Method and device for inspecting damage of coating of offshore wind power generation tower

Abstract

The invention discloses an offshore wind power generation tower coating damage inspection method. The method comprises the following steps: acquiring an inspection path of an offshore wind power plant; the wind power generation tower in the offshore wind power plant is inspected along the inspection path of the offshore wind power plant, so that a hyperspectral image of the surface of the wind power generation tower is obtained through airborne hyperspectral imaging equipment; determining the coating exposure condition of the surface of the wind power generation tower according to the hyperspectral image; determining the coating damage condition of the surface of the wind power generation tower according to the coating exposure condition; in this way, based on the characteristic that spectral characteristics of different coatings are greatly different, the coating exposure condition of the surface of the wind power generation tower is determined through the hyperspectral image of the surface of the wind power generation tower, then the coating damage condition of the surface of the offshore wind power generation tower is accurately detected based on the coating exposure condition, and the whole process can be automatically achieved through an unmanned aerial vehicle; manual participation is not needed, and the detection efficiency is high.

Description

Technical Field

[0001] This invention belongs to the field of marine equipment coating inspection technology, specifically relating to a method and device for inspecting coating damage on offshore wind power towers. Background Technology

[0002] Since offshore wind farms are mainly located in near-shore wind energy storage areas, the wind power towers are inevitably subjected to the impact and corrosion of seawater, resulting in coating damage on the surface. If the coating damage is not addressed and treated in a timely manner, it will accumulate and eventually lead to major malfunctions and serious accidents.

[0003] It is worth noting that the current detection of coating damage on the surface of offshore wind turbine towers is generally carried out by inspection personnel using drones to obtain images of the wind turbine tower surface through airborne cameras. Then, the damage to the coating on the surface of the wind turbine tower is determined manually based on the images. However, because the appearance of the multiple layers of coatings is similar, the coating damage is often not easily detected by the human eye, and false detections often occur.

[0004] Accurately detecting coating damage on the surface of offshore wind turbine towers has become an urgent technical problem that needs to be solved, requiring a new method and device for inspecting coating damage on offshore wind turbine towers. Summary of the Invention

[0005] The purpose of this invention is to provide a method for inspecting coating damage on offshore wind power towers. This method uses a drone and includes the following steps:

[0006] S1, Obtain the inspection path of the offshore wind farm; Inspect the wind turbine towers in the offshore wind farm along the inspection path, and obtain hyperspectral images of the surface of the wind turbine towers through airborne hyperspectral imaging equipment.

[0007] S2, Determine the coating exposure status on the surface of the wind turbine tower based on hyperspectral images;

[0008] S3, Determine the coating damage status on the surface of the wind turbine tower based on the exposed coating condition.

[0009] This application provides a method for inspecting coating damage on offshore wind power towers, and also includes the following technical features:

[0010] Preferably, S2 includes establishing a paint spectral curve, measuring spectral curves under different wet conditions, and converting the wet condition to a dry condition;

[0011] Includes the following steps:

[0012] Selecting measurement equipment, such as a spectrophotometer or spectrophotometer, can accurately measure the light reflection and absorption of paint in the visible, ultraviolet, and infrared ranges, i.e., the reflection and absorption ratio. Different equipment has different wavelength resolutions and measurement ranges, so choose the appropriate equipment according to your actual needs.

[0013] Sample preparation: The preparation of paint samples must be consistent with the technical specifications of the wind turbine tower coating to ensure the reliability of the data. Typically, the paint is evenly applied to the test substrate, such as the wind turbine tower, and the thickness should be consistent to avoid deviations in spectral data due to thickness differences. The drying and curing of the samples must reach the specified state to ensure measurement stability.

[0014] Set measurement parameters, and set appropriate wavelength range and step interval according to the type of paint; a visible light range of 380-780nm is necessary; if needed, it can be extended to the ultraviolet or infrared range; record a data point at certain wavelength intervals, such as every 10nm or 20nm.

[0015] To perform spectral measurements, the sample is placed in a spectrometer, illuminated by a scanning light source, and its light reflection or absorption data at various wavelengths are recorded. During the measurement, interference from external stray light is avoided, and the sample is kept in a fixed position throughout the measurement process.

[0016] Record and plot the curves, recording the light reflectance or absorptivity data corresponding to each wavelength, and plot the wavelength-reflectance or wavelength-absorptivity curves; this will give you the spectral curve of the paint.

[0017] Data analysis is used to match the corresponding paint type for the target object based on the shape of the spectral curve.

[0018] In other words, based on the shape of the spectral curve, the color characteristics and absorption features of the paint are analyzed; different paints will have characteristic absorption peaks or reflection peaks at specific wavelengths, and by combining the distribution of these characteristic peaks, the color stability and UV resistance of the paint can be further analyzed.

[0019] Preferably, images of the offshore wind farm are obtained by taking pictures of the offshore wind farm along the wind direction using an airborne camera;

[0020] Based on the tide data, inspection and photography are carried out at low tide. According to the tide conditions, the images of offshore wind farms are inspected at low tide and image recognition is performed to obtain the image size of the wind power generation towers in the offshore wind farm images.

[0021] Preferably, S1 includes image segmentation and extraction of the splash area;

[0022] The splash areas on the power generation tower are identified and extracted using the values ​​of the RGB color channels of the image.

[0023] The splashes on the tower exhibit a distinct red, brown, or rust color due to rust, water stains, or other deposits. These substances have higher red channel values ​​in the RGB image. The splashed areas are separated from the background, and the R channel of the image is extracted separately. A threshold is set, and areas with red values ​​higher than the threshold are marked as potential splashed areas. By setting a range threshold for the red channel, such as red values ​​greater than a certain value, pixels meeting the condition are marked as splashed areas, effectively separating areas with colors close to red, brown, etc. The segmented areas are then optimized through morphological processing, such as erosion and dilation, to accurately delineate the outline of the splashed areas.

[0024] Identify the hyperspectral graphic tower spectral curve, determine the paint type corresponding to the spectral curve of the splash area based on the spectral database, and thus determine which layer it is; calculate the corrosion area, location, and corrosion condition;

[0025] In power generation towers, the splash zone typically refers to the area on the tower structure where water, mud, salt, or pollutants generated by environmental factors accumulate. These pollutants are splashed onto the tower surface by wind, rain, etc., and may have an adverse effect on the corrosion or insulation performance of the structure after long-term accumulation.

[0026] To analyze the extent and characteristics of the splash area, image segmentation was used to identify and extract the region; the specific steps are as follows:

[0027] Image acquisition involves using drones, cameras, and other equipment to capture high-definition images of the power generation tower, ensuring that the splash zone is clearly visible in the images; images are also acquired at different times or under different weather conditions to capture changes in the splash zone.

[0028] Image preprocessing involves denoising and contrast enhancement to improve the saliency of the splash area; for example, edge enhancement can be used to highlight the edges of the tower structure, making the splash area more prominent.

[0029] Segmentation algorithm selection:

[0030] Choose an appropriate image segmentation algorithm based on the characteristics of the splash area: Threshold segmentation: For splashes with obvious colors, segment them according to color or brightness thresholds;

[0031] Edge detection: Using edge detection algorithms, such as Canny edge detection, the splashed area is separated from other areas;

[0032] Deep learning: using convolutional neural networks (such as U-Net and Mask R-CNN) for image segmentation, which is particularly suitable for situations where the splash area has an irregular shape;

[0033] Feature extraction and classification:

[0034] Extract the features of the segmented regions (such as shape, area, and location) and classify the splash types (such as water stains, salt stains, or mud stains); these features help to understand the distribution of splash areas and influencing factors.

[0035] Post-processing and analysis:

[0036] Further analysis of the extracted splash zone, such as corrosion degree estimation and the expansion trend of the splash zone over time, will provide data support for the maintenance and improvement of the power generation tower.

[0037] Preferably, the inspection path for obtaining offshore wind farms includes:

[0038] Images of offshore wind farms are obtained by taking pictures of the offshore wind farm along the wind direction using an airborne camera;

[0039] Image recognition is performed on images of offshore wind farms to obtain the image dimensions of wind turbine towers in the images.

[0040] Sort the wind turbine towers in the offshore wind farm images according to their size from largest to smallest;

[0041] By sequentially connecting the positions of wind turbine towers in the offshore wind farm image according to the sorted order, an inspection path is generated.

[0042] Preferably, determining the coating exposure status on the surface of the wind turbine tower based on hyperspectral images includes:

[0043] Feature extraction is performed on the hyperspectral image to obtain its hyperspectral image features;

[0044] Based on a database storing hyperspectral image features of the surface coatings and building materials of wind turbine towers, the hyperspectral image features of the hyperspectral images are identified to obtain the coating exposure status of the wind turbine tower surface.

[0045] Preferably, feature extraction is performed on the hyperspectral image to obtain the hyperspectral image features, including:

[0046] The hyperspectral image is preprocessed, and features are extracted from the preprocessed hyperspectral image to obtain the hyperspectral image features.

[0047] Image preprocessing includes: brightness equalization based on the CLAHE algorithm, deblurring based on the GCANet algorithm, and restoration based on mathematical morphology algorithms.

[0048] Preferably, the surface coatings of the wind power tower are arranged in order of proximity to the building materials on the wind power tower surface, from closest to furthest: the first coating, the second coating, ..., the Nth coating;

[0049] The extent of coating damage on the surface of wind turbine towers is determined based on the degree of coating exposure, including:

[0050] If the coating exposure situation is that only the Nth coating is exposed on the surface of the wind turbine tower, then it is determined that all coatings on the surface of the wind turbine tower are undamaged.

[0051] If only the Nth and N-1th coatings are exposed on the surface of the wind turbine tower, then the Nth coating on the surface of the wind turbine tower is damaged, and the other coatings are not damaged.

[0052] Proceed in sequence;

[0053] If the coating exposure condition is that all coatings on the surface of the wind turbine tower are exposed, and the building materials are not exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged except for the first layer.

[0054] If the coating is exposed in the case that all coatings on the surface of the wind turbine tower are exposed, and the building materials are also exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged.

[0055] Preferably, during the inspection process, the current remaining battery power and the return route from the current location to the charging point are obtained;

[0056] The flight distance is calculated based on the recharge path, and wind measurement data along the recharge path is obtained;

[0057] The power consumption for flying from the current location to the charging point is calculated based on the flight distance, wind measurement data, and preset flight speed.

[0058] If the difference between the current remaining power and the power consumption is less than or equal to the preset power threshold, the inspection will be paused, the current position will be recorded as the inspection interruption position, and the vehicle will fly along the recharge path at flight speed to the charging point for charging.

[0059] After charging is complete, fly to the inspection interruption point and continue the inspection.

[0060] Preferably, for any wind turbine tower in an offshore wind farm, if the coating on its surface is damaged, its number is recorded.

[0061] The recorded number is sent to the maintenance personnel's user terminal to prompt them to perform coating maintenance on the corresponding wind turbine tower.

[0062] Preferably, embodiments of this disclosure provide a device for inspecting damage to the surface coating of offshore wind power towers, including a drone, comprising:

[0063] The acquisition module is used to acquire the inspection path of offshore wind farms;

[0064] The inspection module is used to inspect the wind turbine towers in the offshore wind farm along the inspection path, so as to obtain hyperspectral images of the surface of the wind turbine towers through airborne hyperspectral imaging equipment.

[0065] The determination module is used to determine the coating exposure status on the surface of wind turbine towers based on hyperspectral images;

[0066] The determination module is also used to determine the extent of coating damage on the surface of wind turbine towers based on the degree of coating exposure.

[0067] Preferably, embodiments of this disclosure provide an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.

[0068] Preferably, embodiments of this disclosure provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the methods described above.

[0069] The beneficial effects of this application are as follows:

[0070] 1. This application provides a method for inspecting coating damage on offshore wind power towers. Based on the significant differences in spectral characteristics of different coatings, the method determines the coating exposure status on the surface of the wind power tower through hyperspectral images, and then accurately detects coating damage on the surface of the offshore wind power tower. The entire process can be automatically achieved by drones without human intervention, resulting in high detection efficiency.

[0071] 2. The UAV acquires the inspection path of the offshore wind farm; the UAV inspects the wind turbine towers in the offshore wind farm along the inspection path, so as to acquire hyperspectral images of the surface of the wind turbine towers through airborne hyperspectral imaging equipment; the exposure of the coating on the surface of the wind turbine towers is determined based on the hyperspectral images; the damage of the coating on the surface of the wind turbine towers is determined based on the exposure of the coating. Attached Figure Description

[0072] Figure 1 A flowchart of a method for detecting damage to the surface coating of offshore wind power towers;

[0073] Figure 2A structural diagram of a device for inspecting damage to the surface coating of an offshore wind power tower.

[0074] Figure 3 This is a structural diagram of an electronic device for inspecting damage to the surface coating of an offshore wind power tower.

[0075] Figure 4 A sample of dotted paint damage obtained by a surface coating damage inspection device for offshore wind power towers;

[0076] Figure 5 A sample of dotted paint damage obtained by a surface coating damage inspection device for offshore wind power towers;

[0077] Figure 6 A linear paint damage sample obtained by a surface coating damage inspection device for offshore wind power towers;

[0078] Figure 7 A cutout of a sample of dotted paint damage obtained by a surface coating damage inspection device for offshore wind power towers;

[0079] Figure 8 A cutout of a sample of dotted paint damage obtained by a surface coating damage inspection device for offshore wind power towers;

[0080] Figure 9 A cutout of a linear paint damage sample obtained by a surface coating damage inspection device for offshore wind power towers;

[0081] Figure 10 Feature identification image of a point-like paint surface damage sample obtained by an inspection device for surface coating damage of offshore wind power generation towers;

[0082] Figure 11 Feature identification image of a point-like paint surface damage sample obtained by an inspection device for surface coating damage of offshore wind power generation towers;

[0083] Figure 12 Linear paint damage feature identification image obtained by a surface coating damage inspection device for offshore wind power generation towers;

[0084] Figure 13 Feature extraction image of a point-like paint surface damage sample obtained by an inspection device for surface coating damage on offshore wind power towers;

[0085] Figure 14 Feature extraction image of a point-like paint surface damage sample obtained by an inspection device for surface coating damage on offshore wind power towers;

[0086] Figure 15 This is a feature extraction image of a linear paint damage sample obtained by a surface coating damage inspection device for offshore wind power generation towers. Detailed Implementation

[0087] The specific embodiments of this application will be further described in detail below with reference to the accompanying drawings. These embodiments are only for illustrating this application and are not intended to limit the invention.

[0088] In the description of this invention, it should be noted that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0089] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0090] Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0091] like Figure 1 A method for inspecting coating damage on offshore wind power towers, comprising the following steps:

[0092] S1, Obtain the inspection path of the offshore wind farm; inspect the wind turbine towers in the offshore wind farm along the inspection path, and acquire hyperspectral images of the wind turbine tower surfaces using airborne hyperspectral imaging equipment, such as... Figure 4 , 5 Samples of 6;

[0093] S2, determine the coating exposure status on the surface of the wind turbine tower based on hyperspectral images, such as... Figure 7 , 8 9. Identify the damaged areas of the paint surface;

[0094] S3, Determine the extent of coating damage on the surface of the wind turbine tower based on the degree of coating exposure, such as... Figure 10 , 11 12, obtain the damaged area, and further process it to obtain, as shown in the figure. Figure 13 , 14 15;

[0095] The technical problem to be solved by this application is how to effectively and automatically detect the coating damage of offshore wind power towers. Due to the rapid corrosion of the coating of wind power towers caused by the offshore environment, traditional manual inspection methods have problems such as low efficiency and low identification accuracy. Moreover, because the subtle changes in the coating layers are not easy to detect, manual inspection may lead to missed or false detection of damage.

[0096] This method acquires hyperspectral images of the surface of offshore wind turbine towers using airborne hyperspectral imaging equipment. Based on the specific spectral reflectance characteristics of each coating layer, it achieves automated detection and improves the accuracy of damage identification. The technical effects are as follows:

[0097] High-precision coating identification: Utilizing the wavelength resolution characteristics of hyperspectral imaging, the spectral features of coatings can be clearly identified, and the exposed state of coatings at different levels can be distinguished, effectively reducing false detections and missed detections.

[0098] Automatic inspection path generation: Automatically sorts the power generation towers according to their size in the image to generate reasonable drone inspection paths, reducing flight time and energy consumption;

[0099] Coating damage early warning and maintenance reminder: The system can automatically record the damaged tower number and send it to the maintenance terminal in real time to remind maintenance personnel to perform maintenance in a timely manner;

[0100] Saves labor costs: The entire process can be completed automatically by drones, reducing reliance on manual inspections and improving testing efficiency.

[0101] Inspection path optimization accuracy: Path generation is achieved by sorting images by size, reducing the average flight distance of drones by more than 10%;

[0102] Coating identification accuracy: Based on hyperspectral characteristic analysis, the coating identification accuracy can be improved to over 95%;

[0103] Image acquisition and analysis response time: The time for acquiring and analyzing hyperspectral images of each tower shall not exceed 1 minute;

[0104] Automatic recharge logic response: When the remaining battery power is below 30%, automatic recharge is triggered, and the inspection task is seamlessly resumed after charging;

[0105] Data acquisition: Hyperspectral images of wind turbine towers were captured using drones, and the condition of exposed coatings was verified based on the coating spectral curves in the database;

[0106] Experimental samples: Spectral data were collected from coatings with different degrees of damage (slight, moderate, and severe damage) to calculate parameters such as the area and location of different damages;

[0107] Comparative experiment: Compare the results with those of manual inspection to evaluate the missed detection rate, false detection rate and inspection efficiency;

[0108] Path optimization verification: Compare the flight mileage and flight time of traditional inspection paths and automatic path generation methods to verify the effectiveness of path optimization.

[0109] The accuracy of coating damage detection in this application is as follows:

[0110] Accuracy: In the sample, coatings with different damaged layers were tested; the combination of hyperspectral imaging and feature extraction enabled the system to achieve an accuracy of 98% in the test.

[0111] False negative rate: For slightly damaged coatings, the false negative rate was reduced to 1% after identification by hyperspectral imaging;

[0112] False detection rate: The system identification error is controlled below 3%, meaning that the misjudgment caused by similar coating conditions is within a controllable range.

[0113] The improvements to inspection efficiency and flight path optimization are as follows:

[0114] Inspection time reduced: Automatically generated inspection paths reduce inspection time by about 15% compared to traditional methods; in actual tests, the total time for drones to complete inspections at offshore wind farms was reduced by about 20 minutes.

[0115] Inspection path optimization: The inspection path generated by sorting images by size reduces the drone's flight distance by an average of about 12%, thereby reducing power consumption and increasing flight time;

[0116] Flight recharge management data: Battery threshold response time: The system responds automatically when the remaining battery level drops to 30%, completing the recharge path calculation within an average of 30 seconds and arriving at the charging station within 3 minutes;

[0117] Automatic recovery of inspection position deviation: After charging is completed, the accuracy of the drone's return to the interruption point is controlled within 1 meter to ensure that there are no missed inspections;

[0118] The extent and area of ​​coating damage are as follows:

[0119] Area detection accuracy: In samples with different degrees of coating damage, such as 5%, 10%, and 20% area damage, the area detection accuracy error is controlled within ±2%.

[0120] Corrosion area measurement error: The system controls the measurement error of the corrosion area of ​​the damaged area to within 5%, which is more than 20% more accurate than the traditional visual inspection.

[0121] Inspection data transmission and maintenance notifications:

[0122] Data transmission response: The transmission delay of inspection data is kept within 5 seconds to ensure that maintenance personnel can obtain the latest damage information in real time;

[0123] Maintenance reminder: After identifying damage, the system automatically sends the wind turbine tower number to the maintenance terminal, with an average response time of about 10 seconds.

[0124] Specifically, in one embodiment of this application, in step S1, obtaining the inspection path for an offshore wind farm includes the following steps:

[0125] Acquiring images of offshore wind farms: Using an airborne camera to photograph offshore wind farms along the wind direction, collecting image data including wind turbine towers;

[0126] Image recognition: Perform image recognition processing on offshore wind farm images to extract and identify the image size information of wind turbine towers in the images;

[0127] Size sorting: Based on the extracted wind turbine tower image size information, the wind turbine towers are sorted from largest to smallest image size;

[0128] Generate inspection path: Based on the sorted order, connect the location points of each wind turbine tower in the offshore wind farm in sequence to form an inspection path that adapts to the actual layout of the wind farm.

[0129] Specifically, in one embodiment of this application, in S1, an airborne camera is used to take pictures of the offshore wind farm along the wind direction to obtain an image of the offshore wind farm; based on the tide data, inspection and shooting are carried out at low tide; based on the tide conditions, the offshore wind farm image is inspected and image recognition is performed to obtain the image size of the wind power generation tower in the offshore wind farm image.

[0130] Specifically, in one embodiment of this application, image segmentation and spatter extraction in S1 includes the following steps:

[0131] The splash areas on the power generation tower are identified and extracted using the values ​​of the RGB color channels of the image.

[0132] The splash area is separated from the background, and the R channel of the image is extracted separately. A threshold is set to mark areas with red values ​​higher than the threshold as possible splash areas. By setting a range threshold for the red channel, pixels that meet the conditions are marked as splash areas, which is used to separate areas with colors close to red and brown. The segmented areas are processed morphologically to outline the splash area.

[0133] Specifically, in one embodiment of this application, S2 includes establishing a paint spectral curve, measuring spectral curves under different wet conditions, and converting the wet condition to a dry condition.

[0134] Includes the following steps:

[0135] Select a measuring device to measure the proportion of light reflection and absorption of the paint in the visible, ultraviolet, and infrared ranges;

[0136] Sample preparation: The preparation of paint samples must be consistent with the technical specifications of the power generation tower coating;

[0137] Set the measurement parameters, and adjust the wavelength range and step interval according to the type of paint.

[0138] Perform spectral measurements and record the light reflection or absorption data of the sample at various wavelengths;

[0139] Record and plot the curves, record the light reflectance or absorptivity data corresponding to each wavelength, and plot the wavelength-reflectance or wavelength-absorptivity curves to obtain the spectral curve of the paint.

[0140] Data analysis is used to match the target object with the corresponding paint type based on the shape of the spectral curve.

[0141] Specifically, in one embodiment of this application, in step S2, feature extraction is performed on the hyperspectral image to obtain the hyperspectral image features, including the following steps:

[0142] Based on a database storing hyperspectral image features of the surface coatings and building materials of wind turbine towers, the hyperspectral image features of the hyperspectral images are identified to obtain the coating exposure status of the wind turbine tower surface.

[0143] Specifically, in one embodiment of this application, in S2, feature extraction is performed on the hyperspectral image to obtain the hyperspectral image features, including the following steps:

[0144] The hyperspectral image is preprocessed, and features are extracted from the preprocessed hyperspectral image to obtain the hyperspectral image features.

[0145] Image preprocessing includes: brightness equalization based on the CLAHE algorithm, deblurring based on the GCANet algorithm, and restoration based on mathematical morphology algorithms.

[0146] Specifically, in one embodiment of this application, the surface coatings of the wind power tower are arranged in order from closest to farthest from the building materials on the surface of the wind power tower as follows: first coating, second coating, ..., Nth coating;

[0147] The extent of coating damage on the surface of wind turbine towers is determined based on the degree of coating exposure, including:

[0148] If the coating exposure situation is that only the Nth coating is exposed on the surface of the wind turbine tower, then it is determined that all coatings on the surface of the wind turbine tower are undamaged.

[0149] If only the Nth and N-1th coatings are exposed on the surface of the wind turbine tower, then the Nth coating on the surface of the wind turbine tower is damaged, and the other coatings are not damaged.

[0150] Proceed in sequence;

[0151] If the coating exposure condition is that all coatings on the surface of the wind turbine tower are exposed, and the building materials are not exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged except for the first layer.

[0152] If the coating is exposed in the case that all coatings on the surface of the wind turbine tower are exposed, and the building materials are also exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged.

[0153] Specifically, in one embodiment of this application, during the inspection process, the current remaining battery power and the return path from the current location to the charging point are obtained;

[0154] The flight distance is calculated based on the recharge path, and wind measurement data along the recharge path is obtained;

[0155] The power consumption for flying from the current location to the charging point is calculated based on the flight distance, wind measurement data, and preset flight speed.

[0156] If the difference between the current remaining power and the power consumption is less than or equal to the preset power threshold, the inspection will be paused, the current position will be recorded as the inspection interruption position, and the vehicle will fly along the recharge path at flight speed to the charging point for charging.

[0157] After charging is complete, fly to the inspection interruption point and continue the inspection.

[0158] Specifically, in one embodiment of this application, for any wind turbine tower in an offshore wind farm, if the coating on its surface is damaged, indicating that the coating is damaged, its number is recorded; the recorded number is sent to the user terminal of the maintenance personnel to prompt the maintenance personnel to perform coating maintenance on the corresponding wind turbine tower.

[0159] Specifically, in one embodiment of this application, such as Figure 2 , 3 A device for inspecting damage to the surface coating of offshore wind power towers, comprising a drone, including:

[0160] The acquisition module is used to acquire the inspection path of offshore wind farms;

[0161] The inspection module is used to inspect the wind turbine towers in the offshore wind farm along the inspection path, so as to obtain hyperspectral images of the surface of the wind turbine towers through airborne hyperspectral imaging equipment.

[0162] The determination module is used to determine the coating exposure status on the surface of wind turbine towers based on hyperspectral images;

[0163] The determination module is also used to determine the extent of coating damage on the surface of wind turbine towers based on the degree of coating exposure.

[0164] Figure 1 A flowchart illustrating a method for detecting damage to the surface coating of an offshore wind power tower, as provided in an embodiment of this disclosure, is shown. Figure 1-3 As shown, a method for detecting damage to the surface coating of offshore wind turbine towers, using unmanned aerial vehicles (UAVs), includes the following steps:

[0165] S110, Obtain the inspection path of the offshore wind farm; take pictures of the offshore wind farm along the wind direction using an airborne camera to obtain images of the offshore wind farm, then perform image recognition on the offshore wind farm images to obtain the image size of the wind turbine towers in the offshore wind farm images, then sort the wind turbine towers in the offshore wind farm images according to the order of image size from largest to smallest, and connect the positions of the wind turbine towers in the offshore wind farm images in the offshore wind farm in the sorted order to generate the inspection path.

[0166] Obviously, considering that the wind turbine towers in offshore wind farms adjust their orientation according to the wind direction, the wind turbine towers in the images of offshore wind farms taken along the wind direction are mostly frontal. This allows for accurate identification of the image size of the wind turbine towers in the images (the side view reflects the distance of the wind turbine towers from the drone). Then, the positions of the wind turbine towers in the offshore wind farm are connected in order from largest to smallest (i.e., from farthest to closest), generating an inspection path quickly and easily.

[0167] The S120 patrols the wind turbine towers in the offshore wind farm along the patrol route, so as to obtain hyperspectral images of the wind turbine tower surface through airborne hyperspectral imaging equipment.

[0168] Among them, hyperspectral imaging equipment can be hyperspectral imagers or hyperspectral cameras, etc.

[0169] S130, determine the coating exposure status on the surface of the wind turbine tower based on hyperspectral images.

[0170] In some embodiments, feature extraction is performed on the hyperspectral image to obtain the hyperspectral image features.

[0171] As an example, hyperspectral images are preprocessed, and then features are extracted from the preprocessed hyperspectral images to obtain the hyperspectral image features.

[0172] Image preprocessing includes: brightness equalization based on the CLAHE algorithm, deblurring based on the GCANet algorithm, and restoration based on mathematical morphology algorithms.

[0173] On the one hand, to address the issue of uneven image brightness, the CLAHE algorithm was used to segment the hyperspectral image into numerous small regions. Local histogram enhancement was applied to each sub-region while limiting the height of the local histogram, and interpolation was used to optimize the transition between sub-regions. This approach balances brightness while limiting contrast, preserving image details, and restricting noise amplification, thus effectively improving the problem of uneven image brightness.

[0174] On the other hand, to address the issue of blurriness in some hyperspectral images, the GCANet algorithm was used to directly learn the residual between the blurred and unblurred images, thereby achieving an end-to-end deblurring model and using it to deblur hyperspectral images.

[0175] On the other hand, mathematical morphology algorithms are used to repair and enhance the image to address the issue of partial detail loss in hyperspectral images.

[0176] It is worth noting that the feature extraction is implemented using preset hyperspectral image feature extraction algorithms, such as spectral reflectance method, principal component analysis method, sparse representation method, etc., and no restrictions are imposed here.

[0177] For example, the principal component analysis method is shown below:

[0178] The covariance matrix of the original hyperspectral image data is calculated using the following formula:

[0179]

[0180] In formula (1), X represents the covariance matrix; O represents the original hyperspectral image data, which is in matrix form; O T represents the transpose of the original data matrix; n represents the dimension of O.

[0181] The formula for calculating multiple eigenvectors and their corresponding eigenvalues ​​based on the covariance matrix is ​​shown below:

[0182] Xξ i =λ i ξ i (2)

[0183] In formula (2), X represents the covariance matrix; λi ξ represents the eigenvalue. i This represents the eigenvector.

[0184] Each eigenvector is sorted from largest to smallest eigenvalue, and the top eigenvectors are selected to generate the principal component matrix. For example, the cumulative contribution rate is calculated sequentially based on the eigenvalues ​​of the eigenvectors in the sorted order until the cumulative contribution rate is greater than or equal to a preset threshold. The k eigenvectors used for the current cumulative contribution rate calculation, i.e., the top k eigenvectors in the sorted list, are then selected to effectively generate the principal component matrix V. k V k =(ξ1, ξ2, ..., ξ) k The above process is represented by the following formula:

[0185]

[0186] In formula (3), α represents the preset threshold, 0≤α≤1; d represents the number of feature values.

[0187] The original hyperspectral image data is projected onto the coordinate axes corresponding to the principal component matrix to obtain the hyperspectral image features. This process is expressed using the following formula:

[0188] A = OV k (4)

[0189] In formula (4), A represents the hyperspectral image features, which is in matrix form; O represents the original hyperspectral image data; and V... k This represents the principal component matrix.

[0190] Based on a database storing hyperspectral image features of the surface coatings and building materials of wind turbine towers, the hyperspectral image features of the hyperspectral images are identified to obtain the coating exposure status of the wind turbine tower surface.

[0191] S140, Determine the coating damage condition on the surface of the wind power tower based on the exposed coating condition.

[0192] In some embodiments, the surface coatings of the wind power tower are arranged in order of proximity to the building materials on the wind power tower surface, from closest to furthest: the first coating, the second coating, ..., the Nth coating.

[0193] If the coating exposure situation is that only the Nth coating is exposed on the surface of the wind turbine tower, then it is determined that all coatings on the surface of the wind turbine tower are undamaged.

[0194] If only the Nth and N-1th coatings are exposed on the surface of the wind turbine tower, then the Nth coating on the surface of the wind turbine tower is damaged, and the other coatings are not damaged.

[0195] Ongoing;

[0196] If the coating exposure condition is that all coatings on the surface of the wind turbine tower are exposed, and the building materials are not exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged except for the first layer.

[0197] If the coating is exposed in the case that all coatings on the surface of the wind turbine tower are exposed, and the building materials are also exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged.

[0198] Taking the surface of a wind turbine tower as an example, the coatings on the surface of the wind turbine tower are arranged in the following order from closest to furthest from the steel material on the surface of the wind turbine tower: the first coating (epoxy zinc-rich primer coating), the second coating (epoxy micaceous iron oxide intermediate coating), and the third coating (polyurethane topcoat coating).

[0199] If the exposed coating condition is that only the third coating (polyurethane topcoat) is exposed on the surface of the wind turbine tower, then it is determined that all coatings on the surface of the wind turbine tower are undamaged.

[0200] If only the third coating (polyurethane topcoat) and the second coating (epoxy micaceous iron oxide intermediate coating) are exposed on the surface of the wind turbine tower, then the third coating (polyurethane topcoat) on the surface of the wind turbine tower is damaged, and the other coatings are not damaged.

[0201] If the coating exposure condition is that all coatings on the surface of the wind turbine tower are exposed, and the steel material is not exposed, then it is determined that the surface of the wind turbine tower is damaged except for the first coating (epoxy zinc-rich primer coating).

[0202] If the coating is exposed in the case that all the coatings on the surface of the wind turbine tower are exposed, and the steel material is also exposed, then it is determined that all the coatings on the surface of the wind turbine tower are damaged.

[0203] It is worth noting that, when determining coating damage, the location and area of ​​coating damage can also be analyzed and calculated based on the spatial information of hyperspectral images.

[0204] In the embodiments of this disclosure, the exposure of the coating on the surface of the wind turbine tower can be determined by using hyperspectral images of the surface of the wind turbine tower, based on the large differences in the spectral characteristics of different coatings. This allows for accurate detection of coating damage on the surface of offshore wind turbine towers. The entire process can be automated by drones without human intervention, resulting in high detection efficiency.

[0205] It is worth noting that, considering the large area of ​​offshore wind farms, drones may need to recharge midway during inspections. To further improve automation, methods for detecting damage to the surface coating of offshore wind turbine towers may also include:

[0206] During the inspection, the system obtains the current remaining battery power and the return path from the current location to the charging point. It calculates the flight distance based on the return path and obtains wind data such as wind speed, wind direction, and vertical airflow along the return path.

[0207] Calculate the power consumption from the current location to the charging point based on the flight distance, wind measurement data, and preset flight speed, and calculate the difference between the current remaining power and the power consumption.

[0208] If the difference between the current remaining power and the power consumption is less than or equal to the preset power threshold, the inspection will be paused, the current position will be recorded as the inspection interruption position, and the vehicle will fly along the recharge path at flight speed to the charging point for charging.

[0209] After charging is complete, fly to the inspection interruption point and continue the inspection.

[0210] Furthermore, to improve efficiency, the method for detecting damage to the surface coating of offshore wind turbine towers may also include:

[0211] When the inspection interruption location is recorded, the inspection path and the inspection interruption location are sent to the backup drone so that it can start the inspection from the inspection interruption location in its place. After charging is completed, it flies to the location of the backup drone and continues the inspection.

[0212] Meanwhile, in order to promptly repair wind turbine towers with damaged coatings, the detection methods for surface coating damage on offshore wind turbine towers may also include:

[0213] For any wind turbine tower in an offshore wind farm, if the coating on its surface is damaged, its number should be recorded.

[0214] The recorded number is sent to the user terminal of the maintenance personnel to prompt them to perform timely coating maintenance on the corresponding wind turbine tower.

[0215] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0216] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0217] Figure 2 The diagram illustrates the structure of a surface coating damage inspection device for offshore wind power towers according to an embodiment of this disclosure. Figure 2 As shown, the device for detecting damage to the surface coating of offshore wind turbine towers utilizes drones, including:

[0218] The acquisition module 210 is used to acquire the inspection path of the offshore wind farm.

[0219] The inspection module 220 is used to inspect the wind turbine towers in the offshore wind farm along the inspection path of the offshore wind farm, so as to obtain hyperspectral images of the surface of the wind turbine towers through airborne hyperspectral imaging equipment.

[0220] The determination module 230 is used to determine the coating exposure status on the surface of the wind power generation tower based on the hyperspectral image;

[0221] The determination module 230 is also used to determine the coating damage condition on the surface of the wind power tower based on the coating exposure condition.

[0222] In some embodiments, the acquisition module 210 is specifically used for:

[0223] Images of offshore wind farms are obtained by taking pictures of the offshore wind farm along the wind direction using an airborne camera;

[0224] Image recognition is performed on images of offshore wind farms to obtain the image dimensions of wind turbine towers in the images.

[0225] Sort the wind turbine towers in the offshore wind farm images according to their size from largest to smallest;

[0226] By sequentially connecting the positions of wind turbine towers in the offshore wind farm image according to the sorted order, an inspection path is generated.

[0227] In some embodiments, the determining module 230 is specifically used for:

[0228] Feature extraction is performed on the hyperspectral image to obtain its hyperspectral image features;

[0229] Based on a database storing hyperspectral image features of the surface coatings and building materials of wind turbine towers, the hyperspectral image features of the hyperspectral images are identified to obtain the coating exposure status of the wind turbine tower surface.

[0230] In some embodiments, the determining module 230 is specifically used for:

[0231] Hyperspectral images are preprocessed, and features are extracted from the preprocessed hyperspectral images to obtain hyperspectral image features.

[0232] Image preprocessing includes: brightness equalization based on the CLAHE algorithm, deblurring based on the GCANet algorithm, and restoration based on mathematical morphology algorithms.

[0233] In some embodiments, the surface coatings of the wind power tower are arranged in order of proximity to the building materials on the wind power tower surface, from closest to furthest: the first coating, the second coating, ..., the Nth coating.

[0234] Module 230 is specifically used for:

[0235] If the coating exposure situation is that only the Nth coating is exposed on the surface of the wind turbine tower, then it is determined that all coatings on the surface of the wind turbine tower are undamaged.

[0236] If only the Nth and N-1th coatings are exposed on the surface of the wind turbine tower, then the Nth coating on the surface of the wind turbine tower is damaged, and the other coatings are not damaged.

[0237] Continue;

[0238] If the coating exposure condition is that all coatings on the surface of the wind turbine tower are exposed, and the building materials are not exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged except for the first layer.

[0239] If the coating is exposed in the case that all coatings on the surface of the wind turbine tower are exposed, and the building materials are also exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged.

[0240] In some embodiments, the offshore wind power tower surface coating damage detection device 200 further includes:

[0241] The acquisition module is used to acquire the current remaining battery power and the return route from the current location to the charging point during the inspection process.

[0242] The calculation module is used to calculate the flight distance based on the recharge path and to obtain wind measurement data along the recharge path.

[0243] The calculation module is also used to calculate the power consumption from the current location to the charging point based on the flight distance, wind measurement data, and preset flight speed.

[0244] The recharge module is used to pause the inspection if the difference between the current remaining power and the power consumption is less than or equal to a preset power threshold, record the current position as the inspection interruption position, and fly to the charging point at flight speed along the recharge path to charge.

[0245] The reconnection module is used to fly to the inspection interruption position after charging is complete and continue the inspection.

[0246] In some embodiments, the offshore wind power tower surface coating damage detection device 200 further includes:

[0247] The recording module is used to record the number of any wind turbine tower in an offshore wind farm if the coating on its surface is damaged.

[0248] The notification module is used to send the recorded number to the maintenance personnel's user terminal to prompt the maintenance personnel to perform coating maintenance on the corresponding wind turbine tower.

[0249] Figure 2 Each module / unit in the offshore wind turbine tower surface coating damage detection device 200 shown has the ability to realize Figure 1 The functions of each step in the method for detecting damage to the surface coating of offshore wind power towers, as shown, and the corresponding technical effects they achieve, will not be elaborated here for the sake of brevity.

[0250] Figure 3 A structural diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Electronic device 300 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 300 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0251] like Figure 3 As shown, the electronic device 300 may include a computing unit 301, which performs various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0252] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0253] The computing unit 301 is a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer program product, including a computer program tangibly contained in a computer-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program is loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0254] The various embodiments described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), payload programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0255] Program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may execute entirely on the machine, partially on the machine, or as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0256] In the context of this disclosure, a computer-readable medium is a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable medium is a computer-readable signal medium or a computer-readable storage medium. Computer-readable media include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0257] It should be noted that this disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute method 100 and achieve the corresponding technical effects achieved by the embodiments of this disclosure in executing the method. For the sake of brevity, these will not be elaborated here.

[0258] In addition, this disclosure also provides a computer program product including a computer program that implements method 100 when executed by a processor.

[0259] To provide interaction with the user, the embodiments described above are implemented on a computer, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including sound input, voice input, or tactile input).

[0260] The embodiments described above can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user interacts with the embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system are interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0261] Computer systems consist of clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server for a distributed system, or a server incorporating blockchain technology.

[0262] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A method for inspecting coating damage on offshore wind power towers, characterized in that, Includes the following steps: S1, Obtain the inspection path of the offshore wind farm; Inspect the wind turbine towers in the offshore wind farm along the inspection path, and obtain hyperspectral images of the surface of the wind turbine towers through airborne hyperspectral imaging equipment. S2, Determine the coating exposure status on the surface of the wind turbine tower based on hyperspectral images; S3, Determine the coating damage status on the surface of the wind turbine tower based on the exposed coating condition.

2. The method for inspecting coating damage on offshore wind power towers as described in claim 1, characterized in that, In S1, the inspection path for offshore wind farms is obtained, including the following steps: Acquiring images of offshore wind farms: Using an airborne camera to photograph offshore wind farms along the wind direction, collecting image data including wind turbine towers; Image recognition: Perform image recognition processing on offshore wind farm images to extract and identify the image size information of wind turbine towers in the images; Size sorting: Based on the extracted wind turbine tower image size information, the wind turbine towers are sorted from largest to smallest image size; Generate inspection path: Based on the sorted order, connect the location points of each wind turbine tower in the offshore wind farm in sequence to form an inspection path that adapts to the actual layout of the wind farm.

3. The method for inspecting coating damage on offshore wind power towers as described in claim 1, characterized in that, In S1, the airborne camera takes pictures of the offshore wind farm along the wind direction to obtain images of the offshore wind farm; based on the tide data, the inspection and shooting are carried out at low tide; based on the tide conditions, the offshore wind farm images are inspected and image recognition is performed to obtain the image size of the wind power generation towers in the offshore wind farm images.

4. The method for inspecting coating damage on offshore wind power towers as described in claim 1, characterized in that, Image segmentation and spatter extraction in S1 include the following steps: The splash areas on the power generation tower are identified and extracted using the values ​​of the RGB color channels of the image. The splash area is separated from the background, and the R channel of the image is extracted separately. A threshold is set to mark the area with a red value higher than the threshold as the splash area. By setting a range threshold for the red channel, pixels that meet the condition are marked as splash areas, which is used to separate areas with colors close to red and brown. The segmented area is processed morphologically to outline the splash area.

5. The method for inspecting coating damage on offshore wind power towers as described in claim 1, characterized in that, S2 includes establishing the paint spectral curve, measuring the spectral curve under different wet conditions, and converting the wet condition to the dry condition; Includes the following steps: Select a measuring device to measure the proportion of light reflection and absorption of the paint in the visible, ultraviolet, and infrared ranges; Sample preparation: The preparation of paint samples must be consistent with the technical specifications of the power generation tower coating; Set the measurement parameters, and adjust the wavelength range and step interval according to the type of paint. Perform spectral measurements and record the light reflection or absorption data of the sample at various wavelengths; Record and plot the curves, record the light reflectance or absorptivity data corresponding to each wavelength, and plot the wavelength-reflectance or wavelength-absorptivity curves to obtain the spectral curve of the paint. Data analysis is used to match the target object with the corresponding paint type based on the shape of the spectral curve.

6. The method for inspecting coating damage on offshore wind power towers as described in claim 1, characterized in that, In S2, feature extraction is performed on the hyperspectral image to obtain its hyperspectral image features, including the following steps: Based on a database storing hyperspectral image features of wind turbine tower surface coatings and building materials, the hyperspectral image features of the hyperspectral images are identified to determine the coating exposure status of the wind turbine tower surface.

7. The method for inspecting coating damage on offshore wind power towers as described in claim 1, characterized in that, In S2, feature extraction is performed on the hyperspectral image to obtain its hyperspectral image features, including the following steps: The hyperspectral image is preprocessed, and features are extracted from the preprocessed hyperspectral image to obtain the hyperspectral image features. Image preprocessing includes: brightness equalization based on the CLAHE algorithm, deblurring based on the GCANet algorithm, and restoration based on mathematical morphology algorithms.

8. The method for inspecting coating damage on offshore wind power towers as described in claim 1, characterized in that, The surface coatings of the wind turbine tower are arranged in order of distance from the building materials on the wind turbine tower surface, from closest to furthest: coating 1, coating 2, ..., coating N; The extent of coating damage on the surface of wind turbine towers is determined based on the degree of coating exposure, including: If the coating exposure situation is that only the Nth coating is exposed on the surface of the wind turbine tower, then it is determined that all coatings on the surface of the wind turbine tower are undamaged. If only the Nth and N-1th coatings are exposed on the surface of the wind turbine tower, then the Nth coating on the surface of the wind turbine tower is damaged, and the other coatings are not damaged. Proceed in sequence; If the coating exposure condition is that all coatings on the surface of the wind turbine tower are exposed, and the building materials are not exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged except for the first layer. If the coating is exposed in the case that all coatings on the surface of the wind turbine tower are exposed, and the building materials are also exposed, then it is determined that all coatings on the surface of the wind turbine tower are damaged.

9. The method for inspecting coating damage on offshore wind power towers as described in claim 1, characterized in that, During the inspection, the current remaining battery power and the return route from the current location to the charging point are obtained; The flight distance is calculated based on the recharge path, and wind measurement data along the recharge path is obtained; The power consumption for flying from the current location to the charging point is calculated based on the flight distance, wind measurement data, and preset flight speed. If the difference between the current remaining power and the power consumption is less than or equal to the preset power threshold, the inspection will be paused, the current position will be recorded as the inspection interruption position, and the vehicle will fly along the recharge path at flight speed to the charging point for charging. After charging is complete, fly to the inspection interruption point and continue the inspection; For any wind turbine tower in an offshore wind farm, if the coating on its surface is damaged, its number should be recorded. The recorded number is sent to the maintenance personnel's user terminal to prompt them to perform coating maintenance on the corresponding wind turbine tower.

10. A device for inspecting damage to the surface coating of offshore wind power towers, comprising a drone, including: The acquisition module is used to acquire the inspection path of offshore wind farms; The inspection module is used to inspect the wind turbine towers in the offshore wind farm along the inspection path, so as to obtain hyperspectral images of the surface of the wind turbine towers through airborne hyperspectral imaging equipment. The determination module is used to determine the coating exposure status on the surface of wind turbine towers based on hyperspectral images; The determination module is also used to determine the extent of coating damage on the surface of wind turbine towers based on the degree of coating exposure.

Images