Description

Technical Field

[0001] This application relates to the field of conveyor belt detection technology, and in particular to an intelligent monitoring and positioning method and system for conveyor belt surface damage. Background Technology

[0002] In modern industrial production, conveyor belts, as key equipment for material handling, are widely used in ports, coal mines, factories, and many other fields. However, during long-term operation, the conveyor belt surface is highly susceptible to various types of damage, such as tearing, bulging, breakage, and deep scratches. These damages not only reduce the service life of the conveyor belt but can also lead to serious production accidents, causing material leakage, equipment downtime, and significant economic losses to enterprises.

[0003] Traditional conveyor belt monitoring methods primarily rely on manual labor. Manual inspection depends on the experience and responsibility of the inspectors, making the results susceptible to subjective factors and compromising accuracy. Furthermore, manual inspection is inefficient and cannot monitor the conveyor belt's operating status in real time. Related technologies also employ mechanical inspection, which, while improving efficiency to some extent, typically requires complex instruments such as probes, sensors, and ultrasonic equipment, resulting in high costs. In addition, in actual operating environments, conveyor belts are subject to external interference from light and electromagnetic signals, leading to poor stability and accuracy of existing monitoring methods, making it difficult to meet the stringent safety requirements of industrial production. Summary of the Invention

[0004] To address the issue of low accuracy in existing technologies for conveyor belt damage monitoring, this application provides an intelligent monitoring and positioning method and system for conveyor belt surface damage.

[0005] Firstly, this application provides an intelligent monitoring and positioning method for conveyor belt surface damage, employing the following technical solution: A method for intelligent monitoring and locating damage to conveyor belt surfaces includes: Acquire images of a conveyor belt containing a preset number of laser lines captured by a camera device, perform image preprocessing on the conveyor belt images, and obtain the preset number of ROI images; Image enhancement processing and connected component number analysis are performed on each ROI image to obtain the number of connected components in each ROI image, and the conveyor belt is judged to be damaged based on the number of connected components in each ROI image. If it is determined that the conveyor belt is initially undamaged, then pixel sampling is performed on each ROI image to obtain a pixel value array, and the conveyor belt is determined to be damaged based on the pixel value array obtained from each ROI image. If the conveyor belt is determined to be damaged based on the number of connected components or the pixel value array, the location of the damage is located, and an alarm signal containing the location of the damage is sent.

[0006] By adopting the above technical solution, images of the conveyor belt are acquired using a camera device. Combined with image preprocessing, ROI extraction, connected component analysis, and pixel value sampling, intelligent monitoring and location of conveyor belt damage are achieved. This enables efficient and accurate detection of surface damage on the conveyor belt. The reliability of damage judgment is improved through dual verification using the number of connected components and the changing characteristics of pixel value sequences. At the same time, by locating the damage location and sending alarm signals, real-time monitoring and rapid response are achieved, effectively reducing the cost of manual inspection and improving the safety and stability of conveyor belt operation.

[0007] In a preferred embodiment, this application can be further configured such that: determining whether the conveyor belt is damaged based on the number of connected components in each ROI image includes: Determine whether the number of connected components in each ROI image is greater than one; If the number of connected components in each ROI image is greater than one, then the conveyor belt is determined to be damaged. If any ROI image has a connected component count of no more than one, then the conveyor belt is determined to be initially undamaged.

[0008] By adopting the above technical solution, since the damaged area usually causes the laser line to break or disperse, thereby increasing the number of connected regions, the number of connected regions in each ROI image can be analyzed to determine whether the conveyor belt is damaged, which can quickly and preliminarily detect whether there are abnormalities on the surface of the conveyor belt.

[0009] In a preferred embodiment, this application can be further configured such that: the step of sampling pixels in each ROI image to obtain a pixel value array includes: Extract the center line of the laser line in each ROI image; The center line of the laser line is sampled based on a preset step size to obtain an array of pixel values ​​for each ROI image.

[0010] By adopting the above technical solution, the center line of the laser line in each ROI image is extracted and pixel sampling is performed based on a preset step size. This avoids image noise and background interference, and can accurately obtain the pixel value array of the laser line, thus achieving efficient extraction and data quantization of laser line features.

[0011] In a preferred embodiment, this application can be further configured such that: determining whether the conveyor belt is damaged based on the pixel value array obtained from each ROI image includes: The pixel value array obtained from each ROI image is represented in the corresponding coordinate system, where the horizontal coordinate of the coordinate system represents the sampling point and the vertical coordinate of the coordinate system represents the pixel value of the sampling pixel point. Traverse the pixels in each coordinate system to determine the number of consecutive transformations of the pixel value sequence in each coordinate system; If the number of consecutive transformations of the pixel value sequence in each coordinate system is not less than the preset number of transformations, then the conveyor belt is determined to be damaged.

[0012] By adopting the above technical solution, the pixel value array of each ROI image is represented as a sequence in the coordinate system, and the number of consecutive transformations of the pixel value sequence is calculated, which quantifies the change characteristics of the pixel values. This enables accurate identification of irregular fluctuations in the laser line caused by damage, thereby effectively detecting the damage on the conveyor belt surface.

[0013] In a preferred embodiment, this application can be further configured such that determining the number of consecutive transformations of the pixel value sequence in each coordinate system includes: For each coordinate system, calculate the difference between the pixel values ​​corresponding to every two adjacent sampling points in the coordinate system to obtain a difference sequence; Each difference in the difference sequence is compared with a preset distance. Differences in the difference sequence that are greater than the preset distance are marked as 1, and differences in the difference sequence that are not greater than the preset distance are marked as 0. Traverse the marked difference sequence, take two consecutive differences in the difference sequence that are transformed from 0 to 1 as a difference group, and determine the number of consecutive difference groups in the difference sequence as the number of consecutive transformations of the pixel value sequence in the coordinate system.

[0014] By adopting the above technical solution, a continuous difference group from 0 to 1 can be identified, which can accurately capture pixel value mutations caused by conveyor belt damage, thereby improving the sensitivity and accuracy of damage detection.

[0015] In a preferred embodiment, this application can be further configured such that: the image preprocessing of the conveyor belt image to obtain the preset number of ROI images includes: The conveyor belt image is sequentially subjected to channel separation, binarization, and dilatation erosion operations to obtain an intermediate image containing the preset number of laser lines; Perform connected component analysis on the intermediate images to obtain the preset number of Mask images; Based on the conveyor belt image and the preset number of mask images, generate the preset number of ROI images.

[0016] By employing the above technical solutions, preprocessing operations such as channel separation, binarization, and dilatation erosion are performed on the conveyor belt image. This effectively extracts the laser line region and removes noise interference. Through connected component analysis and mask image generation, accurate positioning and segmentation of the laser line region are achieved, thereby generating a high-quality ROI image.

[0017] In a preferred embodiment, this application can be further configured such that: performing connected component analysis on the intermediate image to obtain the preset number of Mask images includes: Identify the light stripes representing laser lines in the intermediate image, and add a number of target pixels above and below each laser line's light stripe to represent the region where the laser line is located; Extract the region where the laser line is formed by each laser line to generate the preset number of Mask images.

[0018] By adopting the above technical solution, the target pixels are expanded vertically and horizontally for each laser line, which can accurately extract the effective area of ​​each laser line.

[0019] In a preferred embodiment, this application can be further configured such that the image enhancement processing for each ROI image includes: For each ROI image, channel separation, binarization, dilation and erosion, and removal of small connected components are performed sequentially.

[0020] By employing the above technical solution, channel separation, binarization, dilation erosion, and small connected component removal are sequentially performed on each ROI image, which can effectively enhance the contrast of the laser line region and remove noise interference, further highlighting the characteristics of the laser line.

[0021] Secondly, this application provides an intelligent monitoring and positioning system for conveyor belt surface damage, which adopts the following technical solution: A smart monitoring and positioning system for conveyor belt surface damage includes: a camera device, a preset number of lasers, and electronic equipment; The preset number of lasers are used to emit the preset number of laser lines on the conveyor belt; The camera device is used to capture images of the conveyor belt containing the preset number of laser lines.

[0022] In a preferred embodiment, this application may be further configured such that the electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform the intelligent monitoring and positioning method for conveyor belt surface damage as described in any of the first aspects.

[0023] In summary, this application includes the following beneficial technical effects: This application utilizes a camera device to acquire images of the conveyor belt, and combines image preprocessing, ROI extraction, connected component analysis, and pixel value sampling to achieve intelligent monitoring and location of conveyor belt damage. It can efficiently and accurately detect surface damage on the conveyor belt, and improve the reliability of damage judgment through dual verification by the change characteristics of the number of connected components and pixel value sequences. At the same time, by locating the damage location and sending alarm signals, it achieves real-time monitoring and rapid response, effectively reducing the cost of manual inspection and improving the safety and stability of conveyor belt operation. Attached Figure Description

[0024] Figure 1 This is a schematic diagram showing the installation positions of the camera device and laser provided in the embodiments of this application; Figure 2 This is a flowchart illustrating an intelligent monitoring and positioning method for conveyor belt surface damage provided in an embodiment of this application. Figure 3 The image is the source image captured by the camera device provided in the embodiments of this application; Figure 4 This is the ROI image provided in the embodiments of this application; Figure 5 This is the Mask image provided in the embodiments of this application; Figure 6 This is a thinned image of the ROI image after extracting the center line of the laser line, provided in the embodiments of this application; Figure 7 The embodiments provided in this application show a sampled image after refining the image; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] The following is in conjunction with the appendix Figure 1 - Appendix Figure 8 This application will be described in further detail.

[0026] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0029] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0030] This application integrates video acquisition, image algorithm analysis, and control signal output on an embedded platform, simplifying the hardware structure and solving the problems of poor accuracy, high cost, and insufficient stability of existing conveyor belt monitoring methods. It enables real-time and accurate monitoring and location of conveyor belt surface damage, improving the safety and reliability of conveyor belt operation and reducing the operating costs of enterprises.

[0031] This application provides an intelligent monitoring and positioning system for conveyor belt surface damage. The system includes a camera device, a preset number of lasers, and electronic equipment. The preset number of lasers emits a preset number of laser lines onto the conveyor belt. The camera device acquires images of the conveyor belt containing the preset number of laser lines. The electronic equipment connects to the camera device wirelessly or via a wired connection, acquires and saves the conveyor belt images captured by the camera device, and processes the acquired images to determine whether the conveyor belt is damaged (e.g., torn) and to pinpoint the location of the damage. The preset number refers to the number of laser emitters, which can be set according to actual needs; this embodiment does not limit this setting, but optionally, the preset number is 3.

[0032] See Figure 1 It shows a schematic diagram of the installation positions of the camera device and the laser, with the first laser ( Figure 1 1) The second laser ( Figure 1 2) and the third laser ( Figure 1 3) Install the laser at a suitable location below the conveyor belt to ensure that the emitted laser forms a clear first laser line (the laser line emitted by the first laser), a second laser line (the laser line emitted by the second laser), and a third laser line (the laser line emitted by the third laser) on the conveyor belt. These three laser line stripes are used to mark the detection area. Install the camera device (which can be an industrial camera) below the conveyor belt downstream of the laser, and adjust its position and angle to ensure that it can capture the conveyor belt area containing the three laser lines. Fine-tune the direction and angle of the laser and camera device to ensure optimal laser emission and image capture results.

[0033] During the operation of the conveyor belt, the camera device captures images of the conveyor belt area, including the laser lines, in real time at a preset frequency. Each acquired image is transmitted to electronic equipment, which processes the image to determine if any damage is present. If damage is detected, the system promptly issues an alarm to notify personnel for appropriate action.

[0034] This application provides an intelligent monitoring and positioning method for conveyor belt surface damage, such as... Figure 2 As shown, the method provided in this application embodiment is executed by an electronic device, which can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application embodiment does not impose any limitations on this. The method is applied to a conveyor belt surface damage intelligent monitoring and positioning system. The method includes steps S201-S204, wherein: S201. Acquire images of the conveyor belt containing a preset number of laser lines captured by the camera device, perform image preprocessing on the conveyor belt images, and obtain a preset number of ROI images.

[0035] Specifically, the images captured by the camera device are used as the source images, see [link to relevant documentation]. Figure 3 It shows the source image captured by the camera device. The source image may contain some invalid areas. The source image can be automatically cropped by image processing tools to obtain a conveyor belt image that only contains the valid laser line area.

[0036] Furthermore, the obtained conveyor belt images are sequentially subjected to channel separation, binarization, erosion dilation, and connected component analysis to obtain a predetermined number of ROI images after preprocessing. Each ROI image can also be numbered, with the number corresponding to the number of the laser emitting the laser line contained in the image. See [link to documentation] Figure 4 It shows three ROI images.

[0037] S202. Perform image enhancement processing and connected component number analysis on each ROI image to obtain the number of connected components in each ROI image, and determine whether the conveyor belt is damaged based on the number of connected components in each ROI image.

[0038] Specifically, image enhancement for each ROI image involves sequentially performing channel separation, binarization, and dilation / erosion operations to obtain a predetermined number of enhanced ROI images. Connectivity analysis is then used to determine the number of connected components in each enhanced ROI image.

[0039] For any enhanced ROI image, if the number of its connected components is 1, then the connected component represents the location of the laser line and the laser line is not broken; if the number of its connected components is greater than 1, it indicates that the laser line in the image may be broken.

[0040] If the number of connected components in a preset number of ROI images is greater than 1, the conveyor belt is determined to be damaged, and each connected component region is marked as the damage location. If the number of connected components in any ROI image is not greater than 1, the conveyor belt is determined to be initially undamaged.

[0041] S203. If it is determined that the conveyor belt is initially undamaged, then pixel sampling is performed on each ROI image to obtain a pixel value array, and the conveyor belt is determined to be damaged based on the pixel value array obtained from each ROI image.

[0042] Specifically, for each ROI image, pixel values ​​are sampled to obtain pixel values. The resulting pixel value array is represented in a coordinate system, and the pixels in the coordinate system are traversed to determine the number of consecutive transformations of the pixel value sequence. If the number of consecutive transformations of the pixel value sequence in each coordinate system is not less than a preset number of transformations, the conveyor belt is considered damaged. If the number of consecutive transformations of the pixel value sequence in a preset number of coordinate systems is less than the preset number of transformations, the conveyor belt is considered undamaged, and the locations of pixels with consecutive transformations less than the preset number of transformations are marked in the ROI image as the damage locations. The preset number of transformations can be set according to actual needs; optionally, the preset number of transformations is 2.

[0043] S204. If the conveyor belt damage is determined based on the number of connected components or the pixel value array, the damage location is located and an alarm signal containing the damage location is sent.

[0044] Specifically, when conveyor belt damage is detected, a stop control signal can be issued to stop the conveyor belt and an alarm signal can be sent, which includes a ROI image marking the location of the damage.

[0045] This embodiment utilizes a camera device to acquire images of the conveyor belt. By combining image preprocessing, ROI extraction, connected component analysis, and pixel value sampling, it achieves intelligent monitoring and location of conveyor belt damage. It can efficiently and accurately detect surface damage on the conveyor belt, and the reliability of damage judgment is improved by dual verification through the changes in the number of connected components and pixel value sequences. At the same time, by locating the damage location and sending alarm signals, it achieves real-time monitoring and rapid response, effectively reducing the cost of manual inspection and improving the safety and stability of conveyor belt operation.

[0046] One possible implementation of this application embodiment involves preprocessing a conveyor belt image to obtain a preset number of ROI images, including: The conveyor belt image is sequentially subjected to channel separation, binarization, and dilatation erosion operations to obtain an intermediate image containing a preset number of laser lines. Perform connected component analysis on the intermediate images to obtain a preset number of mask images; Generate a preset number of ROI images based on the conveyor belt image and a preset number of mask images.

[0047] In this embodiment, channel separation includes separating the conveyor belt image into three channels: RGB, and extracting the red channel (R). Binarization converts the channel-separated image into a binary image; after binarization, the laser line areas are white, and the background is black. Dilation and erosion are used to remove noise, resulting in an intermediate image containing a predetermined number of clear laser lines. Connected component analysis is then performed on the intermediate image to extract each laser line and form a new mask image. See also... Figure 5 It displays three mask images.

[0048] Furthermore, each mask image is aligned with the conveyor belt image. The white areas (laser lines) of the mask image are retained in the conveyor belt image, while other areas are turned black. This extraction yields a Region of Interest (ROI) image. Following the same process, each mask image is used as a template to extract the corresponding region from the conveyor belt image, resulting in a predetermined number of ROI images.

[0049] This embodiment effectively extracts the laser line region and removes noise interference by performing preprocessing operations such as channel separation, binarization, and dilatation erosion on the conveyor belt image. Through connected component analysis and mask image generation, it achieves accurate positioning and segmentation of the laser line region, thereby generating a high-quality ROI image.

[0050] One possible implementation of this application embodiment involves performing connected component analysis on intermediate images to obtain a preset number of mask images, including: Identify the light stripes representing laser lines in the intermediate image, and add target pixels above and below each laser line's light stripe to represent the region where the laser line is located; Extract the area containing each laser line to generate a preset number of Mask images.

[0051] In this embodiment, the conveyor belt image is subjected to dilatational erosion during image preprocessing. Each laser line in the intermediate image is dilatated into a light stripe. The number of target pixels is determined according to the actual situation to ensure that the area where the laser line is formed can completely contain the light stripe formed by the laser line in the intermediate image after image processing. Optionally, the number of target pixels is 20.

[0052] Create three blank mask images and initialize them to 0 (black). For each blank mask image, align it with the middle image. Select the area containing a laser line in the middle image. In the blank mask image, set the pixel value of the area containing the laser line to 255 (white), while keeping the other areas at 0 (black). This will result in one mask image. Repeat the above process to obtain three mask images corresponding to the three laser line areas.

[0053] This embodiment can accurately extract the effective area of ​​each laser line by expanding the target pixel vertically and horizontally along the light stripe of each laser line.

[0054] One possible implementation of this application embodiment involves performing image enhancement processing on each ROI image, including: For each ROI image, channel separation, binarization, dilation and erosion, and small connected component removal are performed sequentially. The small connected component removal involves setting an area threshold and removing connected regions in the image with an area smaller than the threshold. This effectively removes noisy small regions in the image while preserving the main target region. The area threshold can be set based on practical experience.

[0055] This embodiment effectively enhances the contrast of the laser line region and removes noise interference by sequentially performing channel separation, binarization, dilation and erosion, and small connected component removal operations on each ROI image, thus further highlighting the characteristics of the laser line.

[0056] One possible implementation of this application embodiment, determining whether the conveyor belt is damaged based on the number of connected components in each ROI image, includes: Determine whether the number of connected components in each ROI image is greater than one; If the number of connected components in each ROI image is greater than one, then the conveyor belt is determined to be damaged. If any ROI image has a connected component count of no more than one, then the conveyor belt is determined to be initially undamaged.

[0057] Since damaged areas often cause laser lines to break or disperse, thereby increasing the number of connected regions, this embodiment analyzes the number of connected regions in each ROI image to determine whether the conveyor belt is damaged, which can quickly and preliminarily detect whether there are abnormalities on the surface of the conveyor belt.

[0058] One possible implementation of this application embodiment involves sampling pixels in each ROI image to obtain a pixel value array, including: Extract the center line of the laser line in each ROI image; The center line of the laser line is sampled based on a preset step size to obtain an array of pixel values ​​for each ROI image.

[0059] In this embodiment, the HilditchThin algorithm can be used to extract the center line of the laser line in each ROI image. The HilditchThin algorithm is a skeletonization algorithm; after thinning, the center line of the laser line is a single-pixel wide center line. See [link to documentation]. Figure 6 It shows a thinned image after extracting the center line of the laser line from any ROI image.

[0060] Furthermore, the centerline of the laser line in each refined image is sampled based on a preset step size, see [link to relevant documentation]. Figure 7 This diagram shows the sampled image after sampling any refined image. The pixel values ​​of the sampled pixels in each sampled image are arranged in the sampling order into a numerical array as the pixel value array of the corresponding ROI image. The preset step size can be flexibly set according to actual needs. Optionally, the preset step size is 10, which means that sampling is performed once every 10 pixels.

[0061] This embodiment extracts the center line of the laser line in each ROI image and samples pixels based on a preset step size, avoiding image noise and background interference. It can accurately obtain the pixel value array of the laser line, achieving efficient extraction and data quantization of laser line features.

[0062] One possible implementation of this application embodiment involves determining whether the conveyor belt is damaged based on the pixel value array obtained from each ROI image, including: The pixel value array obtained from each ROI image is represented in the corresponding coordinate system, where the horizontal coordinate of the coordinate system represents the sampling point and the vertical coordinate of the coordinate system represents the pixel value of the sampling pixel. Traverse the pixels in each coordinate system to determine the number of consecutive transformations of the pixel value sequence in each coordinate system; If the number of consecutive transformations of the pixel value sequence in each coordinate system is not less than the preset number of transformations, then the conveyor belt is determined to be damaged.

[0063] In this embodiment, for any coordinate system, the horizontal axis represents the sequence number of the sampling point (first sampling point, second sampling point, third sampling point, etc.), and the vertical axis represents the pixel value of the sampling point. Traversing the pixels in the obtained coordinate system, a transformation indicates a change in the difference between the pixel values ​​of two adjacent pixels. The preset number of transformations can be set based on practical experience; optionally, the preset number of transformations is 2.

[0064] This embodiment represents the pixel value array of each ROI image as a sequence in a coordinate system and calculates the number of consecutive transformations of the pixel value sequence, quantifying the change characteristics of pixel values. It can accurately identify irregular fluctuations in the laser line caused by damage, thereby effectively detecting the damage on the conveyor belt surface.

[0065] One possible implementation of this application embodiment, determining the number of consecutive transformations of the pixel value sequence in each coordinate system, includes: For each coordinate system, calculate the difference between the pixel values ​​corresponding to every two adjacent sampling points in the coordinate system to obtain the difference sequence; Compare each difference in the difference sequence with a preset distance, mark the difference in the difference sequence that is greater than the preset distance as 1, and mark the difference in the difference sequence that is not greater than the preset distance as 0; Traverse the marked difference sequence, and take two consecutive differences in the difference sequence that are transformed from 0 to 1 as a difference group. Determine the number of consecutive difference groups in the difference sequence as the number of consecutive transformations of the pixel value sequence in the coordinate system.

[0066] In this embodiment, for any coordinate system, the difference between the pixel values ​​of the preceding and following sampling points in every two adjacent sampling points is calculated, and the resulting differences are arranged into a difference sequence according to the sampling order. The preset distance is set in advance based on practical experience and is used to determine whether the difference between the pixel values ​​of two adjacent points is significant.

[0067] Traverse the difference sequence, marking differences greater than a preset distance as 1 and differences less than or equal to a preset distance as 0, resulting in a marker sequence consisting of 0s and 1s. A difference group represents two consecutive differences from 0 to 1 in the marker sequence. Two difference groups are considered consecutive if the next difference in one group is adjacent to the previous difference in the other group. For example, if the marker sequence is [0, 1, 0, 1, 1, 0], then there are two difference groups [0, 1]. Since these two difference groups are consecutive in the marker sequence, the number of consecutive difference groups is represented as 2.

[0068] This embodiment can accurately capture pixel value mutations caused by conveyor belt damage by identifying continuous difference groups from 0 to 1, thereby improving the sensitivity and accuracy of damage detection.

[0069] Compared to existing mechanical monitoring equipment, this invention requires only two lasers and one camera, along with electronic equipment, to monitor conveyor belt surface damage, significantly reducing equipment costs. By using dual lasers to form laser lines on the conveyor belt, and the camera acquiring images, the detection module analyzes the laser line signals to determine the damage condition. Furthermore, multiple laser lines can be cross-verified, effectively improving detection accuracy. During conveyor belt operation, images can be acquired and processed in real time to quickly determine the conveyor belt's damage status, promptly identify potential safety hazards, and provide sufficient time for enterprises to take appropriate measures. Additionally, by incorporating dustproof glass, a cleaning system, and adjustable laser and camera structures, interference from external environmental factors on the monitoring process is effectively reduced, ensuring stable operation of the monitoring device and improving the reliability of the monitoring results.

[0070] This application provides an electronic device, such as... Figure 8 As shown, Figure 8 The illustrated electronic device 800 includes a processor 801 and a memory 803. The processor 801 and the memory 803 are connected, for example, via a bus 802. Optionally, the electronic device 800 may also include a transceiver 804. It should be noted that in practical applications, the transceiver 804 is not limited to one type, and the structure of this electronic device 800 does not constitute a limitation on the embodiments of this application.

[0071] Processor 801 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 801 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0072] Bus 802 may include a pathway for transmitting information between the aforementioned components. Bus 802 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 802 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0073] The memory 803 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0074] The memory 803 stores the application code that executes the solution of this application, and its execution is controlled by the processor 801. The processor 801 executes the application code stored in the memory 803 to implement the content shown in the aforementioned embodiment of the intelligent monitoring and positioning method for conveyor belt surface damage.

[0075] Figure 8 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0076] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0077] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for intelligent monitoring and locating damage to conveyor belt surfaces, characterized in that, The method, applied to an intelligent monitoring and positioning system for conveyor belt surface damage, includes: Acquire images of a conveyor belt containing a preset number of laser lines captured by a camera device, perform image preprocessing on the conveyor belt images, and obtain the preset number of ROI images; Image enhancement processing and connected component number analysis are performed on each ROI image to obtain the number of connected components in each ROI image, and the conveyor belt is judged to be damaged based on the number of connected components in each ROI image. If it is determined that the conveyor belt is initially undamaged, then pixel sampling is performed on each ROI image to obtain a pixel value array, and the conveyor belt is determined to be damaged based on the pixel value array obtained from each ROI image. If the conveyor belt is determined to be damaged based on the number of connected components or the pixel value array, the location of the damage is located and an alarm signal containing the location of the damage is sent. The step of determining whether the conveyor belt is damaged based on the number of connected components in each ROI image includes: Determine whether the number of connected components in each ROI image is greater than one; If the number of connected components in each ROI image is greater than one, then the conveyor belt is determined to be damaged. If any ROI image has a connected component count of no more than one, then the conveyor belt is determined to be initially undamaged. The step of determining whether the conveyor belt is damaged based on the pixel value array obtained from each ROI image includes: The pixel value array obtained from each ROI image is represented in the corresponding coordinate system, where the horizontal coordinate of the coordinate system represents the sampling point and the vertical coordinate of the coordinate system represents the pixel value of the sampling pixel point. Traverse the pixels in each coordinate system to determine the number of consecutive transformations of the pixel value sequence in each coordinate system; If the number of consecutive transformations of the pixel value sequence in each coordinate system is not less than the preset number of transformations, then the conveyor belt is determined to be damaged. Determining the number of consecutive transformations of the pixel value sequence in each coordinate system includes: For each coordinate system, calculate the difference between the pixel values ​​corresponding to every two adjacent sampling points in the coordinate system to obtain a difference sequence; Each difference in the difference sequence is compared with a preset distance. Differences in the difference sequence that are greater than the preset distance are marked as 1, and differences in the difference sequence that are not greater than the preset distance are marked as 0. Traverse the marked difference sequence, take two consecutive differences in the difference sequence that are transformed from 0 to 1 as a difference group, and determine the number of consecutive difference groups in the difference sequence as the number of consecutive transformations of the pixel value sequence in the coordinate system.

2. The intelligent monitoring and positioning method for conveyor belt surface damage according to claim 1, characterized in that, The step of sampling pixels in each ROI image to obtain a pixel value array includes: Extract the center line of the laser line in each ROI image; The center line of the laser line is sampled based on a preset step size to obtain an array of pixel values ​​for each ROI image.

3. The intelligent monitoring and positioning method for conveyor belt surface damage according to claim 1, characterized in that, The step of preprocessing the conveyor belt image to obtain the preset number of ROI images includes: The conveyor belt image is sequentially subjected to channel separation, binarization, and dilatation erosion operations to obtain an intermediate image containing the preset number of laser lines; Perform connected component analysis on the intermediate images to obtain the preset number of Mask images; Based on the conveyor belt image and the preset number of mask images, generate the preset number of ROI images.

4. The intelligent monitoring and positioning method for conveyor belt surface damage according to claim 3, characterized in that, The process of performing connected component analysis on the intermediate images to obtain the preset number of Mask images includes: Identify the light stripes representing laser lines in the intermediate image, and add target pixels above and below each laser line's light stripe to represent the region where the laser line is located; Extract the region where the laser line is formed by each laser line to generate the preset number of Mask images.

5. The intelligent monitoring and positioning method for conveyor belt surface damage according to claim 1, characterized in that, The image enhancement processing for each ROI image includes: For each ROI image, channel separation, binarization, dilation and erosion, and removal of small connected components are performed sequentially.

6. A smart monitoring and positioning system for conveyor belt surface damage, characterized in that, include: Camera equipment, a predetermined number of lasers and electronic devices; The preset number of lasers are used to emit the preset number of laser lines on the conveyor belt; The camera device is installed below the conveyor belt downstream of the laser, with the shooting direction facing downwards from the conveyor belt, and is used to acquire images of the conveyor belt containing the preset number of laser lines; The electronic device is communicatively connected to the camera device, and the electronic device is configured to perform the intelligent monitoring and positioning method for conveyor belt surface damage as described in any one of claims 1-5.

7. The intelligent monitoring and positioning system for conveyor belt surface damage according to claim 6, characterized in that, The electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the intelligent monitoring and positioning method for conveyor belt surface damage as described in any one of claims 1-5.

Images