A method and system for spraying coating on the wall of a wind turbine tower

Abstract

This invention relates to the field of spraying control technology, and discloses a method and system for spraying the wall of a wind turbine tower. The method includes: scanning the wall of the wind turbine tower with a sensor to obtain its surface contour data and form reference geometric information; extracting the curvature distribution characteristics of the reference geometric information, extracting key path points based on the curvature distribution characteristics, and introducing robot kinematic constraints to perform integrated processing for smoothing and coverage completeness, thereby generating a spraying path; establishing a mapping relationship between the curvature of the spraying path and nozzle parameters, and setting initial values ​​for atomization pressure and paint flow rate based on the mapping relationship; during spraying execution, integrating real-time thickness monitoring data and preset thickness to generate parameter adjustment instructions, driving the actuator to perform online correction of the spraying parameters; this invention achieves precise adaptation between the spraying path and the tower contour, dynamic matching of spraying parameters, ensuring that the coating thickness always conforms to the preset standard, and improving the stability and controllability of the spraying process.

Description

Technical Field

[0001] This invention relates to the field of spraying control technology, and in particular to a method and system for spraying the wall of a wind turbine tower. Background Technology

[0002] As a large supporting structure, the quality of the anti-corrosion coating on the walls of wind turbine towers directly affects the service life and safety of the structure. Due to the narrow and confined internal space of the tower, as well as the presence of many complex curved surfaces, traditional manual spraying presents problems such as high operational risks, high labor intensity, and difficulty in ensuring coating uniformity. Therefore, the use of automated painting robots has become an inevitable trend in the industry, aiming to improve the consistency of spraying quality and operational efficiency through automation technology.

[0003] However, existing automated spraying methods still have significant limitations. On the one hand, most systems employ relatively crude spraying path planning methods, failing to accurately consider the complex curvature variations of the tower wall and the motion constraints of the robot itself. This results in poor adaptability of the generated paths in areas of abrupt curvature changes, leading to spraying overlaps or omissions, and insufficient robot motion stability. On the other hand, the setting of spraying parameters often relies on pre-set fixed programs or operator experience, lacking the ability to dynamically adjust based on the real-time coating condition during operation. This makes it difficult for the system to cope with fluctuations and environmental interference during the spraying process, ultimately leading to defects such as uneven coating thickness, localized overspray or underspray, affecting the overall protective effect of the coating and material utilization efficiency. Summary of the Invention

[0004] This invention provides a method and system for spraying the wall of a wind turbine tower to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for spraying the wall surface of a wind turbine tower, comprising:

[0006] S1. Obtain the surface contour data of the wind turbine tower to form the reference geometric information;

[0007] S2. Extract the curvature distribution features of the reference geometry information, extract key path points based on the curvature distribution features, and introduce robot kinematic constraints to perform integrated processing of smoothing and coverage completeness, thereby generating the spraying path;

[0008] S3. Establish the mapping relationship between the curvature of the spraying path and the nozzle parameters, and set the initial values ​​of atomization pressure and paint flow rate based on the mapping relationship;

[0009] S4. During the spraying process, real-time thickness monitoring data and preset thickness are integrated to generate parameter adjustment instructions, which drive the actuator to correct the spraying parameters online.

[0010] S5. Based on the parameters after online calibration, control the spraying device to move to the already sprayed area to perform secondary spraying, and simultaneously move the detection device to the area where the initial spraying has not been completed to monitor the coating thickness.

[0011] Preferably, the step of acquiring the surface contour data of the wind turbine tower to form reference geometric information includes:

[0012] The system scans the wind turbine tower wall using sensors and monitors the acquisition process of surface contour data, recording the spatial sequence of data points.

[0013] Assess the spatial uniformity of data point distribution and identify areas with insufficient coverage or data distortion;

[0014] Control the data acquisition strategy to acquire supplementary data for identified areas and update the dataset;

[0015] Integrate all datasets, perform geometric consistency checks and error elimination, and output highly complete baseline geometric information.

[0016] Preferably, the step of extracting the curvature distribution features of the reference geometric information and extracting key path points based on the curvature distribution features includes:

[0017] Analyze the local surface undulations of the reference geometry, identify the transition zones of surface unevenness, and mark the boundary feature points of these transition zones;

[0018] Candidate path points are selected based on the distribution density of boundary feature points, the regional coverage capability of the candidate point set is evaluated, and redundant dense points are eliminated.

[0019] The selected candidate path points are validated for path coherence, and the spatial relationships between adjacent points are correlated to generate a sequence of critical path points for topological connections.

[0020] Preferably, the process of introducing robot kinematic constraints to perform integrated smoothing and coverage completion processing to generate a spraying path includes:

[0021] Measure the turning angle between adjacent critical path points, compare it with the robot joint mobility limits, and adjust the spatial coordinates of the critical path points.

[0022] Construct transition paths between path segments, control the rate of change of the end effector's speed, and form a continuous motion trajectory;

[0023] Scan the covered area of ​​the continuous motion trajectory, detect the uncovered segments, and insert supplementary path points;

[0024] By integrating all path points and transition paths, balancing motion smoothness and coverage density, the final spraying path sequence is generated.

[0025] Preferably, establishing the mapping relationship between the curvature of the spraying path and the nozzle parameters, and setting the initial values ​​of the atomization pressure and paint flow rate based on the mapping relationship, includes:

[0026] The spraying path is divided into multiple path segments, the curvature characteristics of each path segment are classified, and the correspondence between curvature range and parameter group is established.

[0027] Match the curvature range with the nozzle parameter combination, select the atomization pressure level range, and determine the adjustment range of the paint flow rate;

[0028] Configure the initial spraying parameters according to the parameter combination, set the reference value of the atomization pressure, and adjust the initial value of the paint flow rate;

[0029] Verify the compatibility of the initial spraying parameters, coordinate the matching relationship between atomization pressure and paint flow rate, and complete the initial setting of nozzle parameters.

[0030] Preferably, step 4, which involves acquiring thickness monitoring data via optical self-reference, includes:

[0031] Broadband probe light is projected onto the coating surface, and mixed spectral signals formed by reflections from the upper interface of the coating and the lower interface of the coating-substrate are collected simultaneously.

[0032] Analyze the interference patterns in mixed spectral signals, identify the constructive and destructive interference characteristics at specific wavelengths, and establish a direct correlation between interference characteristics and the optical thickness of the coating;

[0033] Based on the interference feature set of the entire spraying area directly correlated and transformed, a coating thickness distribution map that does not depend on the absolute position information of the substrate is generated.

[0034] Preferably, the acquisition of thickness monitoring data via optical self-reference further includes:

[0035] The incident angle and focusing plane of the probe beam are dynamically adjusted to match the curvature changes of the coating surface and optimize the signal-to-noise ratio of the interference signal.

[0036] Separate the specular and diffuse reflection components from the mixed spectral signal, extract the coherent signal component containing thickness information, and suppress ambient light interference;

[0037] The characteristic interference patterns corresponding to different thickness ranges are calibrated, and a thickness-spectral feature lookup table is established to realize real-time analysis of coating thickness;

[0038] By integrating real-time analysis results from multiple locations, a three-dimensional distribution field of coating thickness is constructed, and the boundary features of areas with abnormal thickness are identified.

[0039] Preferably, the step of fusing real-time thickness monitoring data with a preset thickness to generate parameter adjustment commands and driving the actuator to perform online correction of the spraying parameters includes:

[0040] Compare the coating thickness distribution map with the preset thickness threshold, identify continuous areas where the thickness deviation exceeds the tolerance, and record the geometric contour and deviation value of the area;

[0041] Based on the deviation value, a parameter adjustment instruction sequence is generated, the instructions are encoded into machine-readable control signals, and the protocol compatibility of the signals with the actuator is verified;

[0042] The control signal is transmitted to the actuator interface, the signal content is parsed into a drive command, and the atomization pressure and coating flow are synchronized and corrected.

[0043] Preferably, the step of controlling the spraying device to move to the already sprayed area to perform a second spraying based on the online calibrated parameters, and simultaneously moving the detection device to the area where the initial spraying has not been completed to monitor the coating thickness, includes:

[0044] The work area is divided based on parameter adjustment instructions, and the boundary coordinates of the area to be sprayed a second time and the area to be monitored are marked.

[0045] Coordinate the operation sequence of the spraying device and the detection device, assign execution permissions to the area to be sprayed a second time, and simultaneously activate the monitoring permissions to the area to be monitored.

[0046] During the secondary coating process, thickness monitoring data is continuously received, and the baseline thickness distribution map is compared with the real-time monitoring data to generate a thickness uniformity evaluation result.

[0047] The operation sequence is dynamically adjusted based on the thickness uniformity assessment results to optimize the equipment's collaborative efficiency and achieve closed-loop control of the coating quality.

[0048] To address the aforementioned problems, the present invention also provides a wind turbine tower wall coating system, the system comprising:

[0049] The data acquisition module is used to acquire surface contour data of wind turbine towers to form reference geometric information;

[0050] The path generation module is used to extract the curvature distribution features of the reference geometry information, extract key path points based on the curvature distribution features, and introduce robot kinematic constraints to perform integrated processing of smoothing and coverage completeness, thereby generating the spraying path.

[0051] The parameter mapping module is used to establish the mapping relationship between the curvature of the spraying path and the nozzle parameters, and to set the initial values ​​of atomization pressure and paint flow rate based on the mapping relationship.

[0052] The online calibration module is used to integrate real-time thickness monitoring data with preset thickness during the spraying process to generate parameter adjustment instructions, driving the actuator to calibrate the spraying parameters online.

[0053] The secondary spraying module is used to control the spraying device to move to the already sprayed area to perform secondary spraying based on the parameters after online calibration, and simultaneously move the detection device to the area where the initial spraying has not been completed to monitor the coating thickness.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. By scanning to obtain precise reference geometric information of the wind turbine tower wall, key path points are extracted by combining curvature distribution characteristics and generated by integrated processing of robot kinematic constraints. The mapping relationship between path curvature and nozzle parameters is established. Combined with real-time thickness monitoring and online parameter correction, as well as the collaborative operation of secondary spraying and synchronous monitoring, the spraying path and tower contour are accurately adapted and the spraying parameters are dynamically matched. This ensures that the coating thickness always conforms to the preset standard and improves the stability and controllability of the spraying process.

[0056] 2. By optimizing the surface contour data acquisition and integration process, the integrity and accuracy of the reference geometric information are ensured. By refining parameter mapping and optical self-reference thickness monitoring, the accuracy of parameter matching and thickness detection is improved. By accurately identifying deviation areas, synchronously correcting parameters, and dynamically adjusting the operation sequence, the uniformity and integrity of the coating are further improved, spraying defects are reduced, and the consistency of spraying quality is ensured. At the same time, the efficiency of equipment collaboration is optimized, and the overall efficiency of spraying operations is improved. Attached Figure Description

[0057] Figure 1 This is a schematic flowchart of a wind turbine tower wall spraying method according to an embodiment of the present invention;

[0058] Figure 2 This is a functional module diagram of a wind turbine tower wall spraying system provided in an embodiment of the present invention;

[0059] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0060] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0061] This application provides a method for spraying coating on the wall of a wind turbine tower. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for spraying coating on the wall of a wind turbine tower can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0062] Example 1, referring to Figure 1 The diagram shown is a flowchart illustrating a wind turbine tower wall coating method according to an embodiment of the present invention. In this embodiment, the wind turbine tower wall coating method is applied to an automated painting robot and includes:

[0063] S1. Obtain the surface contour data of the wind turbine tower wall to form reference geometric information; preferably, the wind turbine tower wall includes the outer wall of the wind turbine tower or the inner wall of the wind turbine tower, and the surface contour data of the outer wall or the inner wall are used to form reference geometric information.

[0064] S2. Extract the curvature distribution features of the reference geometry information, extract key path points based on the curvature distribution features, and introduce robot kinematic constraints to perform integrated processing of smoothing and coverage completeness, thereby generating the spraying path;

[0065] S3. Establish the mapping relationship between the curvature of the spraying path and the nozzle parameters, and set the initial values ​​of atomization pressure and paint flow rate based on the mapping relationship;

[0066] S4. During the spraying process, real-time thickness monitoring data and preset thickness are integrated to generate parameter adjustment instructions, which drive the actuator to correct the spraying parameters online.

[0067] S5. Based on the parameters after online calibration, control the spraying device to move to the already sprayed area to perform secondary spraying, and simultaneously move the detection device to the area where the initial spraying has not been completed to monitor the coating thickness.

[0068] In a preferred embodiment, acquiring surface contour data of the wind turbine tower wall to form reference geometric information includes:

[0069] The system scans the wind turbine tower wall using sensors and monitors the acquisition process of surface contour data, recording the spatial sequence of data points.

[0070] Assess the spatial uniformity of data point distribution and identify areas with insufficient coverage or data distortion;

[0071] Control the data acquisition strategy to acquire supplementary data for identified areas and update the dataset;

[0072] Integrate all datasets, perform geometric consistency checks and error elimination, and output highly complete baseline geometric information.

[0073] Specifically, the automatic painting robot carries a laser displacement sensor and moves at a constant speed along a preset path on the wall of the wind turbine tower. The sensor emits a laser beam in real time and receives the reflected signal, and synchronously records the three-dimensional coordinates of the data point at each sampling moment to form a continuous spatial sequence.

[0074] Furthermore, the actual spacing between adjacent data points in the spatial sequence is compared with the preset sampling spacing one by one. When the actual spacing is greater than the preset value, it is determined to be an area with insufficient coverage. When the deviation between the coordinates of the data point and the fitted trajectory of multiple consecutive data points in the surrounding area exceeds the allowable range, it is determined to be a data distortion area. The start and end positions of the two types of areas are clearly marked.

[0075] The robot adjusts its movement speed based on the location of the marked area. It slows down and increases the sampling density in areas with insufficient coverage. In areas with data distortion, it replans the local sampling path and collects data again to update the original dataset.

[0076] The updated data points are imported into the geometry processing module. By comparing the coordinates of overlapping areas and removing outliers, continuous trajectory fitting is performed on the remaining data points to complete the geometric consistency verification and error elimination, and output highly complete benchmark geometric information.

[0077] In summary, by monitoring data collection, identifying defective areas and supplementing data collection, combined with geometric consistency verification and error elimination, high-completeness benchmark geometric information is output to avoid insufficient data coverage or distortion problems; the surface contour and curvature distribution characteristics of the tower wall are accurately captured, providing reliable data support for subsequent extraction of key path points and adaptation of robot motion constraints, and reducing path planning deviations.

[0078] Overall, this provides an accurate basis for mapping curvature and nozzle parameters and setting initial spraying parameters, avoiding parameter adaptation errors caused by inaccurate geometric data, indirectly ensuring coating uniformity, improving the intelligence and reliability of the entire spraying process from the source, laying the foundation for subsequent online correction, secondary spraying and other links, and helping to solve the problems of incomplete coverage and uneven quality in traditional spraying.

[0079] It is understood that the surface contour data acquisition via sensor scanning in this embodiment is merely an exemplary method. In practical applications, other methods can also be used to acquire surface contour data to form reference geometric information. For example, with high-precision design drawings available, a preset 3D model of the wind turbine tower can be directly imported, and the geometric information of this 3D model can be used as the reference geometric information. Alternatively, preferably, to balance the overall accuracy of the model with the local details of the actual object, the preset 3D model can be fused with the sensor scanning data, and through registration, correction, and other processing, more accurate reference geometric information can be generated. Regardless of the method used to acquire the surface contour data, it does not affect subsequent steps such as path planning based on curvature features, dynamic parameter matching, and closed-loop quality control.

[0080] In a preferred embodiment, the curvature distribution features of the reference geometry are extracted, and key path points are extracted based on the curvature distribution features, including:

[0081] Analyze the local surface undulations of the reference geometry, identify the transition zones of surface unevenness, and mark the boundary feature points of these transition zones;

[0082] Candidate path points are selected based on the distribution density of boundary feature points, the regional coverage capability of the candidate point set is evaluated, and redundant dense points are eliminated.

[0083] The selected candidate path points are validated for path coherence, and the spatial relationships between adjacent points are correlated to generate a sequence of critical path points for topological connections.

[0084] Specifically, examine the surface morphology corresponding to the reference geometric information segment by segment, compare the convex height and concave depth of adjacent areas, and the area where the adjacent area changes from a convex state to a concave state or vice versa is the transition zone of the surface convexity and concavity. Directly record the three-dimensional coordinates of the start and end positions of these transition zones to form boundary feature points.

[0085] The number of boundary feature points in each unit area is counted. Points whose number meets the area coverage requirements are retained as candidate path points. The candidate point set is fully checked to see if it can cover all transition zones and flat areas on the tower wall. Points that are too close and do not affect the coverage integrity are judged as redundant dense points and directly removed.

[0086] Following the circumferential direction and axial sequence of the wind turbine tower wall, the spatial positions of the selected candidate path points are checked sequentially to confirm that there are no spatial barriers between adjacent candidate points and that the path directions are consistent. By establishing direct connection relationships between adjacent points, a sequence of critical path points for topological connection is formed.

[0087] In this embodiment, robot kinematic constraints are introduced to perform integrated processing of smoothing and coverage completeness, thereby generating a spraying path, including:

[0088] Measure the turning angle between adjacent critical path points, compare it with the robot joint mobility limits, and adjust the spatial coordinates of the critical path points.

[0089] Construct transition paths between path segments, control the rate of change of the end effector's speed, and form a continuous motion trajectory;

[0090] Scan the covered area of ​​the continuous motion trajectory, detect the uncovered segments, and insert supplementary path points;

[0091] By integrating all path points and transition paths, balancing motion smoothness and coverage density, the final spraying path sequence is generated.

[0092] Specifically, the direction of the line connecting two points is determined by the three-dimensional coordinates of adjacent critical path points, and the angle between adjacent connecting lines is calculated to obtain the turning angle. This angle is directly compared with the maximum allowable movement angle of each joint of the robot. If the turning angle exceeds the joint movement limit, the spatial position of the corresponding critical path point is finely adjusted to ensure that the turning angle meets the constraints.

[0093] Using the endpoints of adjacent path segments as the starting and ending points, an arc-shaped transition path is constructed to allow the end effector to smoothly transition from the speed of the previous path segment to the speed of the next path segment, avoiding sudden speed changes and forming a continuous motion trajectory without any abrupt changes.

[0094] The robot uses a distance sensor to perform a full-coverage scan along a continuous motion trajectory to detect whether there are any uncovered gaps between the trajectories. Once an uncovered area is found, a supplementary path point is inserted at the center of the gap to ensure that no area is missed.

[0095] Connect all original critical path points, fine-tuned critical path points, supplementary path points, and transition paths in spatial order, check the smoothness of the trajectory and the uniformity of the coverage area, ensure that the smoothness of the movement and the coverage density are balanced, and finally generate a complete spraying path sequence.

[0096] In summary, by analyzing the local surface undulations of the reference geometry to identify the concave-convex transition zone, screening candidate path points and eliminating redundant dense points, and generating a sequence of key path points for topological connections, the path points can accurately match the complex curved surface features of the tower wall, avoiding local omissions or repeated coverage in subsequent spraying, and laying the path foundation for uniform spraying.

[0097] By introducing robot kinematic constraints to adjust the coordinates of critical path points to adapt to the joint activity limits, and constructing a transition path to control the velocity change rate of the end effector, the robot can avoid joint over-limits or sudden velocity changes during movement, ensure continuous and smooth motion trajectory, and reduce coating thickness fluctuations caused by robot motion instability.

[0098] Scanning continuous motion trajectories to detect uncovered sections and inserting supplementary path points balances motion smoothness and coverage density, ensuring complete coverage of the tower wall by the spraying path, reducing blind spots, improving path usability and reliability, and facilitating subsequent parameter adaptation and spraying quality control.

[0099] In a preferred embodiment, a mapping relationship is established between the curvature of the spraying path and the nozzle parameters, and initial values ​​for atomization pressure and paint flow rate are set based on this mapping relationship, including:

[0100] The spraying path is divided into multiple path segments, the curvature characteristics of each path segment are classified, and the correspondence between curvature range and parameter group is established.

[0101] Match the curvature range with the nozzle parameter combination, select the atomization pressure level range, and determine the adjustment range of the paint flow rate;

[0102] Configure the initial spraying parameters according to the parameter combination, set the reference value of the atomization pressure, and adjust the initial value of the paint flow rate;

[0103] Verify the compatibility of the initial spraying parameters, coordinate the matching relationship between atomization pressure and paint flow rate, and complete the initial setting of nozzle parameters.

[0104] Specifically, the path is divided into segments according to the continuous spatial curvature change of the spraying path. Each segment maintains a single curvature characteristic. Path segments with the same or similar curvature are grouped into one category. For each type of curvature, a parameter set consisting of atomization pressure and paint flow rate is defined, and the correspondence between curvature range and parameter set is established.

[0105] Based on the curvature category of each path segment, find the matching nozzle parameter combination in the established correspondence, select the corresponding atomization pressure level range according to the curvature, and determine the adjustment range of paint flow rate according to the spray coverage requirements under this curvature.

[0106] Based on the matched parameter combination, the median value of the atomization pressure level range is selected as the benchmark value. Combined with the actual coverage area requirements of the path segment, the paint flow rate is adjusted to the appropriate value within the adjustment range to complete the initial spraying parameter configuration.

[0107] The initial parameters of the short-distance simulated spraying test are used to observe the adhesion and uniformity of the coating. If the coating is too thick or too thin, the atomization pressure or paint flow rate is finely adjusted to ensure that the two are coordinated and matched to form a coating that meets the requirements, thus completing the initial setting of the nozzle parameters.

[0108] In summary, dividing the spraying path into multiple segments and classifying the curvature characteristics of each segment, and establishing the correspondence between curvature range and parameter group, allows the nozzle parameters to be accurately matched with path segments of different curvatures. This avoids paint accumulation or insufficient coverage in complex curved areas due to fixed parameters, and provides a clear matching basis for subsequent spraying parameter optimization.

[0109] By matching the curvature range with the nozzle parameter combination, selecting the atomization pressure level range, determining the coating flow rate adjustment range, and then setting the atomization pressure reference value and the initial coating flow rate value, the initial parameter setting can be freed from experience dependence and form a scientific configuration based on the actual geometric characteristics of the path, reducing the impact of initial parameter deviations on coating quality.

[0110] Verifying the compatibility of initial spraying parameters and coordinating the matching relationship between atomization pressure and paint flow rate can ensure that the two are matched to the spraying requirements of the current path segment, avoiding problems such as poor atomization effect and uneven paint adhesion caused by parameter incompatibility. This provides a stable parameter basis for the first spraying and reduces the adjustment range of subsequent online correction.

[0111] In a preferred embodiment, thickness monitoring data is acquired via optical self-reference, including:

[0112] Broadband probe light is projected onto the coating surface, and mixed spectral signals formed by reflections from the upper interface of the coating and the lower interface of the coating-substrate are collected simultaneously.

[0113] Analyze the interference patterns in mixed spectral signals, identify the constructive and destructive interference characteristics at specific wavelengths, and establish a direct correlation between interference characteristics and the optical thickness of the coating;

[0114] Based on the interference feature set of the entire spraying area directly correlated and transformed, a coating thickness distribution map that does not depend on the absolute position information of the substrate is generated.

[0115] Specifically, a wide-band light source of the thickness monitoring device vertically projects a probe light containing multiple wavelengths onto the surface of the sprayed coating. When the probe light contacts the upper interface of the coating, part of it is directly reflected, and the other part penetrates the coating to reach the lower interface of the coating and substrate and is reflected. The two reflected light signals are superimposed on the propagation path to form a mixed spectral signal, and the spectral acquisition module of the monitoring device synchronously captures the mixed spectral signal.

[0116] The collected mixed spectral signals are expanded in wavelength order, and the changes in signal intensity at different wavelength positions are observed. The position where the signal intensity reaches the peak corresponds to the constructive interference feature, and the position where the signal intensity drops to the trough corresponds to the destructive interference feature. By recording the wavelength information corresponding to these features, the direct correspondence between the interference features and the optical thickness of the coating is clarified.

[0117] By traversing all mixed spectral signals in the entire spraying area, the interference features of each monitoring point are extracted. Based on the established direct correlation, the interference features of each monitoring point are converted into corresponding coating optical thickness values. These values ​​are arranged according to the spatial coordinates of each monitoring point to form a coating thickness distribution map that does not depend on the absolute position information of the substrate.

[0118] In this embodiment, the interference pattern in the mixed spectral signal can be analyzed by calculating the coating optical thickness through a preferred implementation method. Specifically, the coating optical thickness... It can be calculated using the following formula:

[0119]

[0120] Indicates the optical thickness of the coating; This represents the phase difference measured at wavelengths λ1 and λ2; This indicates the refractive index of the coating, which is an inherent property of the coating and can be pre-calibrated. and These are two characteristic wavelengths selected from the acquired mixed spectral signal.

[0121] It should be noted that the refractive index of the coating is an inherent property of the coating itself. The coating is calibrated and stored in advance by performing optical tests on this type of coating. The two characteristic wavelengths are specific wavelengths selected from the acquired mixed spectral signals based on the characteristics of constructive or destructive interference. The phase difference is obtained by analyzing the interference patterns corresponding to the two characteristic wavelengths in the mixed spectral signal and comparing the phase states of the two.

[0122] This calculation method is a preferred implementation for analyzing interference patterns in mixed spectral signals. It can directly calculate the optical thickness of the coating by utilizing the phase difference between two characteristic wavelengths, the pre-calibrated refractive index of the coating, and the characteristic wavelength itself. This enables accurate acquisition of the coating optical thickness without relying on the absolute position information of the substrate, which is in complete agreement with the core requirements of optical self-reference methods.

[0123] When the refractive index of the coating remains constant, the greater the phase difference between the two characteristic wavelengths, the greater the calculated optical thickness of the coating. When the phase difference and refractive index remain constant, the closer the values ​​of the two characteristic wavelengths are, the greater the optical thickness of the coating, and vice versa.

[0124] In this embodiment, obtaining thickness monitoring data via optical self-reference also includes:

[0125] The incident angle and focusing plane of the probe beam are dynamically adjusted to match the curvature changes of the coating surface and optimize the signal-to-noise ratio of the interference signal.

[0126] Separate the specular and diffuse reflection components from the mixed spectral signal, extract the coherent signal component containing thickness information, and suppress ambient light interference;

[0127] The characteristic interference patterns corresponding to different thickness ranges are calibrated, and a thickness-spectral feature lookup table is established to realize real-time analysis of coating thickness;

[0128] By integrating real-time analysis results from multiple locations, a three-dimensional distribution field of coating thickness is constructed, and the boundary features of areas with abnormal thickness are identified.

[0129] Specifically, the thickness monitoring device changes the incident direction of the probe beam through a built-in angle adjustment mechanism based on the coating surface curvature data in the reference geometry information, so that the beam is always perpendicular to the coating surface at the monitoring point. At the same time, the position of the focusing lens is adjusted to change the focusing plane, so that the beam is accurately focused on the coating surface, reducing scattered light interference and improving the signal-to-noise ratio of the interference signal.

[0130] By utilizing the difference in the propagation direction of reflected light, mixed spectral signals are separated. Specular reflected light propagates along a fixed direction and is captured by a directional receiving device. Diffuse reflected light is scattered in all directions and blocked by a light-shielding component. Then, a narrow-band filter is used to filter out clutter signals in the ambient light, and the coherent signal component carrying coating thickness information is extracted separately.

[0131] A series of standard coating samples with known thicknesses were selected, and broadband probe light was projected onto them to record the corresponding interference patterns. The samples were divided into intervals according to their thickness values, and the characteristic interference patterns in each interval were associated with the corresponding thicknesses and stored to establish a thickness-spectral feature lookup table. During real-time monitoring, the collected patterns were compared with the lookup table to directly obtain the coating thickness.

[0132] Collect real-time thickness analysis results of all monitoring points in the spraying area, arrange and combine them according to the three-dimensional spatial coordinates of each monitoring point to construct a complete three-dimensional distribution field of coating thickness, compare the thickness value of each location with the preset thickness, and identify the area that exceeds the allowable range as the thickness anomaly area. Check each point along the edge of the anomaly area, mark the outer coordinates of continuous anomaly points, and clarify the boundary characteristics of the thickness anomaly area.

[0133] In this embodiment, real-time thickness monitoring data and preset thickness are integrated to generate parameter adjustment commands, driving the actuator to perform online correction of spraying parameters, including:

[0134] Compare the coating thickness distribution map with the preset thickness threshold, identify continuous areas where the thickness deviation exceeds the tolerance, and record the geometric contour and deviation value of the area;

[0135] Based on the deviation value, a parameter adjustment instruction sequence is generated, the instructions are encoded into machine-readable control signals, and the protocol compatibility of the signals with the actuator is verified;

[0136] The control signal is transmitted to the actuator interface, the signal content is parsed into a drive command, and the atomization pressure and coating flow are synchronized and corrected.

[0137] Specifically, the thickness value of each monitoring point on the coating thickness distribution map is compared with the preset thickness threshold one by one. When the thickness deviation of multiple consecutive monitoring points exceeds the set tolerance, the area formed by these consecutive points is determined as a continuous area where the thickness deviation exceeds the tolerance. The geometric contour is determined by recording the coordinates of all boundary monitoring points in the area, the difference between the thickness of each point and the preset threshold is calculated, and the average deviation is statistically analyzed to obtain the deviation value.

[0138] Based on the recorded deviation values, when the thickness is too small, instructions are generated to increase the paint flow rate and appropriately increase the atomization pressure; when the thickness is too large, instructions are generated to decrease the paint flow rate and appropriately decrease the atomization pressure. These instructions are arranged in the order of regions to form a parameter adjustment instruction sequence. Each instruction is converted into a binary control signal that the actuator can recognize. By comparing the signal format with the requirements of the actuator's preset communication protocol, it is confirmed that the signal can be correctly recognized by the actuator, thus completing the protocol compatibility verification.

[0139] The verified control signal is transmitted to the actuator interface via a wired communication line. The interface module reads the control signal bit by bit and converts it into a drive command that the actuator can execute. The drive command acts on both the atomization pressure regulating mechanism and the paint flow control valve. When the thickness is too small, the valve opening and pressure output are increased simultaneously. When the thickness is too large, the valve opening and pressure output are decreased simultaneously, thus realizing online correction of the spraying parameters.

[0140] In this embodiment, the thickness deviation value is obtained. Then, parameter adjustment commands can be generated using a preferred closed-loop control algorithm. For example, the following formula can be used for calculation.

[0141]

[0142] In the formula, This indicates the amount of parameter adjustment that needs to be output; This indicates the thickness deviation detected in real time. , , These are the proportional, integral, and differential coefficients, which are pre-tuned based on the characteristics of the spraying system and the coating.

[0143] It should be noted that the thickness deviation is the difference obtained by comparing the real-time coating thickness monitoring data acquired by optical self-reference method with the preset thickness. The proportional, integral, and derivative coefficients are tested and calibrated before the spraying operation based on the mechanical response characteristics of the spraying system and the adhesion and flow characteristics of the coating. After determining the fixed values, they are stored for later use. The parameter adjustment amount is the result directly output by the closed-loop control algorithm combined with the above values.

[0144] This calculation method is a preferred closed-loop control algorithm for generating parameter adjustment commands. By combining the thickness deviation monitored in real time and the three pre-tuned coefficients, and comprehensively considering the current deviation magnitude, the cumulative deviation, and the rate of change of the deviation, the required parameter adjustment amount is calculated. This adjustment amount is directly used to generate a machine-readable control signal to drive the actuator to perform online correction of the atomization pressure and coating flow rate, ensuring that the coating thickness meets the preset requirements.

[0145] When the proportional, integral, and derivative coefficients are fixed, the larger the thickness deviation detected in real time, the greater the influence of the corresponding proportional part on the parameter adjustment amount, and the parameter adjustment amount increases accordingly. When the deviation persists, the total cumulative deviation will continue to increase, and the corresponding integral part will drive the parameter adjustment amount to continue to increase until the deviation is eliminated. The faster the rate of change of the deviation, the greater the influence of the corresponding derivative part on the parameter adjustment amount, and the parameter adjustment amount will respond quickly to the deviation change and adjust in advance to suppress the expansion of the deviation.

[0146] In summary, by comparing the coating thickness distribution map with the preset thickness threshold, identifying continuous areas where the thickness deviation exceeds the tolerance, and recording their geometric contours and deviation values, the spraying defect area can be accurately located, avoiding the expansion of the defect range due to failure to locate the problem in time. This provides a clear target for parameter adjustment and reduces paint waste and efficiency loss caused by blind adjustment.

[0147] Based on the deviation value, a parameter adjustment command sequence is generated, encoded into a machine-readable control signal, and its protocol compatibility with the actuator is verified. This ensures that the command can be accurately recognized and responded to by the actuator, avoids adjustment failure due to signal format mismatch, and guarantees the reliability and accuracy of the parameter calibration process.

[0148] The system transmits control signals to the actuator interface and parses them into drive commands. It coordinates the synchronous correction of atomization pressure and paint flow rate, and can respond to fluctuations in working conditions and environmental interference during the spraying process in real time. This avoids the problem that fixed parameters are difficult to adapt to dynamic scenarios, effectively improves local overspray or underspray, and enhances the uniformity of coating thickness.

[0149] In a preferred embodiment, based on the parameters calibrated online, the spraying device is controlled to move to the already sprayed area to perform a second spray, and a detection device is simultaneously moved to the area where the initial spray has not been completed to monitor the coating thickness, including:

[0150] The work area is divided based on parameter adjustment instructions, and the boundary coordinates of the area to be sprayed a second time and the area to be monitored are marked.

[0151] Coordinate the operation sequence of the spraying device and the detection device, assign execution permissions to the area to be sprayed a second time, and simultaneously activate the monitoring permissions to the area to be monitored.

[0152] During the secondary coating process, thickness monitoring data is continuously received, and the baseline thickness distribution map is compared with the real-time monitoring data to generate a thickness uniformity evaluation result.

[0153] The operation sequence is dynamically adjusted based on the thickness uniformity assessment results to optimize the equipment's collaborative efficiency and achieve closed-loop control of the coating quality.

[0154] Specifically, based on the continuous area where the thickness deviation exceeds the tolerance marked in the parameter adjustment command, the range of the area to be sprayed a second time is determined, and the area on the wind turbine tower wall that has not been initially sprayed is designated as the monitoring area. By recording the three-dimensional coordinates of the monitoring points at the edges of the two areas, the boundary coordinates between the area to be sprayed a second time and the monitoring area are determined.

[0155] The synchronous start-up sequence of the spraying device and the detection device is set. When the spraying device moves to the starting coordinate of the area to be sprayed a second time according to the planned path, the detection device moves to the starting coordinate of the area to be monitored, sends an execution permission signal to the spraying device to allocate execution authority, and sends a start signal to the detection device to activate the monitoring authority, so as to ensure that the two operations are connected in a consistent manner.

[0156] During the secondary spraying process, the detection device continuously collects coating thickness data of the area to be monitored and transmits it in real time. It compares the pre-stored benchmark thickness distribution map with the real-time monitoring data point by point, counts the proportion of monitoring points in each area whose thickness meets the preset requirements, judges whether the coating thickness is uniform, and generates a thickness uniformity assessment result.

[0157] If the thickness uniformity assessment results show that there are still local deviations in the area to be coated a second time, shorten the moving speed of the spraying device in that local area to extend the spraying time, and at the same time increase the monitoring frequency of the detection device in the corresponding area; if the assessment results meet the requirements, maintain the original work sequence, and optimize the synergistic efficiency of spraying and monitoring by dynamically adjusting to ensure that the spraying quality meets the preset standard and complete the closed-loop control of spraying quality.

[0158] In summary, by dividing the work area based on parameter adjustment instructions and marking the boundary coordinates of the areas to be recoated and the areas to be monitored, the thickness defect areas that need to be recoated and the areas that have not been initially coated can be accurately located. This avoids missing or accidentally covering the normal coating during the second coating process. It also defines a clear monitoring range for the detection device, reduces ineffective work, and ensures the targeted nature of the work.

[0159] By coordinating the operation sequence of the spraying device and the testing device, and allocating execution permissions and activating monitoring permissions, the two devices can work in parallel, avoiding the spraying device waiting for test results or the testing device being idle, thus greatly improving the overall operation efficiency. At the same time, it ensures that the secondary spraying to repair defects and the monitoring of the initial spraying area are carried out simultaneously, avoiding process disconnect.

[0160] During the secondary spraying process, continuous monitoring data is received. The evaluation results are generated by comparing the baseline thickness distribution map with the real-time data. The operation sequence is then dynamically adjusted. This allows for real-time verification of the secondary spraying quality, timely detection of any remaining thickness deviations, and simultaneous monitoring of the pretreatment status of unsprayed areas. This forms a closed-loop control of spraying quality, further ensuring the overall uniformity and integrity of the coating.

[0161] Example 2, as Figure 2 The diagram shown is a functional block diagram of a wind turbine tower wall spraying system provided in an embodiment of the present invention.

[0162] This invention discloses a wind turbine tower wall coating system 100 that can be installed in an electronic device. Depending on the functions implemented, the wind turbine tower wall coating system 100 may include a data acquisition module 101, a path generation module 102, a parameter mapping module 103, an online correction module 104, and a secondary coating module 105. The module in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, and are stored in the memory of the electronic device.

[0163] In this embodiment, the functions of each module / unit are as follows:

[0164] Data acquisition module 101 is used to acquire surface contour data of wind turbine tower to form reference geometric information;

[0165] The path generation module 102 is used to extract the curvature distribution features of the reference geometry information, extract key path points based on the curvature distribution features, and introduce robot kinematic constraints to perform integrated processing of smoothing and coverage completeness, thereby generating the spraying path.

[0166] The parameter mapping module 103 is used to establish the mapping relationship between the curvature of the spraying path and the nozzle parameters, and to set the initial values ​​of atomization pressure and paint flow rate based on the mapping relationship.

[0167] The online calibration module 104 is used to integrate real-time thickness monitoring data with preset thickness during spraying to generate parameter adjustment instructions and drive the actuator to calibrate the spraying parameters online.

[0168] The secondary spraying module 105 is used to control the spraying device to move to the already sprayed area to perform secondary spraying based on the parameters after online calibration, and simultaneously move the detection device to the area where the initial spraying has not been completed to monitor the coating thickness.

[0169] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0170] The modules described as separate components may or may not be physically separate. The components shown as modules 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 the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0171] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0172] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0173] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for spraying the wall surface of a wind turbine tower, applied to an automated painting robot, characterized in that, The method includes the following steps: S1. Obtain the surface contour data of the wind turbine tower wall to form reference geometric information; S2. Extract the curvature distribution features of the reference geometry information, extract key path points based on the curvature distribution features, and introduce robot kinematic constraints to perform integrated processing of smoothing and coverage completeness, thereby generating the spraying path; The step of extracting the curvature distribution features of the reference geometric information and extracting key path points based on the curvature distribution features includes: analyzing the local surface undulations of the reference geometric information, identifying transition zones where the surface becomes uneven, and marking the boundary feature points of these transition zones; filtering candidate path points based on the distribution density of the boundary feature points, evaluating the regional coverage capability of the candidate point set, and removing redundant dense points; verifying the path coherence of the filtered candidate path points, associating the spatial positional relationships of adjacent points, and generating a sequence of key path points for topological connections. The process of introducing robot kinematic constraints to perform smoothing and coverage integrity integrated processing to generate a spraying path includes: measuring the turning angle between adjacent critical path points, comparing the robot joint motion limits, and adjusting the spatial coordinates of the critical path points; constructing transition paths between path segments, controlling the velocity change rate of the end effector, and forming a continuous motion trajectory; scanning the coverage area of ​​the continuous motion trajectory, detecting uncovered segments, and inserting supplementary path points; integrating all path points and transition paths, balancing motion smoothness and coverage density, and generating the final spraying path sequence. S3. Establish the mapping relationship between the curvature of the spraying path and the nozzle parameters, and set the initial values ​​of atomization pressure and paint flow rate based on the mapping relationship; S4. During the spraying process, real-time thickness monitoring data and preset thickness are integrated to generate parameter adjustment instructions, which drive the actuator to correct the spraying parameters online. S5. Based on the parameters after online calibration, control the spraying device to move to the already sprayed area to perform secondary spraying, and simultaneously move the detection device to the area where the initial spraying has not been completed to monitor the coating thickness. The process of controlling the spraying device to move to the already sprayed area to perform a second spraying based on online calibrated parameters, and simultaneously moving the detection device to the area where the first spraying has not been completed to monitor the coating thickness, includes: dividing the work area based on parameter adjustment instructions and marking the boundary coordinates between the area to be sprayed a second time and the area to be monitored; coordinating the work sequence of the spraying device and the detection device, allocating execution permissions to the area to be sprayed a second time, and simultaneously activating monitoring permissions to the area to be monitored; continuously receiving thickness monitoring data during the second spraying process, comparing the baseline thickness distribution map with the real-time monitoring data, and generating a thickness uniformity assessment result; and dynamically adjusting the work sequence based on the thickness uniformity assessment result to optimize the device's collaborative efficiency and complete the closed-loop control of the spraying quality.

2. The method for spraying the wall surface of a wind turbine tower as described in claim 1, characterized in that, The process of acquiring its surface contour data and forming reference geometric information includes: The system scans the wind turbine tower wall using sensors and monitors the acquisition process of surface contour data, recording the spatial sequence of data points. Assess the spatial uniformity of data point distribution and identify areas with insufficient coverage or data distortion; Control the data acquisition strategy to acquire supplementary data for identified areas and update the dataset; Integrate all datasets, perform geometric consistency checks and error elimination, and output highly complete baseline geometric information.

3. The method for spraying the wall surface of a wind turbine tower as described in claim 1, characterized in that, The process of establishing a mapping relationship between the curvature of the spraying path and the nozzle parameters, and setting initial values ​​for atomization pressure and paint flow rate based on this mapping relationship, includes: The spraying path is divided into multiple path segments, the curvature characteristics of each path segment are classified, and the correspondence between curvature range and parameter group is established. Match the curvature range with the nozzle parameter combination, select the atomization pressure level range, and determine the adjustment range of the paint flow rate; Configure the initial spraying parameters according to the parameter combination, set the reference value of the atomization pressure, and adjust the initial value of the paint flow rate; Verify the compatibility of the initial spraying parameters, coordinate the matching relationship between atomization pressure and paint flow rate, and complete the initial setting of nozzle parameters.

4. The method for spraying the wall surface of a wind turbine tower as described in claim 1, characterized in that, Step 4 involves acquiring thickness monitoring data using an optical self-reference method, including: Broadband probe light is projected onto the coating surface, and mixed spectral signals formed by reflections from the upper interface of the coating and the lower interface of the coating-substrate are collected simultaneously. Analyze the interference patterns in mixed spectral signals, identify the constructive and destructive interference characteristics at specific wavelengths, and establish a direct correlation between interference characteristics and the optical thickness of the coating; Based on the interference feature set of the entire spraying area directly correlated and transformed, a coating thickness distribution map that does not depend on the absolute position information of the substrate is generated.

5. The method for spraying the wall surface of a wind turbine tower as described in claim 4, characterized in that, The method of acquiring thickness monitoring data via optical self-reference also includes: The incident angle and focusing plane of the probe beam are dynamically adjusted to match the curvature changes of the coating surface and optimize the signal-to-noise ratio of the interference signal. Separate the specular and diffuse reflection components from the mixed spectral signal, extract the coherent signal component containing thickness information, and suppress ambient light interference; The characteristic interference patterns corresponding to different thickness ranges are calibrated, and a thickness-spectral feature lookup table is established to realize real-time analysis of coating thickness; By integrating real-time analysis results from multiple locations, a three-dimensional distribution field of coating thickness is constructed, and the boundary features of areas with abnormal thickness are identified.

6. The method for spraying the wall surface of a wind turbine tower as described in claim 5, characterized in that, The process of integrating real-time thickness monitoring data with preset thickness to generate parameter adjustment commands, driving the actuator to perform online correction of spraying parameters, includes: Compare the coating thickness distribution map with the preset thickness threshold, identify continuous areas where the thickness deviation exceeds the tolerance, and record the geometric contour and deviation value of the area; Based on the deviation value, a parameter adjustment instruction sequence is generated, the instructions are encoded into machine-readable control signals, and the protocol compatibility of the signals with the actuator is verified; The control signal is transmitted to the actuator interface, the signal content is parsed into a drive command, and the atomization pressure and coating flow are synchronized and corrected.

7. A wind turbine tower wall coating system for implementing the wind turbine tower wall coating method according to any one of claims 1-6, characterized in that, The system includes: The data acquisition module is used to acquire surface contour data of wind turbine towers to form reference geometric information; The path generation module is used to extract the curvature distribution features of the reference geometry information, extract key path points based on the curvature distribution features, and introduce robot kinematic constraints to perform integrated processing of smoothing and coverage completeness, thereby generating the spraying path. The parameter mapping module is used to establish the mapping relationship between the curvature of the spraying path and the nozzle parameters, and to set the initial values ​​of atomization pressure and paint flow rate based on the mapping relationship. The online calibration module is used to integrate real-time thickness monitoring data with preset thickness during the spraying process to generate parameter adjustment instructions, driving the actuator to calibrate the spraying parameters online. The secondary spraying module is used to control the spraying device to move to the already sprayed area to perform secondary spraying based on the parameters after online calibration, and simultaneously move the detection device to the area where the initial spraying has not been completed to monitor the coating thickness.

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