Large complex workpiece space alignment real-time judgment method, system and device

Abstract

The invention discloses a large complex workpiece space alignment real-time judgment method, system and device, and the method comprises the steps: setting a preset marker at a preset position of a crane, collecting and screening an image of the preset marker in real time, obtaining a clear and complete image of the preset marker, and converting the image, after obtaining the HSV color space image, carrying out noise reduction processing on the HSV color space image to obtain a pre-processed image; extracting V channel information from the preprocessed image through an HSV color space, calculating average brightness, dynamically adjusting a threshold range of a color corresponding to a V channel, segmenting a preset marker region according to the threshold range, and calculating a central region; and calculating the offset relative to the central base line by utilizing the corresponding relation between the actual size of the known preset marker and the pixel size in the image and the preset position information of the central base line to obtain the position information of the main beam and the auxiliary beam corresponding to the preset marker, and judging whether the main beam and the auxiliary beam are aligned or not according to the position information. The method is high in detection precision and high in environmental adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial crane equipment, and in particular to a method, system and device for real-time determination of spatial alignment of large and complex workpieces. Background Art

[0002] In industrial production, especially in scenarios involving the assembly of large crane equipment, such as heavy machinery manufacturing and shipbuilding, the spatial alignment accuracy of large, complex components such as main beams and auxiliary beams is directly related to the equipment's operational safety and service life. Traditional spatial alignment determination technology relies primarily on manual operation, with skilled workers using simple measuring tools such as plumb bobs and levels to perform multi-dimensional measurements. This method has significant technical drawbacks: First, manual measurement inevitably leads to inefficiencies, especially for large workpieces exceeding 30 meters in length and weighing hundreds of tons, where a single full-scale measurement can take as long as 4-6 hours. Second, measurement accuracy is significantly affected by operator experience, with repeated measurement errors of up to ±3mm between workers of varying skill levels. Third, traditional optical measurement tools are prone to visual errors under complex lighting conditions, particularly when strong light sources from the workshop ceiling overlap with reflective workpiece surfaces, leading to significant error amplification. Furthermore, given the complex structures and diverse working environments of large, complex workpieces, traditional methods struggle to achieve real-time, accurate determination.

[0003] With the development of machine vision and image processing technologies, it has become possible to use image acquisition and analysis to determine the spatial alignment of workpieces. However, existing vision-based methods suffer from insufficient detection accuracy and poor adaptability when dealing with large targets in complex lighting conditions and large workpieces. For example, in scenes with significant lighting variations, it is difficult to accurately identify key feature targets, resulting in deviations in alignment determination. Summary of the Invention

[0004] In order to solve some or all of the technical problems existing in the above-mentioned prior art, the present invention provides a method, system and device for real-time determination of spatial alignment of large and complex workpieces, which has the advantages of high detection accuracy, strong environmental adaptability, good real-time performance and high degree of automation. It can effectively solve the problems existing in the existing detection methods and has good application prospects.

[0005] The technical solutions of the present invention are as follows:

[0006] In a first aspect, a real-time determination method for spatial alignment of large and complex workpieces is provided, which is used for alignment between main beams and auxiliary beams of a crane, and the method comprises:

[0007] Setting a preset marker at a preset position of the crane, wherein the preset position includes a first main beam, a second main beam, and two auxiliary beams of the crane, and the preset marker includes a target device;

[0008] Collecting images of the preset marker in real time, screening the collected images, and obtaining clear and complete images of the preset marker;

[0009] The collected image is converted using a color space conversion algorithm to obtain an HSV color space image;

[0010] The HSV color space image is subjected to noise reduction processing using a noise reduction algorithm to obtain a preprocessed image;

[0011] Extract V channel information from the obtained preprocessed image using the HSV color space, calculate the average brightness of the color corresponding to the V channel and dynamically adjust the threshold range of the color corresponding to the V channel, segment the area of ​​the preset marker according to the threshold range, and calculate the central area of ​​the preset marker using a geometric algorithm;

[0012] Using the known correspondence between the actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information, the offset of the preset marker relative to the central baseline is calculated to obtain the position information of the corresponding preset markers on the first main beam, the second main beam and the two auxiliary beams. Based on the obtained position information, it is determined whether the first main beam and the second main beam, and the two auxiliary beams are aligned.

[0013] In some optional implementations, the target device includes a circular target of a specific color.

[0014] In some optional implementations, the specific color includes blue.

[0015] Furthermore, in the above-mentioned real-time determination method for spatial alignment of large and complex workpieces, extracting V channel information from the obtained pre-processed image using the HSV color space, calculating the average brightness of the color corresponding to the V channel, and dynamically adjusting the threshold range of the color corresponding to the V channel include:

[0016] If the average brightness is greater than 110 and less than or equal to 145, set the lower limit of HSV to (87, the value of average brightness - 5, the value of average brightness - 25), the upper limit to (160, 255, 255), and extract the blue area to the mask;

[0017] If the average brightness is greater than 145, set the HSV lower limit to (87, 135, the value of average brightness - 5) and the upper limit to (160, 180, 255);

[0018] If the average brightness does not meet the above conditions, it is represented as low brightness, and the threshold range with an HSV lower limit of (90, 150, 50) and an upper limit of (150, 255, 255) is used.

[0019] Furthermore, in the above-mentioned method for real-time determination of spatial alignment of large and complex workpieces, utilizing the known correspondence between the actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information includes:

[0020] If the offset of each preset marker relative to the central baseline is within the allowable error range, it means that the first main beam is aligned with the second main beam and the two auxiliary beams respectively;

[0021] If the offset of each preset marker relative to the central baseline exceeds the allowable error range, it means that the first main beam, the second main beam and the two auxiliary beams are not aligned with each other. At the same time, based on the offset direction and offset amount, the adjustment direction of the misaligned main beam or auxiliary beam is determined and moved accordingly.

[0022] In a second aspect, a real-time determination system for spatial alignment of large and complex workpieces is provided for aligning the main beams and the secondary beams of a crane. The system comprises:

[0023] an identification module, the identification module being used to set preset markers at preset positions of the crane, wherein the preset positions include a first main beam, a second main beam, and two auxiliary beams of the crane, and the preset markers include target devices;

[0024] An image acquisition and screening module, which is used to acquire images of the preset marker in real time and screen the acquired images to obtain clear and complete images of the preset marker;

[0025] An image conversion module is used to convert the collected image using a color space conversion algorithm to obtain an HSV color space image;

[0026] An image processing module is used to perform noise reduction processing on the HSV color space image using a noise reduction algorithm to obtain a preprocessed image;

[0027] a calculation and analysis module for extracting V channel information from the obtained preprocessed image using the HSV color space, calculating the average brightness of the color corresponding to the V channel and dynamically adjusting the threshold range of the color corresponding to the V channel, segmenting the area of ​​the preset marker according to the threshold range, and calculating the central area of ​​the preset marker using a geometric algorithm;

[0028] A determination module is used to calculate the offset of the preset marker relative to the central baseline by using the known correspondence between the actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information, obtain the position information of the corresponding preset markers on the first main beam, the second main beam and the two auxiliary beams, and determine whether the first main beam and the second main beam, and the two auxiliary beams are aligned based on the obtained position information.

[0029] In a third aspect, a determination device is provided that applies the above-mentioned real-time determination method for spatial alignment of large and complex workpieces, and is used for aligning main beams and auxiliary beams of a crane. The device comprises:

[0030] Preset markers, which are arranged on the main beam and the secondary beam of the crane;

[0031] An image acquisition device is provided on the outside of the crane, wherein the range of images acquired by the image acquisition device completely covers the preset markers on the main beam and the secondary beam, so that clear images can be acquired when the preset markers are at any angle on the crane main beam or the secondary beam;

[0032] A control module is pre-installed with a color space conversion algorithm and a noise reduction algorithm, which are respectively used to convert the image captured by the image acquisition device using the color space conversion algorithm to obtain an HSV color space image, and to perform noise reduction processing on the HSV color space image using the noise reduction algorithm to obtain a preprocessed image. At the same time, the control module can extract V channel information from the obtained preprocessed image through the HSV color space, calculate the average brightness of the color corresponding to the V channel, and dynamically adjust the threshold range of the color corresponding to the V channel. The area of ​​the preset marker is divided according to the threshold range, and the center area of ​​the preset marker is calculated through a geometric algorithm. The corresponding relationship between the known actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information are used to calculate the offset of the preset marker relative to the central baseline, obtain the position information of the corresponding preset markers on the first main beam, the second main beam and the two auxiliary beams, and determine whether the first main beam and the second main beam, and the two auxiliary beams are aligned based on the obtained position information.

[0033] In some optional implementations, the preset marker includes a circular target of a specific color.

[0034] In some optional implementations, the image acquisition device includes one or more of a camera, a digital camera, a scanner, an industrial camera, an image acquisition card, a light source device, and an adjustable image acquisition device.

[0035] Furthermore, in the above-mentioned real-time determination device for spatial alignment of large and complex workpieces, an interactive module is also included, and a display interface is provided on the interactive module so that the actual status of the preset markers on the main beam and the auxiliary beam can be displayed through the display interface, and adjustment buttons for the exposure parameters and gain parameters required by the image acquisition device are provided, so that the user can adjust the parameters of the image acquisition device according to the actual light conditions, and the switch button and working status of the image acquisition device are set to display the alignment status, adjustment direction and actual distance between each main beam and auxiliary beam in real time, providing intuitive information for the operator.

[0036] The main advantages of the technical solution of the present invention are as follows:

[0037] The present invention provides a method, system and device for real-time determination of spatial alignment of large and complex workpieces, which has the advantages of high detection accuracy, strong environmental adaptability, good real-time performance and high degree of automation. It can effectively solve the problems existing in existing detection methods and has good application prospects. Specifically, the traditional method relies on manual operation and has low efficiency. In terms of accuracy and reliability, with the help of circular targets of specific colors and advanced algorithms, the center position and offset of the circle are accurately calculated, which effectively reduces errors and ensures stable and safe operation of the equipment. In the face of complex lighting conditions, the threshold is dynamically adjusted to ensure accurate target identification in different environments, and it has a wide range of applicability. At the same time, the interactive window developed based on MFC intuitively displays various types of information, provides parameter adjustment and switch buttons, which are convenient for operators to flexibly adjust and reduce the difficulty of operation. Compared with traditional alignment determination methods that rely on manual measurement and simple measuring tools, the real-time determination method for spatial alignment of large and complex workpieces of the present invention has obvious advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0039] Figure 1 A schematic flow chart of a method for real-time determination of spatial alignment of large and complex workpieces provided by one embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the structure of a real-time determination system for spatial alignment of large and complex workpieces provided by one embodiment of the present invention;

[0041] Figure 3 A schematic structural diagram of a device for real-time determination of spatial alignment of large and complex workpieces provided by one embodiment of the present invention;

[0042] Description of reference numerals:

[0043] 10. Identification module; 20. Image acquisition and screening module; 30. Image conversion module; 40. Image processing module; 50. Calculation and analysis module and determination module;

[0044] 100. Preset marker; 200. Image acquisition device; 300. Control module; 400. Interaction module; 401. Display interface. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] The following is combined with Figure 1-3 , describes in detail the technical solution provided by the embodiments of the present invention.

[0047] As attached Figure 1 As shown, an embodiment of the present invention provides a real-time determination method for spatial alignment of large and complex workpieces, which is used for alignment between main beams and auxiliary beams of a crane. The method includes the following steps:

[0048] Step S1: setting preset markers at preset positions of the crane, wherein the preset positions include a first main beam, a second main beam, and two auxiliary beams of the crane, and the preset markers include target devices;

[0049] In some optional implementations of this embodiment, the target device includes a circular target of a specific color, preferably the specific color is set to blue.

[0050] Because the target devices have distinct geometric features and are easily identifiable, they are placed at pre-set locations on each beam, serving as targets for subsequent image detection. By placing the target devices on each beam, clear detection markers are provided for image acquisition and processing, facilitating the subsequent extraction and analysis of beam position information. This arrangement, through the use of clear, pre-set markers, allows for targeted image acquisition and processing, avoiding direct detection of complex beam structures, simplifying the detection process, and improving detection feasibility.

[0051] Step S2: collecting images of preset markers in real time, screening the collected images, and obtaining clear and complete images of the preset markers;

[0052] In some optional implementations of this embodiment, the image acquisition device includes one or more of a camera, a digital camera, a scanner, an industrial camera, an image acquisition card, a light source device, and an adjustable image acquisition device.

[0053] In order to capture images clearly, completely and accurately, the image capture device is set on the outside of the crane. It is only necessary to ensure that the range of the image captured by the image capture device completely covers the preset markers on the main beam and auxiliary beam, so that the image can be clearly captured when the preset markers are at any angle on the main beam or auxiliary beam of the crane.

[0054] This setup enables image acquisition devices (such as cameras) to capture image information containing preset markers in real time. Due to factors such as lighting variations and noise in real-world environments, the captured images are inevitably blurry and incomplete. Image filtering algorithms filter captured images based on preset clarity and integrity standards (such as contrast and edge sharpness), removing images that do not meet the requirements while retaining clear and complete images, providing high-quality input data for subsequent image processing.

[0055] This arrangement ensures that the features of the preset markers in the subsequently processed images can be accurately identified and analyzed, avoiding detection errors caused by image quality issues and improving the accuracy and reliability of detection.

[0056] Step S3: converting the acquired image using a color space conversion algorithm to obtain an HSV color space image;

[0057] As is well known, conventional images are RGB images, and the images captured in the embodiments of the present invention are of this type. However, the common RGB color space has certain limitations in image processing, such as being sensitive to changes in lighting. The HSV color space is more consistent with the human visual perception of color and can separate color information from brightness information. Therefore, in the embodiments of the present invention, the RGB image is converted into an HSV image through a color space conversion algorithm. This facilitates the subsequent separate processing and analysis of the image brightness information (V channel), and can better adapt to image detection under different lighting conditions.

[0058] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the HSV color space is briefly described:

[0059] The HSV color model, also known as the Hexcone Model, is a color space created by A.R. Smith in 1978 based on the intuitive properties of color. The HSV color model refers to a subset of visible light within the H, S, and V three-dimensional color space. It encompasses all colors within a given color domain, where H stands for Hue, S for Saturation, and V for Value. This provides a more suitable color space for subsequent noise reduction and threshold segmentation, making brightness-based processing more effective and improving the accuracy of extracting pre-defined marker areas within an image.

[0060] Step S4: performing noise reduction processing on the HSV color space image using a noise reduction algorithm to obtain a preprocessed image;

[0061] Specifically, during the image acquisition and transmission process, various noises (such as Gaussian noise, salt and pepper noise, etc.) will inevitably be introduced, which will interfere with the extraction of the preset marker features. Noise reduction algorithms (such as median filtering, Gaussian filtering, etc.) can remove noise from the image, smooth the image edges, and retain the main features of the preset marker. Therefore, in an embodiment of the present invention, by performing noise reduction processing on the V channel (brightness channel) in the HSV color space, the image quality can be improved without affecting the color information, and the outline of the preset marker can be made clearer, thereby reducing the interference of noise on subsequent image processing, improving the signal-to-noise ratio of the image, and laying the foundation for accurately segmenting the preset marker area.

[0062] Step S5: Extracting V channel information from the obtained pre-processed image using the HSV color space, calculating the average brightness of the color corresponding to the V channel and dynamically adjusting the threshold range of the color corresponding to the V channel, segmenting the area of ​​the preset marker according to the threshold range, and calculating the center area of ​​the preset marker using a geometric algorithm;

[0063] In the real-time determination process of spatial alignment of large and complex workpieces, different lighting conditions will have a significant impact on the color performance of preset markers in the image. If a fixed threshold range is used to segment the preset marker area, when the light intensity changes, the preset marker area will not be accurately extracted, thereby affecting the determination result of the workpiece alignment. Therefore, by calculating the average brightness of the color corresponding to the V channel and dynamically adjusting the threshold range, the purpose is to enable the threshold range to adaptively change according to the lighting conditions of the current image, to ensure that the preset marker area can be accurately segmented under different lighting conditions, and to provide a reliable basis for the subsequent accurate calculation of the central area of ​​the preset marker and the judgment of whether the workpiece is aligned. In some optional implementation methods of the present embodiment, in order to avoid the influence of the above-mentioned lighting and other factors, the accurate extraction of the preset marker is achieved, the V channel information of the obtained preprocessed image is extracted through the HSV color space, the average brightness of the color corresponding to the V channel is calculated, and the threshold range of the color corresponding to the V channel is dynamically adjusted in the following manner:

[0064] If the average brightness is greater than 110 and less than or equal to 145, set the lower limit of HSV to (87, the value of average brightness - 5, the value of average brightness - 25), the upper limit to (160, 255, 255), and extract the blue area to the mask;

[0065] If the average brightness is greater than 145, set the HSV lower limit to (87, 135, the value of average brightness - 5) and the upper limit to (160, 180, 255);

[0066] If the average brightness does not meet the above conditions, it is represented as low brightness, and the threshold range with an HSV lower limit of (90, 150, 50) and an upper limit of (150, 255, 255) is used.

[0067] In the embodiment of the present invention, after the above analysis and description, the V channel represents the brightness information of the image. Calculating the average brightness of the color corresponding to the V channel can quantify the overall brightness of the current image, which serves as an important basis for judging the lighting conditions. This is the significance of average brightness in the embodiment of the present invention.

[0068] Specifically, in an embodiment of the present invention, after extracting the V channel information, the image only contains brightness information. Calculating the average brightness of the color corresponding to the V channel can reflect the overall brightness level of the current image. Since the lighting conditions may change in the actual environment, a fixed threshold range is difficult to adapt to different situations. Therefore, a method of dynamically adjusting the threshold range is adopted to automatically adjust the upper and lower limits of the threshold according to the average brightness, so that the preset marker area can be accurately segmented under different lighting conditions. In an embodiment of the present invention, the preset marker is separated from the background by threshold segmentation to obtain the area where it is located. Then, a geometric algorithm (such as a centroid algorithm, a minimum circumscribed rectangle algorithm, etc.) is used to calculate the central area of ​​the preset marker, so that the central area can accurately represent the position of the preset marker in the image.

[0069] Furthermore, in order to more clearly and completely explain the purpose and reason for setting the dynamic threshold in the embodiment of the present invention, it is described in detail:

[0070] The purpose and reason for the average brightness to be between 87-145 is that when the average brightness is greater than 110 and less than or equal to 145, the light intensity is at a medium level. Under such lighting conditions, the color of the preset marker (taking the blue area as an example) is distributed in the HSV space with a certain regularity. The lower limit of HSV is set to (87, the value of average brightness -5, the value of average brightness -25), and the upper limit is set to (160, 255, 255). This is because as the average brightness changes, the hue (H) of the blue area is relatively stable between 87-160, but the saturation (S) and brightness (V) are affected by the overall lighting. By allowing the lower limit of saturation to decrease by 5 as the average brightness decreases, and the lower limit of brightness to decrease by 25 as the average brightness decreases, it can better fit the color change range of the blue preset marker under medium light, thereby accurately extracting the blue area to the mask.

[0071] The purpose and reason for setting the average brightness to be greater than 145 is that when the average brightness is greater than 145, it indicates strong light intensity. In this case, the color of the preset marker will be brighter and the saturation may be reduced. The HSV lower limit is set to (87, 135, the value of the average brightness - 5), and the upper limit is set to (160, 180, 255). By limiting the saturation upper limit to 180, we avoid color distortion caused by excessive brightness from interfering with region extraction. At the same time, the lower brightness limit is still adjusted according to the average brightness to ensure that the range of the blue preset marker can still be accurately defined in strong light.

[0072] Low Average Brightness: If the average brightness does not meet the above conditions, i.e., in a low-brightness environment, the preset marker color is darker. In this case, a fixed HSV threshold range with a lower limit of (90, 150, 50) and an upper limit of (150, 255, 255) is used. In this embodiment of the present invention, this set of thresholds is derived from a large amount of image data tested in low-brightness environments. It can effectively overcome the impact of low light on the preset marker color and accurately segment the target area.

[0073] The method of dynamically adjusting the threshold in the embodiment of the present invention has at least the following beneficial effects:

[0074] 1. Improved segmentation accuracy: By dynamically adjusting the threshold range, the segmentation of the preset marker area can closely match its actual color performance under different lighting conditions, avoiding missed or false detections caused by lighting changes, and greatly improving the accuracy of the preset marker area extraction. For example, in medium lighting conditions, traditional fixed thresholds may miss the slightly darker edge of the preset marker, but dynamic threshold adjustment can fully extract it.

[0075] 2. Enhanced environmental adaptability: The method of the embodiment of the present invention fully considers the influence of light intensity on image color. No matter it is strong light, medium light or low brightness environment, it can achieve accurate segmentation of the preset marker area through reasonable threshold setting, which significantly enhances the adaptability of the real-time judgment method of spatial alignment of large and complex workpieces in different environments, ensures the reliability and stability of the alignment detection results, and provides a guarantee for high-precision installation and real-time monitoring of large and complex workpieces.

[0076] This configuration improves the adaptability of the method of the present invention to different lighting environments by dynamically adjusting the threshold range, ensuring the accuracy of the preset marker area segmentation. Accurately calculating the center area provides precise position coordinates for subsequent offset calculations, improving detection accuracy.

[0077] Step S6: Using the known correspondence between the actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information, calculate the offset of the preset marker relative to the central baseline, obtain the position information of the corresponding preset markers on the first main beam, the second main beam and the two auxiliary beams, and determine whether the first main beam and the second main beam, and the two auxiliary beams are aligned based on the obtained position information.

[0078] During the installation and use of large, complex workpieces, ensuring precise alignment between key components (such as the crane's main beam and secondary beams) is crucial for ensuring the safe and stable operation of the equipment and achieving its designed performance. In this embodiment of the present invention, by quantifying the offset of each preset marker relative to the central baseline, the alignment of the first and second main beams and the two secondary beams can be accurately determined. Furthermore, when misalignment is detected, the offset information provides clear direction and data support for subsequent adjustments, thereby improving the efficiency and quality of workpiece installation and maintenance and reducing the safety hazards and equipment failure risks caused by component misalignment.

[0079] Specifically, in an embodiment of the present invention, utilizing the known correspondence between the actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information includes:

[0080] If the offset of each preset marker relative to the central baseline is within the allowable error range, it means that the first main beam is aligned with the second main beam and the two auxiliary beams respectively;

[0081] If the offset of each preset marker relative to the central baseline exceeds the allowable error range, it means that the first main beam, the second main beam and the two auxiliary beams are not aligned with each other. At the same time, based on the offset direction and offset amount, the adjustment direction of the misaligned main beam or auxiliary beam is determined and moved accordingly.

[0082] It should be noted that the central baseline is a pre-set ideal alignment reference line based on workpiece design requirements, representing the ideal positional relationship between the beams. The central baseline serves as a reference for determining beam alignment. It is pre-set in the crane's design drawings or actual structure. In the embodiments of this invention, it serves only as a reference and benchmark for determining alignment of the crane's main and secondary beams. The specific configuration of the central baseline is not discussed or explored.

[0083] In addition, it is also conceivable that once the preset marker is determined, its actual size is known and fixed, and the proportional relationship between the pixel size and the actual size in the image (i.e., pixel resolution) can be determined through calibration. In actual application, the coordinates of the central area of ​​the preset marker in the image can be converted into actual physical coordinates in combination with the pixel resolution. By calculating the offset of the physical coordinates of the preset markers on each beam relative to the central baseline, the position information of each beam can be obtained. If the offset of the preset markers on the first main beam and the second main beam relative to the central baseline is within the allowable error range, it means that the two main beams are aligned; similarly, it can be judged whether the two sub-beams are aligned.

[0084] Specifically, in an embodiment of the present invention, the correspondence between the actual size of the preset marker and the pixel size in the image (i.e., pixel resolution) is determined through preliminary system calibration work, which establishes a connection between the image space and the actual physical space. By calculating the coordinates of the central area of ​​the preset marker in the image and converting them into actual physical coordinates using pixel resolution, the actual physical coordinates of the preset marker on each beam are compared with the coordinates of the central baseline, and the difference obtained is the offset. The allowable error range is set to take into account the inevitable minor deviations in the actual manufacturing, installation and testing processes. If the offset is within the allowable error range, it means that the position of the beam is within an acceptable accuracy range, that is, the first main beam is aligned with the second main beam and the two auxiliary beams; otherwise, it is determined to be misaligned.

[0085] When the offset exceeds the allowable error range, the direction of the offset (e.g., horizontally to the left or right, vertically upward or downward, etc.) directly reflects the tendency of the misaligned beam to deviate from its ideal position. The magnitude of the offset reflects the degree of deviation. In embodiments of the present invention, based on this information, combined with the structural characteristics of the beam and the operating principle of the adjustment mechanism, the specific adjustment direction of the misaligned main beam or auxiliary beam can be determined.

[0086] It should also be noted that the allowable error range in the embodiment of the present invention is set according to actual needs, so that when the offset is determined according to the allowable error range, it can ensure that the aligned devices are accurately aligned without affecting the use accuracy of the equipment, while ensuring the safety of the equipment.

[0087] For example, if the preset marker on the first main beam is offset to the right by a certain distance in the horizontal direction relative to the central baseline, in order to achieve alignment, the first main beam needs to be moved to the left by a corresponding distance; it can be understood that the greater the offset, the greater the distance required to be moved, so as to gradually narrow the gap with the ideal position and achieve precise alignment of the beam body.

[0088] Thus, the above-described method of the embodiment of the present invention, through the use of clear offset judgment criteria and allowable error range settings, can quickly and accurately determine whether the first main beam, second main beam, and two auxiliary beams are aligned. This avoids the subjectivity and ambiguity of manual judgment, greatly improving the accuracy and reliability of alignment detection results, and providing a scientific and objective basis for workpiece installation acceptance and routine maintenance. Furthermore, based on the adjustment direction and offset amount, operators can quickly develop targeted adjustment plans, avoiding blind adjustments and significantly improving adjustment efficiency. Furthermore, accurate offset data ensures adjustment accuracy, reduces the number of repeated adjustments, and reduces labor and time costs, facilitating the rapid restoration of workpieces to normal use and ensuring the continuity of production operations. Furthermore, beam misalignment issues can be promptly detected and corrected, effectively avoiding potential risks such as uneven force and increased vibration caused by component misalignment. This significantly improves the safety and stability of large and complex workpiece operations, extends the service life of the equipment, and reduces the probability of equipment failure and safety accidents, thus providing significant economic and safety benefits.

[0089] This configuration allows the present invention to accurately measure the position of each beam by converting pixel coordinates in the image into actual physical coordinates. Furthermore, offset calculations based on the central baseline allow for intuitive assessment of beam alignment, providing an accurate basis for crane installation, commissioning, and maintenance.

[0090] Second, as Figure 2 As shown, an embodiment of the present invention further provides a real-time determination system for spatial alignment of large and complex workpieces, which is used for alignment between main beams and auxiliary beams of a crane. The system includes: an identification module 10, an image acquisition and screening module 20, an image conversion module 30, an image processing module 40, a calculation and analysis module, and a determination module 50, wherein:

[0091] The identification module 10 is used to set a preset marker 100 at a preset position of the crane, wherein the preset position includes the first main beam, the second main beam and two auxiliary beams of the crane, and the preset marker 100 includes a target device;

[0092] The image acquisition and screening module 20 is used to acquire images of the preset marker 100 in real time and screen the acquired images to obtain a clear and complete image of the preset marker 100. The image conversion module 30 is used to convert the acquired images using a color space conversion algorithm to obtain an HSV color space image. The image processing module 40 is used to perform noise reduction processing on the HSV color space image using a noise reduction algorithm to obtain a preprocessed image. The calculation and analysis module is used to extract V channel information from the obtained preprocessed image using the HSV color space, calculate the average brightness of the colors corresponding to the V channel, dynamically adjust the threshold range of the colors corresponding to the V channel, segment the area of ​​the preset marker 100 according to the threshold range, and calculate the center area of ​​the preset marker 100 using a geometric algorithm. The determination module is used to calculate the offset of the preset marker 100 relative to the central baseline using the known correspondence between the actual size of the preset marker 100 and the pixel size in the image and the pre-set central baseline position information, obtain the position information of the preset marker 100 on the first main beam, the second main beam, and the two auxiliary beams, and determine whether the first main beam and the second main beam, and the two auxiliary beams, are aligned based on the obtained position information.

[0093] Thirdly, as Figure 3 As shown, an embodiment of the present invention further provides a determination device applying the above-mentioned real-time determination method for spatial alignment of large and complex workpieces, which is used for alignment between main beams and between auxiliary beams of a crane. The device includes: a preset marker 100, an image acquisition device 200, and a control module 300, wherein:

[0094] The preset markers 100 are set on the main beam and the secondary beam of the crane; the image acquisition device 200 is set on the outside of the crane, and the range of the image acquired by the image acquisition device 200 completely covers the preset markers 100 on the main beam and the secondary beam, so that when the preset markers 100 are at any angle on the main beam or the secondary beam of the crane, the image can be clearly acquired; the control module 300 is pre-set with a color space conversion algorithm and a noise reduction algorithm, which are respectively used to convert the image acquired by the image acquisition device 200 using the color space conversion algorithm to obtain an HSV color space image, and to perform noise reduction processing on the HSV color space image using the noise reduction algorithm to obtain a pre-processed image. At the same time, the control module 300 can convert the obtained image into a color space conversion algorithm and a noise reduction algorithm. The preprocessed image extracts V channel information through the HSV color space, calculates the average brightness of the color corresponding to the V channel and dynamically adjusts the threshold range of the color corresponding to the V channel, divides the area of ​​the preset marker 100 according to the threshold range, and calculates the central area of ​​the preset marker 100 through a geometric algorithm. The known correspondence between the actual size of the preset marker 100 and the pixel size in the image and the pre-set central baseline position information are used to calculate the offset of the preset marker 100 relative to the central baseline, and obtain the position information of the corresponding preset marker 100 on the first main beam, the second main beam and the two auxiliary beams. Based on the obtained position information, it is determined whether the first main beam and the second main beam, and the two auxiliary beams are aligned.

[0095] In some optional implementations of this embodiment, the preset marker 100 includes a circular target of a specific color, preferably a blue circular target.

[0096] Because the target devices have distinct geometric features and are easily identifiable, they are placed at pre-set locations on each beam, serving as targets for subsequent image detection. By placing target devices on each beam, clear detection markers are provided for image acquisition and processing, facilitating the subsequent extraction and analysis of beam position information. This arrangement, through the clear, pre-set markers 100, allows for targeted subsequent image acquisition and processing, avoiding direct detection of complex beam structures, simplifying the detection process, and improving detection feasibility.

[0097] In some optional implementations of this embodiment, the image acquisition device 200 includes one or more of a camera, a digital camera, a scanner, an industrial camera, an image acquisition card, a light source device and an adjustable image acquisition device 200. It can be understood that, in addition to the above-mentioned optional devices, the embodiment of the present invention also includes a control module 300, a light source device and a storage device corresponding to the above-mentioned acquisition device, etc., which are only exemplarily described in the embodiment of the present invention and are not the only limitation thereto.

[0098] In addition, in order to facilitate the user to adjust the parameters of the image acquisition device 200 according to actual lighting conditions, set the switch button and working status of the image acquisition device 200, and display the alignment status, adjustment direction and actual distance between each main beam and auxiliary beam in real time to provide intuitive information for the operator, an interactive module 400 is also provided on a large and complex workpiece spatial alignment real-time judgment device in an embodiment of the present invention. The interactive module 400 is connected to the preset marker 100, the image acquisition device 200 and the control module 300, and a display interface 401 is provided on the interactive module 400, so that the actual status of the preset marker 100 on the main beam and the auxiliary beam can be displayed through the display interface 401, and adjustment buttons for the exposure parameters and gain parameters required by the image acquisition device 200 are provided.

[0099] Therefore, compared with the prior art, the method, system, and device for real-time determination of spatial alignment of large and complex workpieces of the present invention have the following technical effects:

[0100] 1. Improve detection accuracy: Through steps such as color space conversion, noise reduction, dynamic threshold segmentation, and geometric algorithm calculation of the center area, the accuracy of extracting the position information of preset markers is effectively improved, thereby accurately judging the alignment of the beam body.

[0101] 2. Adaptability to different environments: The method of dynamically adjusting the V channel threshold range enables the detection process to adapt to different lighting conditions, improving the environmental adaptability of the method.

[0102] 3. Real-time detection: Real-time image acquisition and processing can obtain real-time beam position information, making it easy to promptly discover and resolve beam alignment issues, thereby improving the safety and reliability of crane operation.

[0103] 4. Automated testing: reduces the workload of manual testing, realizes the automation of the testing process, improves testing efficiency and reduces testing costs.

[0104] In summary, the present invention provides a method, system, and device for real-time spatial alignment determination of large, complex workpieces. These methods offer advantages such as high detection accuracy, strong environmental adaptability, good real-time performance, and a high degree of automation. These methods effectively address the challenges of existing detection methods. For example, the multi-camera visual alignment system proposed in patent ZLCN20181034567.X attempts to address large-scale inspection challenges, but its fixed camera array solution is ill-suited for dynamic inspection of mobile lifting equipment within a workshop. Furthermore, the laser-assisted vision method disclosed in patent US20210063215A1 improves inspection accuracy in some scenarios, but its complex optomechanical integration increases deployment costs by over 300%, and reduces maintenance cycles to one-third of conventional systems. These technical limitations severely limit the practical application value of visual inspection technology in industrial settings. The present invention, however, offers a spatial alignment determination method with strong environmental adaptability, high-precision real-time processing capabilities, and strong scalability, breaking through existing technical bottlenecks and possessing promising application prospects.

[0105] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In addition, "front", "back", "left", "right", "upper" and "lower" in this document are all referenced to the placement states shown in the accompanying drawings.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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. However, 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 invention.

Claims

1. A real-time determination method for spatial alignment of large and complex workpieces, characterized in that: Used for aligning the main beams and the auxiliary beams of a crane, the method includes: Setting a preset marker at a preset position of the crane, wherein the preset position includes a first main beam, a second main beam, and two auxiliary beams of the crane, and the preset marker includes a target device; Collecting images of the preset marker in real time, screening the collected images, and obtaining clear and complete images of the preset marker; The collected image is converted using a color space conversion algorithm to obtain an HSV color space image; The HSV color space image is subjected to noise reduction processing using a noise reduction algorithm to obtain a preprocessed image; Extract V channel information from the obtained preprocessed image using the HSV color space, calculate the average brightness of the color corresponding to the V channel and dynamically adjust the threshold range of the color corresponding to the V channel, segment the area of ​​the preset marker according to the threshold range, and calculate the central area of ​​the preset marker using a geometric algorithm; Using the known correspondence between the actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information, the offset of the preset marker relative to the central baseline is calculated to obtain the position information of the corresponding preset markers on the first main beam, the second main beam and the two auxiliary beams. Based on the obtained position information, it is determined whether the first main beam and the second main beam, and the two auxiliary beams are aligned.

2. A method for real-time determination of spatial alignment of large and complex workpieces according to claim 1, characterized in that: The target device includes a circular target of a specific color.

3. A method for real-time determination of spatial alignment of large and complex workpieces according to claim 2, characterized in that: The specific color includes blue.

4. The method for real-time determination of spatial alignment of large and complex workpieces according to claim 1, characterized in that: Extract the V channel information of the obtained preprocessed image through the HSV color space, calculate the average brightness of the color corresponding to the V channel, and dynamically adjust the threshold range of the color corresponding to the V channel. If the average brightness is greater than 110 and less than or equal to 145, set the lower limit of HSV to (87, the value of average brightness - 5, the value of average brightness - 25), the upper limit to (160, 255, 255), and extract the blue area to the mask; If the average brightness is greater than 145, set the HSV lower limit to (87, 135, the value of average brightness - 5) and the upper limit to (160, 180, 255); If the average brightness does not meet the above conditions, it is represented as low brightness, and the threshold range with an HSV lower limit of (90, 150, 50) and an upper limit of (150, 255, 255) is used.

5. The method for real-time determination of spatial alignment of large and complex workpieces according to claim 1, characterized in that: Using the known correspondence between the actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information includes: If the offset of each preset marker relative to the central baseline is within the allowable error range, it means that the first main beam is aligned with the second main beam and the two auxiliary beams respectively; If the offset of each preset marker relative to the central baseline exceeds the allowable error range, it means that the first main beam, the second main beam and the two auxiliary beams are not aligned with each other. At the same time, based on the offset direction and offset amount, the adjustment direction of the misaligned main beam or auxiliary beam is determined and moved accordingly.

6. A real-time determination system for spatial alignment of large and complex workpieces, characterized by: Used for alignment between crane main beams and between auxiliary beams, the system includes: an identification module, the identification module being used to set preset markers at preset positions of the crane, wherein the preset positions include a first main beam, a second main beam, and two auxiliary beams of the crane, and the preset markers include target devices; An image acquisition and screening module, which is used to acquire images of the preset marker in real time and screen the acquired images to obtain clear and complete images of the preset marker; An image conversion module is used to convert the collected image using a color space conversion algorithm to obtain an HSV color space image; An image processing module is used to perform noise reduction processing on the HSV color space image using a noise reduction algorithm to obtain a preprocessed image; a calculation and analysis module for extracting V channel information from the obtained preprocessed image using the HSV color space, calculating the average brightness of the color corresponding to the V channel and dynamically adjusting the threshold range of the color corresponding to the V channel, segmenting the area of ​​the preset marker according to the threshold range, and calculating the central area of ​​the preset marker using a geometric algorithm; A determination module is used to calculate the offset of the preset marker relative to the central baseline by using the known correspondence between the actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information, obtain the position information of the corresponding preset markers on the first main beam, the second main beam and the two auxiliary beams, and determine whether the first main beam and the second main beam, and the two auxiliary beams are aligned based on the obtained position information.

7. A determination device using the real-time determination method for spatial alignment of large and complex workpieces according to any one of claims 1 to 5, characterized in that: Used for aligning the main beams and the auxiliary beams of a crane, the device includes: Preset markers, which are arranged on the main beam and the secondary beam of the crane; An image acquisition device is provided on the outside of the crane, wherein the range of images acquired by the image acquisition device completely covers the preset markers on the main beam and the secondary beam, so that clear images can be acquired when the preset markers are at any angle on the crane main beam or the secondary beam; A control module is pre-installed with a color space conversion algorithm and a noise reduction algorithm, which are respectively used to convert the image captured by the image acquisition device using the color space conversion algorithm to obtain an HSV color space image, and to perform noise reduction processing on the HSV color space image using the noise reduction algorithm to obtain a preprocessed image. At the same time, the control module can extract V channel information from the obtained preprocessed image through the HSV color space, calculate the average brightness of the color corresponding to the V channel, and dynamically adjust the threshold range of the color corresponding to the V channel. The area of ​​the preset marker is divided according to the threshold range, and the center area of ​​the preset marker is calculated through a geometric algorithm. The corresponding relationship between the known actual size of the preset marker and the pixel size in the image and the pre-set central baseline position information are used to calculate the offset of the preset marker relative to the central baseline, obtain the position information of the corresponding preset markers on the first main beam, the second main beam and the two auxiliary beams, and determine whether the first main beam and the second main beam, and the two auxiliary beams are aligned based on the obtained position information.

8. The device for real-time determination of spatial alignment of large and complex workpieces according to claim 7, characterized in that: The preset marker includes a circular target of a specific color.

9. The device for real-time determination of spatial alignment of large and complex workpieces according to claim 7, characterized in that: The image acquisition device includes one or more of a camera, a digital camera, a scanner, an industrial camera, an image acquisition card, a light source device and an adjustable image acquisition device.

10. A large-scale complex workpiece spatial alignment real-time determination device according to any one of claims 7 to 9, characterized in that: It also includes an interactive module, on which a display interface is provided, so that the actual status of the preset markers on the main beam and the auxiliary beam can be displayed through the display interface, and adjustment buttons for the exposure parameters and gain parameters required by the image acquisition device are provided, so that the user can adjust the parameters of the image acquisition device according to the actual light conditions, and the switch button and working status of the image acquisition device are set, and the alignment status, adjustment direction and actual distance between each main beam and auxiliary beam are displayed in real time, providing intuitive information for the operator.

Images