Automatic paint make-up control method and device for sound barrier

Abstract

The invention discloses an automatic paint make-up control method and device for a sound barrier, and relates to the field of data processing. The method comprises the following steps: acquiring a sound barrier image, and dividing the sound barrier image into a plurality of detection areas; performing paint surface aging detection on each detection area to obtain paint surface aging degree data of each detection area; according to the paint surface aging degree data of each detection area, determining a to-be-painted area; obtaining three-dimensional size information of the to-be-painted area, and constructing a three-dimensional model of the to-be-painted area in a three-dimensional space according to the three-dimensional size information; according to the three-dimensional model of the area to be painted, a painting make-up path is established, and the painting make-up path comprises a plurality of painting make-up points connected in sequence and three-dimensional coordinates and painting make-up angles corresponding to the painting make-up points; and according to the paint make-up path, the pose, the paint outlet amount and the paint outlet speed of the paint spraying device are adjusted, so that the paint spraying device conducts paint make-up treatment on all the paint make-up points. By implementing the technical scheme provided by the invention, the paint make-up accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to an automatic paint touch-up control method and device for sound barriers. Background Art

[0002] With the acceleration of urbanization and the rapid development of transportation infrastructure, urban noise issues are becoming increasingly prominent. As an effective noise control measure, sound barriers are widely used in a variety of applications, including highways, railways, and urban rail transit. Sound barriers must not only provide excellent sound insulation but also maintain the integrity and aesthetics of their appearance and structure. Therefore, regular maintenance and repainting are essential.

[0003] Currently, the maintenance of sound barriers mainly relies on manual inspection and repainting, which is not only time-consuming and labor-intensive, but also inefficient. Manual operation is highly subjective and may lead to inaccurate paint repairs.

[0004] Therefore, there is an urgent need for an automatic paint touch-up control method and device for sound barriers. Summary of the Invention

[0005] The present application provides an automatic paint touch-up control method and device for a sound barrier, which improves the accuracy of paint touch-up.

[0006] In a first aspect of the present application, a method for automatic paint touch-up control of a sound barrier is provided, the method comprising: acquiring a sound barrier image, and dividing the sound barrier image into a plurality of detection areas; performing paint aging detection on each of the detection areas to obtain paint aging degree data for each of the detection areas; determining an area to be touch-up based on the paint aging degree data for each of the detection areas; acquiring three-dimensional size information of the area to be touch-up, and constructing a three-dimensional model of the area to be touch-up in a three-dimensional space based on the three-dimensional size information; establishing a paint touch-up path based on the three-dimensional model of the area to be touch-up, the paint touch-up path comprising a plurality of sequentially connected paint touch-up points and three-dimensional coordinates and paint touch-up angles corresponding to each of the paint touch-up points; adjusting the posture, paint output and paint output speed of the paint spraying device according to the paint touch-up path, so that the paint spraying device performs paint touch-up processing on each of the paint touch-up points.

[0007] By adopting the above technical solution, by acquiring the image of the sound barrier and dividing the detection area, and performing paint aging detection on each area, the degree of aging of the sound barrier can be accurately assessed and the areas that need to be repainted can be determined. Then, by obtaining the three-dimensional size information of the area to be repainted and constructing a three-dimensional model, the shape and position of the area to be repainted can be accurately represented in the virtual space. Next, a repainting path containing the position and angle information of the repainting points is established based on the three-dimensional model, which can guide the movement and posture control of the paint spraying device. Finally, by adjusting the posture, paint output and speed of the paint spraying device in real time, precise spraying control of each repainting point can be achieved. The entire method realizes the automation, intelligence and refinement of the sound barrier repainting operation, greatly improving the efficiency, accuracy and quality of repainting.

[0008] Optionally, the paint aging detection is performed on each of the detection areas to obtain paint aging degree data for each of the detection areas, specifically including: performing color space conversion on the image of each of the detection areas to obtain HSV images of each of the detection areas, the HSV images including hue component, saturation component and brightness component; extracting the hue component of each of the HSV images, and calculating the histogram data of the hue component, the histogram data being used to reflect the color distribution characteristics of each of the detection areas; calculating the saturation mean and the brightness mean of each of the detection areas to obtain saturation feature data and brightness feature data of each of the detection areas; and inputting the histogram data, saturation feature data and brightness feature data of each of the detection areas into a preset BP neural network model to obtain paint aging degree data for each of the detection areas.

[0009] By adopting the above technical solution, using color space conversion and HSV component analysis, the color distribution characteristics of the detection area can be effectively extracted. By calculating the histogram data of the hue component, the main color components of the detection area and their proportions can be reflected. By calculating the mean of saturation and brightness, the color vividness and brightness level of the detection area can be reflected. By inputting these feature data into the pre-trained BP neural network model, the paint aging patterns and evaluation experience in the sample data can be fully utilized to quickly and accurately predict the aging degree of the detection area. This method overcomes the subjectivity and instability of manual visual inspection, realizes the quantification and automation of the sound barrier aging assessment, and provides a reliable basis for the formulation of subsequent repainting plans.

[0010] Optionally, the three-dimensional size information of the area to be repaired is obtained, and a three-dimensional model of the area to be repaired is constructed in a three-dimensional space according to the three-dimensional size information, specifically including: arranging optical identification dot arrays on the surface of the sound barrier to construct a three-dimensional optical measurement benchmark for the surface of the sound barrier; using a binocular stereo camera to shoot the area to be repaired from multiple perspectives to obtain multiple visual images of the area to be repaired at different angles; identifying and matching the optical identification dot arrays in each of the visual images, and calculating the three-dimensional spatial coordinate transformation relationship of the optical identification dot arrays between each of the visual images; spatially aligning each of the visual images according to the three-dimensional spatial coordinate transformation relationship of the optical identification dot array to obtain three-dimensional surface texture data of the area to be repaired; extracting the contour edge information of the area to be repaired from the three-dimensional surface texture data to obtain a three-dimensional contour curve of the area to be repaired; fitting the three-dimensional surface of the area to be repaired according to the three-dimensional contour curve, and performing gridding processing to obtain the three-dimensional model.

[0011] By adopting the above technical solution and constructing a three-dimensional measurement benchmark using an optical marker dot matrix, a precise and reliable reference can be provided for measuring the spatial dimensions of the area to be repainted. Using a binocular stereo camera with multiple perspectives, multiple visual images of the area to be repainted can be obtained, fully capturing the surface topography of the area. By identifying, matching, and calculating a three-dimensional transformation of the marker dot matrix in the visual image, the spatial correspondence between images from different perspectives can be accurately restored. Leveraging the three-dimensional spatial coordinate transformation of the marker dot matrix, spatial registration of multi-perspective images can be achieved, resulting in a complete and continuous three-dimensional surface texture. Through contour extraction and three-dimensional surface fitting, the precise shape and dimensions of the area to be repainted can be reconstructed in three-dimensional space.

[0012] Optionally, establishing a paint touch-up path based on the three-dimensional model of the area to be touch-up specifically includes: laying out a three-dimensional grid on the three-dimensional model, and dividing the area to be touch-up into multiple grid units based on the three-dimensional grid; calculating the normal vector of each of the grid units as the paint touch-up angle of the corresponding paint touch-up point; establishing topological connection information of the grid units based on the spatial adjacency relationship of each of the grid units; and planning the motion trajectory of the paint touch-up point based on the topological connection information to generate the paint touch-up path.

[0013] By adopting the above technical solution, by laying out the grid on the three-dimensional model, the irregular area to be repainted can be discretized into regular grid units, which is convenient for localized repainting path planning and parameter control. By calculating the normal vector of the grid unit, the orientation and inclination of each unit surface can be obtained, providing a basis for determining the repainting angle. By analyzing the spatial adjacency relationship of the grid units, the topological connection information between the grid units can be established to characterize the overall structure and connectivity of the area to be repainted. Based on the topological connection information, the motion trajectory of the repainting points is planned to obtain a repainting sequence that covers the entire area to be repainted and has the optimal path, thereby reducing the ineffective movement of the paint spraying device and improving the repainting efficiency. This method realizes the three-dimensionalization, gridding and topology of the repainting path planning, thereby adapting to the complex and changeable shape characteristics of the sound barrier surface and realizing differentiated and refined repainting strategies for different areas.

[0014] Optionally, the movement trajectory of the paint touch-up points is planned based on the topological connection information to generate the paint touch-up path, specifically including: determining the center point of each grid unit as the paint touch-up point, and connecting the center points of adjacent grid units in sequence according to the topological connection information to form the movement trajectory of the paint touch-up point; determining the distance threshold between adjacent paint touch-up points according to the size of the grid units; when the distance between the adjacent paint touch-up points is greater than or equal to the distance threshold, inserting additional paint touch-up points between the adjacent paint touch-up points, and updating the movement trajectory of the paint touch-up points to generate the paint touch-up path.

[0015] By adopting the above technical solution, the center point of the grid unit is used as the touch-up point, and the adjacent touch-up points are connected in sequence through topological connection information, forming a motion trajectory covering the entire area to be touch-up, ensuring the continuity and integrity of the touch-up. By determining the distance threshold of adjacent touch-up points through the grid unit size, the density of the distribution of touch-up points can be judged. When the distance between adjacent touch-up points exceeds the distance threshold, an additional touch-up point is inserted between the two points, and the motion trajectory is updated. This touch-up point insertion mechanism can effectively deal with situations such as uneven grid unit size and drastic surface changes, increase the spraying density in sparse touch-up points, avoid blind spots or overspraying, and thus improve the uniformity and consistency of the touch-up. This method fully considers the geometric characteristics and grid division of the area to be touch-up, realizes dynamic optimization and intelligent adjustment of the touch-up path, and further improves the refinement level of the touch-up operation.

[0016] Optionally, adjusting the position, paint output and paint output speed of the paint spraying device according to the paint touch-up path specifically includes: obtaining real-time position information of the paint spraying device, the real-time position information including position information and posture information; controlling the motion mechanism of the paint spraying device based on the three-dimensional coordinates of each paint touch-up point on the paint touch-up path, and adjusting the position of the paint spraying device so that the paint spraying center of the paint spraying device is aligned with the paint touch-up point; controlling the rotation mechanism of the paint spraying device based on the paint touch-up angle of the paint touch-up point, and adjusting the posture of the paint spraying device so that the paint spraying direction of the paint spraying device is consistent with the paint touch-up angle of the paint touch-up point; adjusting the paint output and paint output speed of the paint spraying device according to the aging degree of the paint surface of the area to be touched up, and performing touch-up treatment on each paint touch-up point.

[0017] By adopting the above technical solution, by acquiring the position and posture information of the paint spraying device in real time, its position and posture information in three-dimensional space can be determined, providing dynamic feedback for motion control. Based on the three-dimensional coordinates of the paint touch-up point, by controlling the motion mechanism of the paint spraying device, the paint spraying center can accurately locate the paint touch-up point, ensuring that the paint liquid is accurately sprayed to the target location. Based on the paint touch-up angle of the paint touch-up point, by controlling the rotation mechanism of the paint spraying device, the paint spraying direction can be adjusted to align with the surface normal vector, ensuring that the paint liquid is sprayed vertically and evenly onto the surface, improving the adhesion and flatness of the coating. By adjusting the paint output and speed of the paint spraying device according to the degree of aging of the area to be touched up, customized touch-up effects can be achieved for different areas, increasing the paint layer thickness in severely aged areas and reducing the spraying amount in slightly aged areas, thus avoiding overspraying and waste of resources. Through the coordinated control of multiple parameters and multiple mechanisms of the paint spraying device, this method achieves flexible, precise, and intelligent touch-up operations, maximizing equipment performance and improving touch-up efficiency and quality.

[0018] Optionally, adjusting the paint output amount and paint output speed of the paint spraying device according to the aging degree of the paint surface in the area to be repainted specifically includes: obtaining the paint surface aging degree data of the area to be repainted, and determining the target paint layer thickness of the area to be repainted according to a preset mapping relationship; obtaining technical parameters of the paint spraying device, the technical parameters including the paint output amount and paint spraying diameter per unit time; based on the target paint layer thickness and the paint output, calculating the residence time of the paint spraying device at each paint touch-up point when repainting the each paint touch-up point; calculating the movement speed of the paint spraying device between adjacent paint touch-up points according to the residence time and the distance between adjacent paint touch-up points, to obtain the paint output speed corresponding to each paint touch-up point.

[0019] By adopting the above technical solution, by obtaining the paint aging data of the area to be repainted and determining the target paint layer thickness using a preset mapping relationship, differentiated repainting strategies can be formulated for different aging states, achieving customized and refined repainting. By obtaining technical parameters such as the paint output and spray diameter of the paint spraying device, the performance limits and spraying characteristics of the equipment can be understood, providing data support for process parameter optimization. Based on the target thickness and paint output, by calculating the residence time of the paint spraying device at each repainting point, the paint layer thickness at each location can be precisely controlled to ensure the uniformity and consistency of the repainting.

[0020] In a second aspect of the present application, an automatic paint touch-up control device for a sound barrier is provided, which includes an acquisition module and a processing module, wherein: the acquisition module is used to acquire a sound barrier image and divide the sound barrier image into multiple detection areas; the processing module is used to perform paint aging detection on each of the detection areas to obtain paint aging degree data for each of the detection areas; the processing module is also used to determine the area to be touch-up based on the paint aging degree data of each of the detection areas; the acquisition module is also used to acquire three-dimensional size information of the area to be touch-up, and construct a three-dimensional model of the area to be touch-up in three-dimensional space based on the three-dimensional size information; the processing module is also used to establish a touch-up path based on the three-dimensional model of the area to be touch-up, and the touch-up path includes multiple touch-up points connected in sequence and three-dimensional coordinates and touch-up angles corresponding to each of the touch-up points; the processing module is also used to adjust the posture, paint output and paint output speed of the paint spraying device according to the touch-up path, so that the paint spraying device can touch up each of the touch-up points.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By acquiring an image of the sound barrier and dividing it into detection areas, and conducting paint aging inspections on each area, the degree of aging of the sound barrier can be accurately assessed and the areas that require touch-up can be determined. Then, by acquiring the three-dimensional size information of the area to be touch-up and constructing a three-dimensional model, the shape and position of the area to be touch-up can be accurately represented in virtual space. Next, a touch-up path containing the position and angle information of the touch-up points is established based on the three-dimensional model to guide the movement and posture control of the paint spraying device. Finally, by adjusting the posture, paint output, and speed of the paint spraying device in real time, precise spraying control of each touch-up point can be achieved. The entire method realizes the automation, intelligence, and refinement of the sound barrier touch-up operation, greatly improving the efficiency, accuracy, and quality of touch-up. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of an automatic paint touch-up control method for a sound barrier disclosed in an embodiment of the present application; Figure 2 This is a module schematic diagram of an automatic paint touch-up control device for a sound barrier disclosed in an embodiment of the present application; Figure 3 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0025] Description of the accompanying drawings: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0028] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] This application provides an automatic paint touch-up control method for a sound barrier, referring to Figure 1 , Figure 1 This is a flow chart of an automatic repainting control method for a sound barrier provided by an embodiment of the present application. The method is applied to a repainting robot and includes steps S101 to S106, which are as follows: Step S101: Acquire a sound barrier image, and divide the sound barrier image into multiple detection areas.

[0030] In step S101, the touch-up robot acquires image information of the sound barrier using its onboard image acquisition device. This image acquisition device can be a high-resolution digital camera or industrial camera, capable of clearly capturing detailed information on the sound barrier's surface, such as color, texture, and defects. To obtain a complete image of the sound barrier, the touch-up robot uses a mobile mechanism to move along the length of the barrier, continuously capturing images as it moves, ultimately obtaining a panoramic image of the barrier.

[0031] After acquiring the sound barrier image, the touch-up paint robot needs to divide the image into multiple detection areas so that it can subsequently perform paint aging detection on each detection area. The touch-up paint robot uses a grid division method to divide the sound barrier image. Specifically, the touch-up paint robot first determines the appropriate grid size, such as 50cm×50cm, based on the size information of the sound barrier. Then, the touch-up paint robot nests the grids one by one on the sound barrier image to form a regular grid array. Each grid corresponds to an area on the surface of the sound barrier, which is a detection area. The color of the grid lines can be selected to contrast sharply with the color of the sound barrier surface, such as bright red, to clearly mark the boundaries of each detection area.

[0032] In practice, sound barriers typically range in height from 2 to 5 meters and can be hundreds or even thousands of meters long. For example, a 3-meter-high and 100-meter-long sound barrier, assuming a 50cm x 50cm grid, can be divided into 6 vertical grids and 200 horizontal grids, for a total of 1,200 detection zones. The touch-up robot inspects each of these 1,200 zones for paint degradation and records the degradation data for each zone to inform subsequent touch-up decisions.

[0033] Step S102: performing paint aging detection on each of the detection areas to obtain paint aging degree data of each of the detection areas.

[0034] In step S102, paint aging detection is performed on each of the detection areas to obtain paint aging degree data for each of the detection areas, specifically including: performing color space conversion on the image of each of the detection areas to obtain an HSV image of each of the detection areas, wherein the HSV image includes a hue component, a saturation component, and a lightness component; extracting the hue component of each of the HSV images, and calculating histogram data of the hue component, wherein the histogram data is used to reflect the color distribution characteristics of each of the detection areas; calculating the saturation mean and the lightness mean of each of the detection areas to obtain saturation feature data and lightness feature data of each of the detection areas; and inputting the histogram data, saturation feature data, and lightness feature data of each of the detection areas into a preset BP neural network model to obtain paint aging degree data for each of the detection areas.

[0035] Specifically, the touch-up robot converts the RGB image of the inspection area into the HSV color space. The HSV color space, composed of three components: hue, saturation, and value, is more suitable for describing color properties. Hue represents the type of color, such as red or green; saturation represents the purity of the color; a higher value indicates a more vivid color; and value represents the brightness of the color; a higher value indicates a brighter color. By converting RGB to HSV, the touch-up robot can better analyze the color characteristics of the inspection area.

[0036] Next, the touch-up paint robot extracts the hue component of the HSV image and calculates the histogram data of the hue component. A histogram is a statistical chart used to represent the distribution of data. Here, the hue histogram reflects the distribution characteristics of different colors in the detection area. Specifically, the touch-up paint robot divides the value range of the hue component (0-360 degrees) into several intervals (such as 36 intervals, each interval is 10 degrees), and then counts the number of pixels in each interval to obtain a hue histogram. The shape and distribution of the hue histogram can reflect the color characteristics of the detection area, such as the main color, color diversity, etc.

[0037] In addition to hue information, the touch-up robot also calculates the mean saturation and mean brightness of the inspection area as saturation and brightness feature data. The mean is a statistical indicator that reflects the central tendency of data. The mean saturation indicates the average vividness of the colors within the inspection area, while the mean brightness indicates the average brightness level within the inspection area. These two metrics quantify the color and brightness characteristics of the inspection area, providing a basis for subsequent paint degradation assessment.

[0038] Finally, the paint touch-up robot inputs the hue histogram data, saturation feature data, and brightness feature data into the preset BP neural network model to obtain the paint aging degree data of the detection area. The preset BP neural network is a machine learning model consisting of an input layer, a hidden layer, and an output layer. It can learn the complex mapping relationship between input and output through training. Here, the input data is the color and brightness characteristics of the detection area, and the output data is a quantitative score of the paint aging degree, such as 0-100 points, where the higher the score, the more severe the aging. The preset BP neural network model is trained in advance using a large amount of sample data to learn the correspondence between color and brightness features and the degree of aging. Once the training is completed, the preset BP neural network model can quickly and accurately predict the degree of paint aging based on the new input data.

[0039] Step S103: determining the area to be repainted according to the paint aging degree data of each detection area.

[0040] In step S103, the touch-up robot first analyzes and evaluates the paint aging data for each inspection area. Typically, paint aging can be expressed as a quantitative indicator, such as a scale of 0-100, with higher scores indicating more severe aging. The touch-up robot pre-sets a threshold for aging (e.g., 70) and marks all inspection areas with scores above this threshold as candidate areas for touch-up. These areas have reached a certain level of paint aging severity and require priority for touch-up.

[0041] For example, a repainting robot inspected a 500-meter section of a sound barrier, collecting paint aging data for 1,000 inspection areas. By setting an aging threshold of 70 points, the robot initially selected 200 candidate areas for repainting.

[0042] Step S104: Acquire the three-dimensional size information of the area to be repainted, and construct a three-dimensional model of the area to be repainted in a three-dimensional space according to the three-dimensional size information.

[0043] In step S104, the three-dimensional size information of the area to be repaired is obtained, and a three-dimensional model of the area to be repaired is constructed in three-dimensional space according to the three-dimensional size information, specifically including: arranging optical identification dot arrays on the surface of the sound barrier to construct a three-dimensional optical measurement benchmark for the surface of the sound barrier; using a binocular stereo camera to shoot the area to be repaired from multiple perspectives to obtain multiple visual images of the area to be repaired at different angles; identifying and matching the optical identification dot arrays in each of the visual images, and calculating the three-dimensional spatial coordinate transformation relationship of the optical identification dot arrays between each of the visual images; spatially aligning each of the visual images according to the three-dimensional spatial coordinate transformation relationship of the optical identification dot array to obtain three-dimensional surface texture data of the area to be repaired; extracting the contour edge information of the area to be repaired from the three-dimensional surface texture data to obtain a three-dimensional contour curve of the area to be repaired; fitting the three-dimensional surface of the area to be repaired according to the three-dimensional contour curve, and performing gridding processing to obtain the three-dimensional model.

[0044] Specifically, the touch-up robot obtains the three-dimensional size information of the area to be touched up and constructs a three-dimensional model of the area to be touched up based on this information, providing an accurate spatial geometric basis for subsequent touch-up path planning and spray parameter control. First, the touch-up robot lays out a set of optical marking dots on the surface of the sound barrier to construct a three-dimensional optical measurement benchmark. These optical marking points can be highly reflective dot stickers or projected dot matrices, arranged in a regular grid, covering the entire surface of the sound barrier. Through these marking points, the touch-up robot can establish a three-dimensional coordinate system as a spatial reference for subsequent measurement and modeling.

[0045] Next, the touch-up robot uses a binocular stereo camera to capture images of the area to be repainted from multiple perspectives, acquiring visual images from different angles. This camera, consisting of two parallel cameras, simulates human binocular vision and captures depth information. By varying the camera's position and angle, the robot can capture images from different angles of the area to be repainted, acquiring a series of images that fully capture the area's surface topography and texture details. During the capture process, the robot controls parameters such as the camera's distance, focal length, and exposure to ensure a stable image.

[0046] After acquiring the visual images, the touch-up robot identifies and matches the optical marker arrays within each visual image. Using image processing algorithms such as threshold segmentation, edge detection, and circular fitting, the robot extracts the pixel coordinates of the marker points within each image. The robot then uses stereo matching algorithms, such as epipolar constraints and regional correlation, to locate the corresponding marker points in images from different perspectives and establish a mapping relationship between them. Furthermore, based on the camera's internal and external parameters and binocular vision principles, the robot calculates the coordinates of each marker point in three-dimensional space, deriving the three-dimensional coordinate transformation relationship of the marker array under different perspectives.

[0047] With the three-dimensional spatial coordinate transformation relationship of the marker points, the touch-up robot can spatially register multiple visual images, unifying them into the same three-dimensional coordinate system. Specifically, the touch-up robot uses an image from a certain perspective as a reference and, utilizing the coordinate transformation relationship of the marker points, aligns images from other perspectives with the reference image through transformations such as rotation and translation, ensuring that their positions and orientations in three-dimensional space remain consistent. After spatial registration, the multiple visual images are fused into a complete, coherent three-dimensional surface texture, describing the true appearance of the area to be repainted.

[0048] After obtaining the 3D surface texture data of the area to be repainted, the repainting robot further extracts the contour and edge information of the area. Using algorithms such as image segmentation and edge detection, the robot locates sudden changes in color, brightness, and other characteristics within the surface texture, outlining the boundary of the area to be repainted. This contour information is represented as a 3D curve, describing the shape and extent of the area to be repainted in 3D space.

[0049] Finally, the touch-up robot fits a 3D surface model of the area to be repainted based on the 3D contour curve. Using surface fitting algorithms such as B-spline and NURBS surfaces, the robot can generate a smooth, continuous 3D surface that approximates the actual surface shape of the area to be repainted. To facilitate subsequent path planning and spray control, the robot also meshes the 3D surface, discretizing it into a mesh model composed of multiple small facets, each with specific vertex coordinates and normal vector information.

[0050] For example, a touch-up robot needs to create a 3D model of a 2m x 3m area to be repainted. First, it places a 10x10 optical marker array around the area, with each marker spaced 30cm apart. Then, it uses a binocular stereo camera to capture three images of the area from the front, left, and right. By identifying and matching the markers in the images and calculating their 3D coordinate transformation, the robot registers the three images to the same coordinate system, obtaining a complete 3D surface texture of the area. Next, the robot extracts the contour edges of the surface texture, generating a closed 3D curve that describes the boundary shape of the area. Finally, the robot fits the 3D surface of the area using a B-spline surface and discretizes it into a mesh model consisting of 1,000 triangular meshes. This 3D model accurately depicts the spatial shape and dimensions of the area to be repainted, providing a reliable geometric basis for subsequent repainting operations.

[0051] Step S105: establishing a paint touch-up path according to the three-dimensional model of the area to be touch-up, wherein the paint touch-up path includes a plurality of sequentially connected paint touch-up points and the three-dimensional coordinates and paint touch-up angles corresponding to each of the paint touch-up points.

[0052] In step S105, a paint touch-up path is established based on the three-dimensional model of the area to be touch-up, specifically including: laying out a three-dimensional grid on the three-dimensional model, and dividing the area to be touch-up into multiple grid units according to the three-dimensional grid; calculating the normal vector of each of the grid units as the paint touch-up angle of the corresponding paint touch-up point; establishing topological connection information of the grid units based on the spatial adjacency relationship of each of the grid units; and planning the motion trajectory of the paint touch-up point based on the topological connection information to generate the paint touch-up path.

[0053] Specifically, the touch-up robot plans a touch-up path—the trajectory and posture of the robot's end-user—based on a 3D model of the area to be painted. The touch-up path consists of a series of touch-up points connected in a specific order. Each touch-up point has a specific 3D coordinate and touch-up angle, guiding the robot to spray the area in an orderly, efficient, and comprehensive manner.

[0054] First, the touch-up robot lays out a 3D grid on the 3D model of the area to be touched up. 3D meshing is a spatial partitioning method that divides the surface of a 3D model into multiple small, regular grid cells, such as triangles and quadrilaterals, according to certain rules and scales. The purpose of meshing is to discretize a continuous 3D surface to facilitate numerical calculations and path planning. After obtaining the 3D mesh, the touch-up robot divides the area to be touched up into multiple grid cells. Each grid cell is a small, planar subregion that approximates a portion of the 3D surface. The grid cell boundaries are formed by 3D grid line segments, and adjacent grid cells are connected by shared edges or vertices. The size of the grid cells is determined by factors such as touch-up accuracy and spraying width, ensuring both continuity and uniformity of the touch-up and efficiency. For example, if the touch-up accuracy requirement is 1 mm and the spraying width is 50 mm, the grid cell size can be set to 20 mm × 20 mm. This meets the required accuracy while avoiding excessive repetitive spraying.

[0055] Next, the touch-up robot calculates the normal vector of each grid cell as the touch-up angle for the touch-up point corresponding to that grid cell. The normal vector is a unit vector perpendicular to the plane and indicates the orientation of the plane. In three-dimensional space, each grid cell can be represented by the normal vector of the plane in which it is located. The touch-up robot can calculate the normal vector of each grid cell through the vertex coordinates of the grid cell. The touch-up angle is the angle between the spraying direction and the normal vector of the grid cell, which determines the posture of the end of the touch-up robot. Ideally, the touch-up angle should be parallel to the normal vector, that is, perpendicular to the plane of the grid cell, to ensure the uniformity and adhesion of the touch-up paint.

[0056] The paint robot then establishes topological connectivity information for each grid cell based on its spatial adjacency. Topological connectivity reflects the relative position and connectivity between grid cells and is an important basis for path planning. Specifically, the paint robot identifies the boundaries of each grid cell, locates its adjacent grid cells, and records the connectivity relationships between them (such as shared edges and vertices). By traversing all grid cells, the paint robot constructs a complete topological connectivity graph, representing the spatial adjacency and reachability between grid cells. This topological connectivity information can be stored and represented using data structures such as adjacency matrices and adjacency lists.

[0057] Finally, the touch-up robot plans the trajectory of the touch-up points based on the topological connectivity information, generating a touch-up path. A trajectory is a three-dimensional curve formed by connecting a series of touch-up points in a specific order, describing the movement path of the robot's end-point. The goal of planning the touch-up path is to cover the entire area to be painted in the shortest time, with the least energy consumption, and with the highest touch-up quality. The robot uses optimization algorithms (such as genetic algorithms and ant colony algorithms) to search for the optimal sequence and connection of touch-up points. Furthermore, the robot interpolates and smoothes the path between touch-up points to ensure continuity and stability. After these steps, the robot ultimately determines a touch-up path that covers all grid cells and satisfies all constraints.

[0058] For example, a touch-up robot plans a paint path for an ellipsoidal area to be repainted, measuring 2m × 2m × 0.5m. It lays out a 3D grid on a 3D model of the area, setting the grid cell size to 50mm × 50mm based on the required touch-up accuracy and spray width, thus dividing the entire area into 1600 grid cells. The robot then calculates the normal vector for each grid cell and determines the touch-up angles for each of the 1600 touch-up points. The robot then analyzes the spatial adjacency of the grid cells and establishes a topological connection graph for the grid cells. Finally, based on this topological connection graph, the robot uses a genetic algorithm to search for the optimal sequence of touch-up points. Based on kinematic constraints, the robot generates a paint path with a total length of approximately 60m, including the 3D coordinates and touch-up angle information for each of the 1600 touch-up points. This path covers all grid cells, minimizing the robot's unloaded movements and improving both efficiency and quality.

[0059] In one possible implementation, based on the topological connection information, the movement trajectory of the paint touch-up points is planned to generate the paint touch-up path, specifically including: determining the center point of each of the grid units as the paint touch-up point, and connecting the center points of adjacent grid units in sequence according to the topological connection information to form the movement trajectory of the paint touch-up point; determining the distance threshold between adjacent paint touch-up points according to the size of the grid units; when the distance between the adjacent paint touch-up points is greater than or equal to the distance threshold, inserting additional paint touch-up points between the adjacent paint touch-up points, and updating the movement trajectory of the paint touch-up points to generate the paint touch-up path.

[0060] Specifically, the touch-up paint robot determines the center point of each grid unit as the touch-up paint point. The center point of a grid unit is a special position inside the unit, which can usually be calculated using the average value of the coordinates of the grid unit vertices. Selecting the center point as the touch-up paint point has the following technical effects: first, the center point is located at the geometric center of the grid unit and can represent the positional characteristics of the entire unit; second, the distance between the center points of adjacent grid units is relatively close, which is conducive to ensuring the continuity and uniformity of the touch-up paint; third, using the center point as the touch-up paint point can simplify the calculation and control of the motion trajectory. Therefore, the touch-up paint robot traverses all grid units, calculates their center point coordinates, and obtains a set of touch-up paint points equal to the number of grid units.

[0061] Next, the touch-up robot connects the center points of adjacent grid cells in sequence according to certain rules based on the topological connection information of the grid cells, forming a motion trajectory for the touch-up points. Specifically, the touch-up robot starts from a certain grid cell, finds all the grid cells adjacent to it, and selects one of them as the next target cell. The touch-up robot then connects the center points of the current cell and the target cell to form a segment of the motion trajectory. The touch-up robot then uses the target cell as the new current cell and repeats the above process until all grid cells have been visited. When connecting the center points of adjacent grid cells, the touch-up robot can adopt different strategies, such as shortest path priority and contour priority, to balance the efficiency and quality of touch-up. After the above steps, the touch-up robot obtains a motion trajectory formed by connecting the center points of the grid cells in sequence, covering the entire area to be touched up.

[0062] However, simply connecting the center points of the grid cells may not be enough, because the distance between adjacent center points may be large, resulting in discontinuous or uneven paint touch-up. To this end, the touch-up robot determines the distance threshold between adjacent touch-up points based on the size of the grid cells. The distance threshold refers to the maximum allowable distance between adjacent touch-up points. If the distance threshold is exceeded, additional touch-up points need to be inserted between the two points to ensure the continuity and uniformity of the touch-up. The size of the distance threshold is related to factors such as the size of the grid cell, the touch-up accuracy, and the spraying width, and can be set to 1 / 2 to 1 / 3 of the grid cell size. For example, if the size of the grid cell is 50mm×50mm and the spraying width is 30mm, the distance threshold can be set to 20mm, that is, the distance between adjacent touch-up points should not exceed 20mm, otherwise additional touch-up points need to be inserted.

[0063] After determining the distance threshold, the touch-up robot checks the distance between every two adjacent touch-up points on the motion trajectory. If the distance is less than the distance threshold, no additional points need to be inserted between the two touch-up points; if the distance is greater than or equal to the distance threshold, one or more additional touch-up points are inserted between the two touch-up points to meet the distance threshold requirement. The positions of the additional touch-up points can be calculated using methods such as linear interpolation or spline interpolation so that they are evenly distributed between the original touch-up points. After inserting the additional touch-up points, the touch-up robot updates the original motion trajectory and obtains a motion trajectory with more touch-up points and smaller spacing, which is the final touch-up path. This touch-up path not only covers all grid cells, but also the distances between adjacent touch-up points meet the threshold requirements, which can guide the touch-up robot to achieve continuous and uniform touch-up operations.

[0064] For example, a touch-up robot lays out a 10x10 grid on a 1mx1m square area to be repainted, with grid cells measuring 100mmx100mm. The robot first calculates the center points of 100 grid cells, generating 100 initial touch-up points. The robot then connects the center points of adjacent grid cells, sequentially from left to right and top to bottom, forming a trajectory. Next, based on the grid cell size and spraying width, the robot determines a 40mm threshold for the distance between adjacent touch-up points. Finally, the robot checks the distance between each adjacent touch-up point on the trajectory and determines that some distances exceed 40mm. The robot then inserts additional touch-up points between these points, reducing the distance between adjacent points to less than 40mm. After inserting these additional points, the number of touch-up points on the trajectory increases to 150, while the spacing is reduced to approximately 30mm. This updated motion trajectory is the final paint touch-up path. The paint touch-up robot densely and evenly covers the entire area to be painted, and can guide the paint touch-up robot to complete the paint touch-up task efficiently and with high quality.

[0065] Step S106: adjusting the position, paint output amount, and paint output speed of the paint spraying device according to the paint touch-up path, so that the paint spraying device performs paint touch-up processing on each of the paint touch-up points.

[0066] In step S106, the posture, paint output and paint output speed of the paint spraying device are adjusted according to the paint touch-up path, specifically including: obtaining real-time posture information of the paint spraying device, the real-time posture information including position information and posture information; based on the three-dimensional coordinates of each paint touch-up point on the paint touch-up path, controlling the motion mechanism of the paint spraying device, adjusting the position of the paint spraying device so that the paint spraying center of the paint spraying device is aligned with the paint touch-up point; based on the paint touch-up angle of the paint touch-up point, controlling the rotation mechanism of the paint spraying device, adjusting the posture of the paint spraying device so that the paint spraying direction of the paint spraying device is consistent with the paint touch-up angle of the paint touch-up point; adjusting the paint output and paint output speed of the paint spraying device according to the aging degree of the paint surface of the area to be touch-up, and performing touch-up treatment on each paint touch-up point.

[0067] Specifically, the touch-up robot acquires the paint sprayer's posture information in real time, including position and attitude information. Position information represents the paint sprayer's coordinates in three-dimensional space and is typically measured by position sensors on the robot (such as encoders and odometers). Attitude information represents the paint sprayer's orientation and angle and is typically measured by attitude sensors (such as gyroscopes and accelerometers). Real-time posture information reflects the paint sprayer's current motion state and is an important basis for controlling its movement and spraying. By collecting and analyzing this posture data in real time, the touch-up robot can accurately determine the paint sprayer's position and attitude in three-dimensional space, providing feedback for subsequent motion control.

[0068] Next, the touch-up robot controls the paint sprayer's kinematic mechanism based on the 3D coordinates of each touch-up point along the paint path, adjusting its position so that the center of the paint spray is aligned with the current touch-up point. Specifically, the touch-up robot sequentially reads the 3D coordinates of each touch-up point along the paint path and uses these as the target position. The robot then calculates the distance and direction the paint sprayer needs to move based on the current position and target position. Based on the kinematic mechanism's degrees of freedom and range of motion, the robot plans a trajectory from the current position to the target position and controls the kinematic mechanism to follow this trajectory until the center of the paint sprayer aligns with the coordinates of the touch-up point. During this process, the robot monitors the position information in real time and, based on the feedback data, makes real-time adjustments and corrections to the kinematic mechanism to eliminate positional errors and improve positioning accuracy. Through this control, the robot can quickly and accurately move the paint sprayer between touch-up points, providing a stable positioning foundation for touch-up operations.

[0069] After adjusting the position of the paint sprayer, the touch-up robot controls the paint sprayer's rotation mechanism based on the touch-up angle at the touch-up point, adjusting its posture to align the spray direction with the touch-up angle. The touch-up angle refers to the angle between the paint sprayer's central axis and the normal vector of the surface to be touched, which determines the angle at which the paint is sprayed onto the surface. A suitable touch-up angle promotes uniform distribution and adhesion of the paint, improving touch-up quality. The touch-up robot reads the touch-up angle of the current touch-up point from the touch-up path and uses this as the target posture. The touch-up robot then calculates the required rotation angle and direction of the paint sprayer based on real-time posture information fed back by the posture sensor. Based on the rotation mechanism's degrees of freedom and rotation range, the touch-up robot plans a rotation trajectory from the current posture to the target posture and controls the rotation mechanism to follow this trajectory until the paint sprayer's posture aligns with the target touch-up angle. Similarly, during the rotation process, the touch-up robot also monitors posture information in real time and performs real-time feedback control to eliminate posture errors and improve the accuracy of the touch-up angle.

[0070] Finally, the touch-up robot also needs to adjust the paint delivery volume and speed of the paint sprayer based on the paint aging level of the area to be touched up to optimize the touch-up effect. The paint delivery volume refers to the volume of paint liquid sprayed from the paint sprayer per unit time and determines the thickness and coverage of the touch-up paint. The paint delivery speed refers to the speed at which the paint sprayer moves along the touch-up path and determines the spraying time for each touch-up point. Areas with more severe paint aging often require a larger paint delivery volume and a slower delivery speed to achieve a better touch-up effect. Conversely, areas with less severe paint aging can appropriately reduce the paint delivery volume and increase the delivery speed to improve touch-up efficiency. Based on the paint aging data for each inspection area obtained in step S102, the touch-up robot determines the aging level of the area where the current touch-up point is located. The touch-up robot then searches for the corresponding paint delivery volume and speed values ​​from a preset paint delivery volume-aging level comparison table and a paint delivery speed-aging level comparison table and sends these values ​​to the paint liquid control unit and speed control unit of the paint sprayer. The paint liquid control unit adjusts the speed or opening of the paint pump according to the paint output value to control the flow of the paint liquid; the speed control unit adjusts the speed of the motion mechanism according to the paint output speed value to control the movement speed of the paint spraying device.

[0071] For example, a touch-up robot is repainting an area with peeling paint and severe rust. It first uses position and attitude sensors to obtain real-time position information from the paint sprayer. It determines that its current position is 20 cm from the target touch-up point, and that the spray direction and angle differ by 10°. The robot then plans a motion trajectory and a rotation trajectory, controlling the motion and rotation mechanisms to adjust the position and attitude of the paint sprayer within 2 seconds. The robot then checks the paint aging data to determine that the area's aging degree is 80, corresponding to a paint delivery rate of 500 ml / min and a paint delivery speed of 50 mm / s. It transmits these parameters to the paint sprayer's control unit and begins spraying the paint at that touch-up point. The robot then continues along the paint path to the next touch-up point, adjusting the paint delivery rate and speed based on the area's aging degree, and repeats the process until the entire area is fully repainted.

[0072] In one possible embodiment, the paint output amount and paint output speed of the paint spraying device are adjusted according to the aging degree of the paint surface in the area to be repainted, specifically including: obtaining the paint surface aging degree data of the area to be repainted, and determining the target paint layer thickness of the area to be repainted according to a preset mapping relationship; obtaining the technical parameters of the paint spraying device, the technical parameters including the paint output amount and paint spraying diameter per unit time; based on the target paint layer thickness and the paint output, calculating the residence time of the paint spraying device at each paint touch-up point when repainting the paint at each paint touch-up point; calculating the movement speed of the paint spraying device between adjacent paint touch-up points according to the residence time and the distance between adjacent paint touch-up points, and obtaining the paint output speed corresponding to each paint touch-up point.

[0073] Specifically, the touch-up robot obtains paint aging data for the area to be touched up. This aging data, derived from the detection results in step S102, reflects the severity of paint aging at various locations within the area to be touched up. Typically, the aging degree of the paint can be represented by a numerical value ranging from 0 to 100, with a higher value indicating more severe aging. The touch-up robot then correlates this aging data with the spatial location of the area to be touched up, generating a three-dimensional paint aging distribution map.

[0074] The touch-up robot then converts the paint aging data into target paint thickness data based on a preset mapping relationship. The mapping relationship refers to the correspondence between the paint aging degree and the touch-up paint thickness, which can be obtained through an empirical formula or table lookup. For example, a paint aging degree-target paint thickness comparison table can be developed, and corresponding target paint thickness values ​​can be set according to different aging degree intervals. Generally speaking, the higher the degree of aging, the thicker the touch-up paint required to achieve a better repair effect. The touch-up robot obtains the target paint thickness data for each location in the area to be touched up by table lookup or calculation.

[0075] Next, the touch-up robot obtains the technical parameters of the paint spraying device, mainly including the paint output per unit time and the paint spray diameter. The paint output refers to the volume of paint liquid that the paint spraying device can spray per unit time, usually measured in milliliters per second (ml / s) or cubic centimeters per second (cm^3 / s). The paint spray diameter refers to the diameter of the paint mist jet formed by the paint spraying device at a certain distance, usually measured in millimeters (mm). With the target paint layer thickness and paint output data, the touch-up robot can calculate the dwell time of the paint spraying device at each touch-up point. The dwell time refers to the time the paint spraying device continuously sprays at a certain touch-up point, which determines the actual paint layer thickness at that point. The touch-up robot calculates the dwell time of each touch-up point according to the formula: dwell time = target paint layer thickness ÷ paint output. For example, if the target paint layer thickness at a touch-up point is 1mm and the paint output of the paint spraying device is 5ml / s, the dwell time at that point is 0.2s. The paint touch-up robot associates the calculated dwell time data with the position coordinates of the paint touch-up points to form a dwell time distribution map.

[0076] Finally, the touch-up robot calculates the speed of the paint sprayer between adjacent touch-up points based on the dwell time and the distance between them, obtaining the corresponding paint delivery speed for each touch-up point. The paint delivery speed refers to the speed at which the paint sprayer moves along the paint delivery path and determines the continuity and uniformity of the spraying between adjacent touch-up points. The touch-up robot first calculates the distance between adjacent touch-up points, which can be obtained using the Euclidean distance formula for the coordinates of the two points. It then divides this distance by the sum of the dwell times of the two touch-up points to obtain the speed of the paint sprayer between the two points. For example, if the distance between two touch-up points is 100 mm, and their dwell times are 0.2 s and 0.3 s, respectively, the speed of the paint sprayer between the two points is 100 mm ÷ (0.2 s + 0.3 s) = 200 mm / s. The touch-up robot performs this calculation for all pairs of adjacent touch-up points, resulting in a paint delivery speed distribution map.

[0077] During actual touch-up operations, the touch-up robot controls the movement of the paint sprayer along the touch-up path based on the coordinates of the touch-up points. When the paint sprayer reaches a touch-up point, the robot controls the paint sprayer to remain at that point for a specified period of time based on the dwell time at that point, and then sprays the paint at the set amount. The robot then controls the paint sprayer to move to the next touch-up point at a specified speed based on the location of the next touch-up point and the paint delivery speed between the two points. This cycle continues until all touch-up points are completed. By precisely controlling the paint delivery amount and speed at each touch-up point, the touch-up robot can achieve customized touch-up effects at different locations in the area to be touched up. This not only compensates for areas of severe paint aging, but also avoids problems with excessively thick or thin paint layers, significantly improving the quality and efficiency of touch-up.

[0078] Reference Figure 2 The present application also provides an automatic paint touch-up control device for a sound barrier, which is a paint touch-up robot. The paint touch-up robot includes an acquisition module and a processing module, wherein: the acquisition module is used to acquire a sound barrier image and divide the sound barrier image into multiple detection areas; the processing module is used to perform paint aging detection on each of the detection areas to obtain paint aging degree data for each of the detection areas; the processing module is also used to determine the area to be touch-up based on the paint aging degree data of each of the detection areas; the acquisition module is also used to acquire three-dimensional size information of the area to be touch-up and construct a three-dimensional model of the area to be touch-up in three-dimensional space based on the three-dimensional size information; the processing module is also used to establish a paint touch-up path based on the three-dimensional model of the area to be touch-up, and the paint touch-up path includes multiple sequentially connected paint touch-up points and three-dimensional coordinates and paint touch-up angles corresponding to each of the paint touch-up points; the processing module is also used to adjust the posture, paint output and paint output speed of the paint spraying device according to the paint touch-up path, so that the paint spraying device can perform paint touch-up processing on each of the paint touch-up points.

[0079] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0080] This application also provides an electronic device. Figure 3 , Figure 33. This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0081] The communication bus 302 is used to implement the connection and communication between these components.

[0082] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0083] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0084] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0085] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also optionally be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program for an automatic paint touch-up control method for a sound barrier.

[0086] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for an automatic paint touch-up control method for a sound barrier. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0087] The present application further provides a computer-readable storage medium storing instructions, which, when executed by one or more processors 301 , enable the electronic device 300 to perform one or more of the methods described in the above embodiments.

[0088] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0090] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0091] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0092] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0093] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0094] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A method for controlling automatic paint touch-up of a sound barrier, characterized in that: The method comprises: Acquire a sound barrier image, and divide the sound barrier image into multiple detection areas; Performing paint aging testing on each of the detection areas to obtain paint aging degree data for each of the detection areas; Determining the area to be repainted based on the paint aging degree data of each of the detection areas; Acquiring three-dimensional size information of the area to be repainted, and constructing a three-dimensional model of the area to be repainted in three-dimensional space according to the three-dimensional size information; Establishing a touch-up path according to the three-dimensional model of the area to be touch-up, wherein the touch-up path includes a plurality of sequentially connected touch-up points and the three-dimensional coordinates and touch-up angles corresponding to each of the touch-up points; According to the paint touch-up path, the position, paint output amount and paint output speed of the paint spraying device are adjusted so that the paint spraying device can perform paint touch-up processing on each of the paint touch-up points.

2. The method according to claim 1, characterized in that The paint aging test is performed on each of the detection areas to obtain paint aging degree data of each of the detection areas, specifically including: Performing color space conversion on the image of each detection area to obtain an HSV image of each detection area, wherein the HSV image includes a hue component, a saturation component, and a lightness component; Extracting hue components of each of the HSV images and calculating histogram data of the hue components, wherein the histogram data is used to reflect the color distribution characteristics of each of the detection areas; Calculating the saturation mean and the brightness mean of each detection area to obtain saturation feature data and brightness feature data of each detection area; The histogram data, saturation characteristic data and brightness characteristic data of each detection area are input into a preset BP neural network model to obtain the paint aging degree data of each detection area.

3. The method according to claim 1, characterized in that The step of obtaining the three-dimensional size information of the area to be repainted and constructing a three-dimensional model of the area to be repainted in a three-dimensional space according to the three-dimensional size information specifically includes: Arranging an optical marking dot matrix on the surface of the sound barrier to construct a three-dimensional optical measurement benchmark for the surface of the sound barrier; Using a binocular stereo camera to shoot the area to be repainted from multiple perspectives to obtain multiple visual images of the area to be repainted from different angles; Identifying and matching the optical identification dot matrix in each of the visual images, and calculating the three-dimensional space coordinate transformation relationship of the optical identification dot matrix between each of the visual images; Performing spatial registration on each of the visual images according to the three-dimensional spatial coordinate transformation relationship of the optical marking dot matrix to obtain three-dimensional surface texture data of the area to be repainted; Extracting the contour edge information of the area to be repainted from the three-dimensional surface texture data to obtain a three-dimensional contour curve of the area to be repainted; The three-dimensional surface of the area to be repainted is fitted according to the three-dimensional contour curve, and meshing is performed to obtain the three-dimensional model.

4. The method according to claim 1, wherein The step of establishing a paint touch-up path according to the three-dimensional model of the area to be touch-up specifically includes: Arranging a three-dimensional grid on the three-dimensional model, and dividing the area to be repainted into a plurality of grid units according to the three-dimensional grid; Calculating the normal vector of each grid cell as the touch-up angle of the corresponding touch-up point; Establishing topological connection information of the grid cells according to the spatial adjacency relationship of each of the grid cells; Based on the topological connection information, the movement trajectory of the paint touch-up point is planned to generate the paint touch-up path.

5. The method according to claim 4, characterized in that The step of planning the movement trajectory of the touch-up paint point based on the topological connection information and generating the touch-up paint path specifically includes: Determining the center point of each of the grid cells as a touch-up point, and sequentially connecting the center points of adjacent grid cells according to the topological connection information to form a motion trajectory of the touch-up point; Determining a distance threshold between adjacent touch-up paint points based on the size of the grid cells; When the distance between the adjacent paint touch-up points is greater than or equal to the distance threshold, additional paint touch-up points are inserted between the adjacent paint touch-up points, and the motion trajectories of the paint touch-up points are updated to generate the paint touch-up path.

6. The method according to claim 1, characterized in that The adjusting of the position, paint output amount, and paint output speed of the paint spraying device according to the paint touch-up path specifically includes: Acquiring real-time posture information of the paint spraying device, wherein the real-time posture information includes position information and posture information; Based on the three-dimensional coordinates of each paint touch-up point on the paint touch-up path, the motion mechanism of the paint spraying device is controlled to adjust the position of the paint spraying device so that the paint spraying center of the paint spraying device is aligned with the paint touch-up point; Based on the paint touch-up angle of the paint touch-up point, controlling the rotation mechanism of the paint spraying device and adjusting the posture of the paint spraying device so that the paint spraying direction of the paint spraying device is consistent with the paint touch-up angle of the paint touch-up point; According to the aging degree of the paint surface in the area to be repainted, the paint output amount and the paint output speed of the paint spraying device are adjusted, and repainting treatment is performed on each repainting point.

7. The method according to claim 6, characterized in that The step of adjusting the paint output amount and the paint output speed of the paint spraying device according to the aging degree of the paint surface of the area to be repainted specifically includes: Acquiring paint aging degree data of the area to be repainted, and determining a target paint layer thickness of the area to be repainted according to a preset mapping relationship; Obtaining technical parameters of the paint spraying device, wherein the technical parameters include the amount of paint output per unit time and the paint spraying diameter; Based on the target paint layer thickness and the paint output, calculating the residence time of the paint spraying device at each touch-up point when performing a touch-up treatment on each touch-up point; According to the dwell time and the distance between adjacent paint touch-up points, the movement speed of the paint spraying device between adjacent paint touch-up points is calculated to obtain the paint delivery speed corresponding to each of the paint touch-up points.

8. An automatic paint touch-up control device for a sound barrier, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire a sound barrier image and divide the sound barrier image into multiple detection areas; The processing module (202) is used to perform paint aging detection on each of the detection areas to obtain paint aging degree data of each of the detection areas; The processing module (202) is further configured to determine the area to be repainted based on the paint aging degree data of each of the detection areas; The acquisition module (201) is further used to acquire three-dimensional size information of the area to be repainted, and construct a three-dimensional model of the area to be repainted in three-dimensional space according to the three-dimensional size information; The processing module (202) is further configured to establish a paint touch-up path based on the three-dimensional model of the area to be touch-up, wherein the paint touch-up path includes a plurality of sequentially connected paint touch-up points and the three-dimensional coordinates and paint touch-up angles corresponding to each of the paint touch-up points; The processing module (202) is further used to adjust the position, paint output amount and paint output speed of the paint spraying device according to the paint touch-up path, so that the paint spraying device performs paint touch-up processing on each of the paint touch-up points.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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