General damage detection method, device and equipment of vibrating screen screen cloth and storage medium

Abstract

The application provides a general damage detection method, device and equipment of a vibrating screen mesh and a storage medium. The method utilizes an infrared temperature measurement camera and combines an image processing algorithm to realize real-time detection of the damage of the mesh during operation of the mesh, locate the specific damaged position, and play a role in a closed and lightless place where the mesh works, is not affected by changes in external light, can be used in various industrial environments such as closed and dark places, can be adapted to various mesh apertures, various types and particle sizes of materials, and is suitable for different screening processes and various vibrating screen devices.

Description

Technical Field

[0001] This invention belongs to the technical field of vibrating screen equipment, and particularly relates to a general method, device, equipment and storage medium for detecting damage to vibrating screen mesh. Background Technology

[0002] Fine-particle ultrasonic vibrating screens offer advantages such as a wide range of sieving material types and particle sizes, high screening efficiency, and high screening accuracy. Therefore, vibrating screens are crucial equipment in production processes requiring particle size separation, such as food and pharmaceutical processing and metal powder processing. However, in actual use, due to prolonged exposure to vibration and friction, the screen mesh is typically a component that wears out quickly. If the screen mesh holes are severely worn or even damaged, the screened material particle size will exceed the required standard, resulting in ineffective screening. This not only reduces product quality but also poses potential malfunctions to subsequent processing equipment, reducing production efficiency. Therefore, timely detection of screen wear and damage is essential in practical use.

[0003] Existing methods often involve manual inspection or visible light machine vision for detection.

[0004] Manual inspection methods typically require workers to disassemble the equipment daily and visually inspect the screens for damage. This method suffers from problems such as untimely and inaccurate problem detection, production interruptions due to screen disassembly, and cumbersome and inefficient inspection processes.

[0005] Patent CN114429544A proposes a computer vision-based method for detecting screen damage in vibrating screens. This method involves image segmentation and particle size detection of the ore conveyed on the conveyor belt after screening, thereby indirectly inferring the health of the screen. However, this method requires algorithm adjustments for different materials, particle sizes, and lighting environments, resulting in limited adaptability. Furthermore, it only targets larger particles and cannot detect the particle size of small-diameter powder-level materials. Patent CN206573495U proposes a screen damage detection device based on linear array optical image technology, which similarly uses visible light machine vision to detect the screened material. CN109975301A proposes a screen damage detection method and device for blast furnaces, which also uses visible light machine vision to detect the particle size of the undersize material.

[0006] Patent CN101884977B also indirectly warns of screen damage by detecting the material after screening. Specifically, it determines the size of the material by the angle of the swing arm when the material hits a rotating arm during the transport process. This method can only screen larger particles and is applicable to specific sand and gravel screening scenarios. Patent CN104148282B proposes a vibrating screen and its screen damage detection device, which similarly uses the method of detecting the material hitting the baffle and detecting the angle of the baffle.

[0007] Patent CN102892519B proposes a method and equipment for monitoring wear and repairing separation screens. It sets up a unified optical inspection station for multiple screens, uses a CCD camera to scan and obtain clear images of the screen surface, and uses machine vision algorithms to find damage points. However, this method can only detect damage when the screens are periodically taken out of use, and cannot detect damage in real time during the screening process. It cannot detect damage in a timely manner, which may lead to material loss. In addition, optical detection requires additional stable supplementary lighting.

[0008] It is evident that existing technologies have shortcomings in terms of real-time performance, accuracy, efficiency, cost, and applicability. Summary of the Invention

[0009] Based on this, and in response to the aforementioned technical problems, a general method, apparatus, equipment, and storage medium for detecting damage to vibrating screen mesh is provided.

[0010] The technical solution adopted in this invention is as follows:

[0011] As a first aspect of the present invention, a general method for detecting damage to vibrating screen mesh is provided, characterized in that it includes:

[0012] S101. Obtain a thermal imaging image of the screen surface under vibration using an infrared thermography camera, wherein the imaging range of the camera fully includes the entire screen surface.

[0013] S102. Determine whether there is an area with abnormal infrared signal intensity in the thermal imaging image through an image processing algorithm. If so, it means that the screen is damaged; otherwise, it means that the screen is not damaged.

[0014] Among them, the abnormal infrared signal intensity area refers to the area where the intensity is higher than the infrared signal intensity of the surrounding screen mesh surface.

[0015] S103, Output the detection results.

[0016] As a second aspect of the present invention, a universal damage detection device for vibrating screen mesh is provided, characterized in that it comprises:

[0017] The first module is used to acquire thermal imaging images of the screen surface under vibration using an infrared thermometer camera, wherein the imaging range of the camera fully includes the entire screen surface.

[0018] The second module is used to determine whether there are areas with abnormal infrared signal intensity in the thermal imaging image through image processing algorithms. If so, it means that the screen is damaged; otherwise, it means that the screen is not damaged.

[0019] Among them, the abnormal infrared signal intensity area refers to the area where the intensity is higher than the infrared signal intensity of the surrounding screen mesh surface.

[0020] The third module is used to output the detection results.

[0021] As a third aspect of the present invention, an electronic device is provided, characterized in that it includes a storage module, the storage module including instructions loaded and executed by a processor, the instructions, when executed, causing the processor to perform a general damage detection method for vibrating screen mesh as described in the first aspect above.

[0022] As a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing one or more programs, characterized in that, when the one or more programs are executed by a processor, they implement the general damage detection method for vibrating screen mesh of the first aspect described above.

[0023] This invention utilizes an infrared temperature measurement camera combined with image processing algorithms to detect the damage to the screen in real time during screen operation. It can also function in enclosed, light-free environments where the screen operates, unaffected by changes in external light. It can be used in various industrial environments, including enclosed and low-light conditions, and is compatible with various screen apertures, different types and particle sizes of materials. It is suitable for different screening processes and various vibrating screen equipment.

[0024] Furthermore, this invention is based on the infrared band, thus enabling rapid and accurate identification of minute damaged areas. The detection process is highly automated, reducing manual intervention and improving efficiency. Compared to common methods for detecting the particle size of sieved samples, this invention is less complex, more accurate, and does not require algorithm adjustments based on specific sieved materials and particle sizes. Compared to common visible light machine vision recognition methods, this invention relies on infrared spectral thermography images, eliminating the need for additional illumination and being less susceptible to interference from changes in ambient light. Attached Figure Description

[0025] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments:

[0026] Figure 1 A flowchart of a general damage detection method for vibrating screen mesh provided in an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of a universal damage detection device for vibrating screen mesh provided in an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of an electronic device provided in an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the application environment of an embodiment of the present invention;

[0030] Figure 5(a) is a schematic diagram of the visible light image of the screen according to an embodiment of the present invention. Figure 5 (b) is a schematic diagram of the mask image obtained after morphological dilation of the initial state image acquired before the screen begins to vibrate. Figure 5 (c) is a schematic diagram of the real-time thermal imaging image of the screen. Figure 5 (d) is a schematic diagram of the difference image between the real-time image and the mask. Figure 5 (e) is a schematic diagram of the image after binarization, morphological filtering, and infrared signal intensity anomaly region identification (edge ​​detection, region filtering and merging, etc.) of the above difference image;

[0031] Figure 6 This is a schematic diagram showing the installation position relationship between the infrared temperature measuring camera and the screen in an embodiment of the present invention. Detailed Implementation

[0032] The embodiments of the present invention will be described below with reference to the accompanying drawings. It should be noted that the embodiments described in this specification are not exhaustive and do not represent the only embodiments of the present invention. The corresponding embodiments below are only for clearly illustrating the inventive content of this patent and are not intended to limit its implementation. For those skilled in the art, different variations and modifications can be made based on the embodiments described. Any variations or modifications that fall within the technical concept and inventive content of this invention and are obvious are also within the protection scope of this invention.

[0033] Figure 4 An exemplary application environment of the embodiments of this application is shown, such as Figure 4 As shown, it includes an infrared temperature measuring camera 101, an image processing device 102, and a display device 103. The image processing device 102 is connected to the infrared temperature measuring camera 101 and the display device 103 via wired or wireless means.

[0034] The infrared temperature camera 101 is used to capture thermal images of the screen surface under vibration in real time. It is located above the screen 2 and fixed to the screen frame. The camera is installed as close to perpendicular to the screen surface as possible for image capture. However, an angle θ between the camera's main axis Z and the screen normal N within a certain range will not significantly degrade the detection capability. An angle θ within 60 degrees is recommended. Additionally, the camera's field of view and installation distance should be appropriately selected to ensure that the camera's imaging range completely covers the entire screen surface. See [reference needed]. Figure 6 Specifically, the infrared temperature measurement camera 101 can be an infrared industrial camera or a thermal imaging sensor.

[0035] The image processing device 102 is used to process thermal imaging images to detect damage to the screen. It can be an edge computing device or a cloud computing platform.

[0036] Display device 103 is used to output the detection results and can be a display.

[0037] This application provides a general method for detecting damage to vibrating screen mesh. The principle of this method is as follows: When the screen mesh is intact, the mesh surface can be approximated as a uniform plane. After vibration, the screen surface heats up. Due to its uniformity, according to heat conduction theory, the mesh surface temperature will be almost uniform or have a very small overall temperature gradient, with heat accumulation only near the edges (where it connects to the frame). When the screen mesh is damaged (wear and / or broken), it is equivalent to creating a new "edge" on this uniform mesh surface. Therefore, heat will accumulate at the damaged area, resulting in a significantly higher temperature at the damaged contour than the surrounding mesh surface. This allows for easy and accurate identification of the damaged area in infrared images using various simple methods.

[0038] Based on the above principles, such as Figure 1 As shown, the specific flow of the method in this application embodiment is as follows:

[0039] S101. Obtain thermal imaging images of the screen surface under vibration using an infrared thermometer camera.

[0040] In this embodiment, the infrared temperature measurement camera selected is a Hikvision infrared industrial camera.

[0041] S102. Using image processing algorithms, identify and determine whether there are areas with abnormal infrared signal intensity in the thermal imaging image. If so, it means the screen is damaged; otherwise, it means the screen is not damaged.

[0042] If the screen is damaged, further determine the location of the damaged area in the thermal imaging image, and determine the degree of damage based on the size of the damaged area.

[0043] For a single pixel, an infrared thermography camera collects the intensity of the infrared signal at that pixel and outputs a numerical value. Of course, some infrared thermography cameras will perform a further processing to output a temperature value. Regardless of the output value, the resulting thermal image can characterize the intensity of the infrared signal at each pixel. Therefore, based on the above principle, when the screen is damaged, areas with higher actual screen temperatures will appear as areas with higher infrared signal intensity in the thermal image. Thus, the intensity of the infrared signal at the damaged area will be higher than the infrared signal intensity of the surrounding screen surface. We define these areas as areas with abnormal infrared signal intensity.

[0044] To improve detection accuracy, the image processing device 102 can first preprocess the thermal imaging image to remove background noise (remove background, suppress noise) and enhance the features of the damaged area. For example, it can filter out the edge heating area at the connection between the screen and the frame, and filter out the material heating area that changes over time. In this embodiment, preprocessing methods such as mask subtraction, binarization and morphological filtering are used.

[0045] Among them, the image processing algorithms are traditional vision algorithms (based on image processing technology and mathematical modeling, without relying on a large amount of data), machine learning vision algorithms, or deep learning vision algorithms.

[0046] The following explanation uses a simple traditional vision algorithm as an example to illustrate step S102.

[0047] First, when the vibrating screen is not started, an infrared temperature measuring camera captures images of the screen mesh. After morphological expansion, a mask image is obtained and stored. Due to the different materials, the image can clearly distinguish the screen mesh surface from the frame that fixes the screen mesh. Figure 5 (a) shows a visible light image of the sieve, revealing multiple damaged areas. Figure 5 (b) is the image obtained by morphological dilation of the initial state image acquired before the screen starts to vibrate, which is used as a mask.

[0048] Then, during the operation of the screen, thermal imaging images are acquired in real time, and preprocessing such as mask subtraction (subtracting the thermal imaging image from the mask image), binarization, and morphological filtering are performed on the thermal imaging images. Mask subtraction can exclude areas in the image that belong to the frame rather than the mesh surface, thereby avoiding false detections caused by different temperatures in the frame parts. Figure 5 (c) is a real-time thermal imaging image of the sieve. Figure 5 (d) is the difference image between the real-time image and the mask.

[0049] Next, edge extraction is performed on the preprocessed thermal imaging image using an edge detection algorithm to find the contours of areas with abnormal infrared signal intensity. Figure 5 As shown in (d), the color of the abnormal infrared signal intensity region is significantly different from the background color, such as abnormal region a. Therefore, it can be extracted using an edge detection algorithm. At this time, there are likely to be many contour targets, so some methods are needed for screening. For example, by combining multiple consecutive frames of images, we can screen out contours that have always existed and use them as abnormal infrared signal intensity regions. For some dense small contour regions, we can merge them into a whole damaged region under certain rules.

[0050] Figure 5 (e) is for the above Figure 5 (d) is the image after binarization, morphological filtering, and infrared signal intensity anomaly region identification (edge ​​detection, contour filtering and merging, etc.) of the difference image.

[0051] S103, Output the detection results.

[0052] In this embodiment, if the screen is damaged, a thermal imaging image is output to the display, and the location and degree of damage of each damaged area are marked in the thermal imaging image. The location of the damaged area can be represented by a detection box, such as... Figure 5 As shown in (e), multiple rectangular detection boxes can be seen. Simultaneously, alarms are triggered, such as audible and visual alarms. The degree of damage can be clearly indicated to a human, for example, by using lines / areas of different colors or different shades of the same color to mark the outline of the damaged area on the image. Conversely, if the screen is undamaged, an "undamaged" message is displayed.

[0053] As can be seen from the above, the universal damage detection method for vibrating screen mesh provided in this application embodiment utilizes an infrared temperature measuring camera combined with image processing algorithms to detect the damage of the screen mesh in real time during screen operation and locate the specific damaged location. At the same time, it can function in enclosed, light-free environments where the screen mesh is in operation, and is not affected by changes in external light. It can be used in various industrial environments such as enclosed and low-light environments, and can also be adapted to various screen mesh apertures, various types and particle sizes of materials, and is suitable for different screening processes and various vibrating screen equipment.

[0054] Furthermore, this application is based on the infrared band, thus enabling rapid and accurate identification of minute damaged areas. The detection process is highly automated, reducing manual intervention and improving efficiency. Compared to common methods for detecting the particle size of sieved samples, this application is less complex, more accurate, and does not require algorithm adjustments based on specific sieved materials and particle sizes. Compared to common visible light machine vision recognition methods, this application relies on infrared spectral thermography images, eliminating the need for additional illumination and being less susceptible to interference from changes in ambient light.

[0055] The following describes in detail one or more embodiments of the universal damage detection device for vibrating screen mesh of the present invention. Those skilled in the art will understand that these devices can be configured using commercially available hardware components through the steps taught in this solution. Figure 2 This invention illustrates a universal damage detection device for vibrating screen mesh provided by an embodiment of the present invention, such as... Figure 2 As shown, the device includes a first module 11, a first module 12, and a third module 13.

[0056] The first module 11 is used in S101 to acquire thermal imaging images of the screen surface under vibration through an infrared temperature measurement camera.

[0057] In this embodiment, the infrared temperature measurement camera selected is a Hikvision infrared industrial camera.

[0058] The second module 12 is used in S102 to identify and determine whether there are areas with abnormal infrared signal intensity in the thermal imaging image through image processing algorithms. If so, it means that the screen is damaged; otherwise, it means that the screen is not damaged.

[0059] If the screen is damaged, further determine the location of the damaged area in the thermal imaging image, and determine the degree of damage based on the size of the damaged area.

[0060] For a single pixel, an infrared thermography camera collects the intensity of the infrared signal at that pixel and outputs a numerical value. Of course, some infrared thermography cameras will perform a further processing to output a temperature value. Regardless of the output value, the resulting thermal image can characterize the intensity of the infrared signal at each pixel. Therefore, based on the above principle, when the screen is damaged, areas with higher actual screen temperatures will appear as areas with higher infrared signal intensity in the thermal image. Thus, the intensity of the infrared signal at the damaged area will be higher than the infrared signal intensity of the surrounding screen surface. We define these areas as areas with abnormal infrared signal intensity.

[0061] To improve detection accuracy, the image processing device 102 can first preprocess the thermal imaging image to remove background noise (remove background, suppress noise) and enhance the features of the damaged area. For example, it can filter out the edge heating area at the connection between the screen and the frame, and filter out the material heating area that changes over time. In this embodiment, preprocessing methods such as mask subtraction, binarization and morphological filtering are used.

[0062] Among them, the image processing algorithms are traditional vision algorithms (based on image processing technology and mathematical modeling, without relying on a large amount of data), machine learning vision algorithms, or deep learning vision algorithms.

[0063] The following explanation uses a simple traditional vision algorithm as an example to illustrate step S102.

[0064] First, when the vibrating screen is not started, an infrared temperature measuring camera captures images of the screen mesh. After morphological expansion, a mask image is obtained and stored. Due to the different materials, the image can clearly distinguish the screen mesh surface from the frame that fixes the screen mesh. Figure 5 (a) shows a visible light image of the sieve, revealing multiple damaged areas. Figure 5 (b) is the image obtained by morphological dilation of the initial state image acquired before the screen starts to vibrate, which is used as a mask.

[0065] Then, during the operation of the screen, thermal imaging images are acquired in real time, and preprocessing such as mask subtraction (subtracting the thermal imaging image from the mask image), binarization, and morphological filtering are performed on the thermal imaging images. Mask subtraction can exclude areas in the image that belong to the frame rather than the mesh surface, thereby avoiding false detections caused by different temperatures in the frame parts. Figure 5 (c) is a real-time thermal imaging image of the sieve. Figure 5 (d) is the difference image between the real-time image and the mask.

[0066] Next, edge extraction is performed on the preprocessed thermal imaging image using an edge detection algorithm to find the contours of areas with abnormal infrared signal intensity. Figure 5 As shown in (d), the color of the abnormal infrared signal intensity region is significantly different from the background color, such as abnormal region a. Therefore, it can be extracted using an edge detection algorithm. At this time, there are likely to be many contour targets, so some methods are needed for screening. For example, by combining multiple consecutive frames of images, we can screen out contours that have always existed and use them as abnormal infrared signal intensity regions. For some dense small contour regions, we can merge them into a whole damaged region under certain rules.

[0067] Figure 5 (e) is for the above Figure 5 (d) is the image after binarization, morphological filtering, and infrared signal intensity anomaly region identification (edge ​​detection, contour filtering and merging, etc.) of the difference image.

[0068] The third module 13 is used for S103 and outputs the detection results.

[0069] In this embodiment, if the screen is damaged, a thermal imaging image is output to the display, and the location and degree of damage of each damaged area are marked in the thermal imaging image. The location of the damaged area can be represented by a detection box, such as... Figure 5 As shown in (e), multiple rectangular detection boxes can be seen. Simultaneously, alarms are triggered, such as audible and visual alarms. The degree of damage can be clearly indicated to a human, for example, by using lines / areas of different colors or different shades of the same color to mark the outline of the damaged area on the image. Conversely, if the screen is undamaged, an "undamaged" message is displayed.

[0070] In summary, the universal damage detection device for vibrating screen mesh provided in the above embodiments can perform the universal damage detection method for vibrating screen mesh provided in the foregoing embodiments.

[0071] Similar to the above concept, the above Figure 2 The structure of the universal damage detection device for vibrating screen mesh shown can be implemented as an electronic device. Figure 3 A schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention is shown.

[0072] For example, the electronic device includes a storage module 21 and a processor 22. The storage module 21 includes instructions loaded and executed by the processor 22, which, when executed, cause the processor 22 to perform the steps described in the above section of this specification, "A General Damage Detection Method for Vibrating Screen Mesh," according to various exemplary embodiments of the present invention.

[0073] It should be understood that processor 22 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, the general-purpose processor can be a microprocessor or any conventional processor.

[0074] This invention also provides a computer-readable storage medium that stores one or more programs, which, when executed by a processor, implement the steps described in the above section on a general method for detecting damage to a vibrating screen mesh according to various exemplary embodiments of the invention.

[0075] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer-readable storage media (or non-transitory media) and communication media (or transient media).

[0076] As is known to those skilled in the art, the term computer-readable storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0077] For example, the computer-readable storage medium may be an internal storage unit of the electronic device described in the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., provided on the electronic device.

[0078] The electronic devices and computer-readable storage media provided in the foregoing embodiments, using infrared temperature measurement cameras and combined with image processing algorithms, can detect the damage to the screen in real time during the operation of the screen and locate the specific damaged location. At the same time, they can function in enclosed, light-free environments where the screen operates, and are not affected by changes in external light. They can be used in various industrial environments such as enclosed and low-light environments, and can be adapted to various screen apertures, various types and particle sizes of materials, and are suitable for different screening processes and various vibrating screen equipment.

[0079] Furthermore, this application is based on the infrared band, thus enabling rapid and accurate identification of minute damaged areas. The detection process is highly automated, reducing manual intervention and improving efficiency. Compared to common methods for detecting the particle size of sieved samples, this application is less complex, more accurate, and does not require algorithm adjustments based on specific sieved materials and particle sizes. Compared to common visible light machine vision recognition methods, this application relies on infrared spectral thermography images, eliminating the need for additional illumination and being less susceptible to interference from changes in ambient light.

[0080] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A general method for detecting damage to vibrating screen mesh, characterized in that, include: S101. Obtain a thermal imaging image of the screen surface under vibration using an infrared thermography camera, wherein the imaging range of the camera fully includes the entire screen surface. S102. Determine whether there is an area with abnormal infrared signal intensity in the thermal imaging image through an image processing algorithm. If so, it means that the screen is damaged; otherwise, it means that the screen is not damaged. Among them, the abnormal infrared signal intensity area refers to the area where the intensity is higher than the infrared signal intensity of the surrounding screen mesh surface. S103, Output the detection results.

2. The method for detecting general damage to vibrating screen mesh according to claim 1, characterized in that, The infrared temperature measuring camera is located above the screen and is fixed to the screen frame.

3. The method for detecting general damage to vibrating screen mesh according to claim 1, characterized in that, Also includes: Before S102, the thermal imaging image is preprocessed to remove background noise and enhance the features of the damaged area.

4. The method for detecting general damage to vibrating screen mesh according to claim 1, characterized in that, The image processing algorithm can be a traditional vision algorithm, a machine learning vision algorithm, or a deep learning vision algorithm.

5. The general damage detection method for vibrating screen mesh according to claim 4, characterized in that, S102 further includes: Edge detection algorithms are used to extract edges from thermal imaging images to find the contours of areas with abnormal infrared signal intensity. The outlines are filtered and merged to determine the location of the damaged areas.

6. The method for detecting general damage to vibrating screen mesh according to claim 5, characterized in that, S102 further includes: The extent of damage is determined by the size of the damaged area.

7. The general damage detection method for vibrating screen mesh according to claim 6, characterized in that, S103 further includes: When the screen is damaged, the thermal imaging image is displayed, marking the location and extent of damage of each damaged area, and triggering an alarm.

8. A universal damage detection device for vibrating screen mesh, characterized in that, include: The first module is used to acquire thermal imaging images of the screen surface under vibration using an infrared thermometer camera, wherein the imaging range of the camera fully includes the entire screen surface. The second module is used to determine whether there are areas with abnormal infrared signal intensity in the thermal imaging image through image processing algorithms. If so, it means that the screen is damaged; otherwise, it means that the screen is not damaged. Among them, the abnormal infrared signal intensity area refers to the area where the intensity is higher than the infrared signal intensity of the surrounding screen mesh surface. The third module is used to output the detection results.

9. An electronic device, characterized in that, The device includes a storage module containing instructions loaded and executed by a processor, which, when executed, cause the processor to perform a general damage detection method for vibrating screen mesh according to any one of claims 1-7.

10. A computer-readable storage medium storing one or more programs, characterized in that, When the one or more programs are executed by the processor, they implement the general damage detection method for vibrating screen mesh as described in any one of claims 1-7.

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